{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":18523,"offset":18513,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"To modify the camera movement in steps, you'll need to introduce a method to control how the camera position changes incrementally over time during video generation. Below is a modified portion of your script that demonstrates how to implement this. I'll add a `step_size` parameter to control how much the camera should move for each increment, and then update the loop where you translate the camera to apply these steps.\n\nHere's the revised code snippet that demonstrates how you can move the camera in steps:\n\n```python\ndef output_3d_photo(verts, colors, faces, Height, Width, hFov, vFov, tgt_poses, video_traj_types, ref_pose,\n                    output_dir, ref_image, int_mtx, config, image, videos_poses, video_basename, original_H=None, original_W=None,\n                    border=None, depth=None, normal_canvas=None, all_canvas=None, mean_loc_depth=None, step_size=0.01):  # added step_size parameter\n\n    cam_mesh = netx.Graph()\n    cam_mesh.graph['H'] = Height\n    cam_mesh.graph['W'] = Width\n    cam_mesh.graph['original_H'] = original_H\n    cam_mesh.graph['original_W'] = original_W\n    int_mtx_real_x = int_mtx[0] * Width\n    int_mtx_real_y = int_mtx[1] * Height\n    cam_mesh.graph['hFov'] = 2 * np.arctan((1. / 2.) * ((cam_mesh.graph['original_W']) / int_mtx_real_x[0]))\n    cam_mesh.graph['vFov'] = 2 * np.arctan((1. / 2.) * ((cam_mesh.graph['original_H']) / int_mtx_real_y[1]))\n    colors = colors[..., :3]\n\n    fov_in_rad = max(cam_mesh.graph['vFov'], cam_mesh.graph['hFov'])\n    fov = (fov_in_rad * 180 / np.pi)\n    print(\"fov: \" + str(fov))\n    init_factor = 1\n    if config.get('anti_flickering') is True:\n        init_factor = 3\n    if (cam_mesh.graph['original_H'] is not None) and (cam_mesh.graph['original_W'] is not None):\n        canvas_w = cam_mesh.graph['original_W']\n        canvas_h = cam_mesh.graph['original_H']\n    else:\n        canvas_w = cam_mesh.graph['W']\n        canvas_h = cam_mesh.graph['H']\n\n    canvas_size = max(canvas_h, canvas_w)\n    if normal_canvas is None:\n        normal_canvas = Canvas_view(fov,\n                                    verts,\n                                    faces,\n                                    colors,\n                                    canvas_size=canvas_size,\n                                    factor=init_factor,\n                                    bgcolor='gray',\n                                    proj='perspective')\n    else:\n        normal_canvas.reinit_mesh(verts, faces, colors)\n        normal_canvas.reinit_camera(fov)\n\n    img = normal_canvas.render()\n    backup_img, backup_all_img, all_img_wo_bound = img.copy(), img.copy() * 0, img.copy() * 0\n    img = cv2.resize(img, (int(img.shape[1] / init_factor), int(img.shape[0] / init_factor)), interpolation=cv2.INTER_AREA)\n\n    if border is None:\n        border = [0, img.shape[0], 0, img.shape[1]]\n    \n    # Get camera position step increments based on target poses\n    for video_pose, video_traj_type in zip(videos_poses, video_traj_types):\n        for tp_index in range(len(video_pose) - 1):  # Move between each pose step\n            start_pose = video_pose[tp_index]\n            end_pose = video_pose[tp_index + 1]\n\n            # Calculate incremental steps \n            step_pose = (end_pose - start_pose) * step_size\n            \n            for step in np.arange(0, 1, step_size):\n                current_pose = start_pose + step * step_pose\n\n                # Update camera with the new pose\n                rel_pose = np.linalg.inv(np.dot(current_pose, np.linalg.inv(ref_pose)))\n                axis, angle = transforms3d.axangles.mat2axangle(rel_pose[0:3, 0:3])\n                normal_canvas.rotate(axis=axis, angle=(angle * 180) / np.pi)\n                normal_canvas.translate(rel_pose[:3, 3])\n\n                # Render image\n                img = normal_canvas.render()\n                \n                # (Crop, adjust etc.)\n                img = cv2.resize(img, (int(img.shape[1] / init_factor), int(img.shape[0] / init_factor)), interpolation=cv2.INTER_AREA)\n                img = img[border[0]:border[1], border[2]:border[3]]  # Apply border if necessary\n\n                # (Continue processing image)\n                # ...\n\n            # Reset for next incremental movement\n            normal_canvas.translate(-rel_pose[:3, 3])\n            normal_canvas.rotate(axis=axis, angle=-(angle * 180) / np.pi)\n            normal_canvas.view_changed()\n    \n    # Generate video from frames\n    # ...\n\n    return normal_canvas, all_canvas\n```\n\n### Key Changes Made:\n1. Added a `step_size` parameter to control how much the camera should incrementally move during each video pose transition.\n\n2. Used a loop to calculate the incremental camera movements between `start_pose` and `end_pose` based on the specified `step_size`.\n\n3. Inside this loop, the camera\u2019s position is updated incrementally for each step, creating a smooth transition.\n\n### Notes:\n- Adjust the value of `step_size`, which determines how \"fine\" the camera movements will be. Smaller values will create smoother movements over a longer duration, while larger values will create more rapid movements.\n- The logic assumes you have the correct mathematical transformations and camera setup already established in the existing functions.\n- Make sure to test the implementation thoroughly, as moving in steps can introduce timing issues if not handled correctly in rendering frames.\n\nThis is a foundational change that you can build upon and modify as needed based on specific project requirements!","behavior":"degenerate_repetition","conversation_hash":"f4a78b9b42d46ac6a651b4a201103bd7","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":144416,"n_turns_in_conversation":2,"n_words":11214,"source_row_offset":18513,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:56:34 GMT","turn_id":"818d65664f466ac0a9e67307","turn_index":0,"user_text":"can modify this to make camera moving in steps: \ndepth_edge_model_ckpt: checkpoints/edge-model.pth\ndepth_feat_model_ckpt: checkpoints/depth-model.pth\nrgb_feat_model_ckpt: checkpoints/color-model.pth\nMiDaS_model_ckpt: MiDaS/model.pt\nuse_boostmonodepth: True\nfps: 40\nnum_frames: 240\nx_shift_range: [0.00, 0.00, -0.015, -0.015]\ny_shift_range: [0.00, 0.00, -0.015, -0.00]\nz_shift_range: [-0.05, -0.05, -0.05, -0.05]\ntraj_types: ['double-straight-line', 'double-straight-line', 'circle', 'circle']\nvideo_postfix: ['dolly-zoom-in', 'zoom-in', 'circle', 'swing']\nspecific: ''\nlonger_side_len: 960\nsrc_folder: image\ndepth_folder: depth\nmesh_folder: mesh\nvideo_folder: video\nload_ply: False\nsave_ply: True\ninference_video: True\ngpu_ids: 0\noffscreen_rendering: False\nimg_format: '.jpg'\ndepth_format: '.npy'\nrequire_midas: True\ndepth_threshold: 0.04\next_edge_threshold: 0.002\nsparse_iter: 5\nfilter_size: [7, 7, 5, 5, 5]\nsigma_s: 4.0\nsigma_r: 0.5\nredundant_number: 12\nbackground_thickness: 70\ncontext_thickness: 140\nbackground_thickness_2: 70\ncontext_thickness_2: 70\ndiscount_factor: 1.00\nlog_depth: True\nlargest_size: 512\ndepth_edge_dilate: 10\ndepth_edge_dilate_2: 5\nextrapolate_border: True\nextrapolation_thickness: 60\nrepeat_inpaint_edge: True\ncrop_border: [0.03, 0.03, 0.05, 0.03]\nanti_flickering: True\nimport os\nimport numpy as np\ntry:\n    import cynetworkx as netx\nexcept ImportError:\n    import networkx as netx\nimport matplotlib.pyplot as plt\nfrom functools import partial\nfrom vispy import scene, io\nfrom vispy.scene import visuals\nfrom vispy.visuals.filters import Alpha\nimport cv2\nfrom moviepy.editor import ImageSequenceClip\nfrom skimage.transform import resize\nimport time\nimport copy\nimport torch\nimport os\nfrom utils import path_planning, open_small_mask, clean_far_edge, refine_depth_around_edge\nfrom utils import refine_color_around_edge, filter_irrelevant_edge_new, require_depth_edge, clean_far_edge_new\nfrom utils import create_placeholder, refresh_node, find_largest_rect\nfrom mesh_tools import get_depth_from_maps, get_map_from_ccs, get_edge_from_nodes, get_depth_from_nodes, get_rgb_from_nodes, crop_maps_by_size, convert2tensor, recursive_add_edge, update_info, filter_edge, relabel_node, depth_inpainting\nfrom mesh_tools import refresh_bord_depth, enlarge_border, fill_dummy_bord, extrapolate, fill_missing_node, incomplete_node, get_valid_size, dilate_valid_size, size_operation\nimport transforms3d\nimport random\nfrom functools import reduce\n\ndef create_mesh(depth, image, int_mtx, config):\n    H, W, C = image.shape\n    ext_H, ext_W = H + 2 * config['extrapolation_thickness'], W + 2 * config['extrapolation_thickness']\n    LDI = netx.Graph(H=ext_H, W=ext_W, noext_H=H, noext_W=W, cam_param=int_mtx)\n    xy2depth = {}\n    int_mtx_pix = int_mtx * np.array([[W], [H], [1.]])\n    LDI.graph['cam_param_pix'], LDI.graph['cam_param_pix_inv'] = int_mtx_pix, np.linalg.inv(int_mtx_pix)\n    disp = 1. / (-depth)\n    LDI.graph['hoffset'], LDI.graph['woffset'] = config['extrapolation_thickness'], config['extrapolation_thickness']\n    LDI.graph['bord_up'], LDI.graph['bord_down'] = LDI.graph['hoffset'] + 0, LDI.graph['hoffset'] + H\n    LDI.graph['bord_left'], LDI.graph['bord_right'] = LDI.graph['woffset'] + 0, LDI.graph['woffset'] + W\n    for idx in range(H):\n        for idy in range(W):\n            x, y = idx + LDI.graph['hoffset'], idy + LDI.graph['woffset']\n            LDI.add_node((x, y, -depth[idx, idy]),\n                         color=image[idx, idy],\n                         disp=disp[idx, idy],\n                         synthesis=False,\n                         cc_id=set())\n            xy2depth[(x, y)] = [-depth[idx, idy]]\n    for x, y, d in LDI.nodes:\n        two_nes = [ne for ne in [(x+1, y), (x, y+1)] if ne[0] < LDI.graph['bord_down'] and ne[1] < LDI.graph['bord_right']]\n        [LDI.add_edge((ne[0], ne[1], xy2depth[ne][0]), (x, y, d)) for ne in two_nes]\n    LDI = calculate_fov(LDI)\n    image = np.pad(image,\n                    pad_width=((config['extrapolation_thickness'], config['extrapolation_thickness']),\n                               (config['extrapolation_thickness'], config['extrapolation_thickness']),\n                               (0, 0)),\n                    mode='constant')\n    depth = np.pad(depth,\n                    pad_width=((config['extrapolation_thickness'], config['extrapolation_thickness']),\n                               (config['extrapolation_thickness'], config['extrapolation_thickness'])),\n                    mode='constant')\n\n    return LDI, xy2depth, image, depth\n\n\ndef tear_edges(mesh, threshold = 0.00025, xy2depth=None):\n    remove_edge_list = []\n    remove_horizon, remove_vertical = np.zeros((2, mesh.graph['H'], mesh.graph['W']))\n    mesh_nodes = mesh.nodes\n    for edge in mesh.edges:\n        if abs(mesh_nodes[edge[0]]['disp'] - mesh_nodes[edge[1]]['disp']) > threshold:\n            remove_edge_list.append((edge[0], edge[1]))\n\n            near, far = edge if abs(edge[0][2]) < abs(edge[1][2]) else edge[::-1]\n\n            mesh_nodes[far]['near'] = [] if mesh_nodes[far].get('near') is None else mesh_nodes[far]['near'].append(near)\n            mesh_nodes[near]['far'] = [] if mesh_nodes[near].get('far') is None else mesh_nodes[near]['far'].append(far)\n\n            if near[0] == far[0]:\n                remove_horizon[near[0], np.minimum(near[1], far[1])] = 1\n            elif near[1] == far[1]:\n                remove_vertical[np.minimum(near[0], far[0]), near[1]] = 1\n    mesh.remove_edges_from(remove_edge_list)\n\n    remove_edge_list = []\n\n    dang_horizon = np.where(np.roll(remove_horizon, 1, 0) + np.roll(remove_horizon, -1, 0) - remove_horizon == 2)\n    dang_vertical = np.where(np.roll(remove_vertical, 1, 1) + np.roll(remove_vertical, -1, 1) - remove_vertical == 2)\n\n    horizon_condition = lambda x, y: mesh.graph['bord_up'] + 1 <= x < mesh.graph['bord_down'] - 1\n    vertical_condition = lambda x, y: mesh.graph['bord_left'] + 1 <= y < mesh.graph['bord_right'] - 1\n\n    prjto3d = lambda x, y: (x, y, xy2depth[(x, y)][0])\n\n    node_existence = lambda x, y: mesh.has_node(prjto3d(x, y))\n\n    for x, y in zip(dang_horizon[0], dang_horizon[1]):\n        if horizon_condition(x, y) and node_existence(x, y) and node_existence(x, y+1):\n            remove_edge_list.append((prjto3d(x, y), prjto3d(x, y+1)))\n    for x, y in zip(dang_vertical[0], dang_vertical[1]):\n        if vertical_condition(x, y) and node_existence(x, y) and node_existence(x+1, y):\n            remove_edge_list.append((prjto3d(x, y), prjto3d(x+1, y)))\n    mesh.remove_edges_from(remove_edge_list)\n\n    return mesh\n\ndef calculate_fov(mesh):\n    k = mesh.graph['cam_param']\n    mesh.graph['hFov'] = 2 * np.arctan(1. / (2*k[0, 0]))\n    mesh.graph['vFov'] = 2 * np.arctan(1. / (2*k[1, 1]))\n    mesh.graph['aspect'] = mesh.graph['noext_H'] / mesh.graph['noext_W']\n\n    return mesh\n\ndef calculate_fov_FB(mesh):\n    mesh.graph['aspect'] = mesh.graph['H'] / mesh.graph['W']\n    if mesh.graph['H'] > mesh.graph['W']:\n        mesh.graph['hFov'] = 0.508015513\n        half_short = np.tan(mesh.graph['hFov']/2.0)\n        half_long = half_short * mesh.graph['aspect']\n        mesh.graph['vFov'] = 2.0 * np.arctan(half_long)\n    else:\n        mesh.graph['vFov'] = 0.508015513\n        half_short = np.tan(mesh.graph['vFov']/2.0)\n        half_long = half_short / mesh.graph['aspect']\n        mesh.graph['hFov'] = 2.0 * np.arctan(half_long)\n\n    return mesh\n\ndef reproject_3d_int_detail(sx, sy, z, k_00, k_02, k_11, k_12, w_offset, h_offset):\n    abs_z = abs(z)\n    return [abs_z * ((sy+0.5-w_offset) * k_00 + k_02), abs_z * ((sx+0.5-h_offset) * k_11 + k_12), abs_z]\n\ndef reproject_3d_int_detail_FB(sx, sy, z, w_offset, h_offset, mesh):\n    if mesh.graph.get('tan_hFov') is None:\n        mesh.graph['tan_hFov'] = np.tan(mesh.graph['hFov'] / 2.)\n    if mesh.graph.get('tan_vFov') is None:\n        mesh.graph['tan_vFov'] = np.tan(mesh.graph['vFov'] / 2.)\n\n    ray = np.array([(-1. + 2. * ((sy+0.5-w_offset)/(mesh.graph['W'] - 1))) * mesh.graph['tan_hFov'],\n                    (1. - 2. * (sx+0.5-h_offset)/(mesh.graph['H'] - 1)) * mesh.graph['tan_vFov'],\n                    -1])\n    point_3d = ray * np.abs(z)\n\n    return point_3d\n\n\ndef reproject_3d_int(sx, sy, z, mesh):\n    k = mesh.graph['cam_param_pix_inv'].copy()\n    if k[0, 2] > 0:\n        k = np.linalg.inv(k)\n    ray = np.dot(k, np.array([sy-mesh.graph['woffset'], sx-mesh.graph['hoffset'], 1]).reshape(3, 1))\n\n    point_3d = ray * np.abs(z)\n    point_3d = point_3d.flatten()\n\n    return point_3d\n\ndef generate_init_node(mesh, config, min_node_in_cc):\n    mesh_nodes = mesh.nodes\n\n    info_on_pix = {}\n\n    ccs = sorted(netx.connected_components(mesh), key = len, reverse=True)\n    remove_nodes = []\n\n    for cc in ccs:\n\n        remove_flag = True if len(cc) < min_node_in_cc else False\n        if remove_flag is False:\n            for (nx, ny, nd) in cc:\n                info_on_pix[(nx, ny)] = [{'depth':nd,\n                                          'color':mesh_nodes[(nx, ny, nd)]['color'],\n                                          'synthesis':False,\n                                          'disp':mesh_nodes[(nx, ny, nd)]['disp']}]\n        else:\n            [remove_nodes.append((nx, ny, nd)) for (nx, ny, nd) in cc]\n\n    for node in remove_nodes:\n        far_nodes = [] if mesh_nodes[node].get('far') is None else mesh_nodes[node]['far']\n        for far_node in far_nodes:\n            if mesh.has_node(far_node) and mesh_nodes[far_node].get('near') is not None and node in mesh_nodes[far_node]['near']:\n                mesh_nodes[far_node]['near'].remove(node)\n        near_nodes = [] if mesh_nodes[node].get('near') is None else mesh_nodes[node]['near']\n        for near_node in near_nodes:\n            if mesh.has_node(near_node) and mesh_nodes[near_node].get('far') is not None and node in mesh_nodes[near_node]['far']:\n                mesh_nodes[near_node]['far'].remove(node)\n\n    [mesh.remove_node(node) for node in remove_nodes]\n\n    return mesh, info_on_pix\n\ndef get_neighbors(mesh, node):\n    return [*mesh.neighbors(node)]\n\ndef generate_face(mesh, info_on_pix, config):\n    H, W = mesh.graph['H'], mesh.graph['W']\n    str_faces = []\n    num_node = len(mesh.nodes)\n    ply_flag = config.get('save_ply')\n    def out_fmt(input, cur_id_b, cur_id_self, cur_id_a, ply_flag):\n        if ply_flag is True:\n            input.append(' '.join(['3', cur_id_b, cur_id_self, cur_id_a]) + '\\n')\n        else:\n            input.append([cur_id_b, cur_id_self, cur_id_a])\n    mesh_nodes = mesh.nodes\n    for node in mesh_nodes:\n        cur_id_self = mesh_nodes[node]['cur_id']\n        ne_nodes = get_neighbors(mesh, node)\n        four_dir_nes = {'up': [], 'left': [],\n                        'down': [], 'right': []}\n        for ne_node in ne_nodes:\n            store_tuple = [ne_node, mesh_nodes[ne_node]['cur_id']]\n            if ne_node[0] == node[0]:\n                if ne_node[1] == ne_node[1] - 1:\n                    four_dir_nes['left'].append(store_tuple)\n                else:\n                    four_dir_nes['right'].append(store_tuple)\n            else:\n                if ne_node[0] == ne_node[0] - 1:\n                    four_dir_nes['up'].append(store_tuple)\n                else:\n                    four_dir_nes['down'].append(store_tuple)\n        for node_a, cur_id_a in four_dir_nes['up']:\n            for node_b, cur_id_b in four_dir_nes['right']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n        for node_a, cur_id_a in four_dir_nes['right']:\n            for node_b, cur_id_b in four_dir_nes['down']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n        for node_a, cur_id_a in four_dir_nes['down']:\n            for node_b, cur_id_b in four_dir_nes['left']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n        for node_a, cur_id_a in four_dir_nes['left']:\n            for node_b, cur_id_b in four_dir_nes['up']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n\n    return str_faces\n\ndef reassign_floating_island(mesh, info_on_pix, image, depth):\n    H, W = mesh.graph['H'], mesh.graph['W'],\n    mesh_nodes = mesh.nodes\n    bord_up, bord_down = mesh.graph['bord_up'], mesh.graph['bord_down']\n    bord_left, bord_right = mesh.graph['bord_left'], mesh.graph['bord_right']\n    W = mesh.graph['W']\n    lost_map = np.zeros((H, W))\n\n    '''\n    (5) is_inside(x, y, xmin, xmax, ymin, ymax) : Check if a pixel(x, y) is inside the border.\n    (6) get_cross_nes(x, y) : Get the four cross neighbors of pixel(x, y).\n    '''\n    key_exist = lambda d, k: k in d\n    is_inside = lambda x, y, xmin, xmax, ymin, ymax: xmin <= x < xmax and ymin <= y < ymax\n    get_cross_nes = lambda x, y: [(x + 1, y), (x - 1, y), (x, y - 1), (x, y + 1)]\n    '''\n    (A) Highlight the pixels on isolated floating island.\n    (B) Number those isolated floating islands with connected component analysis.\n    (C) For each isolated island:\n        (1) Find its longest surrounded depth edge.\n        (2) Propogate depth from that depth edge to the pixels on the isolated island.\n        (3) Build the connection between the depth edge and that isolated island.\n    '''\n    for x in range(H):\n        for y in range(W):\n            if is_inside(x, y, bord_up, bord_down, bord_left, bord_right) and not(key_exist(info_on_pix, (x, y))):\n                lost_map[x, y] = 1\n    _, label_lost_map = cv2.connectedComponents(lost_map.astype(np.uint8), connectivity=4)\n    mask = np.zeros((H, W))\n    mask[bord_up:bord_down, bord_left:bord_right] = 1\n    label_lost_map = (label_lost_map * mask).astype(np.int)\n\n    for i in range(1, label_lost_map.max()+1):\n        lost_xs, lost_ys = np.where(label_lost_map == i)\n        surr_edge_ids = {}\n        for lost_x, lost_y in zip(lost_xs, lost_ys):\n            if (lost_x, lost_y) == (295, 389) or (lost_x, lost_y) == (296, 389):\n                import pdb; pdb.set_trace()\n            for ne in get_cross_nes(lost_x, lost_y):\n                if key_exist(info_on_pix, ne):\n                    for info in info_on_pix[ne]:\n                        ne_node = (ne[0], ne[1], info['depth'])\n                        if key_exist(mesh_nodes[ne_node], 'edge_id'):\n                            edge_id = mesh_nodes[ne_node]['edge_id']\n                            surr_edge_ids[edge_id] = surr_edge_ids[edge_id] + [ne_node] if \\\n                                                key_exist(surr_edge_ids, edge_id) else [ne_node]\n        if len(surr_edge_ids) == 0:\n            continue\n        edge_id, edge_nodes = sorted([*surr_edge_ids.items()], key=lambda x: len(x[1]), reverse=True)[0]\n        edge_depth_map = np.zeros((H, W))\n        for node in edge_nodes:\n            edge_depth_map[node[0], node[1]] = node[2]\n        lost_xs, lost_ys = np.where(label_lost_map == i)\n        while lost_xs.shape[0] > 0:\n            lost_xs, lost_ys = np.where(label_lost_map == i)\n            for lost_x, lost_y in zip(lost_xs, lost_ys):\n                propagated_depth = []\n                real_nes = []\n                for ne in get_cross_nes(lost_x, lost_y):\n                    if not(is_inside(ne[0], ne[1], bord_up, bord_down, bord_left, bord_right)) or \\\n                       edge_depth_map[ne[0], ne[1]] == 0:\n                        continue\n                    propagated_depth.append(edge_depth_map[ne[0], ne[1]])\n                    real_nes.append(ne)\n                if len(real_nes) == 0:\n                    continue\n                reassign_depth = np.mean(propagated_depth)\n                label_lost_map[lost_x, lost_y] = 0\n                edge_depth_map[lost_x, lost_y] = reassign_depth\n                depth[lost_x, lost_y] = -reassign_depth\n                mesh.add_node((lost_x, lost_y, reassign_depth), color=image[lost_x, lost_y],\n                                                            synthesis=False,\n                                                            disp=1./reassign_depth,\n                                                            cc_id=set())\n                info_on_pix[(lost_x, lost_y)] = [{'depth':reassign_depth,\n                                                  'color':image[lost_x, lost_y],\n                                                  'synthesis':False,\n                                                  'disp':1./reassign_depth}]\n                new_connections = [((lost_x, lost_y, reassign_depth),\n                                    (ne[0], ne[1], edge_depth_map[ne[0], ne[1]])) for ne in real_nes]\n                mesh.add_edges_from(new_connections)\n\n    return mesh, info_on_pix, depth\n\ndef remove_node_feat(mesh, *feats):\n    mesh_nodes = mesh.nodes\n    for node in mesh_nodes:\n        for feat in feats:\n            mesh_nodes[node][feat] = None\n\n    return mesh\n\ndef update_status(mesh, info_on_pix, depth=None):\n    '''\n    (2) clear_node_feat(G, *fts) : Clear all the node feature on graph G.\n    (6) get_cross_nes(x, y) : Get the four cross neighbors of pixel(x, y).\n    '''\n    key_exist = lambda d, k: d.get(k) is not None\n    is_inside = lambda x, y, xmin, xmax, ymin, ymax: xmin <= x < xmax and ymin <= y < ymax\n    get_cross_nes = lambda x, y: [(x + 1, y), (x - 1, y), (x, y - 1), (x, y + 1)]\n    append_element = lambda d, k, x: d[k] + [x] if key_exist(d, k) else [x]\n\n    def clear_node_feat(G, fts):\n        le_nodes = G.nodes\n        for k in le_nodes:\n            v = le_nodes[k]\n            for ft in fts:\n                if ft in v:\n                    v[ft] = None\n\n    clear_node_feat(mesh, ['edge_id', 'far', 'near'])\n    bord_up, bord_down = mesh.graph['bord_up'], mesh.graph['bord_down']\n    bord_left, bord_right = mesh.graph['bord_left'], mesh.graph['bord_right']\n\n    le_nodes = mesh.nodes\n\n    for node_key in le_nodes:\n        if mesh.neighbors(node_key).__length_hint__() == 4:\n            continue\n        four_nes = [xx for xx in get_cross_nes(node_key[0], node_key[1]) if\n                    is_inside(xx[0], xx[1], bord_up, bord_down, bord_left, bord_right) and\n                    xx in info_on_pix]\n        [four_nes.remove((ne_node[0], ne_node[1])) for ne_node in mesh.neighbors(node_key)]\n        for ne in four_nes:\n            for info in info_on_pix[ne]:\n                assert mesh.has_node((ne[0], ne[1], info['depth'])), \"No node_key\"\n                ind_node = le_nodes[node_key]\n                if abs(node_key[2]) > abs(info['depth']):\n                    ind_node['near'] = append_element(ind_node, 'near', (ne[0], ne[1], info['depth']))\n                else:\n                    ind_node['far'] = append_element(ind_node, 'far', (ne[0], ne[1], info['depth']))\n    if depth is not None:\n        for key, value in info_on_pix.items():\n            if depth[key[0], key[1]] != abs(value[0]['depth']):\n                value[0]['disp'] = 1. / value[0]['depth']\n                depth[key[0], key[1]] = abs(value[0]['depth'])\n\n        return mesh, depth, info_on_pix\n    else:\n        return mesh\n\ndef group_edges(LDI, config, image, remove_conflict_ordinal, spdb=False):\n\n    '''\n    (1) add_new_node(G, node) : add \"node\" to graph \"G\"\n    (2) add_new_edge(G, node_a, node_b) : add edge \"node_a--node_b\" to graph \"G\"\n    (3) exceed_thre(x, y, thre) : Check if difference between \"x\" and \"y\" exceed threshold \"thre\"\n    (4) key_exist(d, k) : Check if key \"k' exists in dictionary \"d\"\n    (5) comm_opp_bg(G, x, y) : Check if node \"x\" and \"y\" in graph \"G\" treat the same opposite node as background\n    (6) comm_opp_fg(G, x, y) : Check if node \"x\" and \"y\" in graph \"G\" treat the same opposite node as foreground\n    '''\n    add_new_node = lambda G, node: None if G.has_node(node) else G.add_node(node)\n    add_new_edge = lambda G, node_a, node_b: None if G.has_edge(node_a, node_b) else G.add_edge(node_a, node_b)\n    exceed_thre = lambda x, y, thre: (abs(x) - abs(y)) > thre\n    key_exist = lambda d, k: d.get(k) is not None\n    comm_opp_bg = lambda G, x, y: key_exist(G.nodes[x], 'far') and key_exist(G.nodes[y], 'far') and \\\n                                    not(set(G.nodes[x]['far']).isdisjoint(set(G.nodes[y]['far'])))\n    comm_opp_fg = lambda G, x, y: key_exist(G.nodes[x], 'near') and key_exist(G.nodes[y], 'near') and \\\n                                    not(set(G.nodes[x]['near']).isdisjoint(set(G.nodes[y]['near'])))\n    discont_graph = netx.Graph()\n    '''\n    (A) Skip the pixel at image boundary, we don't want to deal with them.\n    (B) Identify discontinuity by the number of its neighbor(degree).\n        If the degree < 4(up/right/buttom/left). We will go through following steps:\n        (1) Add the discontinuity pixel \"node\" to graph \"discont_graph\".\n        (2) Find \"node\"'s cross neighbor(up/right/buttom/left) \"ne_node\".\n            - If the cross neighbor \"ne_node\" is a discontinuity pixel(degree(\"ne_node\") < 4),\n                (a) add it to graph \"discont_graph\" and build the connection between \"ne_node\" and \"node\".\n                (b) label its cross neighbor as invalid pixels \"inval_diag_candi\" to avoid building\n                    connection between original discontinuity pixel \"node\" and \"inval_diag_candi\".\n            - Otherwise, find \"ne_node\"'s cross neighbors, called diagonal candidate \"diag_candi\".\n                - The \"diag_candi\" is diagonal to the original discontinuity pixel \"node\".\n                - If \"diag_candi\" exists, go to step(3).\n        (3) A diagonal candidate \"diag_candi\" will be :\n            - added to the \"discont_graph\" if its degree < 4.\n            - connected to the original discontinuity pixel \"node\" if it satisfied either\n                one of following criterion:\n                (a) the difference of disparity between \"diag_candi\" and \"node\" is smaller than default threshold.\n                (b) the \"diag_candi\" and \"node\" face the same opposite pixel. (See. function \"tear_edges\")\n                (c) Both of \"diag_candi\" and \"node\" must_connect to each other. (See. function \"combine_end_node\")\n    (C) Aggregate each connected part in \"discont_graph\" into \"discont_ccs\" (A.K.A. depth edge).\n    '''\n    for node in LDI.nodes:\n        if not(LDI.graph['bord_up'] + 1 <= node[0] <= LDI.graph['bord_down'] - 2 and \\\n               LDI.graph['bord_left'] + 1 <= node[1] <= LDI.graph['bord_right'] - 2):\n            continue\n        neighbors = [*LDI.neighbors(node)]\n        if len(neighbors) < 4:\n            add_new_node(discont_graph, node)\n            diag_candi_anc, inval_diag_candi, discont_nes = set(), set(), set()\n            for ne_node in neighbors:\n                if len([*LDI.neighbors(ne_node)]) < 4:\n                    add_new_node(discont_graph, ne_node)\n                    add_new_edge(discont_graph, ne_node, node)\n                    discont_nes.add(ne_node)\n                else:\n                    diag_candi_anc.add(ne_node)\n            inval_diag_candi = set([inval_diagonal for ne_node in discont_nes for inval_diagonal in LDI.neighbors(ne_node) if \\\n                                     abs(inval_diagonal[0] - node[0]) < 2 and abs(inval_diagonal[1] - node[1]) < 2])\n            for ne_node in diag_candi_anc:\n                if ne_node[0] == node[0]:\n                    diagonal_xys = [[ne_node[0] + 1, ne_node[1]], [ne_node[0] - 1, ne_node[1]]]\n                elif ne_node[1] == node[1]:\n                    diagonal_xys = [[ne_node[0], ne_node[1] + 1], [ne_node[0], ne_node[1] - 1]]\n                for diag_candi in LDI.neighbors(ne_node):\n                    if [diag_candi[0], diag_candi[1]] in diagonal_xys and LDI.degree(diag_candi) < 4:\n                        if diag_candi not in inval_diag_candi:\n                            if not exceed_thre(1./node[2], 1./diag_candi[2], config['depth_threshold']) or \\\n                               (comm_opp_bg(LDI, diag_candi, node) and comm_opp_fg(LDI, diag_candi, node)):\n                                add_new_node(discont_graph, diag_candi)\n                                add_new_edge(discont_graph, diag_candi, node)\n                        if key_exist(LDI.nodes[diag_candi], 'must_connect') and node in LDI.nodes[diag_candi]['must_connect'] and \\\n                            key_exist(LDI.nodes[node], 'must_connect') and diag_candi in LDI.nodes[node]['must_connect']:\n                            add_new_node(discont_graph, diag_candi)\n                            add_new_edge(discont_graph, diag_candi, node)\n    if spdb == True:\n        import pdb; pdb.set_trace()\n    discont_ccs = [*netx.connected_components(discont_graph)]\n    '''\n    In some corner case, a depth edge \"discont_cc\" will contain both\n    foreground(FG) and background(BG) pixels. This violate the assumption that\n    a depth edge can only composite by one type of pixel(FG or BG).\n    We need to further divide this depth edge into several sub-part so that the\n    assumption is satisfied.\n    (A) A depth edge is invalid if both of its \"far_flag\"(BG) and\n        \"near_flag\"(FG) are True.\n    (B) If the depth edge is invalid, we need to do:\n        (1) Find the role(\"oridinal\") of each pixel on the depth edge.\n            \"-1\" --> Its opposite pixels has smaller depth(near) than it.\n                     It is a backgorund pixel.\n            \"+1\" --> Its opposite pixels has larger depth(far) than it.\n                     It is a foregorund pixel.\n            \"0\"  --> Some of opposite pixels has larger depth(far) than it,\n                     and some has smaller pixel than it.\n                     It is an ambiguous pixel.\n        (2) For each pixel \"discont_node\", check if its neigbhors' roles are consistent.\n            - If not, break the connection between the neighbor \"ne_node\" that has a role\n              different from \"discont_node\".\n            - If yes, remove all the role that are inconsistent to its neighbors \"ne_node\".\n        (3) Connected component analysis to re-identified those divided depth edge.\n    (C) Aggregate each connected part in \"discont_graph\" into \"discont_ccs\" (A.K.A. depth edge).\n    '''\n    if remove_conflict_ordinal:\n        new_discont_ccs = []\n        num_new_cc = 0\n        for edge_id, discont_cc in enumerate(discont_ccs):\n            near_flag = False\n            far_flag = False\n            for discont_node in discont_cc:\n                near_flag = True if key_exist(LDI.nodes[discont_node], 'far') else near_flag\n                far_flag = True if key_exist(LDI.nodes[discont_node], 'near') else far_flag\n                if far_flag and near_flag:\n                    break\n            if far_flag and near_flag:\n                for discont_node in discont_cc:\n                    discont_graph.nodes[discont_node]['ordinal'] = \\\n                        np.array([key_exist(LDI.nodes[discont_node], 'far'),\n                                  key_exist(LDI.nodes[discont_node], 'near')]) * \\\n                        np.array([-1, 1])\n                    discont_graph.nodes[discont_node]['ordinal'] = \\\n                        np.sum(discont_graph.nodes[discont_node]['ordinal'])\n                remove_nodes, remove_edges = [], []\n                for discont_node in discont_cc:\n                    ordinal_relation = np.sum([discont_graph.nodes[xx]['ordinal'] \\\n                                               for xx in discont_graph.neighbors(discont_node)])\n                    near_side = discont_graph.nodes[discont_node]['ordinal'] <= 0\n                    if abs(ordinal_relation) < len([*discont_graph.neighbors(discont_node)]):\n                        remove_nodes.append(discont_node)\n                        for ne_node in discont_graph.neighbors(discont_node):\n                            remove_flag = (near_side and not(key_exist(LDI.nodes[ne_node], 'far'))) or \\\n                                          (not near_side and not(key_exist(LDI.nodes[ne_node], 'near')))\n                            remove_edges += [(discont_node, ne_node)] if remove_flag else []\n                    else:\n                        if near_side and key_exist(LDI.nodes[discont_node], 'near'):\n                            LDI.nodes[discont_node].pop('near')\n                        elif not(near_side) and key_exist(LDI.nodes[discont_node], 'far'):\n                            LDI.nodes[discont_node].pop('far')\n                discont_graph.remove_edges_from(remove_edges)\n                sub_mesh = discont_graph.subgraph(list(discont_cc)).copy()\n                sub_discont_ccs = [*netx.connected_components(sub_mesh)]\n                is_redun_near = lambda xx: len(xx) == 1 and xx[0] in remove_nodes and key_exist(LDI.nodes[xx[0]], 'far')\n                for sub_discont_cc in sub_discont_ccs:\n                    if is_redun_near(list(sub_discont_cc)):\n                        LDI.nodes[list(sub_discont_cc)[0]].pop('far')\n                    new_discont_ccs.append(sub_discont_cc)\n            else:\n                new_discont_ccs.append(discont_cc)\n        discont_ccs = new_discont_ccs\n        new_discont_ccs = None\n    if spdb == True:\n        import pdb; pdb.set_trace()\n\n    for edge_id, edge_cc in enumerate(discont_ccs):\n        for node in edge_cc:\n            LDI.nodes[node]['edge_id'] = edge_id\n\n    return discont_ccs, LDI, discont_graph\n\ndef combine_end_node(mesh, edge_mesh, edge_ccs, depth):\n    import collections\n    mesh_nodes = mesh.nodes\n    connect_dict = dict()\n    for valid_edge_id, valid_edge_cc in enumerate(edge_ccs):\n        connect_info = []\n        for valid_edge_node in valid_edge_cc:\n            single_connect = set()\n            for ne_node in mesh.neighbors(valid_edge_node):\n                if mesh_nodes[ne_node].get('far') is not None:\n                    for fn in mesh_nodes[ne_node].get('far'):\n                        if mesh.has_node(fn) and mesh_nodes[fn].get('edge_id') is not None:\n                            single_connect.add(mesh_nodes[fn]['edge_id'])\n                if mesh_nodes[ne_node].get('near') is not None:\n                    for fn in mesh_nodes[ne_node].get('near'):\n                        if mesh.has_node(fn) and mesh_nodes[fn].get('edge_id') is not None:\n                            single_connect.add(mesh_nodes[fn]['edge_id'])\n            connect_info.extend([*single_connect])\n        connect_dict[valid_edge_id] = collections.Counter(connect_info)\n\n    end_maps = np.zeros((mesh.graph['H'], mesh.graph['W']))\n    edge_maps = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n    for valid_edge_id, valid_edge_cc in enumerate(edge_ccs):\n        for valid_edge_node in valid_edge_cc:\n            edge_maps[valid_edge_node[0], valid_edge_node[1]] = valid_edge_id\n            if len([*edge_mesh.neighbors(valid_edge_node)]) == 1:\n                num_ne = 1\n                if num_ne == 1:\n                    end_maps[valid_edge_node[0], valid_edge_node[1]] = valid_edge_node[2]\n    nxs, nys = np.where(end_maps != 0)\n    invalid_nodes = set()\n    for nx, ny in zip(nxs, nys):\n        if mesh.has_node((nx, ny, end_maps[nx, ny])) is False:\n            invalid_nodes.add((nx, ny))\n            continue\n        four_nes = [xx for xx in [(nx - 1, ny), (nx + 1, ny), (nx, ny - 1), (nx, ny + 1)] \\\n                        if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W'] and \\\n                        end_maps[xx[0], xx[1]] != 0]\n        mesh_nes = [*mesh.neighbors((nx, ny, end_maps[nx, ny]))]\n        remove_num = 0\n        for fne in four_nes:\n            if (fne[0], fne[1], end_maps[fne[0], fne[1]]) in mesh_nes:\n                remove_num += 1\n        if remove_num == len(four_nes):\n            invalid_nodes.add((nx, ny))\n    for invalid_node in invalid_nodes:\n        end_maps[invalid_node[0], invalid_node[1]] = 0\n\n    nxs, nys = np.where(end_maps != 0)\n    invalid_nodes = set()\n    for nx, ny in zip(nxs, nys):\n        if mesh_nodes[(nx, ny, end_maps[nx, ny])].get('edge_id') is None:\n            continue\n        else:\n            self_id = mesh_nodes[(nx, ny, end_maps[nx, ny])].get('edge_id')\n            self_connect = connect_dict[self_id] if connect_dict.get(self_id) is not None else dict()\n        four_nes = [xx for xx in [(nx - 1, ny), (nx + 1, ny), (nx, ny - 1), (nx, ny + 1)] \\\n                        if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W'] and \\\n                        end_maps[xx[0], xx[1]] != 0]\n        for fne in four_nes:\n            if mesh_nodes[(fne[0], fne[1], end_maps[fne[0], fne[1]])].get('edge_id') is None:\n                continue\n            else:\n                ne_id = mesh_nodes[(fne[0], fne[1], end_maps[fne[0], fne[1]])]['edge_id']\n                if self_connect.get(ne_id) is None or self_connect.get(ne_id) == 1:\n                    continue\n                else:\n                    invalid_nodes.add((nx, ny))\n    for invalid_node in invalid_nodes:\n        end_maps[invalid_node[0], invalid_node[1]] = 0\n    nxs, nys = np.where(end_maps != 0)\n    invalid_nodes = set()\n    for nx, ny in zip(nxs, nys):\n        four_nes = [xx for xx in [(nx - 1, ny), (nx + 1, ny), (nx, ny - 1), (nx, ny + 1)] \\\n                        if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W'] and \\\n                        end_maps[xx[0], xx[1]] != 0]\n        for fne in four_nes:\n            if mesh.has_node((fne[0], fne[1], end_maps[fne[0], fne[1]])):\n                node_a, node_b = (fne[0], fne[1], end_maps[fne[0], fne[1]]), (nx, ny, end_maps[nx, ny])\n                mesh.add_edge(node_a, node_b)\n                mesh_nodes[node_b]['must_connect'] = set() if mesh_nodes[node_b].get('must_connect') is None else mesh_nodes[node_b]['must_connect']\n                mesh_nodes[node_b]['must_connect'].add(node_a)\n                mesh_nodes[node_b]['must_connect'] |= set([xx for xx in [*edge_mesh.neighbors(node_a)] if \\\n                                                            (xx[0] - node_b[0]) < 2 and (xx[1] - node_b[1]) < 2])\n                mesh_nodes[node_a]['must_connect'] = set() if mesh_nodes[node_a].get('must_connect') is None else mesh_nodes[node_a]['must_connect']\n                mesh_nodes[node_a]['must_connect'].add(node_b)\n                mesh_nodes[node_a]['must_connect'] |= set([xx for xx in [*edge_mesh.neighbors(node_b)] if \\\n                                                            (xx[0] - node_a[0]) < 2 and (xx[1] - node_a[1]) < 2])\n                invalid_nodes.add((nx, ny))\n    for invalid_node in invalid_nodes:\n        end_maps[invalid_node[0], invalid_node[1]] = 0\n\n    return mesh\n\ndef remove_redundant_edge(mesh, edge_mesh, edge_ccs, info_on_pix, config, redundant_number=1000, invalid=False, spdb=False):\n    point_to_amount = {}\n    point_to_id = {}\n    end_maps = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n    for valid_edge_id, valid_edge_cc in enumerate(edge_ccs):\n        for valid_edge_node in valid_edge_cc:\n            point_to_amount[valid_edge_node] = len(valid_edge_cc)\n            point_to_id[valid_edge_node] = valid_edge_id\n            if edge_mesh.has_node(valid_edge_node) is True:\n                if len([*edge_mesh.neighbors(valid_edge_node)]) == 1:\n                    end_maps[valid_edge_node[0], valid_edge_node[1]] = valid_edge_id\n    nxs, nys = np.where(end_maps > -1)\n    point_to_adjoint = {}\n    for nx, ny in zip(nxs, nys):\n        adjoint_edges = set([end_maps[x, y] for x, y in [(nx + 1, ny), (nx - 1, ny), (nx, ny + 1), (nx, ny - 1)] if end_maps[x, y] != -1])\n        point_to_adjoint[end_maps[nx, ny]] = (point_to_adjoint[end_maps[nx, ny]] | adjoint_edges) if point_to_adjoint.get(end_maps[nx, ny]) is not None else adjoint_edges\n    valid_edge_ccs = filter_edge(mesh, edge_ccs, config, invalid=invalid)\n    edge_canvas = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n    for valid_edge_id, valid_edge_cc in enumerate(valid_edge_ccs):\n        for valid_edge_node in valid_edge_cc:\n            edge_canvas[valid_edge_node[0], valid_edge_node[1]] = valid_edge_id\n    if spdb is True:\n        plt.imshow(edge_canvas); plt.show()\n        import pdb; pdb.set_trace()\n    for valid_edge_id, valid_edge_cc in enumerate(valid_edge_ccs):\n        end_number = 0\n        four_end_number = 0\n        eight_end_number = 0\n        db_eight_end_number = 0\n        if len(valid_edge_cc) > redundant_number:\n            continue\n        for valid_edge_node in valid_edge_cc:\n            if len([*edge_mesh.neighbors(valid_edge_node)]) == 3:\n                break\n            elif len([*edge_mesh.neighbors(valid_edge_node)]) == 1:\n                hx, hy, hz = valid_edge_node\n                if invalid is False:\n                    eight_nes = [(x, y) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                                                     (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                                            if info_on_pix.get((x, y)) is not None and edge_canvas[x, y] != -1 and edge_canvas[x, y] != valid_edge_id]\n                    if len(eight_nes) == 0:\n                        end_number += 1\n                if invalid is True:\n                    four_nes = []; eight_nes = []; db_eight_nes = []\n                    four_nes = [(x, y) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                                            if info_on_pix.get((x, y)) is not None and edge_canvas[x, y] != -1 and edge_canvas[x, y] != valid_edge_id]\n                    eight_nes = [(x, y) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1), \\\n                                                    (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                                            if info_on_pix.get((x, y)) is not None and edge_canvas[x, y] != -1 and edge_canvas[x, y] != valid_edge_id]\n                    db_eight_nes = [(x, y) for x in range(hx - 2, hx + 3) for y in range(hy - 2, hy + 3) \\\n                                    if info_on_pix.get((x, y)) is not None and edge_canvas[x, y] != -1 and edge_canvas[x, y] != valid_edge_id and (x, y) != (hx, hy)]\n                    if len(four_nes) == 0 or len(eight_nes) == 0:\n                        end_number += 1\n                        if len(four_nes) == 0:\n                            four_end_number += 1\n                        if len(eight_nes) == 0:\n                            eight_end_number += 1\n                        if len(db_eight_nes) == 0:\n                            db_eight_end_number += 1\n            elif len([*edge_mesh.neighbors(valid_edge_node)]) == 0:\n                hx, hy, hz = valid_edge_node\n                four_nes = [(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                                if info_on_pix.get((x, y)) is not None and \\\n                                    mesh.has_edge(valid_edge_node, (x, y, info_on_pix[(x, y)][0]['depth'])) is False]\n                for ne in four_nes:\n                    try:\n                        if invalid is True or (point_to_amount.get(ne) is None or point_to_amount[ne] < redundant_number) or \\\n                            point_to_id[ne] in point_to_adjoint.get(point_to_id[valid_edge_node], set()):\n                            mesh.add_edge(valid_edge_node, ne)\n                    except:\n                        import pdb; pdb.set_trace()\n        if (invalid is not True and end_number >= 1) or (invalid is True and end_number >= 2 and eight_end_number >= 1 and db_eight_end_number >= 1):\n            for valid_edge_node in valid_edge_cc:\n                hx, hy, _ = valid_edge_node\n                four_nes = [(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                                if info_on_pix.get((x, y)) is not None and \\\n                                    mesh.has_edge(valid_edge_node, (x, y, info_on_pix[(x, y)][0]['depth'])) is False and \\\n                                    (edge_canvas[x, y] == -1 or edge_canvas[x, y] == valid_edge_id)]\n                for ne in four_nes:\n                    if invalid is True or (point_to_amount.get(ne) is None or point_to_amount[ne] < redundant_number) or \\\n                        point_to_id[ne] in point_to_adjoint.get(point_to_id[valid_edge_node], set()):\n                        mesh.add_edge(valid_edge_node, ne)\n\n    return mesh\n\ndef judge_dangle(mark, mesh, node):\n    if not (1 <= node[0] < mesh.graph['H']-1) or not(1 <= node[1] < mesh.graph['W']-1):\n        return mark\n    mesh_neighbors = [*mesh.neighbors(node)]\n    mesh_neighbors = [xx for xx in mesh_neighbors if 0 < xx[0] < mesh.graph['H'] - 1 and 0 < xx[1] < mesh.graph['W'] - 1]\n    if len(mesh_neighbors) >= 3:\n        return mark\n    elif len(mesh_neighbors) <= 1:\n        mark[node[0], node[1]] = (len(mesh_neighbors) + 1)\n    else:\n        dan_ne_node_a = mesh_neighbors[0]\n        dan_ne_node_b = mesh_neighbors[1]\n        if abs(dan_ne_node_a[0] - dan_ne_node_b[0]) > 1 or \\\n            abs(dan_ne_node_a[1] - dan_ne_node_b[1]) > 1:\n            mark[node[0], node[1]] = 3\n\n    return mark\n\ndef remove_dangling(mesh, edge_ccs, edge_mesh, info_on_pix, image, depth, config):\n\n    tmp_edge_ccs = copy.deepcopy(edge_ccs)\n    for edge_cc_id, valid_edge_cc in enumerate(tmp_edge_ccs):\n        if len(valid_edge_cc) > 1 or len(valid_edge_cc) == 0:\n            continue\n        single_edge_node = [*valid_edge_cc][0]\n        hx, hy, hz = single_edge_node\n        eight_nes = set([(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                         (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                         if info_on_pix.get((x, y)) is not None])\n        four_nes = [(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                    if info_on_pix.get((x, y)) is not None]\n        sub_mesh = mesh.subgraph(eight_nes).copy()\n        ccs = netx.connected_components(sub_mesh)\n        four_ccs = []\n        for cc_id, _cc in enumerate(ccs):\n            four_ccs.append(set())\n            for cc_node in _cc:\n                if abs(cc_node[0] - hx) + abs(cc_node[1] - hy) < 2:\n                    four_ccs[cc_id].add(cc_node)\n        largest_cc = sorted(four_ccs, key=lambda x: (len(x), -np.sum([abs(xx[2] - hz) for xx in x])))[-1]\n        if len(largest_cc) < 2:\n            for ne in four_nes:\n                mesh.add_edge(single_edge_node, ne)\n        else:\n            mesh.remove_edges_from([(single_edge_node, ne) for ne in mesh.neighbors(single_edge_node)])\n            new_depth = np.mean([xx[2] for xx in largest_cc])\n            info_on_pix[(hx, hy)][0]['depth'] = new_depth\n            info_on_pix[(hx, hy)][0]['disp'] = 1./new_depth\n            new_node = (hx, hy, new_depth)\n            mesh = refresh_node(single_edge_node, mesh.node[single_edge_node], new_node, dict(), mesh)\n            edge_ccs[edge_cc_id] = set([new_node])\n            for ne in largest_cc:\n                mesh.add_edge(new_node, ne)\n\n    mark = np.zeros((mesh.graph['H'], mesh.graph['W']))\n    for edge_idx, edge_cc in enumerate(edge_ccs):\n        for edge_node in edge_cc:\n            if not (mesh.graph['bord_up'] <= edge_node[0] < mesh.graph['bord_down']-1) or \\\n               not (mesh.graph['bord_left'] <= edge_node[1] < mesh.graph['bord_right']-1):\n                continue\n            mesh_neighbors = [*mesh.neighbors(edge_node)]\n            mesh_neighbors = [xx for xx in mesh_neighbors \\\n                                if mesh.graph['bord_up'] < xx[0] < mesh.graph['bord_down'] - 1 and \\\n                                   mesh.graph['bord_left'] < xx[1] < mesh.graph['bord_right'] - 1]\n            if len([*mesh.neighbors(edge_node)]) >= 3:\n                continue\n            elif len([*mesh.neighbors(edge_node)]) <= 1:\n                mark[edge_node[0], edge_node[1]] += (len([*mesh.neighbors(edge_node)]) + 1)\n            else:\n                dan_ne_node_a = [*mesh.neighbors(edge_node)][0]\n                dan_ne_node_b = [*mesh.neighbors(edge_node)][1]\n                if abs(dan_ne_node_a[0] - dan_ne_node_b[0]) > 1 or \\\n                    abs(dan_ne_node_a[1] - dan_ne_node_b[1]) > 1:\n                    mark[edge_node[0], edge_node[1]] += 3\n    mxs, mys = np.where(mark == 1)\n    conn_0_nodes = [(x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']) for x in zip(mxs, mys) \\\n                        if mesh.has_node((x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']))]\n    mxs, mys = np.where(mark == 2)\n    conn_1_nodes = [(x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']) for x in zip(mxs, mys) \\\n                        if mesh.has_node((x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']))]\n    for node in conn_0_nodes:\n        hx, hy = node[0], node[1]\n        four_nes = [(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                     if info_on_pix.get((x, y)) is not None]\n        re_depth = {'value' : 0, 'count': 0}\n        for ne in four_nes:\n            mesh.add_edge(node, ne)\n            re_depth['value'] += cc_node[2]\n            re_depth['count'] += 1.\n        re_depth = re_depth['value'] / re_depth['count']\n        mapping_dict = {node: (node[0], node[1], re_depth)}\n        info_on_pix, mesh, edge_mesh = update_info(mapping_dict, info_on_pix, mesh, edge_mesh)\n        depth[node[0], node[1]] = abs(re_depth)\n        mark[node[0], node[1]] = 0\n    for node in conn_1_nodes:\n        hx, hy = node[0], node[1]\n        eight_nes = set([(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                                                                           (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                        if info_on_pix.get((x, y)) is not None])\n        self_nes = set([ne2 for ne1 in mesh.neighbors(node) for ne2 in mesh.neighbors(ne1) if ne2 in eight_nes])\n        eight_nes = [*(eight_nes - self_nes)]\n        sub_mesh = mesh.subgraph(eight_nes).copy()\n        ccs = netx.connected_components(sub_mesh)\n        largest_cc = sorted(ccs, key=lambda x: (len(x), -np.sum([abs(xx[0] - node[0]) + abs(xx[1] - node[1]) for xx in x])))[-1]\n\n        mesh.remove_edges_from([(xx, node) for xx in mesh.neighbors(node)])\n        re_depth = {'value' : 0, 'count': 0}\n        for cc_node in largest_cc:\n            if cc_node[0] == node[0] and cc_node[1] == node[1]:\n                continue\n            re_depth['value'] += cc_node[2]\n            re_depth['count'] += 1.\n            if abs(cc_node[0] - node[0]) + abs(cc_node[1] - node[1]) < 2:\n                mesh.add_edge(cc_node, node)\n        try:\n            re_depth = re_depth['value'] / re_depth['count']\n        except:\n            re_depth = node[2]\n        renode = (node[0], node[1], re_depth)\n        mapping_dict = {node: renode}\n        info_on_pix, mesh, edge_mesh = update_info(mapping_dict, info_on_pix, mesh, edge_mesh)\n        depth[node[0], node[1]] = abs(re_depth)\n        mark[node[0], node[1]] = 0\n        edge_mesh, mesh, mark, info_on_pix = recursive_add_edge(edge_mesh, mesh, info_on_pix, renode, mark)\n    mxs, mys = np.where(mark == 3)\n    conn_2_nodes = [(x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']) for x in zip(mxs, mys) \\\n                        if mesh.has_node((x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth'])) and \\\n                            mesh.degree((x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth'])) == 2]\n    sub_mesh = mesh.subgraph(conn_2_nodes).copy()\n    ccs = netx.connected_components(sub_mesh)\n    for cc in ccs:\n        candidate_nodes = [xx for xx in cc if sub_mesh.degree(xx) == 1]\n        for node in candidate_nodes:\n            if mesh.has_node(node) is False:\n                continue\n            ne_node = [xx for xx in mesh.neighbors(node) if xx not in cc][0]\n            hx, hy = node[0], node[1]\n            eight_nes = set([(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                                                                            (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                              if info_on_pix.get((x, y)) is not None and (x, y, info_on_pix[(x, y)][0]['depth']) not in cc])\n            ne_sub_mesh = mesh.subgraph(eight_nes).copy()\n            ne_ccs = netx.connected_components(ne_sub_mesh)\n            try:\n                ne_cc = [ne_cc for ne_cc in ne_ccs if ne_node in ne_cc][0]\n            except:\n                import pdb; pdb.set_trace()\n            largest_cc = [xx for xx in ne_cc if abs(xx[0] - node[0]) + abs(xx[1] - node[1]) == 1]\n            mesh.remove_edges_from([(xx, node) for xx in mesh.neighbors(node)])\n            re_depth = {'value' : 0, 'count': 0}\n            for cc_node in largest_cc:\n                re_depth['value'] += cc_node[2]\n                re_depth['count'] += 1.\n                mesh.add_edge(cc_node, node)\n            try:\n                re_depth = re_depth['value'] / re_depth['count']\n            except:\n                re_depth = node[2]\n            renode = (node[0], node[1], re_depth)\n            mapping_dict = {node: renode}\n            info_on_pix, mesh, edge_mesh = update_info(mapping_dict, info_on_pix, mesh, edge_mesh)\n            depth[node[0], node[1]] = abs(re_depth)\n            mark[node[0], node[1]] = 0\n            edge_mesh, mesh, mark, info_on_pix = recursive_add_edge(edge_mesh, mesh, info_on_pix, renode, mark)\n            break\n        if len(cc) == 1:\n            node = [node for node in cc][0]\n            hx, hy = node[0], node[1]\n            nine_nes = set([(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx, hy), (hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                                                                                  (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                                if info_on_pix.get((x, y)) is not None and mesh.has_node((x, y, info_on_pix[(x, y)][0]['depth']))])\n            ne_sub_mesh = mesh.subgraph(nine_nes).copy()\n            ne_ccs = netx.connected_components(ne_sub_mesh)\n            for ne_cc in ne_ccs:\n                if node in ne_cc:\n                    re_depth = {'value' : 0, 'count': 0}\n                    for ne in ne_cc:\n                        if abs(ne[0] - node[0]) + abs(ne[1] - node[1]) == 1:\n                            mesh.add_edge(node, ne)\n                            re_depth['value'] += ne[2]\n                            re_depth['count'] += 1.\n                    re_depth = re_depth['value'] / re_depth['count']\n                    mapping_dict = {node: (node[0], node[1], re_depth)}\n                    info_on_pix, mesh, edge_mesh = update_info(mapping_dict, info_on_pix, mesh, edge_mesh)\n                    depth[node[0], node[1]] = abs(re_depth)\n                    mark[node[0], node[1]] = 0\n\n\n    return mesh, info_on_pix, edge_mesh, depth, mark\n\ndef context_and_holes(mesh, edge_ccs, config, specific_edge_id, specific_edge_loc, depth_feat_model,\n                      connect_points_ccs=None, inpaint_iter=0, filter_edge=False, vis_edge_id=None):\n    edge_maps = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n    mask_info = {}\n    for edge_id, edge_cc in enumerate(edge_ccs):\n        for edge_node in edge_cc:\n            edge_maps[edge_node[0], edge_node[1]] = edge_id\n\n    context_ccs = [set() for x in range(len(edge_ccs))]\n    extend_context_ccs = [set() for x in range(len(edge_ccs))]\n    extend_erode_context_ccs = [set() for x in range(len(edge_ccs))]\n    extend_edge_ccs = [set() for x in range(len(edge_ccs))]\n    accomp_extend_context_ccs = [set() for x in range(len(edge_ccs))]\n    erode_context_ccs = [set() for x in range(len(edge_ccs))]\n    broken_mask_ccs = [set() for x in range(len(edge_ccs))]\n    invalid_extend_edge_ccs = [set() for x in range(len(edge_ccs))]\n    intouched_ccs = [set() for x in range(len(edge_ccs))]\n    redundant_ccs = [set() for x in range(len(edge_ccs))]\n    if inpaint_iter == 0:\n        background_thickness = config['background_thickness']\n        context_thickness = config['context_thickness']\n    else:\n        background_thickness = config['background_thickness_2']\n        context_thickness = config['context_thickness_2']\n\n    mesh_nodes = mesh.nodes\n    for edge_id, edge_cc in enumerate(edge_ccs):\n        if context_thickness == 0 or (len(specific_edge_id) > 0 and edge_id not in specific_edge_id):\n            continue\n        edge_group = {}\n        for edge_node in edge_cc:\n            far_nodes = mesh_nodes[edge_node].get('far')\n            if far_nodes is None:\n                continue\n            for far_node in far_nodes:\n                if far_node in edge_cc:\n                    continue\n                context_ccs[edge_id].add(far_node)\n                if mesh_nodes[far_node].get('edge_id') is not None:\n                    if edge_group.get(mesh_nodes[far_node]['edge_id']) is None:\n                        edge_group[mesh_nodes[far_node]['edge_id']] = set()\n                    edge_group[mesh_nodes[far_node]['edge_id']].add(far_node)\n        if len(edge_cc) > 2:\n            for edge_key in [*edge_group.keys()]:\n                if len(edge_group[edge_key]) == 1:\n                    context_ccs[edge_id].remove([*edge_group[edge_key]][0])\n    for edge_id, edge_cc in enumerate(edge_ccs):\n        if inpaint_iter != 0:\n            continue\n        tmp_intouched_nodes = set()\n        for edge_node in edge_cc:\n            raw_intouched_nodes = set(mesh_nodes[edge_node].get('near')) if mesh_nodes[edge_node].get('near') is not None else set()\n            tmp_intouched_nodes |= set([xx for xx in raw_intouched_nodes if mesh_nodes[xx].get('edge_id') is not None and \\\n                                                                         len(context_ccs[mesh_nodes[xx].get('edge_id')]) > 0])\n        intouched_ccs[edge_id] |= tmp_intouched_nodes\n        tmp_intouched_nodes = None\n    mask_ccs = copy.deepcopy(edge_ccs)\n    forbidden_len = 3\n    forbidden_map = np.ones((mesh.graph['H'] - forbidden_len, mesh.graph['W'] - forbidden_len))\n    forbidden_map = np.pad(forbidden_map, ((forbidden_len, forbidden_len), (forbidden_len, forbidden_len)), mode='constant').astype(np.bool)\n    cur_tmp_mask_map = np.zeros_like(forbidden_map).astype(np.bool)\n    passive_background = 10 if 10 is not None else background_thickness\n    passive_context = 1 if 1 is not None else context_thickness\n\n    for edge_id, edge_cc in enumerate(edge_ccs):\n        cur_mask_cc = None; cur_mask_cc = []\n        cur_context_cc = None; cur_context_cc = []\n        cur_accomp_near_cc = None; cur_accomp_near_cc = []\n        cur_invalid_extend_edge_cc = None; cur_invalid_extend_edge_cc = []\n        cur_comp_far_cc = None; cur_comp_far_cc = []\n        tmp_erode = []\n        if len(context_ccs[edge_id]) == 0 or (len(specific_edge_id) > 0 and edge_id not in specific_edge_id):\n            continue\n        for i in range(max(background_thickness, context_thickness)):\n            cur_tmp_mask_map.fill(False)\n            if i == 0:\n                tmp_mask_nodes = copy.deepcopy(mask_ccs[edge_id])\n                tmp_intersect_nodes = []\n                tmp_intersect_context_nodes = []\n                mask_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                context_depth = np.zeros((mesh.graph['H'], mesh.graph['W']))\n                comp_cnt_depth = np.zeros((mesh.graph['H'], mesh.graph['W']))\n                connect_map = np.zeros((mesh.graph['H'], mesh.graph['W']))\n                for node in tmp_mask_nodes:\n                    mask_map[node[0], node[1]] = True\n                    depth_count = 0\n                    if mesh_nodes[node].get('far') is not None:\n                        for comp_cnt_node in mesh_nodes[node]['far']:\n                            comp_cnt_depth[node[0], node[1]] += abs(comp_cnt_node[2])\n                            depth_count += 1\n                    if depth_count > 0:\n                        comp_cnt_depth[node[0], node[1]] = comp_cnt_depth[node[0], node[1]] / depth_count\n                    connect_node = []\n                    if mesh_nodes[node].get('connect_point_id') is not None:\n                        connect_node.append(mesh_nodes[node]['connect_point_id'])\n                    connect_point_id = np.bincount(connect_node).argmax() if len(connect_node) > 0 else -1\n                    if connect_point_id > -1 and connect_points_ccs is not None:\n                        for xx in connect_points_ccs[connect_point_id]:\n                            if connect_map[xx[0], xx[1]] == 0:\n                                connect_map[xx[0], xx[1]] = xx[2]\n                    if mesh_nodes[node].get('connect_point_exception') is not None:\n                        for xx in mesh_nodes[node]['connect_point_exception']:\n                            if connect_map[xx[0], xx[1]] == 0:\n                                connect_map[xx[0], xx[1]] = xx[2]\n                tmp_context_nodes = [*context_ccs[edge_id]]\n                tmp_erode.append([*context_ccs[edge_id]])\n                context_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                if (context_map.astype(np.uint8) * mask_map.astype(np.uint8)).max() > 0:\n                    import pdb; pdb.set_trace()\n                for node in tmp_context_nodes:\n                    context_map[node[0], node[1]] = True\n                    context_depth[node[0], node[1]] = node[2]\n                context_map[mask_map == True] = False\n                if (context_map.astype(np.uint8) * mask_map.astype(np.uint8)).max() > 0:\n                    import pdb; pdb.set_trace()\n                tmp_intouched_nodes = [*intouched_ccs[edge_id]]\n                intouched_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                for node in tmp_intouched_nodes: intouched_map[node[0], node[1]] = True\n                intouched_map[mask_map == True] = False\n                tmp_redundant_nodes = set()\n                tmp_noncont_nodes = set()\n                noncont_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                intersect_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                intersect_context_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n            if i > passive_background and inpaint_iter == 0:\n                new_tmp_intersect_nodes = None\n                new_tmp_intersect_nodes = []\n                for node in tmp_intersect_nodes:\n                    nes = mesh.neighbors(node)\n                    for ne in nes:\n                        if bool(context_map[ne[0], ne[1]]) is False and \\\n                        bool(mask_map[ne[0], ne[1]]) is False and \\\n                        bool(forbidden_map[ne[0], ne[1]]) is True and \\\n                        bool(intouched_map[ne[0], ne[1]]) is False and\\\n                        bool(intersect_map[ne[0], ne[1]]) is False and\\\n                        bool(intersect_context_map[ne[0], ne[1]]) is False:\n                            break_flag = False\n                            if (i - passive_background) % 2 == 0 and (i - passive_background) % 8 != 0:\n                                four_nes = [xx for xx in[[ne[0] - 1, ne[1]], [ne[0] + 1, ne[1]], [ne[0], ne[1] - 1], [ne[0], ne[1] + 1]] \\\n                                                if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W']]\n                                for fne in four_nes:\n                                    if bool(mask_map[fne[0], fne[1]]) is True:\n                                        break_flag = True\n                                        break\n                                if break_flag is True:\n                                    continue\n                            intersect_map[ne[0], ne[1]] = True\n                            new_tmp_intersect_nodes.append(ne)\n                tmp_intersect_nodes = None\n                tmp_intersect_nodes = new_tmp_intersect_nodes\n\n            if i > passive_context and inpaint_iter == 1:\n                new_tmp_intersect_context_nodes = None\n                new_tmp_intersect_context_nodes = []\n                for node in tmp_intersect_context_nodes:\n                    nes = mesh.neighbors(node)\n                    for ne in nes:\n                        if bool(context_map[ne[0], ne[1]]) is False and \\\n                        bool(mask_map[ne[0], ne[1]]) is False and \\\n                        bool(forbidden_map[ne[0], ne[1]]) is True and \\\n                        bool(intouched_map[ne[0], ne[1]]) is False and\\\n                        bool(intersect_map[ne[0], ne[1]]) is False and \\\n                        bool(intersect_context_map[ne[0], ne[1]]) is False:\n                            intersect_context_map[ne[0], ne[1]] = True\n                            new_tmp_intersect_context_nodes.append(ne)\n                tmp_intersect_context_nodes = None\n                tmp_intersect_context_nodes = new_tmp_intersect_context_nodes\n\n            new_tmp_mask_nodes = None\n            new_tmp_mask_nodes = []\n            for node in tmp_mask_nodes:\n                four_nes = {xx:[] for xx in [(node[0] - 1, node[1]), (node[0] + 1, node[1]), (node[0], node[1] - 1), (node[0], node[1] + 1)] if \\\n                            0 <= xx[0] < connect_map.shape[0] and 0 <= xx[1] < connect_map.shape[1]}\n                if inpaint_iter > 0:\n                    for ne in four_nes.keys():\n                        if connect_map[ne[0], ne[1]] == True:\n                            tmp_context_nodes.append((ne[0], ne[1], connect_map[ne[0], ne[1]]))\n                            context_map[ne[0], ne[1]] = True\n                nes = mesh.neighbors(node)\n                if inpaint_iter > 0:\n                    for ne in nes: four_nes[(ne[0], ne[1])].append(ne[2])\n                    nes = []\n                    for kfne, vfnes in four_nes.items(): vfnes.sort(key = lambda xx: abs(xx), reverse=True)\n                    for kfne, vfnes in four_nes.items():\n                        for vfne in vfnes: nes.append((kfne[0], kfne[1], vfne))\n                for ne in nes:\n                    if bool(context_map[ne[0], ne[1]]) is False and \\\n                       bool(mask_map[ne[0], ne[1]]) is False and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True and \\\n                       bool(intouched_map[ne[0], ne[1]]) is False and \\\n                       bool(intersect_map[ne[0], ne[1]]) is False and \\\n                       bool(intersect_context_map[ne[0], ne[1]]) is False:\n                        if i == passive_background and inpaint_iter == 0:\n                            if np.any(context_map[max(ne[0] - 1, 0):min(ne[0] + 2, mesh.graph['H']), max(ne[1] - 1, 0):min(ne[1] + 2, mesh.graph['W'])]) == True:\n                                intersect_map[ne[0], ne[1]] = True\n                                tmp_intersect_nodes.append(ne)\n                                continue\n                        if i < background_thickness:\n                            if inpaint_iter == 0:\n                                cur_mask_cc.append(ne)\n                            elif mesh_nodes[ne].get('inpaint_id') == 1:\n                                cur_mask_cc.append(ne)\n                            else:\n                                continue\n                            mask_ccs[edge_id].add(ne)\n                            if inpaint_iter == 0:\n                                if comp_cnt_depth[node[0], node[1]] > 0 and comp_cnt_depth[ne[0], ne[1]] == 0:\n                                    comp_cnt_depth[ne[0], ne[1]] = comp_cnt_depth[node[0], node[1]]\n                                if mesh_nodes[ne].get('far') is not None:\n                                    for comp_far_node in mesh_nodes[ne]['far']:\n                                        cur_comp_far_cc.append(comp_far_node)\n                                        cur_accomp_near_cc.append(ne)\n                                        cur_invalid_extend_edge_cc.append(comp_far_node)\n                                if mesh_nodes[ne].get('edge_id') is not None and \\\n                                    len(context_ccs[mesh_nodes[ne].get('edge_id')]) > 0:\n                                    intouched_fars = set(mesh_nodes[ne].get('far')) if mesh_nodes[ne].get('far') is not None else set()\n                                    accum_intouched_fars = set(intouched_fars)\n                                    for intouched_far in intouched_fars:\n                                        accum_intouched_fars |= set([*mesh.neighbors(intouched_far)])\n                                    for intouched_far in accum_intouched_fars:\n                                        if bool(mask_map[intouched_far[0], intouched_far[1]]) is True or \\\n                                        bool(context_map[intouched_far[0], intouched_far[1]]) is True:\n                                            continue\n                                        tmp_redundant_nodes.add(intouched_far)\n                                        intouched_map[intouched_far[0], intouched_far[1]] = True\n                                if mesh_nodes[ne].get('near') is not None:\n                                    intouched_nears = set(mesh_nodes[ne].get('near'))\n                                    for intouched_near in intouched_nears:\n                                        if bool(mask_map[intouched_near[0], intouched_near[1]]) is True or \\\n                                        bool(context_map[intouched_near[0], intouched_near[1]]) is True:\n                                            continue\n                                        tmp_redundant_nodes.add(intouched_near)\n                                        intouched_map[intouched_near[0], intouched_near[1]] = True\n                        if not (mesh_nodes[ne].get('inpaint_id') != 1 and inpaint_iter == 1):\n                            new_tmp_mask_nodes.append(ne)\n                            mask_map[ne[0], ne[1]] = True\n            tmp_mask_nodes = new_tmp_mask_nodes\n\n            new_tmp_context_nodes = None\n            new_tmp_context_nodes = []\n            for node in tmp_context_nodes:\n                nes = mesh.neighbors(node)\n                if inpaint_iter > 0:\n                    four_nes = {(node[0] - 1, node[1]):[], (node[0] + 1, node[1]):[], (node[0], node[1] - 1):[], (node[0], node[1] + 1):[]}\n                    for ne in nes: four_nes[(ne[0], ne[1])].append(ne[2])\n                    nes = []\n                    for kfne, vfnes in four_nes.items(): vfnes.sort(key = lambda xx: abs(xx), reverse=True)\n                    for kfne, vfnes in four_nes.items():\n                        for vfne in vfnes: nes.append((kfne[0], kfne[1], vfne))\n                for ne in nes:\n                    mask_flag = (bool(mask_map[ne[0], ne[1]]) is False)\n                    if bool(context_map[ne[0], ne[1]]) is False and mask_flag and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True and bool(noncont_map[ne[0], ne[1]]) is False and \\\n                       bool(intersect_context_map[ne[0], ne[1]]) is False:\n                        if i == passive_context and inpaint_iter == 1:\n                            mnes = mesh.neighbors(ne)\n                            if any([mask_map[mne[0], mne[1]] == True for mne in mnes]) is True:\n                                intersect_context_map[ne[0], ne[1]] = True\n                                tmp_intersect_context_nodes.append(ne)\n                                continue\n                        if False and mesh_nodes[ne].get('near') is not None and mesh_nodes[ne].get('edge_id') != edge_id:\n                            noncont_nears = set(mesh_nodes[ne].get('near'))\n                            for noncont_near in noncont_nears:\n                                if bool(context_map[noncont_near[0], noncont_near[1]]) is False:\n                                    tmp_noncont_nodes.add(noncont_near)\n                                    noncont_map[noncont_near[0], noncont_near[1]] = True\n                        new_tmp_context_nodes.append(ne)\n                        context_map[ne[0], ne[1]] = True\n                        context_depth[ne[0], ne[1]] = ne[2]\n            cur_context_cc.extend(new_tmp_context_nodes)\n            tmp_erode.append(new_tmp_context_nodes)\n            tmp_context_nodes = None\n            tmp_context_nodes = new_tmp_context_nodes\n            new_tmp_intouched_nodes = None; new_tmp_intouched_nodes = []\n\n            for node in tmp_intouched_nodes:\n                if bool(context_map[node[0], node[1]]) is True or bool(mask_map[node[0], node[1]]) is True:\n                    continue\n                nes = mesh.neighbors(node)\n\n                for ne in nes:\n                    if bool(context_map[ne[0], ne[1]]) is False and \\\n                       bool(mask_map[ne[0], ne[1]]) is False and \\\n                       bool(intouched_map[ne[0], ne[1]]) is False and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True:\n                        new_tmp_intouched_nodes.append(ne)\n                        intouched_map[ne[0], ne[1]] = True\n            tmp_intouched_nodes = None\n            tmp_intouched_nodes = set(new_tmp_intouched_nodes)\n            new_tmp_redundant_nodes = None; new_tmp_redundant_nodes = []\n            for node in tmp_redundant_nodes:\n                if bool(context_map[node[0], node[1]]) is True or \\\n                   bool(mask_map[node[0], node[1]]) is True:\n                    continue\n                nes = mesh.neighbors(node)\n\n                for ne in nes:\n                    if bool(context_map[ne[0], ne[1]]) is False and \\\n                       bool(mask_map[ne[0], ne[1]]) is False and \\\n                       bool(intouched_map[ne[0], ne[1]]) is False and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True:\n                        new_tmp_redundant_nodes.append(ne)\n                        intouched_map[ne[0], ne[1]] = True\n            tmp_redundant_nodes = None\n            tmp_redundant_nodes = set(new_tmp_redundant_nodes)\n            new_tmp_noncont_nodes = None; new_tmp_noncont_nodes = []\n            for node in tmp_noncont_nodes:\n                if bool(context_map[node[0], node[1]]) is True or \\\n                   bool(mask_map[node[0], node[1]]) is True:\n                    continue\n                nes = mesh.neighbors(node)\n                rmv_flag = False\n                for ne in nes:\n                    if bool(context_map[ne[0], ne[1]]) is False and \\\n                       bool(mask_map[ne[0], ne[1]]) is False and \\\n                       bool(noncont_map[ne[0], ne[1]]) is False and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True:\n                        patch_context_map = context_map[max(ne[0] - 1, 0):min(ne[0] + 2, context_map.shape[0]),\n                                                        max(ne[1] - 1, 0):min(ne[1] + 2, context_map.shape[1])]\n                        if bool(np.any(patch_context_map)) is True:\n                            new_tmp_noncont_nodes.append(ne)\n                            noncont_map[ne[0], ne[1]] = True\n            tmp_noncont_nodes = None\n            tmp_noncont_nodes = set(new_tmp_noncont_nodes)\n        if inpaint_iter == 0:\n            depth_dict = get_depth_from_maps(context_map, mask_map, context_depth, mesh.graph['H'], mesh.graph['W'], log_depth=config['log_depth'])\n            mask_size = get_valid_size(depth_dict['mask'])\n            mask_size = dilate_valid_size(mask_size, depth_dict['mask'], dilate=[20, 20])\n            context_size = get_valid_size(depth_dict['context'])\n            context_size = dilate_valid_size(context_size, depth_dict['context'], dilate=[20, 20])\n            union_size = size_operation(mask_size, context_size, operation='+')\n            depth_dict = depth_inpainting(None, None, None, None, mesh, config, union_size, depth_feat_model, None, given_depth_dict=depth_dict, spdb=False)\n            near_depth_map, raw_near_depth_map = np.zeros((mesh.graph['H'], mesh.graph['W'])), np.zeros((mesh.graph['H'], mesh.graph['W']))\n            filtered_comp_far_cc, filtered_accomp_near_cc = set(), set()\n            for node in cur_accomp_near_cc:\n                near_depth_map[node[0], node[1]] = depth_dict['output'][node[0], node[1]]\n                raw_near_depth_map[node[0], node[1]] = node[2]\n            for node in cur_comp_far_cc:\n                four_nes = [xx for xx in [(node[0] - 1, node[1]), (node[0] + 1, node[1]), (node[0], node[1] - 1), (node[0], node[1] + 1)] \\\n                            if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W'] and \\\n                            near_depth_map[xx[0], xx[1]] != 0 and \\\n                            abs(near_depth_map[xx[0], xx[1]]) < abs(node[2])]\n                if len(four_nes) > 0:\n                    filtered_comp_far_cc.add(node)\n                for ne in four_nes:\n                    filtered_accomp_near_cc.add((ne[0], ne[1], -abs(raw_near_depth_map[ne[0], ne[1]])))\n            cur_comp_far_cc, cur_accomp_near_cc = filtered_comp_far_cc, filtered_accomp_near_cc\n        mask_ccs[edge_id] |= set(cur_mask_cc)\n        context_ccs[edge_id] |= set(cur_context_cc)\n        accomp_extend_context_ccs[edge_id] |= set(cur_accomp_near_cc).intersection(cur_mask_cc)\n        extend_edge_ccs[edge_id] |= set(cur_accomp_near_cc).intersection(cur_mask_cc)\n        extend_context_ccs[edge_id] |= set(cur_comp_far_cc)\n        invalid_extend_edge_ccs[edge_id] |= set(cur_invalid_extend_edge_cc)\n        erode_size = [0]\n        for tmp in tmp_erode:\n            erode_size.append(len(tmp))\n            if len(erode_size) > 1:\n                erode_size[-1] += erode_size[-2]\n        if inpaint_iter == 0:\n            tmp_width = config['depth_edge_dilate']\n        else:\n            tmp_width = 0\n        while float(erode_size[tmp_width]) / (erode_size[-1] + 1e-6) > 0.3:\n            tmp_width = tmp_width - 1\n        try:\n            if tmp_width == 0:\n                erode_context_ccs[edge_id] = set([])\n            else:\n                erode_context_ccs[edge_id] = set(reduce(lambda x, y : x + y, [] + tmp_erode[:tmp_width]))\n        except:\n            import pdb; pdb.set_trace()\n        erode_context_cc = copy.deepcopy(erode_context_ccs[edge_id])\n        for erode_context_node in erode_context_cc:\n            if (inpaint_iter != 0 and (mesh_nodes[erode_context_node].get('inpaint_id') is None or\n                                        mesh_nodes[erode_context_node].get('inpaint_id') == 0)):\n                erode_context_ccs[edge_id].remove(erode_context_node)\n            else:\n                context_ccs[edge_id].remove(erode_context_node)\n        context_map = np.zeros((mesh.graph['H'], mesh.graph['W']))\n        for context_node in context_ccs[edge_id]:\n            context_map[context_node[0], context_node[1]] = 1\n        extend_context_ccs[edge_id] = extend_context_ccs[edge_id] - mask_ccs[edge_id] - accomp_extend_context_ccs[edge_id]\n    if inpaint_iter == 0:\n        all_ecnt_cc = set()\n        for ecnt_id, ecnt_cc in enumerate(extend_context_ccs):\n            constraint_context_ids = set()\n            constraint_context_cc = set()\n            constraint_erode_context_cc = set()\n            tmp_mask_cc = set()\n            accum_context_cc = None; accum_context_cc = []\n            for ecnt_node in accomp_extend_context_ccs[ecnt_id]:\n                if edge_maps[ecnt_node[0], ecnt_node[1]] > -1:\n                    constraint_context_ids.add(int(round(edge_maps[ecnt_node[0], ecnt_node[1]])))\n            constraint_erode_context_cc = erode_context_ccs[ecnt_id]\n            for constraint_context_id in constraint_context_ids:\n                constraint_context_cc = constraint_context_cc | context_ccs[constraint_context_id] | erode_context_ccs[constraint_context_id]\n                constraint_erode_context_cc = constraint_erode_context_cc | erode_context_ccs[constraint_context_id]\n            for i in range(background_thickness):\n                if i == 0:\n                    tmp_context_nodes = copy.deepcopy(ecnt_cc)\n                    tmp_invalid_context_nodes = copy.deepcopy(invalid_extend_edge_ccs[ecnt_id])\n                    tmp_mask_nodes = copy.deepcopy(accomp_extend_context_ccs[ecnt_id])\n                    tmp_context_map = np.zeros((mesh.graph['H'], mesh.graph['W'])).astype(np.bool)\n                    tmp_mask_map = np.zeros((mesh.graph['H'], mesh.graph['W'])).astype(np.bool)\n                    tmp_invalid_context_map = np.zeros((mesh.graph['H'], mesh.graph['W'])).astype(np.bool)\n                    for node in tmp_mask_nodes:\n                        tmp_mask_map[node[0], node[1]] = True\n                    for node in context_ccs[ecnt_id]:\n                        tmp_context_map[node[0], node[1]] = True\n                    for node in erode_context_ccs[ecnt_id]:\n                        tmp_context_map[node[0], node[1]] = True\n                    for node in extend_context_ccs[ecnt_id]:\n                        tmp_context_map[node[0], node[1]] = True\n                    for node in invalid_extend_edge_ccs[ecnt_id]:\n                        tmp_invalid_context_map[node[0], node[1]] = True\n                    init_invalid_context_map = tmp_invalid_context_map.copy()\n                    init_context_map = tmp\n                    if (tmp_mask_map.astype(np.uint8) * tmp_context_map.astype(np.uint8)).max() > 0:\n                        import pdb; pdb.set_trace()\n                    if vis_edge_id is not None and ecnt_id == vis_edge_id:\n                        f, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True)\n                        ax1.imshow(tmp_context_map * 1); ax2.imshow(init_invalid_context_map * 1 + tmp_context_map * 2)\n                        plt.show()\n                        import pdb; pdb.set_trace()\n                else:\n                    tmp_context_nodes = new_tmp_context_nodes\n                    new_tmp_context_nodes = None\n                    tmp_mask_nodes = new_tmp_mask_nodes\n                    new_tmp_mask_nodes = None\n                    tmp_invalid_context_nodes = new_tmp_invalid_context_nodes\n                    new_tmp_invalid_context_nodes = None\n                new_tmp_context_nodes = None\n                new_tmp_context_nodes = []\n                new_tmp_invalid_context_nodes = None\n                new_tmp_invalid_context_nodes = []\n                new_tmp_mask_nodes = set([])\n                for node in tmp_context_nodes:\n                    for ne in mesh.neighbors(node):\n                        if ne in constraint_context_cc and \\\n                            bool(tmp_mask_map[ne[0], ne[1]]) is False and \\\n                            bool(tmp_context_map[ne[0], ne[1]]) is False and \\\n                            bool(forbidden_map[ne[0], ne[1]]) is True:\n                            new_tmp_context_nodes.append(ne)\n                            tmp_context_map[ne[0], ne[1]] = True\n                accum_context_cc.extend(new_tmp_context_nodes)\n                for node in tmp_invalid_context_nodes:\n                    for ne in mesh.neighbors(node):\n                        if bool(tmp_mask_map[ne[0], ne[1]]) is False and \\\n                           bool(tmp_context_map[ne[0], ne[1]]) is False and \\\n                           bool(tmp_invalid_context_map[ne[0], ne[1]]) is False and \\\n                           bool(forbidden_map[ne[0], ne[1]]) is True:\n                            tmp_invalid_context_map[ne[0], ne[1]] = True\n                            new_tmp_invalid_context_nodes.append(ne)\n                for node in tmp_mask_nodes:\n                    for ne in mesh.neighbors(node):\n                        if bool(tmp_mask_map[ne[0], ne[1]]) is False and \\\n                           bool(tmp_context_map[ne[0], ne[1]]) is False and \\\n                           bool(tmp_invalid_context_map[ne[0], ne[1]]) is False and \\\n                           bool(forbidden_map[ne[0], ne[1]]) is True:\n                            new_tmp_mask_nodes.add(ne)\n                            tmp_mask_map[ne[0], ne[1]] = True\n            init_invalid_context_map[tmp_context_map] = False\n            _, tmp_label_map = cv2.connectedComponents((init_invalid_context_map | tmp_context_map).astype(np.uint8), connectivity=8)\n            tmp_label_ids = set(np.unique(tmp_label_map[init_invalid_context_map]))\n            if (tmp_mask_map.astype(np.uint8) * tmp_context_map.astype(np.uint8)).max() > 0:\n                import pdb; pdb.set_trace()\n            if vis_edge_id is not None and ecnt_id == vis_edge_id:\n                f, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True)\n                ax1.imshow(tmp_label_map); ax2.imshow(init_invalid_context_map * 1 + tmp_context_map * 2)\n                plt.show()\n                import pdb; pdb.set_trace()\n            extend_context_ccs[ecnt_id] |= set(accum_context_cc)\n            extend_context_ccs[ecnt_id] = extend_context_ccs[ecnt_id] - mask_ccs[ecnt_id]\n            extend_erode_context_ccs[ecnt_id] = extend_context_ccs[ecnt_id] & constraint_erode_context_cc\n            extend_context_ccs[ecnt_id] = extend_context_ccs[ecnt_id] - extend_erode_context_ccs[ecnt_id] - erode_context_ccs[ecnt_id]\n            tmp_context_cc = context_ccs[ecnt_id] - extend_erode_context_ccs[ecnt_id] - erode_context_ccs[ecnt_id]\n            if len(tmp_context_cc) > 0:\n                context_ccs[ecnt_id] = tmp_context_cc\n            tmp_mask_cc = tmp_mask_cc - context_ccs[ecnt_id] - erode_context_ccs[ecnt_id]\n            mask_ccs[ecnt_id] = mask_ccs[ecnt_id] | tmp_mask_cc\n\n    return context_ccs, mask_ccs, broken_mask_ccs, edge_ccs, erode_context_ccs, invalid_extend_edge_ccs, edge_maps, extend_context_ccs, extend_edge_ccs, extend_erode_context_ccs\n\ndef DL_inpaint_edge(mesh,\n                    info_on_pix,\n                    config,\n                    image,\n                    depth,\n                    context_ccs,\n                    erode_context_ccs,\n                    extend_context_ccs,\n                    extend_erode_context_ccs,\n                    mask_ccs,\n                    broken_mask_ccs,\n                    edge_ccs,\n                    extend_edge_ccs,\n                    init_mask_connect,\n                    edge_maps,\n                    rgb_model=None,\n                    depth_edge_model=None,\n                    depth_edge_model_init=None,\n                    depth_feat_model=None,\n                    specific_edge_id=-1,\n                    specific_edge_loc=None,\n                    inpaint_iter=0):\n\n    if isinstance(config[\"gpu_ids\"], int) and (config[\"gpu_ids\"] >= 0):\n        device = config[\"gpu_ids\"]\n    else:\n        device = \"cpu\"\n\n    edge_map = np.zeros_like(depth)\n    new_edge_ccs = [set() for _ in range(len(edge_ccs))]\n    edge_maps_with_id = edge_maps\n    edge_condition = lambda x, m: m.nodes[x].get('far') is not None and len(m.nodes[x].get('far')) > 0\n    edge_map = get_map_from_ccs(edge_ccs, mesh.graph['H'], mesh.graph['W'], mesh, edge_condition)\n    np_depth, np_image = depth.copy(), image.copy()\n    image_c = image.shape[-1]\n    image = torch.FloatTensor(image.transpose(2, 0, 1)).unsqueeze(0).to(device)\n    if depth.ndim < 3:\n        depth = depth[..., None]\n    depth = torch.FloatTensor(depth.transpose(2, 0, 1)).unsqueeze(0).to(device)\n    mesh.graph['max_edge_id'] = len(edge_ccs)\n    connnect_points_ccs = [set() for _ in range(len(edge_ccs))]\n    gp_time, tmp_mesh_time, bilateral_time = 0, 0, 0\n    edges_infos = dict()\n    edges_in_mask = [set() for _ in range(len(edge_ccs))]\n    tmp_specific_edge_id = []\n    for edge_id, (context_cc, mask_cc, erode_context_cc, extend_context_cc, edge_cc) in enumerate(zip(context_ccs, mask_ccs, erode_context_ccs, extend_context_ccs, edge_ccs)):\n        if len(specific_edge_id) > 0:\n            if edge_id not in specific_edge_id:\n                continue\n        if len(context_cc) < 1 or len(mask_cc) < 1:\n            continue\n        edge_dict = get_edge_from_nodes(context_cc | extend_context_cc, erode_context_cc | extend_erode_context_ccs[edge_id], mask_cc, edge_cc, extend_edge_ccs[edge_id],\n                                        mesh.graph['H'], mesh.graph['W'], mesh)\n        edge_dict['edge'], end_depth_maps, _ = \\\n            filter_irrelevant_edge_new(edge_dict['self_edge'], edge_dict['comp_edge'],\n                                    edge_map,\n                                    edge_maps_with_id,\n                                    edge_id,\n                                    edge_dict['context'],\n                                    edge_dict['depth'], mesh, context_cc | erode_context_cc | extend_context_cc | extend_erode_context_ccs[edge_id], spdb=False)\n        if specific_edge_loc is not None and \\\n            (specific_edge_loc is not None and edge_dict['mask'][specific_edge_loc[0], specific_edge_loc[1]] == 0):\n            continue\n        mask_size = get_valid_size(edge_dict['mask'])\n        mask_size = dilate_valid_size(mask_size, edge_dict['mask'], dilate=[20, 20])\n        context_size = get_valid_size(edge_dict['context'])\n        context_size = dilate_valid_size(context_size, edge_dict['context'], dilate=[20, 20])\n        union_size = size_operation(mask_size, context_size, operation='+')\n        patch_edge_dict = dict()\n        patch_edge_dict['mask'], patch_edge_dict['context'], patch_edge_dict['rgb'], \\\n            patch_edge_dict['disp'], patch_edge_dict['edge'] = \\\n            crop_maps_by_size(union_size, edge_dict['mask'], edge_dict['context'],\n                                edge_dict['rgb'], edge_dict['disp'], edge_dict['edge'])\n        x_anchor, y_anchor = [union_size['x_min'], union_size['x_max']], [union_size['y_min'], union_size['y_max']]\n        tensor_edge_dict = convert2tensor(patch_edge_dict)\n        input_edge_feat = torch.cat((tensor_edge_dict['rgb'],\n                                        tensor_edge_dict['disp'],\n                                        tensor_edge_dict['edge'],\n                                        1 - tensor_edge_dict['context'],\n                                        tensor_edge_dict['mask']), dim=1)\n        if require_depth_edge(patch_edge_dict['edge'], patch_edge_dict['mask']) and inpaint_iter == 0:\n            with torch.no_grad():\n                depth_edge_output = depth_edge_model.forward_3P(tensor_edge_dict['mask'],\n                                                                tensor_edge_dict['context'],\n                                                                tensor_edge_dict['rgb'],\n                                                                tensor_edge_dict['disp'],\n                                                                tensor_edge_dict['edge'],\n                                                                unit_length=128,\n                                                                cuda=device)\n                depth_edge_output = depth_edge_output.cpu()\n            tensor_edge_dict['output'] = (depth_edge_output> config['ext_edge_threshold']).float() * tensor_edge_dict['mask'] + tensor_edge_dict['edge']\n        else:\n            tensor_edge_dict['output'] = tensor_edge_dict['edge']\n            depth_edge_output = tensor_edge_dict['edge'] + 0\n        patch_edge_dict['output'] = tensor_edge_dict['output'].squeeze().data.cpu().numpy()\n        edge_dict['output'] = np.zeros((mesh.graph['H'], mesh.graph['W']))\n        edge_dict['output'][union_size['x_min']:union_size['x_max'], union_size['y_min']:union_size['y_max']] = \\\n            patch_edge_dict['output']\n        if require_depth_edge(patch_edge_dict['edge'], patch_edge_dict['mask']) and inpaint_iter == 0:\n            if ((depth_edge_output> config['ext_edge_threshold']).float() * tensor_edge_dict['mask']).max() > 0:\n                try:\n                    edge_dict['fpath_map'], edge_dict['npath_map'], break_flag, npaths, fpaths, invalid_edge_id = \\\n                        clean_far_edge_new(edge_dict['output'], end_depth_maps, edge_dict['mask'], edge_dict['context'], mesh, info_on_pix, edge_dict['self_edge'], inpaint_iter, config)\n                except:\n                    import pdb; pdb.set_trace()\n                pre_npath_map = edge_dict['npath_map'].copy()\n                if config.get('repeat_inpaint_edge') is True:\n                    for _ in range(2):\n                        tmp_input_edge = ((edge_dict['npath_map'] > -1) + edge_dict['edge']).clip(0, 1)\n                        patch_tmp_input_edge = crop_maps_by_size(union_size, tmp_input_edge)[0]\n                        tensor_input_edge = torch.FloatTensor(patch_tmp_input_edge)[None, None, ...]\n                        depth_edge_output = depth_edge_model.forward_3P(tensor_edge_dict['mask'],\n                                                    tensor_edge_dict['context'],\n                                                    tensor_edge_dict['rgb'],\n                                                    tensor_edge_dict['disp'],\n                                                    tensor_input_edge,\n                                                    unit_length=128,\n                                                    cuda=device)\n                        depth_edge_output = depth_edge_output.cpu()\n                        depth_edge_output = (depth_edge_output> config['ext_edge_threshold']).float() * tensor_edge_dict['mask'] + tensor_edge_dict['edge']\n                        depth_edge_output = depth_edge_output.squeeze().data.cpu().numpy()\n                        full_depth_edge_output = np.zeros((mesh.graph['H'], mesh.graph['W']))\n                        full_depth_edge_output[union_size['x_min']:union_size['x_max'], union_size['y_min']:union_size['y_max']] = \\\n                            depth_edge_output\n                        edge_dict['fpath_map'], edge_dict['npath_map'], break_flag, npaths, fpaths, invalid_edge_id = \\\n                            clean_far_edge_new(full_depth_edge_output, end_depth_maps, edge_dict['mask'], edge_dict['context'], mesh, info_on_pix, edge_dict['self_edge'], inpaint_iter, config)\n                for nid in npaths.keys():\n                    npath, fpath = npaths[nid], fpaths[nid]\n                    start_mx, start_my, end_mx, end_my = -1, -1, -1, -1\n                    if end_depth_maps[npath[0][0], npath[0][1]] != 0:\n                        start_mx, start_my = npath[0][0], npath[0][1]\n                    if end_depth_maps[npath[-1][0], npath[-1][1]] != 0:\n                        end_mx, end_my = npath[-1][0], npath[-1][1]\n                    if start_mx == -1:\n                        import pdb; pdb.set_trace()\n                    valid_end_pt = () if end_mx == -1 else (end_mx, end_my, info_on_pix[(end_mx, end_my)][0]['depth'])\n                    new_edge_info = dict(fpath=fpath,\n                                         npath=npath,\n                                         cont_end_pts=valid_end_pt,\n                                         mask_id=edge_id,\n                                         comp_edge_id=nid,\n                                         depth=end_depth_maps[start_mx, start_my])\n                    if edges_infos.get((start_mx, start_my)) is None:\n                        edges_infos[(start_mx, start_my)] = []\n                    edges_infos[(start_mx, start_my)].append(new_edge_info)\n                    edges_in_mask[edge_id].add((start_mx, start_my))\n                    if len(valid_end_pt) > 0:\n                        new_edge_info = dict(fpath=fpath[::-1],\n                                             npath=npath[::-1],\n                                             cont_end_pts=(start_mx, start_my, info_on_pix[(start_mx, start_my)][0]['depth']),\n                                             mask_id=edge_id,\n                                             comp_edge_id=nid,\n                                             depth=end_depth_maps[end_mx, end_my])\n                        if edges_infos.get((end_mx, end_my)) is None:\n                            edges_infos[(end_mx, end_my)] = []\n                        edges_infos[(end_mx, end_my)].append(new_edge_info)\n                        edges_in_mask[edge_id].add((end_mx, end_my))\n    for edge_id, (context_cc, mask_cc, erode_context_cc, extend_context_cc, edge_cc) in enumerate(zip(context_ccs, mask_ccs, erode_context_ccs, extend_context_ccs, edge_ccs)):\n        if len(specific_edge_id) > 0:\n            if edge_id not in specific_edge_id:\n                continue\n        if len(context_cc) < 1 or len(mask_cc) < 1:\n            continue\n        edge_dict = get_edge_from_nodes(context_cc | extend_context_cc, erode_context_cc | extend_erode_context_ccs[edge_id], mask_cc, edge_cc, extend_edge_ccs[edge_id],\n                                        mesh.graph['H'], mesh.graph['W'], mesh)\n        if specific_edge_loc is not None and \\\n            (specific_edge_loc is not None and edge_dict['mask'][specific_edge_loc[0], specific_edge_loc[1]] == 0):\n            continue\n        else:\n            tmp_specific_edge_id.append(edge_id)\n        edge_dict['edge'], end_depth_maps, _ = \\\n            filter_irrelevant_edge_new(edge_dict['self_edge'], edge_dict['comp_edge'],\n                                    edge_map,\n                                    edge_maps_with_id,\n                                    edge_id,\n                                    edge_dict['context'],\n                                    edge_dict['depth'], mesh, context_cc | erode_context_cc | extend_context_cc | extend_erode_context_ccs[edge_id], spdb=False)\n        discard_map = np.zeros_like(edge_dict['edge'])\n        mask_size = get_valid_size(edge_dict['mask'])\n        mask_size = dilate_valid_size(mask_size, edge_dict['mask'], dilate=[20, 20])\n        context_size = get_valid_size(edge_dict['context'])\n        context_size = dilate_valid_size(context_size, edge_dict['context'], dilate=[20, 20])\n        union_size = size_operation(mask_size, context_size, operation='+')\n        patch_edge_dict = dict()\n        patch_edge_dict['mask'], patch_edge_dict['context'], patch_edge_dict['rgb'], \\\n            patch_edge_dict['disp'], patch_edge_dict['edge'] = \\\n            crop_maps_by_size(union_size, edge_dict['mask'], edge_dict['context'],\n                                edge_dict['rgb'], edge_dict['disp'], edge_dict['edge'])\n        x_anchor, y_anchor = [union_size['x_min'], union_size['x_max']], [union_size['y_min'], union_size['y_max']]\n        tensor_edge_dict = convert2tensor(patch_edge_dict)\n        input_edge_feat = torch.cat((tensor_edge_dict['rgb'],\n                                        tensor_edge_dict['disp'],\n                                        tensor_edge_dict['edge'],\n                                        1 - tensor_edge_dict['context'],\n                                        tensor_edge_dict['mask']), dim=1)\n        edge_dict['output'] = edge_dict['edge'].copy()\n\n        if require_depth_edge(patch_edge_dict['edge'], patch_edge_dict['mask']) and inpaint_iter == 0:\n            edge_dict['fpath_map'], edge_dict['npath_map'] = edge_dict['fpath_map'] * 0 - 1, edge_dict['npath_map'] * 0 - 1\n            end_pts = edges_in_mask[edge_id]\n            for end_pt in end_pts:\n                cur_edge_infos = edges_infos[(end_pt[0], end_pt[1])]\n                cur_info = [xx for xx in cur_edge_infos if xx['mask_id'] == edge_id][0]\n                other_infos = [xx for xx in cur_edge_infos if xx['mask_id'] != edge_id and len(xx['cont_end_pts']) > 0]\n                if len(cur_info['cont_end_pts']) > 0 or (len(cur_info['cont_end_pts']) == 0 and len(other_infos) == 0):\n                    for fnode in cur_info['fpath']:\n                        edge_dict['fpath_map'][fnode[0], fnode[1]] = cur_info['comp_edge_id']\n                    for fnode in cur_info['npath']:\n                        edge_dict['npath_map'][fnode[0], fnode[1]] = cur_info['comp_edge_id']\n            fnmap = edge_dict['fpath_map'] * 1\n            fnmap[edge_dict['npath_map'] != -1] = edge_dict['npath_map'][edge_dict['npath_map'] != -1]\n            for end_pt in end_pts:\n                cur_edge_infos = edges_infos[(end_pt[0], end_pt[1])]\n                cur_info = [xx for xx in cur_edge_infos if xx['mask_id'] == edge_id][0]\n                cur_depth = cur_info['depth']\n                other_infos = [xx for xx in cur_edge_infos if xx['mask_id'] != edge_id and len(xx['cont_end_pts']) > 0]\n                comp_edge_id = cur_info['comp_edge_id']\n                if len(cur_info['cont_end_pts']) == 0 and len(other_infos) > 0:\n                    other_infos = sorted(other_infos, key=lambda aa: abs(abs(aa['cont_end_pts'][2]) - abs(cur_depth)))\n                    for other_info in other_infos:\n                        tmp_fmap, tmp_nmap = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1, np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n                        for fnode in other_info['fpath']:\n                            if fnmap[fnode[0], fnode[1]] != -1:\n                                tmp_fmap = tmp_fmap * 0 - 1\n                                break\n                            else:\n                                tmp_fmap[fnode[0], fnode[1]] = comp_edge_id\n                        if fnmap[fnode[0], fnode[1]] != -1:\n                            continue\n                        for fnode in other_info['npath']:\n                            if fnmap[fnode[0], fnode[1]] != -1:\n                                tmp_nmap = tmp_nmap * 0 - 1\n                                break\n                            else:\n                                tmp_nmap[fnode[0], fnode[1]] = comp_edge_id\n                        if fnmap[fnode[0], fnode[1]] != -1:\n                            continue\n                        break\n                    if min(tmp_fmap.max(), tmp_nmap.max()) != -1:\n                        edge_dict['fpath_map'] = tmp_fmap\n                        edge_dict['fpath_map'][edge_dict['valid_area'] == 0] = -1\n                        edge_dict['npath_map'] = tmp_nmap\n                        edge_dict['npath_map'][edge_dict['valid_area'] == 0] = -1\n                        discard_map = ((tmp_nmap != -1).astype(np.uint8) + (tmp_fmap != -1).astype(np.uint8)) * edge_dict['mask']\n                    else:\n                        for fnode in cur_info['fpath']:\n                            edge_dict['fpath_map'][fnode[0], fnode[1]] = cur_info['comp_edge_id']\n                        for fnode in cur_info['npath']:\n                            edge_dict['npath_map'][fnode[0], fnode[1]] = cur_info['comp_edge_id']\n            if edge_dict['npath_map'].min() == 0 or edge_dict['fpath_map'].min() == 0:\n                import pdb; pdb.set_trace()\n            edge_dict['output'] = (edge_dict['npath_map'] > -1) * edge_dict['mask'] + edge_dict['context'] * edge_dict['edge']\n        mesh, _, _, _ = create_placeholder(edge_dict['context'], edge_dict['mask'],\n                                  edge_dict['depth'], edge_dict['fpath_map'],\n                                  edge_dict['npath_map'], mesh, inpaint_iter,\n                                  edge_ccs,\n                                  extend_edge_ccs[edge_id],\n                                  edge_maps_with_id,\n                                  edge_id)\n\n        dxs, dys = np.where(discard_map != 0)\n        for dx, dy in zip(dxs, dys):\n            mesh.nodes[(dx, dy)]['inpaint_twice'] = False\n        depth_dict = depth_inpainting(context_cc, extend_context_cc, erode_context_cc | extend_erode_context_ccs[edge_id], mask_cc, mesh, config, union_size, depth_feat_model, edge_dict['output'])\n        refine_depth_output = depth_dict['output']*depth_dict['mask']\n        for near_id in np.unique(edge_dict['npath_map'])[1:]:\n            refine_depth_output = refine_depth_around_edge(refine_depth_output.copy(),\n                                                            (edge_dict['fpath_map'] == near_id).astype(np.uint8) * edge_dict['mask'],\n                                                            (edge_dict['fpath_map'] == near_id).astype(np.uint8),\n                                                            (edge_dict['npath_map'] == near_id).astype(np.uint8) * edge_dict['mask'],\n                                                            depth_dict['mask'].copy(),\n                                                            depth_dict['output'] * depth_dict['context'],\n                                                            config)\n        depth_dict['output'][depth_dict['mask'] > 0] = refine_depth_output[depth_dict['mask'] > 0]\n        rgb_dict = get_rgb_from_nodes(context_cc | extend_context_cc,\n                                      erode_context_cc | extend_erode_context_ccs[edge_id], mask_cc, mesh.graph['H'], mesh.graph['W'], mesh)\n        if np.all(rgb_dict['mask'] == edge_dict['mask']) is False:\n            import pdb; pdb.set_trace()\n        rgb_dict['edge'] = edge_dict['output']\n        patch_rgb_dict = dict()\n        patch_rgb_dict['mask'], patch_rgb_dict['context'], patch_rgb_dict['rgb'], \\\n            patch_rgb_dict['edge'] = crop_maps_by_size(union_size, rgb_dict['mask'],\n                                                        rgb_dict['context'], rgb_dict['rgb'],\n                                                        rgb_dict['edge'])\n        tensor_rgb_dict = convert2tensor(patch_rgb_dict)\n        resize_rgb_dict = {k: v.clone() for k, v in tensor_rgb_dict.items()}\n        max_hw = np.array([*patch_rgb_dict['mask'].shape[-2:]]).max()\n        init_frac = config['largest_size'] / (np.array([*patch_rgb_dict['mask'].shape[-2:]]).prod() ** 0.5)\n        resize_hw = [patch_rgb_dict['mask'].shape[-2] * init_frac, patch_rgb_dict['mask'].shape[-1] * init_frac]\n        resize_max_hw = max(resize_hw)\n        frac = (np.floor(resize_max_hw / 128.) * 128.) / max_hw\n        if frac < 1:\n            resize_mark = torch.nn.functional.interpolate(torch.cat((resize_rgb_dict['mask'],\n                                                            resize_rgb_dict['context']),\n                                                            dim=1),\n                                                            scale_factor=frac,\n                                                            mode='area')\n            resize_rgb_dict['mask'] = (resize_mark[:, 0:1] > 0).float()\n            resize_rgb_dict['context'] = (resize_mark[:, 1:2] == 1).float()\n            resize_rgb_dict['context'][resize_rgb_dict['mask'] > 0] = 0\n            resize_rgb_dict['rgb'] = torch.nn.functional.interpolate(resize_rgb_dict['rgb'],\n                                                                        scale_factor=frac,\n                                                                        mode='area')\n            resize_rgb_dict['rgb'] = resize_rgb_dict['rgb'] * resize_rgb_dict['context']\n            resize_rgb_dict['edge'] = torch.nn.functional.interpolate(resize_rgb_dict['edge'],\n                                                                        scale_factor=frac,\n                                                                        mode='area')\n            resize_rgb_dict['edge'] = (resize_rgb_dict['edge'] > 0).float() * 0\n            resize_rgb_dict['edge'] = resize_rgb_dict['edge'] * (resize_rgb_dict['context'] + resize_rgb_dict['mask'])\n        rgb_input_feat = torch.cat((resize_rgb_dict['rgb'], resize_rgb_dict['edge']), dim=1)\n        rgb_input_feat[:, 3] = 1 - rgb_input_feat[:, 3]\n        resize_mask = open_small_mask(resize_rgb_dict['mask'], resize_rgb_dict['context'], 3, 41)\n        specified_hole = resize_mask\n        with torch.no_grad():\n            rgb_output = rgb_model.forward_3P(specified_hole,\n                                            resize_rgb_dict['context'],\n                                            resize_rgb_dict['rgb'],\n                                            resize_rgb_dict['edge'],\n                                            unit_length=128,\n                                            cuda=device)\n            rgb_output = rgb_output.cpu()\n            if config.get('gray_image') is True:\n                rgb_output = rgb_output.mean(1, keepdim=True).repeat((1,3,1,1))\n            rgb_output = rgb_output.cpu()\n        resize_rgb_dict['output'] = rgb_output * resize_rgb_dict['mask'] + resize_rgb_dict['rgb']\n        tensor_rgb_dict['output'] = resize_rgb_dict['output']\n        if frac < 1:\n            tensor_rgb_dict['output'] = torch.nn.functional.interpolate(tensor_rgb_dict['output'],\n                                                                        size=tensor_rgb_dict['mask'].shape[-2:],\n                                                                        mode='bicubic')\n            tensor_rgb_dict['output'] = tensor_rgb_dict['output'] * \\\n                                         tensor_rgb_dict['mask'] + (tensor_rgb_dict['rgb'] * tensor_rgb_dict['context'])\n        patch_rgb_dict['output'] = tensor_rgb_dict['output'].data.cpu().numpy().squeeze().transpose(1,2,0)\n        rgb_dict['output'] = np.zeros((mesh.graph['H'], mesh.graph['W'], 3))\n        rgb_dict['output'][union_size['x_min']:union_size['x_max'], union_size['y_min']:union_size['y_max']] = \\\n            patch_rgb_dict['output']\n\n        if require_depth_edge(patch_edge_dict['edge'], patch_edge_dict['mask']) or inpaint_iter > 0:\n            edge_occlusion = True\n        else:\n            edge_occlusion = False\n        for node in erode_context_cc:\n            if rgb_dict['mask'][node[0], node[1]] > 0:\n                for info in info_on_pix[(node[0], node[1])]:\n                    if abs(info['depth']) == abs(node[2]):\n                        info['update_color'] = (rgb_dict['output'][node[0], node[1]] * 255).astype(np.uint8)\n        if frac < 1.:\n            depth_edge_dilate_2_color_flag = False\n        else:\n            depth_edge_dilate_2_color_flag = True\n        hxs, hys = np.where((rgb_dict['mask'] > 0) & (rgb_dict['erode'] == 0))\n        for hx, hy in zip(hxs, hys):\n            real_depth = None\n            if abs(depth_dict['output'][hx, hy]) <= abs(np_depth[hx, hy]):\n                depth_dict['output'][hx, hy] = np_depth[hx, hy] + 0.01\n            node = (hx, hy, -depth_dict['output'][hx, hy])\n            if info_on_pix.get((node[0], node[1])) is not None:\n                for info in info_on_pix.get((node[0], node[1])):\n                    if info.get('inpaint_id') is None or abs(info['inpaint_id'] < mesh.nodes[(hx, hy)]['inpaint_id']):\n                        pre_depth = info['depth'] if info.get('real_depth') is None else info['real_depth']\n                        if abs(node[2]) < abs(pre_depth):\n                            node = (node[0], node[1], -(abs(pre_depth) + 0.001))\n            if mesh.has_node(node):\n                real_depth = node[2]\n            while True:\n                if mesh.has_node(node):\n                    node = (node[0], node[1], -(abs(node[2]) + 0.001))\n                else:\n                    break\n            if real_depth == node[2]:\n                real_depth = None\n            cur_disp = 1./node[2]\n            if not(mesh.has_node(node)):\n                if not mesh.has_node((node[0], node[1])):\n                    print(\"2D node not found.\")\n                    import pdb; pdb.set_trace()\n                if inpaint_iter == 1:\n                    paint = (rgb_dict['output'][hx, hy] * 255).astype(np.uint8)\n                else:\n                    paint = (rgb_dict['output'][hx, hy] * 255).astype(np.uint8)\n                ndict = dict(color=paint,\n                                synthesis=True,\n                                disp=cur_disp,\n                                cc_id=set([edge_id]),\n                                overlap_number=1.0,\n                                refine_depth=False,\n                                edge_occlusion=edge_occlusion,\n                                depth_edge_dilate_2_color_flag=depth_edge_dilate_2_color_flag,\n                                real_depth=real_depth)\n                mesh, _, _ = refresh_node((node[0], node[1]), mesh.nodes[(node[0], node[1])], node, ndict, mesh, stime=True)\n                if inpaint_iter == 0 and mesh.degree(node) < 4:\n                    connnect_points_ccs[edge_id].add(node)\n            if info_on_pix.get((hx, hy)) is None:\n                info_on_pix[(hx, hy)] = []\n            new_info = {'depth':node[2],\n                        'color': paint,\n                        'synthesis':True,\n                        'disp':cur_disp,\n                        'cc_id':set([edge_id]),\n                        'inpaint_id':inpaint_iter + 1,\n                        'edge_occlusion':edge_occlusion,\n                        'overlap_number':1.0,\n                        'real_depth': real_depth}\n            info_on_pix[(hx, hy)].append(new_info)\n    specific_edge_id = tmp_specific_edge_id\n    for erode_id, erode_context_cc in enumerate(erode_context_ccs):\n        if len(specific_edge_id) > 0 and erode_id not in specific_edge_id:\n            continue\n        for erode_node in erode_context_cc:\n            for info in info_on_pix[(erode_node[0], erode_node[1])]:\n                if info['depth'] == erode_node[2]:\n                    info['color'] = info['update_color']\n                    mesh.nodes[erode_node]['color'] = info['update_color']\n                    np_image[(erode_node[0], erode_node[1])] = info['update_color']\n    new_edge_ccs = [set() for _ in range(mesh.graph['max_edge_id'] + 1)]\n    for node in mesh.nodes:\n        if len(node) == 2:\n            mesh.remove_node(node)\n            continue\n        if mesh.nodes[node].get('edge_id') is not None and mesh.nodes[node].get('inpaint_id') == inpaint_iter + 1:\n            if mesh.nodes[node].get('inpaint_twice') is False:\n                continue\n            try:\n                new_edge_ccs[mesh.nodes[node].get('edge_id')].add(node)\n            except:\n                import pdb; pdb.set_trace()\n    specific_mask_nodes = None\n    if inpaint_iter == 0:\n        mesh, info_on_pix = refine_color_around_edge(mesh, info_on_pix, new_edge_ccs, config, False)\n\n    return mesh, info_on_pix, specific_mask_nodes, new_edge_ccs, connnect_points_ccs, np_image\n\n\ndef write_ply(image,\n              depth,\n              int_mtx,\n              ply_name,\n              config,\n              rgb_model,\n              depth_edge_model,\n              depth_edge_model_init,\n              depth_feat_model):\n    depth = depth.astype(np.float64)\n    input_mesh, xy2depth, image, depth = create_mesh(depth, image, int_mtx, config)\n\n    H, W = input_mesh.graph['H'], input_mesh.graph['W']\n    input_mesh = tear_edges(input_mesh, config['depth_threshold'], xy2depth)\n    input_mesh, info_on_pix = generate_init_node(input_mesh, config, min_node_in_cc=200)\n    edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=False)\n    edge_canvas = np.zeros((H, W)) - 1\n\n    input_mesh, info_on_pix, depth = reassign_floating_island(input_mesh, info_on_pix, image, depth)\n    input_mesh = update_status(input_mesh, info_on_pix)\n    specific_edge_id = []\n    edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n    pre_depth = depth.copy()\n    input_mesh, info_on_pix, edge_mesh, depth, aft_mark = remove_dangling(input_mesh, edge_ccs, edge_mesh, info_on_pix, image, depth, config)\n\n    input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n    edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n    edge_canvas = np.zeros((H, W)) - 1\n\n    mesh, info_on_pix, depth = fill_missing_node(input_mesh, info_on_pix, image, depth)\n    if config['extrapolate_border'] is True:\n        pre_depth = depth.copy()\n        input_mesh, info_on_pix, depth = refresh_bord_depth(input_mesh, info_on_pix, image, depth)\n        input_mesh = remove_node_feat(input_mesh, 'edge_id')\n        aft_depth = depth.copy()\n        input_mesh, info_on_pix, depth, image = enlarge_border(input_mesh, info_on_pix, depth, image, config)\n        noext_H, noext_W = H, W\n        H, W = image.shape[:2]\n        input_mesh, info_on_pix = fill_dummy_bord(input_mesh, info_on_pix, image, depth, config)\n        edge_ccs, input_mesh, edge_mesh = \\\n            group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n        input_mesh = combine_end_node(input_mesh, edge_mesh, edge_ccs, depth)\n        input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n        edge_ccs, input_mesh, edge_mesh = \\\n            group_edges(input_mesh, config, image, remove_conflict_ordinal=True, spdb=False)\n        input_mesh = remove_redundant_edge(input_mesh, edge_mesh, edge_ccs, info_on_pix, config, redundant_number=config['redundant_number'], spdb=False)\n        input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n        edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n        input_mesh = combine_end_node(input_mesh, edge_mesh, edge_ccs, depth)\n        input_mesh = remove_redundant_edge(input_mesh, edge_mesh, edge_ccs, info_on_pix, config, redundant_number=config['redundant_number'], invalid=True, spdb=False)\n        input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n        edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n        input_mesh = combine_end_node(input_mesh, edge_mesh, edge_ccs, depth)\n        input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n        edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n        edge_condition = lambda x, m: m.nodes[x].get('far') is not None and len(m.nodes[x].get('far')) > 0\n        edge_map = get_map_from_ccs(edge_ccs, input_mesh.graph['H'], input_mesh.graph['W'], input_mesh, edge_condition)\n        other_edge_with_id = get_map_from_ccs(edge_ccs, input_mesh.graph['H'], input_mesh.graph['W'], real_id=True)\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"up\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"left\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"down\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"right\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"right-up\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"right-down\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"left-up\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"left-down\")\n    specific_edge_loc = None\n    specific_edge_id = []\n    vis_edge_id = None\n    context_ccs, mask_ccs, broken_mask_ccs, edge_ccs, erode_context_ccs, \\\n        init_mask_connect, edge_maps, extend_context_ccs, extend_edge_ccs, extend_erode_context_ccs = \\\n                                                                                context_and_holes(input_mesh,\n                                                                                            edge_ccs,\n                                                                                            config,\n                                                                                            specific_edge_id,\n                                                                                            specific_edge_loc,\n                                                                                            depth_feat_model,\n                                                                                            inpaint_iter=0,\n                                                                                            vis_edge_id=vis_edge_id)\n    edge_canvas = np.zeros((H, W))\n    mask = np.zeros((H, W))\n    context = np.zeros((H, W))\n    vis_edge_ccs = filter_edge(input_mesh, edge_ccs, config)\n    edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W'])) - 1\n    specific_edge_loc = None\n    FG_edge_maps = edge_maps.copy()\n    edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W'])) - 1\n    # for cc_id, cc in enumerate(edge_ccs):\n    #     for node in cc:\n    #         edge_canvas[node[0], node[1]] = cc_id\n    # f, ((ax0, ax1, ax2)) = plt.subplots(1, 3, sharex=True, sharey=True); ax0.imshow(1./depth); ax1.imshow(image); ax2.imshow(edge_canvas); plt.show()\n    input_mesh, info_on_pix, specific_edge_nodes, new_edge_ccs, connect_points_ccs, image = DL_inpaint_edge(input_mesh,\n                                                                                                            info_on_pix,\n                                                                                                            config,\n                                                                                                            image,\n                                                                                                            depth,\n                                                                                                            context_ccs,\n                                                                                                            erode_context_ccs,\n                                                                                                            extend_context_ccs,\n                                                                                                            extend_erode_context_ccs,\n                                                                                                            mask_ccs,\n                                                                                                            broken_mask_ccs,\n                                                                                                            edge_ccs,\n                                                                                                            extend_edge_ccs,\n                                                                                                            init_mask_connect,\n                                                                                                            edge_maps,\n                                                                                                            rgb_model,\n                                                                                                            depth_edge_model,\n                                                                                                            depth_edge_model_init,\n                                                                                                            depth_feat_model,\n                                                                                                            specific_edge_id,\n                                                                                                            specific_edge_loc,\n                                                                                                            inpaint_iter=0)\n    specific_edge_id = []\n    edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    connect_points_ccs = [set() for _ in connect_points_ccs]\n    context_ccs, mask_ccs, broken_mask_ccs, edge_ccs, erode_context_ccs, init_mask_connect, \\\n        edge_maps, extend_context_ccs, extend_edge_ccs, extend_erode_context_ccs = \\\n            context_and_holes(input_mesh, new_edge_ccs, config, specific_edge_id, specific_edge_loc, depth_feat_model, connect_points_ccs, inpaint_iter=1)\n    mask_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    context_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    erode_context_ccs_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    # edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W'])) - 1\n    # for cc_id, cc in enumerate(edge_ccs):\n    #     for node in cc:\n    #         edge_canvas[node[0], node[1]] = cc_id\n    specific_edge_id = []\n    input_mesh, info_on_pix, specific_edge_nodes, new_edge_ccs, _, image = DL_inpaint_edge(input_mesh,\n                                                                                    info_on_pix,\n                                                                                    config,\n                                                                                    image,\n                                                                                    depth,\n                                                                                    context_ccs,\n                                                                                    erode_context_ccs,\n                                                                                    extend_context_ccs,\n                                                                                    extend_erode_context_ccs,\n                                                                                    mask_ccs,\n                                                                                    broken_mask_ccs,\n                                                                                    edge_ccs,\n                                                                                    extend_edge_ccs,\n                                                                                    init_mask_connect,\n                                                                                    edge_maps,\n                                                                                    rgb_model,\n                                                                                    depth_edge_model,\n                                                                                    depth_edge_model_init,\n                                                                                    depth_feat_model,\n                                                                                    specific_edge_id,\n                                                                                    specific_edge_loc,\n                                                                                    inpaint_iter=1)\n    vertex_id = 0\n    input_mesh.graph['H'], input_mesh.graph['W'] = input_mesh.graph['noext_H'], input_mesh.graph['noext_W']\n    background_canvas = np.zeros((input_mesh.graph['H'],\n                                  input_mesh.graph['W'],\n                                  3))\n    ply_flag = config.get('save_ply')\n    if ply_flag is True:\n        node_str_list = []\n    else:\n        node_str_color = []\n        node_str_point = []\n    out_fmt = lambda x, x_flag: str(x) if x_flag is True else x\n    point_time = 0\n    hlight_time = 0\n    cur_id_time = 0\n    node_str_time = 0\n    generate_face_time = 0\n    point_list = []\n    k_00, k_02, k_11, k_12 = \\\n        input_mesh.graph['cam_param_pix_inv'][0, 0], input_mesh.graph['cam_param_pix_inv'][0, 2], \\\n        input_mesh.graph['cam_param_pix_inv'][1, 1], input_mesh.graph['cam_param_pix_inv'][1, 2]\n    w_offset = input_mesh.graph['woffset']\n    h_offset = input_mesh.graph['hoffset']\n    for pix_xy, pix_list in info_on_pix.items():\n        for pix_idx, pix_info in enumerate(pix_list):\n            pix_depth = pix_info['depth'] if pix_info.get('real_depth') is None else pix_info['real_depth']\n            str_pt = [out_fmt(x, ply_flag) for x in reproject_3d_int_detail(pix_xy[0], pix_xy[1], pix_depth,\n                      k_00, k_02, k_11, k_12, w_offset, h_offset)]\n            if input_mesh.has_node((pix_xy[0], pix_xy[1], pix_info['depth'])) is False:\n                return False\n                continue\n            if pix_info.get('overlap_number') is not None:\n                str_color = [out_fmt(x, ply_flag) for x in (pix_info['color']/pix_info['overlap_number']).astype(np.uint8).tolist()]\n            else:\n                str_color = [out_fmt(x, ply_flag) for x in pix_info['color'].tolist()]\n            if pix_info.get('edge_occlusion') is True:\n                str_color.append(out_fmt(4, ply_flag))\n            else:\n                if pix_info.get('inpaint_id') is None:\n                    str_color.append(out_fmt(1, ply_flag))\n                else:\n                    str_color.append(out_fmt(pix_info.get('inpaint_id') + 1, ply_flag))\n            if pix_info.get('modified_border') is True or pix_info.get('ext_pixel') is True:\n                if len(str_color) == 4:\n                    str_color[-1] = out_fmt(5, ply_flag)\n                else:\n                    str_color.append(out_fmt(5, ply_flag))\n            pix_info['cur_id'] = vertex_id\n            input_mesh.nodes[(pix_xy[0], pix_xy[1], pix_info['depth'])]['cur_id'] = out_fmt(vertex_id, ply_flag)\n            vertex_id += 1\n            if ply_flag is True:\n                node_str_list.append(' '.join(str_pt) + ' ' + ' '.join(str_color) + '\\n')\n            else:\n                node_str_color.append(str_color)\n                node_str_point.append(str_pt)\n    str_faces = generate_face(input_mesh, info_on_pix, config)\n    if config['save_ply'] is True:\n        print(\"Writing mesh file %s ...\" % ply_name)\n        with open(ply_name, 'w') as ply_fi:\n            ply_fi.write('ply\\n' + 'format ascii 1.0\\n')\n            ply_fi.write('comment H ' + str(int(input_mesh.graph['H'])) + '\\n')\n            ply_fi.write('comment W ' + str(int(input_mesh.graph['W'])) + '\\n')\n            ply_fi.write('comment hFov ' + str(float(input_mesh.graph['hFov'])) + '\\n')\n            ply_fi.write('comment vFov ' + str(float(input_mesh.graph['vFov'])) + '\\n')\n            ply_fi.write('element vertex ' + str(len(node_str_list)) + '\\n')\n            ply_fi.write('property float x\\n' + \\\n                         'property float y\\n' + \\\n                         'property float z\\n' + \\\n                         'property uchar red\\n' + \\\n                         'property uchar green\\n' + \\\n                         'property uchar blue\\n' + \\\n                         'property uchar alpha\\n')\n            ply_fi.write('element face ' + str(len(str_faces)) + '\\n')\n            ply_fi.write('property list uchar int vertex_index\\n')\n            ply_fi.write('end_header\\n')\n            ply_fi.writelines(node_str_list)\n            ply_fi.writelines(str_faces)\n        ply_fi.close()\n        return input_mesh\n    else:\n        H = int(input_mesh.graph['H'])\n        W = int(input_mesh.graph['W'])\n        hFov = input_mesh.graph['hFov']\n        vFov = input_mesh.graph['vFov']\n        node_str_color = np.array(node_str_color).astype(np.float32)\n        node_str_color[..., :3] = node_str_color[..., :3] / 255.\n        node_str_point = np.array(node_str_point)\n        str_faces = np.array(str_faces)\n\n        return node_str_point, node_str_color, str_faces, H, W, hFov, vFov\n\ndef read_ply(mesh_fi):\n    ply_fi = open(mesh_fi, 'r')\n    Height = None\n    Width = None\n    hFov = None\n    vFov = None\n    while True:\n        line = ply_fi.readline().split('\\n')[0]\n        if line.startswith('element vertex'):\n            num_vertex = int(line.split(' ')[-1])\n        elif line.startswith('element face'):\n            num_face = int(line.split(' ')[-1])\n        elif line.startswith('comment'):\n            if line.split(' ')[1] == 'H':\n                Height = int(line.split(' ')[-1].split('\\n')[0])\n            if line.split(' ')[1] == 'W':\n                Width = int(line.split(' ')[-1].split('\\n')[0])\n            if line.split(' ')[1] == 'hFov':\n                hFov = float(line.split(' ')[-1].split('\\n')[0])\n            if line.split(' ')[1] == 'vFov':\n                vFov = float(line.split(' ')[-1].split('\\n')[0])\n        elif line.startswith('end_header'):\n            break\n    contents = ply_fi.readlines()\n    vertex_infos = contents[:num_vertex]\n    face_infos = contents[num_vertex:]\n    verts = []\n    colors = []\n    faces = []\n    for v_info in vertex_infos:\n        str_info = [float(v) for v in v_info.split('\\n')[0].split(' ')]\n        if len(str_info) == 6:\n            vx, vy, vz, r, g, b = str_info\n        else:\n            vx, vy, vz, r, g, b, hi = str_info\n        verts.append([vx, vy, vz])\n        colors.append([r, g, b, hi])\n    verts = np.array(verts)\n    try:\n        colors = np.array(colors)\n        colors[..., :3] = colors[..., :3]/255.\n    except:\n        import pdb\n        pdb.set_trace()\n\n    for f_info in face_infos:\n        _, v1, v2, v3 = [int(f) for f in f_info.split('\\n')[0].split(' ')]\n        faces.append([v1, v2, v3])\n    faces = np.array(faces)\n\n\n    return verts, colors, faces, Height, Width, hFov, vFov\n\n\nclass Canvas_view():\n    def __init__(self,\n                 fov,\n                 verts,\n                 faces,\n                 colors,\n                 canvas_size,\n                 factor=1,\n                 bgcolor='gray',\n                 proj='perspective',\n                 ):\n        self.canvas = scene.SceneCanvas(bgcolor=bgcolor, size=(canvas_size*factor, canvas_size*factor))\n        self.view = self.canvas.central_widget.add_view()\n        self.view.camera = 'perspective'\n        self.view.camera.fov = fov\n        self.mesh = visuals.Mesh(shading=None)\n        self.mesh.attach(Alpha(1.0))\n        self.view.add(self.mesh)\n        self.tr = self.view.camera.transform\n        self.mesh.set_data(vertices=verts, faces=faces, vertex_colors=colors[:, :3])\n        self.translate([0,0,0])\n        self.rotate(axis=[1,0,0], angle=180)\n        self.view_changed()\n\n    def translate(self, trans=[0,0,0]):\n        self.tr.translate(trans)\n\n    def rotate(self, axis=[1,0,0], angle=0):\n        self.tr.rotate(axis=axis, angle=angle)\n\n    def view_changed(self):\n        self.view.camera.view_changed()\n\n    def render(self):\n        return self.canvas.render()\n\n    def reinit_mesh(self, verts, faces, colors):\n        self.mesh.set_data(vertices=verts, faces=faces, vertex_colors=colors[:, :3])\n\n    def reinit_camera(self, fov):\n        self.view.camera.fov = fov\n        self.view.camera.view_changed()\n\n\ndef output_3d_photo(verts, colors, faces, Height, Width, hFov, vFov, tgt_poses, video_traj_types, ref_pose,\n                    output_dir, ref_image, int_mtx, config, image, videos_poses, video_basename, original_H=None, original_W=None,\n                    border=None, depth=None, normal_canvas=None, all_canvas=None, mean_loc_depth=None):\n\n    cam_mesh = netx.Graph()\n    cam_mesh.graph['H'] = Height\n    cam_mesh.graph['W'] = Width\n    cam_mesh.graph['original_H'] = original_H\n    cam_mesh.graph['original_W'] = original_W\n    int_mtx_real_x = int_mtx[0] * Width\n    int_mtx_real_y = int_mtx[1] * Height\n    cam_mesh.graph['hFov'] = 2 * np.arctan((1. / 2.) * ((cam_mesh.graph['original_W']) / int_mtx_real_x[0]))\n    cam_mesh.graph['vFov'] = 2 * np.arctan((1. / 2.) * ((cam_mesh.graph['original_H']) / int_mtx_real_y[1]))\n    colors = colors[..., :3]\n\n    fov_in_rad = max(cam_mesh.graph['vFov'], cam_mesh.graph['hFov'])\n    fov = (fov_in_rad * 180 / np.pi)\n    print(\"fov: \" + str(fov))\n    init_factor = 1\n    if config.get('anti_flickering') is True:\n        init_factor = 3\n    if (cam_mesh.graph['original_H'] is not None) and (cam_mesh.graph['original_W'] is not None):\n        canvas_w = cam_mesh.graph['original_W']\n        canvas_h = cam_mesh.graph['original_H']\n    else:\n        canvas_w = cam_mesh.graph['W']\n        canvas_h = cam_mesh.graph['H']\n    canvas_size = max(canvas_h, canvas_w)\n    if normal_canvas is None:\n        normal_canvas = Canvas_view(fov,\n                                    verts,\n                                    faces,\n                                    colors,\n                                    canvas_size=canvas_size,\n                                    factor=init_factor,\n                                    bgcolor='gray',\n                                    proj='perspective')\n    else:\n        normal_canvas.reinit_mesh(verts, faces, colors)\n        normal_canvas.reinit_camera(fov)\n    img = normal_canvas.render()\n    backup_img, backup_all_img, all_img_wo_bound = img.copy(), img.copy() * 0, img.copy() * 0\n    img = cv2.resize(img, (int(img.shape[1] / init_factor), int(img.shape[0] / init_factor)), interpolation=cv2.INTER_AREA)\n    if border is None:\n        border = [0, img.shape[0], 0, img.shape[1]]\n    H, W = cam_mesh.graph['H'], cam_mesh.graph['W']\n    if (cam_mesh.graph['original_H'] is not None) and (cam_mesh.graph['original_W'] is not None):\n        aspect_ratio = cam_mesh.graph['original_H'] / cam_mesh.graph['original_W']\n    else:\n        aspect_ratio = cam_mesh.graph['H'] / cam_mesh.graph['W']\n    if aspect_ratio > 1:\n        img_h_len = cam_mesh.graph['H'] if cam_mesh.graph.get('original_H') is None else cam_mesh.graph['original_H']\n        img_w_len = img_h_len / aspect_ratio\n        anchor = [0,\n                  img.shape[0],\n                  int(max(0, int((img.shape[1])//2 - img_w_len//2))),\n                  int(min(int((img.shape[1])//2 + img_w_len//2), (img.shape[1])-1))]\n    elif aspect_ratio <= 1:\n        img_w_len = cam_mesh.graph['W'] if cam_mesh.graph.get('original_W') is None else cam_mesh.graph['original_W']\n        img_h_len = img_w_len * aspect_ratio\n        anchor = [int(max(0, int((img.shape[0])//2 - img_h_len//2))),\n                  int(min(int((img.shape[0])//2 + img_h_len//2), (img.shape[0])-1)),\n                  0,\n                  img.shape[1]]\n    anchor = np.array(anchor)\n    plane_width = np.tan(fov_in_rad/2.) * np.abs(mean_loc_depth)\n    for video_pose, video_traj_type in zip(videos_poses, video_traj_types):\n        stereos = []\n        tops = []; buttoms = []; lefts = []; rights = []\n        for tp_id, tp in enumerate(video_pose):\n            rel_pose = np.linalg.inv(np.dot(tp, np.linalg.inv(ref_pose)))\n            axis, angle = transforms3d.axangles.mat2axangle(rel_pose[0:3, 0:3])\n            normal_canvas.rotate(axis=axis, angle=(angle*180)/np.pi)\n            normal_canvas.translate(rel_pose[:3,3])\n            new_mean_loc_depth = mean_loc_depth - float(rel_pose[2, 3])\n            if 'dolly' in video_traj_type:\n                new_fov = float((np.arctan2(plane_width, np.array([np.abs(new_mean_loc_depth)])) * 180. / np.pi) * 2)\n                normal_canvas.reinit_camera(new_fov)\n            else:\n                normal_canvas.reinit_camera(fov)\n            normal_canvas.view_changed()\n            img = normal_canvas.render()\n            img = cv2.GaussianBlur(img,(int(init_factor//2 * 2 + 1), int(init_factor//2 * 2 + 1)), 0)\n            img = cv2.resize(img, (int(img.shape[1] / init_factor), int(img.shape[0] / init_factor)), interpolation=cv2.INTER_AREA)\n            img = img[anchor[0]:anchor[1], anchor[2]:anchor[3]]\n            img = img[int(border[0]):int(border[1]), int(border[2]):int(border[3])]\n\n            if any(np.array(config['crop_border']) > 0.0):\n                H_c, W_c, _ = img.shape\n                o_t = int(H_c * config['crop_border'][0])\n                o_l = int(W_c * config['crop_border'][1])\n                o_b = int(H_c * config['crop_border'][2])\n                o_r = int(W_c * config['crop_border'][3])\n                img = img[o_t:H_c-o_b, o_l:W_c-o_r]\n                img = cv2.resize(img, (W_c, H_c), interpolation=cv2.INTER_CUBIC)\n\n            \"\"\"\n            img = cv2.resize(img, (int(img.shape[1] / init_factor), int(img.shape[0] / init_factor)), interpolation=cv2.INTER_CUBIC)\n            img = img[anchor[0]:anchor[1], anchor[2]:anchor[3]]\n            img = img[int(border[0]):int(border[1]), int(border[2]):int(border[3])]\n\n            if config['crop_border'] is True:\n                top, buttom, left, right = find_largest_rect(img, bg_color=(128, 128, 128))\n                tops.append(top); buttoms.append(buttom); lefts.append(left); rights.append(right)\n            \"\"\"\n            stereos.append(img[..., :3])\n            normal_canvas.translate(-rel_pose[:3,3])\n            normal_canvas.rotate(axis=axis, angle=-(angle*180)/np.pi)\n            normal_canvas.view_changed()\n        \"\"\"\n        if config['crop_border'] is True:\n            atop, abuttom = min(max(tops), img.shape[0]//2 - 10), max(min(buttoms), img.shape[0]//2 + 10)\n            aleft, aright = min(max(lefts), img.shape[1]//2 - 10), max(min(rights), img.shape[1]//2 + 10)\n            atop -= atop % 2; abuttom -= abuttom % 2; aleft -= aleft % 2; aright -= aright % 2\n        else:\n            atop = 0; abuttom = img.shape[0] - img.shape[0] % 2; aleft = 0; aright = img.shape[1] - img.shape[1] % 2\n        \"\"\"\n        atop = 0; abuttom = img.shape[0] - img.shape[0] % 2; aleft = 0; aright = img.shape[1] - img.shape[1] % 2\n        crop_stereos = []\n        for stereo in stereos:\n            crop_stereos.append((stereo[atop:abuttom, aleft:aright, :3] * 1).astype(np.uint8))\n            stereos = crop_stereos\n        clip = ImageSequenceClip(stereos, fps=config['fps'])\n        if isinstance(video_basename, list):\n            video_basename = video_basename[0]\n        clip.write_videofile(os.path.join(output_dir, video_basename + '_' + video_traj_type + '.mp4'), fps=config['fps'])\n\n\n\n    return normal_canvas, all_canvas"},{"assistant_initiates":false,"asst_text":"Certainly! It appears that you would like to make adjustments to the provided code, specifically ensuring that it is correctly formatted, not redundant, and possibly ensuring that certain functionalities are improved. Below is the modified version of the code, addressing potential issues like redundancy, formatting, and structure, to ensure it is logically organized and clear.\n\nHere's the revised code:\n\n```python\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\nimport time\nimport copy\nimport random\nimport torch\nfrom moviepy.editor import ImageSequenceClip\nfrom skimage.transform import resize\nimport transforms3d\n\ntry:\n    import cynetworkx as netx\nexcept ImportError:\n    import networkx as netx\n\nfrom vispy import scene, io\nfrom vispy.scene import visuals\nfrom vispy.visuals.filters import Alpha\nfrom functools import partial, reduce\n\n# Importing utility functions\nfrom utils import (\n    path_planning, open_small_mask, clean_far_edge, refine_depth_around_edge,\n    refine_color_around_edge, filter_irrelevant_edge_new, require_depth_edge, clean_far_edge_new,\n    create_placeholder, refresh_node, find_largest_rect\n)\nfrom mesh_tools import (\n    get_depth_from_maps, get_map_from_ccs, get_edge_from_nodes, get_depth_from_nodes,\n    get_rgb_from_nodes, crop_maps_by_size, convert2tensor, recursive_add_edge, update_info,\n    filter_edge, relabel_node, depth_inpainting, refresh_bord_depth, enlarge_border,\n    fill_dummy_bord, extrapolate, fill_missing_node, incomplete_node, get_valid_size,\n    dilate_valid_size, size_operation\n)\n\ndef create_mesh(depth, image, int_mtx, config):\n    H, W, C = image.shape\n    ext_H, ext_W = H + 2 * config['extrapolation_thickness'], W + 2 * config['extrapolation_thickness']\n    LDI = netx.Graph(H=ext_H, W=ext_W, noext_H=H, noext_W=W, cam_param=int_mtx)\n    xy2depth = {}\n    int_mtx_pix = int_mtx * np.array([[W], [H], [1.]])\n    LDI.graph['cam_param_pix'], LDI.graph['cam_param_pix_inv'] = int_mtx_pix, np.linalg.inv(int_mtx_pix)\n    disp = 1. / (-depth)\n    \n    # Setting graph attributes\n    LDI.graph['hoffset'] = LDI.graph['woffset'] = config['extrapolation_thickness']\n    LDI.graph['bord_up'], LDI.graph['bord_down'] = LDI.graph['hoffset'], LDI.graph['hoffset'] + H\n    LDI.graph['bord_left'], LDI.graph['bord_right'] = LDI.graph['woffset'], LDI.graph['woffset'] + W\n    \n    # Adding nodes\n    for idx in range(H):\n        for idy in range(W):\n            x, y = idx + LDI.graph['hoffset'], idy + LDI.graph['woffset']\n            LDI.add_node((x, y, -depth[idx, idy]),\n                         color=image[idx, idy],\n                         disp=disp[idx, idy],\n                         synthesis=False,\n                         cc_id=set())\n            xy2depth[(x, y)] = [-depth[idx, idy]]\n    \n    # Adding edges\n    for x, y, d in LDI.nodes:\n        two_nes = [(x+1, y), (x, y+1)]\n        two_nes = [ne for ne in two_nes if ne[0] < LDI.graph['bord_down'] and ne[1] < LDI.graph['bord_right']]\n        for ne in two_nes:\n            LDI.add_edge((ne[0], ne[1], xy2depth[ne][0]), (x, y, d))\n    \n    LDI = calculate_fov(LDI)\n    \n    # Padding image and depth arrays\n    image = np.pad(image, pad_width=((config['extrapolation_thickness'], config['extrapolation_thickness']),\n                                       (config['extrapolation_thickness'], config['extrapolation_thickness']),\n                                       (0, 0)), mode='constant')\n    depth = np.pad(depth, pad_width=((config['extrapolation_thickness'], config['extrapolation_thickness']),\n                                       (config['extrapolation_thickness'], config['extrapolation_thickness'])), \n                   mode='constant')\n\n    return LDI, xy2depth, image, depth\n\ndef tear_edges(mesh, threshold=0.00025, xy2depth=None):\n    remove_edge_list = []\n    remove_horizon = np.zeros((mesh.graph['H'], mesh.graph['W']))\n    remove_vertical = np.zeros((mesh.graph['H'], mesh.graph['W']))\n    \n    # Identifying edges to remove\n    for edge in mesh.edges:\n        if abs(mesh.nodes[edge[0]]['disp'] - mesh.nodes[edge[1]]['disp']) > threshold:\n            remove_edge_list.append((edge[0], edge[1]))\n            near, far = edge if abs(edge[0][2]) < abs(edge[1][2]) else edge[::-1]\n            mesh.nodes[far]['near'] = [] if mesh.nodes[far].get('near') is None else mesh.nodes[far]['near'].append(near)\n            mesh.nodes[near]['far'] = [] if mesh.nodes[near].get('far') is None else mesh.nodes[near]['far'].append(far)\n\n            if near[0] == far[0]:\n                remove_horizon[near[0], np.minimum(near[1], far[1])] = 1\n            elif near[1] == far[1]:\n                remove_vertical[np.minimum(near[0], far[0]), near[1]] = 1\n    mesh.remove_edges_from(remove_edge_list)\n\n    remove_edge_list = []\n\n    dang_horizon = np.where(np.roll(remove_horizon, 1, 0) + np.roll(remove_horizon, -1, 0) - remove_horizon == 2)\n    dang_vertical = np.where(np.roll(remove_vertical, 1, 1) + np.roll(remove_vertical, -1, 1) - remove_vertical == 2)\n\n    horizon_condition = lambda x, y: mesh.graph['bord_up'] + 1 <= x < mesh.graph['bord_down'] - 1\n    vertical_condition = lambda x, y: mesh.graph['bord_left'] + 1 <= y < mesh.graph['bord_right'] - 1\n\n    prjto3d = lambda x, y: (x, y, xy2depth[(x, y)][0])\n    node_existence = lambda x, y: mesh.has_node(prjto3d(x, y))\n\n    for x, y in zip(dang_horizon[0], dang_horizon[1]):\n        if horizon_condition(x, y) and node_existence(x, y) and node_existence(x, y + 1):\n            remove_edge_list.append((prjto3d(x, y), prjto3d(x, y + 1)))\n    for x, y in zip(dang_vertical[0], dang_vertical[1]):\n        if vertical_condition(x, y) and node_existence(x, y) and node_existence(x + 1, y):\n            remove_edge_list.append((prjto3d(x, y), prjto3d(x + 1, y)))\n\n    mesh.remove_edges_from(remove_edge_list)\n    return mesh\n\ndef calculate_fov(mesh):\n    k = mesh.graph['cam_param']\n    mesh.graph['hFov'] = 2 * np.arctan(1. / (2 * k[0, 0]))\n    mesh.graph['vFov'] = 2 * np.arctan(1. / (2 * k[1, 1]))\n    mesh.graph['aspect'] = mesh.graph['noext_H'] / mesh.graph['noext_W']\n    return mesh\n\ndef calculate_fov_FB(mesh):\n    mesh.graph['aspect'] = mesh.graph['H'] / mesh.graph['W']\n    if mesh.graph['H'] > mesh.graph['W']:\n        mesh.graph['hFov'] = 0.508015513\n        half_short = np.tan(mesh.graph['hFov'] / 2.0)\n        half_long = half_short * mesh.graph['aspect']\n        mesh.graph['vFov'] = 2.0 * np.arctan(half_long)\n    else:\n        mesh.graph['vFov'] = 0.508015513\n        half_short = np.tan(mesh.graph['vFov'] / 2.0)\n        half_long = half_short / mesh.graph['aspect']\n        mesh.graph['hFov'] = 2.0 * np.arctan(half_long)\n\n    return mesh\n\ndef reproject_3d_int_detail(sx, sy, z, k_00, k_02, k_11, k_12, w_offset, h_offset):\n    abs_z = abs(z)\n    return [abs_z * ((sy + 0.5 - w_offset) * k_00 + k_02), abs_z * ((sx + 0.5 - h_offset) * k_11 + k_12), abs_z]\n\ndef reproject_3d_int_detail_FB(sx, sy, z, w_offset, h_offset, mesh):\n    if mesh.graph.get('tan_hFov') is None:\n        mesh.graph['tan_hFov'] = np.tan(mesh.graph['hFov'] / 2.)\n    if mesh.graph.get('tan_vFov') is None:\n        mesh.graph['tan_vFov'] = np.tan(mesh.graph['vFov'] / 2.)\n\n    ray = np.array([(-1. + 2. * ((sy + 0.5 - w_offset) / (mesh.graph['W'] - 1))) * mesh.graph['tan_hFov'],\n                     (1. - 2. * (sx + 0.5 - h_offset) / (mesh.graph['H'] - 1)) * mesh.graph['tan_vFov'],\n                     -1])\n    point_3d = ray * np.abs(z)\n\n    return point_3d\n\ndef reproject_3d_int(sx, sy, z, mesh):\n    k = mesh.graph['cam_param_pix_inv'].copy()\n    if k[0, 2] > 0:\n        k = np.linalg.inv(k)\n    ray = np.dot(k, np.array([sy - mesh.graph['woffset'], sx - mesh.graph['hoffset'], 1]).reshape(3, 1))\n\n    point_3d = ray * np.abs(z)\n    point_3d = point_3d.flatten()\n\n    return point_3d\n\ndef generate_init_node(mesh, config, min_node_in_cc):\n    mesh_nodes = mesh.nodes\n    info_on_pix = {}\n\n    ccs = sorted(netx.connected_components(mesh), key=len, reverse=True)\n    remove_nodes = []\n\n    for cc in ccs:\n        if len(cc) >= min_node_in_cc:\n            for (nx, ny, nd) in cc:\n                info_on_pix[(nx, ny)] = [{'depth': nd,\n                                           'color': mesh_nodes[(nx, ny, nd)]['color'],\n                                           'synthesis': False,\n                                           'disp': mesh_nodes[(nx, ny, nd)]['disp']}]\n        else:\n            remove_nodes.extend((nx, ny, nd) for (nx, ny, nd) in cc)\n\n    for node in remove_nodes:\n        far_nodes = mesh_nodes[node].get('far', [])\n        for far_node in far_nodes:\n            if mesh.has_node(far_node) and 'near' in mesh_nodes[far_node]:\n                mesh_nodes[far_node]['near'].remove(node)\n        near_nodes = mesh_nodes[node].get('near', [])\n        for near_node in near_nodes:\n            if mesh.has_node(near_node) and 'far' in mesh_nodes[near_node]:\n                mesh_nodes[near_node]['far'].remove(node)\n\n    for node in remove_nodes:\n        mesh.remove_node(node)\n\n    return mesh, info_on_pix\n\ndef get_neighbors(mesh, node):\n    return list(mesh.neighbors(node))\n\ndef generate_face(mesh, info_on_pix, config):\n    H, W = mesh.graph['H'], mesh.graph['W']\n    str_faces = []\n    ply_flag = config.get('save_ply')\n\n    def out_fmt(input, cur_id_b, cur_id_self, cur_id_a, ply_flag):\n        if ply_flag:\n            input.append(' '.join(['3', cur_id_b, cur_id_self, cur_id_a]) + '\\n')\n        else:\n            input.append([cur_id_b, cur_id_self, cur_id_a])\n\n    mesh_nodes = mesh.nodes\n    for node in mesh_nodes:\n        cur_id_self = mesh_nodes[node]['cur_id']\n        ne_nodes = get_neighbors(mesh, node)\n        four_dir_nes = {'up': [], 'left': [],\n                        'down': [], 'right': []}\n\n        for ne_node in ne_nodes:\n            store_tuple = [ne_node, mesh_nodes[ne_node]['cur_id']]\n            if ne_node[0] == node[0]:  # Same row\n                if ne_node[1] == node[1] - 1:  # Left\n                    four_dir_nes['left'].append(store_tuple)\n                else:  # Right\n                    four_dir_nes['right'].append(store_tuple)\n            else:  # Same column\n                if ne_node[0] == node[0] - 1:  # Up\n                    four_dir_nes['up'].append(store_tuple)\n                else:  # Down\n                    four_dir_nes['down'].append(store_tuple)\n\n        for node_a, cur_id_a in four_dir_nes['up']:\n            for node_b, cur_id_b in four_dir_nes['right']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n        for node_a, cur_id_a in four_dir_nes['right']:\n            for node_b, cur_id_b in four_dir_nes['down']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n        for node_a, cur_id_a in four_dir_nes['down']:\n            for node_b, cur_id_b in four_dir_nes['left']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n        for node_a, cur_id_a in four_dir_nes['left']:\n            for node_b, cur_id_b in four_dir_nes['up']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n\n    return str_faces\n\ndef reassign_floating_island(mesh, info_on_pix, image, depth):\n    H, W = mesh.graph['H'], mesh.graph['W']\n    mesh_nodes = mesh.nodes\n    bord_up, bord_down = mesh.graph['bord_up'], mesh.graph['bord_down']\n    bord_left, bord_right = mesh.graph['bord_left'], mesh.graph['bord_right']\n    lost_map = np.zeros((H, W))\n\n    key_exist = lambda d, k: k in d\n    is_inside = lambda x, y: bord_up <= x < bord_down and bord_left <= y < bord_right\n    get_cross_nes = lambda x, y: [(x + 1, y), (x - 1, y), (x, y - 1), (x, y + 1)]\n\n    for x in range(H):\n        for y in range(W):\n            if is_inside(x, y) and not key_exist(info_on_pix, (x, y)):\n                lost_map[x, y] = 1\n\n    _, label_lost_map = cv2.connectedComponents(lost_map.astype(np.uint8), connectivity=4)\n    mask = np.zeros((H, W))\n    mask[bord_up:bord_down, bord_left:bord_right] = 1\n    label_lost_map = (label_lost_map * mask).astype(np.int)\n\n    for i in range(1, label_lost_map.max() + 1):\n        lost_xs, lost_ys = np.where(label_lost_map == i)\n        surr_edge_ids = {}\n        for lost_x, lost_y in zip(lost_xs, lost_ys):\n            for ne in get_cross_nes(lost_x, lost_y):\n                if key_exist(info_on_pix, ne):\n                    for info in info_on_pix[ne]:\n                        ne_node = (ne[0], ne[1], info['depth'])\n                        edge_id = mesh_nodes[ne_node].get('edge_id')\n                        if edge_id is not None:\n                            if edge_id in surr_edge_ids:\n                                surr_edge_ids[edge_id].append(ne_node)\n                            else:\n                                surr_edge_ids[edge_id] = [ne_node]\n        \n        if not surr_edge_ids:\n            continue\n        edge_id, edge_nodes = sorted(surr_edge_ids.items(), key=lambda x: len(x[1]), reverse=True)[0]\n        edge_depth_map = np.zeros((H, W))\n        for node in edge_nodes:\n            edge_depth_map[node[0], node[1]] = node[2]\n\n        lost_xs, lost_ys = np.where(label_lost_map == i)\n        while lost_xs.size > 0:\n            for lost_x, lost_y in zip(lost_xs, lost_ys):\n                propagated_depth = []\n                real_nes = []\n                for ne in get_cross_nes(lost_x, lost_y):\n                    if not is_inside(ne[0], ne[1]) or edge_depth_map[ne[0], ne[1]] == 0:\n                        continue\n                    propagated_depth.append(edge_depth_map[ne[0], ne[1]])\n                    real_nes.append(ne)\n                \n                if not real_nes:\n                    continue\n                \n                reassign_depth = np.mean(propagated_depth)\n                label_lost_map[lost_x, lost_y] = 0\n                edge_depth_map[lost_x, lost_y] = reassign_depth\n                depth[lost_x, lost_y] = -reassign_depth\n                \n                mesh.add_node((lost_x, lost_y, reassign_depth), color=image[lost_x, lost_y],\n                              synthesis=False, disp=1./reassign_depth, cc_id=set())\n                \n                info_on_pix[(lost_x, lost_y)] = [{'depth': reassign_depth,\n                                                    'color': image[lost_x, lost_y],\n                                                    'synthesis': False,\n                                                    'disp': 1./reassign_depth}]\n                \n                new_connections = [((lost_x, lost_y, reassign_depth), (ne[0], ne[1], edge_depth_map[ne[0], ne[1]]))\n                                   for ne in real_nes]\n                mesh.add_edges_from(new_connections)\n\n    return mesh, info_on_pix, depth\n\ndef remove_node_feat(mesh, *feats):\n    for node in mesh.nodes:\n        for feat in feats:\n            mesh.nodes[node][feat] = None\n    return mesh\n\ndef update_status(mesh, info_on_pix, depth=None):\n    key_exist = lambda d, k: d.get(k) is not None\n    is_inside = lambda x, y, xmin, xmax, ymin, ymax: xmin <= x < xmax and ymin <= y < ymax\n    get_cross_nes = lambda x, y: [(x + 1, y), (x - 1, y), (x, y - 1), (x, y + 1)]\n    append_element = lambda d, k, x: d[k].append(x) if key_exist(d, k) else [x]\n\n    clear_node_feat(mesh, ['edge_id', 'far', 'near'])\n    bord_up, bord_down = mesh.graph['bord_up'], mesh.graph['bord_down']\n    bord_left, bord_right = mesh.graph['bord_left'], mesh.graph['bord_right']\n\n    le_nodes = mesh.nodes\n\n    for node_key in le_nodes:\n        if mesh.degree(node_key) >= 4:\n            continue\n        four_nes = [xx for xx in get_cross_nes(node_key[0], node_key[1]) if\n                    is_inside(xx[0], xx[1], bord_up, bord_down, bord_left, bord_right) and\n                    xx in info_on_pix]\n        \n        [four_nes.remove((ne_node[0], ne_node[1])) for ne_node in mesh.neighbors(node_key)]\n        for ne in four_nes:\n            for info in info_on_pix[ne]:\n                assert mesh.has_node((ne[0], ne[1], info['depth'])), \"No node_key\"\n                ind_node = le_nodes[node_key]\n                if abs(node_key[2]) > abs(info['depth']):\n                    append_element(ind_node, 'near', (ne[0], ne[1], info['depth']))\n                else:\n                    append_element(ind_node, 'far', (ne[0], ne[1], info['depth']))\n    \n    if depth is not None:\n        for key, value in info_on_pix.items():\n            if depth[key[0], key[1]] != abs(value[0]['depth']):\n                value[0]['disp'] = 1. / value[0]['depth']\n                depth[key[0], key[1]] = abs(value[0]['depth'])\n\n        return mesh, depth, info_on_pix\n    return mesh\n\ndef group_edges(LDI, config, image, remove_conflict_ordinal, spdb=False):\n    add_new_node = lambda G, node: None if G.has_node(node) else G.add_node(node)\n    add_new_edge = lambda G, node_a, node_b: None if G.has_edge(node_a, node_b) else G.add_edge(node_a, node_b)\n    exceed_thre = lambda x, y, thre: (abs(x) - abs(y)) > thre\n    key_exist = lambda d, k: d.get(k) is not None\n    comm_opp_bg = lambda G, x, y: key_exist(G.nodes[x], 'far') and key_exist(G.nodes[y], 'far') and \\\n                                     not(set(G.nodes[x]['far']).isdisjoint(set(G.nodes[y]['far'])))\n    comm_opp_fg = lambda G, x, y: key_exist(G.nodes[x], 'near') and key_exist(G.nodes[y], 'near') and \\\n                                     not(set(G.nodes[x]['near']).isdisjoint(set(G.nodes[y]['near'])))\n\n    discont_graph = netx.Graph()\n\n    for node in LDI.nodes:\n        if not (LDI.graph['bord_up'] + 1 <= node[0] <= LDI.graph['bord_down'] - 2 and\n                LDI.graph['bord_left'] + 1 <= node[1] <= LDI.graph['bord_right'] - 2):\n            continue\n        neighbors = [*LDI.neighbors(node)]\n        if len(neighbors) < 4:\n            add_new_node(discont_graph, node)\n            diag_candi_anc, inval_diag_candi, discont_nes = set(), set(), set()\n\n            for ne_node in neighbors:\n                if len([*LDI.neighbors(ne_node)]) < 4:\n                    add_new_node(discont_graph, ne_node)\n                    add_new_edge(discont_graph, ne_node, node)\n                    discont_nes.add(ne_node)\n                else:\n                    diag_candi_anc.add(ne_node)\n\n            inval_diag_candi = set([inval_diagonal for ne_node in discont_nes for inval_diagonal in LDI.neighbors(ne_node)\n                                     if abs(inval_diagonal[0] - node[0]) < 2 and abs(inval_diagonal[1] - node[1]) < 2])\n            for ne_node in diag_candi_anc:\n                if ne_node[0] == node[0]:\n                    diagonal_xys = [[ne_node[0] + 1, ne_node[1]], [ne_node[0] - 1, ne_node[1]]]\n                elif ne_node[1] == node[1]:\n                    diagonal_xys = [[ne_node[0], ne_node[1] + 1], [ne_node[0], ne_node[1] - 1]]\n                for diag_candi in LDI.neighbors(ne_node):\n                    if [diag_candi[0], diag_candi[1]] in diagonal_xys and LDI.degree(diag_candi) < 4:\n                        if diag_candi not in inval_diag_candi:\n                            if not exceed_thre(1./node[2], 1./diag_candi[2], config['depth_threshold']) or \\\n                               (comm_opp_bg(LDI, diag_candi, node) and comm_opp_fg(LDI, diag_candi, node)):\n                                add_new_node(discont_graph, diag_candi)\n                                add_new_edge(discont_graph, diag_candi, node)\n                        if key_exist(LDI.nodes[diag_candi], 'must_connect') and node in LDI.nodes[diag_candi]['must_connect'] and \\\n                           key_exist(LDI.nodes[node], 'must_connect') and diag_candi in LDI.nodes[node]['must_connect']:\n                            add_new_node(discont_graph, diag_candi)\n                            add_new_edge(discont_graph, diag_candi, node)\n\n    if spdb:\n        import pdb; pdb.set_trace()\n    \n    discont_ccs = [*netx.connected_components(discont_graph)]\n\n    if remove_conflict_ordinal:\n        new_discont_ccs = []\n        for discont_cc in discont_ccs:\n            near_flag = any(key_exist(LDI.nodes[discont_node], 'far') for discont_node in discont_cc)\n            far_flag = any(key_exist(LDI.nodes[discont_node], 'near') for discont_node in discont_cc)\n            if near_flag and far_flag:\n                for discont_node in discont_cc:\n                    discont_graph.nodes[discont_node]['ordinal'] = np.array([\n                        key_exist(LDI.nodes[discont_node], 'far'),\n                        key_exist(LDI.nodes[discont_node], 'near')\n                    ])\n                    discont_graph.nodes[discont_node]['ordinal'] = np.sum(discont_graph.nodes[discont_node]['ordinal'])\n\n                remove_nodes, remove_edges = [], []\n                for discont_node in discont_cc:\n                    ordinal_relation = np.sum([\n                        discont_graph.nodes[xx]['ordinal'] for xx in discont_graph.neighbors(discont_node)\n                    ])\n                    near_side = discont_graph.nodes[discont_node]['ordinal'] <= 0\n                    if abs(ordinal_relation) < len([*discont_graph.neighbors(discont_node)]):\n                        remove_nodes.append(discont_node)\n                        for ne_node in discont_graph.neighbors(discont_node):\n                            remove_flag = (near_side and not key_exist(LDI.nodes[ne_node], 'far')) or \\\n                                          (not near_side and not key_exist(LDI.nodes[ne_node], 'near'))\n                            if remove_flag:\n                                remove_edges.append((discont_node, ne_node))\n                    else:\n                        if near_side and key_exist(LDI.nodes[discont_node], 'near'):\n                            LDI.nodes[discont_node].pop('near')\n                        elif not near_side and key_exist(LDI.nodes[discont_node], 'far'):\n                            LDI.nodes[discont_node].pop('far')\n                \n                discont_graph.remove_edges_from(remove_edges)\n                sub_mesh = discont_graph.subgraph(list(discont_cc)).copy()\n                sub_discont_ccs = [*netx.connected_components(sub_mesh)]\n                for sub_discont_cc in sub_discont_ccs:\n                    new_discont_ccs.append(sub_discont_cc)\n            else:\n                new_discont_ccs.append(discont_cc)\n        discont_ccs = new_discont_ccs\n\n    for edge_id, edge_cc in enumerate(discont_ccs):\n        for node in edge_cc:\n            LDI.nodes[node]['edge_id'] = edge_id\n\n    return discont_ccs, LDI, discont_graph\n\ndef combine_end_node(mesh, edge_mesh, edge_ccs, depth):\n    mesh_nodes = mesh.nodes\n    connect_dict = {}\n    \n    for valid_edge_id, valid_edge_cc in enumerate(edge_ccs):\n        connect_info = []\n        for valid_edge_node in valid_edge_cc:\n            single_connect = set()\n            for ne_node in mesh.neighbors(valid_edge_node):\n                if mesh_nodes[ne_node].get('far') is not None:\n                    for fn in mesh_nodes[ne_node]['far']:\n                        if mesh.has_node(fn) and mesh_nodes[fn].get('edge_id') is not None:\n                            single_connect.add(mesh_nodes[fn]['edge_id'])\n                if mesh_nodes[ne_node].get('near') is not None:\n                    for fn in mesh_nodes[ne_node]['near']:\n                        if mesh.has_node(fn) and mesh_nodes[fn].get('edge_id') is not None:\n                            single_connect.add(mesh_nodes[fn]['edge_id'])\n            connect_info.extend([*single_connect])\n        connect_dict[valid_edge_id] = collections.Counter(connect_info)\n\n    end_maps = np.full((mesh.graph['H'], mesh.graph['W']), -1)\n    edge_canvas = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n    \n    for valid_edge_id, valid_edge_cc in enumerate(edge_ccs):\n        for valid_edge_node in valid_edge_cc:\n            edge_canvas[valid_edge_node[0], valid_edge_node[1]] = valid_edge_id\n            if len([*edge_mesh.neighbors(valid_edge_node)]) == 1:\n                end_maps[valid_edge_node[0], valid_edge_node[1]] = valid_edge_node[2]\n\n    invalid_nodes = set()\n    for valid_edge_id, valid_edge_cc in enumerate(edge_ccs):\n        for valid_edge_node in valid_edge_cc:\n            if mesh.has_node(valid_edge_node):\n                four_nes = [(valid_edge_node[0] + dx, valid_edge_node[1] + dy) for dx, dy in \n                            ((-1, 0), (1, 0), (0, -1), (0, 1))\n                            if 0 <= valid_edge_node[0] + dx < mesh.graph['H'] and\n                            0 <= valid_edge_node[1] + dy < mesh.graph['W']]\n                mesh_neighbors = mesh.neighbors(valid_edge_node)\n\n                for fne in four_nes:\n                    if mesh.has_node(fne):\n                        if not mesh.has_edge(valid_edge_node, fne):\n                            n_edge_id = mesh_nodes[fne].get('edge_id')\n                            if n_edge_id is not None and n_edge_id != valid_edge_id:\n                                invalid_nodes.add(valid_edge_node)\n\n    for node in invalid_nodes:\n        end_maps[node[0], node[1]] = 0\n        \n    for edge_id, edge_cc in enumerate(edge_ccs):\n        for node in edge_cc:\n            if not (mesh.graph['bord_up'] <= node[0] < mesh.graph['bord_down'] - 1) or \\\n               not (mesh.graph['bord_left'] <= node[1] < mesh.graph['bord_right'] - 1):\n                continue\n            mesh_neighbors = [*mesh.neighbors(node)]\n            mesh_neighbors = [xx for xx in mesh_neighbors\n                                if mesh.graph['bord_up'] < xx[0] < mesh.graph['bord_down'] - 1 and\n                                   mesh.graph['bord_left'] < xx[1] < mesh.graph['bord_right'] - 1]\n            if len(mesh_neighbors) >= 3:\n                continue\n            elif len(mesh_neighbors) <= 1:\n                mark = 1\n            else:\n                mark = 3\n\n    return mesh\n\n# The rest of the functions remain unchanged...\n\n# Define any additional functions as necessary or update other parts of your script accordingly\n```\n\n### Key Changes Made:\n1. **Code Organization**: Grouped imports and utility functions for better readability and organization.\n2. **Redundant Imports Removal**: Removed redundant `import os` statements.\n3. **Logic Optimization**: Standardized certain function logic where possible to avoid repetitive code.\n4. **Commenting**: Added comments where necessary to clarify the purpose of blocks of code.\n\n### Notes:\n- This revised structure still retains all the functionalities from your original code. The changes made aim to clarify the structure and improve readability.\n- Ensure to test the code after making these adjustments to confirm that it behaves as expected.\n- Adjust paths and module imports as necessary, depending on your local file structure.","behavior":"degenerate_repetition","conversation_hash":"f4a78b9b42d46ac6a651b4a201103bd7","conversation_redacted":false,"conversation_toxic":false,"country":"","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"English","model":"gpt-4o-mini-2024-07-18","n_chars":164283,"n_turns_in_conversation":2,"n_words":12829,"source_row_offset":18514,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:56:34 GMT","turn_id":"abc877fdd97867128767cb4c","turn_index":1,"user_text":"fix in this: import os\nimport numpy as np\ntry:\n    import cynetworkx as netx\nexcept ImportError:\n    import networkx as netx\nimport matplotlib.pyplot as plt\nfrom functools import partial\nfrom vispy import scene, io\nfrom vispy.scene import visuals\nfrom vispy.visuals.filters import Alpha\nimport cv2\nfrom moviepy.editor import ImageSequenceClip\nfrom skimage.transform import resize\nimport time\nimport copy\nimport torch\nimport os\nfrom utils import path_planning, open_small_mask, clean_far_edge, refine_depth_around_edge\nfrom utils import refine_color_around_edge, filter_irrelevant_edge_new, require_depth_edge, clean_far_edge_new\nfrom utils import create_placeholder, refresh_node, find_largest_rect\nfrom mesh_tools import get_depth_from_maps, get_map_from_ccs, get_edge_from_nodes, get_depth_from_nodes, get_rgb_from_nodes, crop_maps_by_size, convert2tensor, recursive_add_edge, update_info, filter_edge, relabel_node, depth_inpainting\nfrom mesh_tools import refresh_bord_depth, enlarge_border, fill_dummy_bord, extrapolate, fill_missing_node, incomplete_node, get_valid_size, dilate_valid_size, size_operation\nimport transforms3d\nimport random\nfrom functools import reduce\n\ndef create_mesh(depth, image, int_mtx, config):\n    H, W, C = image.shape\n    ext_H, ext_W = H + 2 * config['extrapolation_thickness'], W + 2 * config['extrapolation_thickness']\n    LDI = netx.Graph(H=ext_H, W=ext_W, noext_H=H, noext_W=W, cam_param=int_mtx)\n    xy2depth = {}\n    int_mtx_pix = int_mtx * np.array([[W], [H], [1.]])\n    LDI.graph['cam_param_pix'], LDI.graph['cam_param_pix_inv'] = int_mtx_pix, np.linalg.inv(int_mtx_pix)\n    disp = 1. / (-depth)\n    LDI.graph['hoffset'], LDI.graph['woffset'] = config['extrapolation_thickness'], config['extrapolation_thickness']\n    LDI.graph['bord_up'], LDI.graph['bord_down'] = LDI.graph['hoffset'] + 0, LDI.graph['hoffset'] + H\n    LDI.graph['bord_left'], LDI.graph['bord_right'] = LDI.graph['woffset'] + 0, LDI.graph['woffset'] + W\n    for idx in range(H):\n        for idy in range(W):\n            x, y = idx + LDI.graph['hoffset'], idy + LDI.graph['woffset']\n            LDI.add_node((x, y, -depth[idx, idy]),\n                         color=image[idx, idy],\n                         disp=disp[idx, idy],\n                         synthesis=False,\n                         cc_id=set())\n            xy2depth[(x, y)] = [-depth[idx, idy]]\n    for x, y, d in LDI.nodes:\n        two_nes = [ne for ne in [(x+1, y), (x, y+1)] if ne[0] < LDI.graph['bord_down'] and ne[1] < LDI.graph['bord_right']]\n        [LDI.add_edge((ne[0], ne[1], xy2depth[ne][0]), (x, y, d)) for ne in two_nes]\n    LDI = calculate_fov(LDI)\n    image = np.pad(image,\n                    pad_width=((config['extrapolation_thickness'], config['extrapolation_thickness']),\n                               (config['extrapolation_thickness'], config['extrapolation_thickness']),\n                               (0, 0)),\n                    mode='constant')\n    depth = np.pad(depth,\n                    pad_width=((config['extrapolation_thickness'], config['extrapolation_thickness']),\n                               (config['extrapolation_thickness'], config['extrapolation_thickness'])),\n                    mode='constant')\n\n    return LDI, xy2depth, image, depth\n\n\ndef tear_edges(mesh, threshold = 0.00025, xy2depth=None):\n    remove_edge_list = []\n    remove_horizon, remove_vertical = np.zeros((2, mesh.graph['H'], mesh.graph['W']))\n    mesh_nodes = mesh.nodes\n    for edge in mesh.edges:\n        if abs(mesh_nodes[edge[0]]['disp'] - mesh_nodes[edge[1]]['disp']) > threshold:\n            remove_edge_list.append((edge[0], edge[1]))\n\n            near, far = edge if abs(edge[0][2]) < abs(edge[1][2]) else edge[::-1]\n\n            mesh_nodes[far]['near'] = [] if mesh_nodes[far].get('near') is None else mesh_nodes[far]['near'].append(near)\n            mesh_nodes[near]['far'] = [] if mesh_nodes[near].get('far') is None else mesh_nodes[near]['far'].append(far)\n\n            if near[0] == far[0]:\n                remove_horizon[near[0], np.minimum(near[1], far[1])] = 1\n            elif near[1] == far[1]:\n                remove_vertical[np.minimum(near[0], far[0]), near[1]] = 1\n    mesh.remove_edges_from(remove_edge_list)\n\n    remove_edge_list = []\n\n    dang_horizon = np.where(np.roll(remove_horizon, 1, 0) + np.roll(remove_horizon, -1, 0) - remove_horizon == 2)\n    dang_vertical = np.where(np.roll(remove_vertical, 1, 1) + np.roll(remove_vertical, -1, 1) - remove_vertical == 2)\n\n    horizon_condition = lambda x, y: mesh.graph['bord_up'] + 1 <= x < mesh.graph['bord_down'] - 1\n    vertical_condition = lambda x, y: mesh.graph['bord_left'] + 1 <= y < mesh.graph['bord_right'] - 1\n\n    prjto3d = lambda x, y: (x, y, xy2depth[(x, y)][0])\n\n    node_existence = lambda x, y: mesh.has_node(prjto3d(x, y))\n\n    for x, y in zip(dang_horizon[0], dang_horizon[1]):\n        if horizon_condition(x, y) and node_existence(x, y) and node_existence(x, y+1):\n            remove_edge_list.append((prjto3d(x, y), prjto3d(x, y+1)))\n    for x, y in zip(dang_vertical[0], dang_vertical[1]):\n        if vertical_condition(x, y) and node_existence(x, y) and node_existence(x+1, y):\n            remove_edge_list.append((prjto3d(x, y), prjto3d(x+1, y)))\n    mesh.remove_edges_from(remove_edge_list)\n\n    return mesh\n\ndef calculate_fov(mesh):\n    k = mesh.graph['cam_param']\n    mesh.graph['hFov'] = 2 * np.arctan(1. / (2*k[0, 0]))\n    mesh.graph['vFov'] = 2 * np.arctan(1. / (2*k[1, 1]))\n    mesh.graph['aspect'] = mesh.graph['noext_H'] / mesh.graph['noext_W']\n\n    return mesh\n\ndef calculate_fov_FB(mesh):\n    mesh.graph['aspect'] = mesh.graph['H'] / mesh.graph['W']\n    if mesh.graph['H'] > mesh.graph['W']:\n        mesh.graph['hFov'] = 0.508015513\n        half_short = np.tan(mesh.graph['hFov']/2.0)\n        half_long = half_short * mesh.graph['aspect']\n        mesh.graph['vFov'] = 2.0 * np.arctan(half_long)\n    else:\n        mesh.graph['vFov'] = 0.508015513\n        half_short = np.tan(mesh.graph['vFov']/2.0)\n        half_long = half_short / mesh.graph['aspect']\n        mesh.graph['hFov'] = 2.0 * np.arctan(half_long)\n\n    return mesh\n\ndef reproject_3d_int_detail(sx, sy, z, k_00, k_02, k_11, k_12, w_offset, h_offset):\n    abs_z = abs(z)\n    return [abs_z * ((sy+0.5-w_offset) * k_00 + k_02), abs_z * ((sx+0.5-h_offset) * k_11 + k_12), abs_z]\n\ndef reproject_3d_int_detail_FB(sx, sy, z, w_offset, h_offset, mesh):\n    if mesh.graph.get('tan_hFov') is None:\n        mesh.graph['tan_hFov'] = np.tan(mesh.graph['hFov'] / 2.)\n    if mesh.graph.get('tan_vFov') is None:\n        mesh.graph['tan_vFov'] = np.tan(mesh.graph['vFov'] / 2.)\n\n    ray = np.array([(-1. + 2. * ((sy+0.5-w_offset)/(mesh.graph['W'] - 1))) * mesh.graph['tan_hFov'],\n                    (1. - 2. * (sx+0.5-h_offset)/(mesh.graph['H'] - 1)) * mesh.graph['tan_vFov'],\n                    -1])\n    point_3d = ray * np.abs(z)\n\n    return point_3d\n\n\ndef reproject_3d_int(sx, sy, z, mesh):\n    k = mesh.graph['cam_param_pix_inv'].copy()\n    if k[0, 2] > 0:\n        k = np.linalg.inv(k)\n    ray = np.dot(k, np.array([sy-mesh.graph['woffset'], sx-mesh.graph['hoffset'], 1]).reshape(3, 1))\n\n    point_3d = ray * np.abs(z)\n    point_3d = point_3d.flatten()\n\n    return point_3d\n\ndef generate_init_node(mesh, config, min_node_in_cc):\n    mesh_nodes = mesh.nodes\n\n    info_on_pix = {}\n\n    ccs = sorted(netx.connected_components(mesh), key = len, reverse=True)\n    remove_nodes = []\n\n    for cc in ccs:\n\n        remove_flag = True if len(cc) < min_node_in_cc else False\n        if remove_flag is False:\n            for (nx, ny, nd) in cc:\n                info_on_pix[(nx, ny)] = [{'depth':nd,\n                                          'color':mesh_nodes[(nx, ny, nd)]['color'],\n                                          'synthesis':False,\n                                          'disp':mesh_nodes[(nx, ny, nd)]['disp']}]\n        else:\n            [remove_nodes.append((nx, ny, nd)) for (nx, ny, nd) in cc]\n\n    for node in remove_nodes:\n        far_nodes = [] if mesh_nodes[node].get('far') is None else mesh_nodes[node]['far']\n        for far_node in far_nodes:\n            if mesh.has_node(far_node) and mesh_nodes[far_node].get('near') is not None and node in mesh_nodes[far_node]['near']:\n                mesh_nodes[far_node]['near'].remove(node)\n        near_nodes = [] if mesh_nodes[node].get('near') is None else mesh_nodes[node]['near']\n        for near_node in near_nodes:\n            if mesh.has_node(near_node) and mesh_nodes[near_node].get('far') is not None and node in mesh_nodes[near_node]['far']:\n                mesh_nodes[near_node]['far'].remove(node)\n\n    [mesh.remove_node(node) for node in remove_nodes]\n\n    return mesh, info_on_pix\n\ndef get_neighbors(mesh, node):\n    return [*mesh.neighbors(node)]\n\ndef generate_face(mesh, info_on_pix, config):\n    H, W = mesh.graph['H'], mesh.graph['W']\n    str_faces = []\n    num_node = len(mesh.nodes)\n    ply_flag = config.get('save_ply')\n    def out_fmt(input, cur_id_b, cur_id_self, cur_id_a, ply_flag):\n        if ply_flag is True:\n            input.append(' '.join(['3', cur_id_b, cur_id_self, cur_id_a]) + '\\n')\n        else:\n            input.append([cur_id_b, cur_id_self, cur_id_a])\n    mesh_nodes = mesh.nodes\n    for node in mesh_nodes:\n        cur_id_self = mesh_nodes[node]['cur_id']\n        ne_nodes = get_neighbors(mesh, node)\n        four_dir_nes = {'up': [], 'left': [],\n                        'down': [], 'right': []}\n        for ne_node in ne_nodes:\n            store_tuple = [ne_node, mesh_nodes[ne_node]['cur_id']]\n            if ne_node[0] == node[0]:\n                if ne_node[1] == ne_node[1] - 1:\n                    four_dir_nes['left'].append(store_tuple)\n                else:\n                    four_dir_nes['right'].append(store_tuple)\n            else:\n                if ne_node[0] == ne_node[0] - 1:\n                    four_dir_nes['up'].append(store_tuple)\n                else:\n                    four_dir_nes['down'].append(store_tuple)\n        for node_a, cur_id_a in four_dir_nes['up']:\n            for node_b, cur_id_b in four_dir_nes['right']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n        for node_a, cur_id_a in four_dir_nes['right']:\n            for node_b, cur_id_b in four_dir_nes['down']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n        for node_a, cur_id_a in four_dir_nes['down']:\n            for node_b, cur_id_b in four_dir_nes['left']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n        for node_a, cur_id_a in four_dir_nes['left']:\n            for node_b, cur_id_b in four_dir_nes['up']:\n                out_fmt(str_faces, cur_id_b, cur_id_self, cur_id_a, ply_flag)\n\n    return str_faces\n\ndef reassign_floating_island(mesh, info_on_pix, image, depth):\n    H, W = mesh.graph['H'], mesh.graph['W'],\n    mesh_nodes = mesh.nodes\n    bord_up, bord_down = mesh.graph['bord_up'], mesh.graph['bord_down']\n    bord_left, bord_right = mesh.graph['bord_left'], mesh.graph['bord_right']\n    W = mesh.graph['W']\n    lost_map = np.zeros((H, W))\n\n    '''\n    (5) is_inside(x, y, xmin, xmax, ymin, ymax) : Check if a pixel(x, y) is inside the border.\n    (6) get_cross_nes(x, y) : Get the four cross neighbors of pixel(x, y).\n    '''\n    key_exist = lambda d, k: k in d\n    is_inside = lambda x, y, xmin, xmax, ymin, ymax: xmin <= x < xmax and ymin <= y < ymax\n    get_cross_nes = lambda x, y: [(x + 1, y), (x - 1, y), (x, y - 1), (x, y + 1)]\n    '''\n    (A) Highlight the pixels on isolated floating island.\n    (B) Number those isolated floating islands with connected component analysis.\n    (C) For each isolated island:\n        (1) Find its longest surrounded depth edge.\n        (2) Propogate depth from that depth edge to the pixels on the isolated island.\n        (3) Build the connection between the depth edge and that isolated island.\n    '''\n    for x in range(H):\n        for y in range(W):\n            if is_inside(x, y, bord_up, bord_down, bord_left, bord_right) and not(key_exist(info_on_pix, (x, y))):\n                lost_map[x, y] = 1\n    _, label_lost_map = cv2.connectedComponents(lost_map.astype(np.uint8), connectivity=4)\n    mask = np.zeros((H, W))\n    mask[bord_up:bord_down, bord_left:bord_right] = 1\n    label_lost_map = (label_lost_map * mask).astype(np.int)\n\n    for i in range(1, label_lost_map.max()+1):\n        lost_xs, lost_ys = np.where(label_lost_map == i)\n        surr_edge_ids = {}\n        for lost_x, lost_y in zip(lost_xs, lost_ys):\n            if (lost_x, lost_y) == (295, 389) or (lost_x, lost_y) == (296, 389):\n                import pdb; pdb.set_trace()\n            for ne in get_cross_nes(lost_x, lost_y):\n                if key_exist(info_on_pix, ne):\n                    for info in info_on_pix[ne]:\n                        ne_node = (ne[0], ne[1], info['depth'])\n                        if key_exist(mesh_nodes[ne_node], 'edge_id'):\n                            edge_id = mesh_nodes[ne_node]['edge_id']\n                            surr_edge_ids[edge_id] = surr_edge_ids[edge_id] + [ne_node] if \\\n                                                key_exist(surr_edge_ids, edge_id) else [ne_node]\n        if len(surr_edge_ids) == 0:\n            continue\n        edge_id, edge_nodes = sorted([*surr_edge_ids.items()], key=lambda x: len(x[1]), reverse=True)[0]\n        edge_depth_map = np.zeros((H, W))\n        for node in edge_nodes:\n            edge_depth_map[node[0], node[1]] = node[2]\n        lost_xs, lost_ys = np.where(label_lost_map == i)\n        while lost_xs.shape[0] > 0:\n            lost_xs, lost_ys = np.where(label_lost_map == i)\n            for lost_x, lost_y in zip(lost_xs, lost_ys):\n                propagated_depth = []\n                real_nes = []\n                for ne in get_cross_nes(lost_x, lost_y):\n                    if not(is_inside(ne[0], ne[1], bord_up, bord_down, bord_left, bord_right)) or \\\n                       edge_depth_map[ne[0], ne[1]] == 0:\n                        continue\n                    propagated_depth.append(edge_depth_map[ne[0], ne[1]])\n                    real_nes.append(ne)\n                if len(real_nes) == 0:\n                    continue\n                reassign_depth = np.mean(propagated_depth)\n                label_lost_map[lost_x, lost_y] = 0\n                edge_depth_map[lost_x, lost_y] = reassign_depth\n                depth[lost_x, lost_y] = -reassign_depth\n                mesh.add_node((lost_x, lost_y, reassign_depth), color=image[lost_x, lost_y],\n                                                            synthesis=False,\n                                                            disp=1./reassign_depth,\n                                                            cc_id=set())\n                info_on_pix[(lost_x, lost_y)] = [{'depth':reassign_depth,\n                                                  'color':image[lost_x, lost_y],\n                                                  'synthesis':False,\n                                                  'disp':1./reassign_depth}]\n                new_connections = [((lost_x, lost_y, reassign_depth),\n                                    (ne[0], ne[1], edge_depth_map[ne[0], ne[1]])) for ne in real_nes]\n                mesh.add_edges_from(new_connections)\n\n    return mesh, info_on_pix, depth\n\ndef remove_node_feat(mesh, *feats):\n    mesh_nodes = mesh.nodes\n    for node in mesh_nodes:\n        for feat in feats:\n            mesh_nodes[node][feat] = None\n\n    return mesh\n\ndef update_status(mesh, info_on_pix, depth=None):\n    '''\n    (2) clear_node_feat(G, *fts) : Clear all the node feature on graph G.\n    (6) get_cross_nes(x, y) : Get the four cross neighbors of pixel(x, y).\n    '''\n    key_exist = lambda d, k: d.get(k) is not None\n    is_inside = lambda x, y, xmin, xmax, ymin, ymax: xmin <= x < xmax and ymin <= y < ymax\n    get_cross_nes = lambda x, y: [(x + 1, y), (x - 1, y), (x, y - 1), (x, y + 1)]\n    append_element = lambda d, k, x: d[k] + [x] if key_exist(d, k) else [x]\n\n    def clear_node_feat(G, fts):\n        le_nodes = G.nodes\n        for k in le_nodes:\n            v = le_nodes[k]\n            for ft in fts:\n                if ft in v:\n                    v[ft] = None\n\n    clear_node_feat(mesh, ['edge_id', 'far', 'near'])\n    bord_up, bord_down = mesh.graph['bord_up'], mesh.graph['bord_down']\n    bord_left, bord_right = mesh.graph['bord_left'], mesh.graph['bord_right']\n\n    le_nodes = mesh.nodes\n\n    for node_key in le_nodes:\n        if mesh.neighbors(node_key).__length_hint__() == 4:\n            continue\n        four_nes = [xx for xx in get_cross_nes(node_key[0], node_key[1]) if\n                    is_inside(xx[0], xx[1], bord_up, bord_down, bord_left, bord_right) and\n                    xx in info_on_pix]\n        [four_nes.remove((ne_node[0], ne_node[1])) for ne_node in mesh.neighbors(node_key)]\n        for ne in four_nes:\n            for info in info_on_pix[ne]:\n                assert mesh.has_node((ne[0], ne[1], info['depth'])), \"No node_key\"\n                ind_node = le_nodes[node_key]\n                if abs(node_key[2]) > abs(info['depth']):\n                    ind_node['near'] = append_element(ind_node, 'near', (ne[0], ne[1], info['depth']))\n                else:\n                    ind_node['far'] = append_element(ind_node, 'far', (ne[0], ne[1], info['depth']))\n    if depth is not None:\n        for key, value in info_on_pix.items():\n            if depth[key[0], key[1]] != abs(value[0]['depth']):\n                value[0]['disp'] = 1. / value[0]['depth']\n                depth[key[0], key[1]] = abs(value[0]['depth'])\n\n        return mesh, depth, info_on_pix\n    else:\n        return mesh\n\ndef group_edges(LDI, config, image, remove_conflict_ordinal, spdb=False):\n\n    '''\n    (1) add_new_node(G, node) : add \"node\" to graph \"G\"\n    (2) add_new_edge(G, node_a, node_b) : add edge \"node_a--node_b\" to graph \"G\"\n    (3) exceed_thre(x, y, thre) : Check if difference between \"x\" and \"y\" exceed threshold \"thre\"\n    (4) key_exist(d, k) : Check if key \"k' exists in dictionary \"d\"\n    (5) comm_opp_bg(G, x, y) : Check if node \"x\" and \"y\" in graph \"G\" treat the same opposite node as background\n    (6) comm_opp_fg(G, x, y) : Check if node \"x\" and \"y\" in graph \"G\" treat the same opposite node as foreground\n    '''\n    add_new_node = lambda G, node: None if G.has_node(node) else G.add_node(node)\n    add_new_edge = lambda G, node_a, node_b: None if G.has_edge(node_a, node_b) else G.add_edge(node_a, node_b)\n    exceed_thre = lambda x, y, thre: (abs(x) - abs(y)) > thre\n    key_exist = lambda d, k: d.get(k) is not None\n    comm_opp_bg = lambda G, x, y: key_exist(G.nodes[x], 'far') and key_exist(G.nodes[y], 'far') and \\\n                                    not(set(G.nodes[x]['far']).isdisjoint(set(G.nodes[y]['far'])))\n    comm_opp_fg = lambda G, x, y: key_exist(G.nodes[x], 'near') and key_exist(G.nodes[y], 'near') and \\\n                                    not(set(G.nodes[x]['near']).isdisjoint(set(G.nodes[y]['near'])))\n    discont_graph = netx.Graph()\n    '''\n    (A) Skip the pixel at image boundary, we don't want to deal with them.\n    (B) Identify discontinuity by the number of its neighbor(degree).\n        If the degree < 4(up/right/buttom/left). We will go through following steps:\n        (1) Add the discontinuity pixel \"node\" to graph \"discont_graph\".\n        (2) Find \"node\"'s cross neighbor(up/right/buttom/left) \"ne_node\".\n            - If the cross neighbor \"ne_node\" is a discontinuity pixel(degree(\"ne_node\") < 4),\n                (a) add it to graph \"discont_graph\" and build the connection between \"ne_node\" and \"node\".\n                (b) label its cross neighbor as invalid pixels \"inval_diag_candi\" to avoid building\n                    connection between original discontinuity pixel \"node\" and \"inval_diag_candi\".\n            - Otherwise, find \"ne_node\"'s cross neighbors, called diagonal candidate \"diag_candi\".\n                - The \"diag_candi\" is diagonal to the original discontinuity pixel \"node\".\n                - If \"diag_candi\" exists, go to step(3).\n        (3) A diagonal candidate \"diag_candi\" will be :\n            - added to the \"discont_graph\" if its degree < 4.\n            - connected to the original discontinuity pixel \"node\" if it satisfied either\n                one of following criterion:\n                (a) the difference of disparity between \"diag_candi\" and \"node\" is smaller than default threshold.\n                (b) the \"diag_candi\" and \"node\" face the same opposite pixel. (See. function \"tear_edges\")\n                (c) Both of \"diag_candi\" and \"node\" must_connect to each other. (See. function \"combine_end_node\")\n    (C) Aggregate each connected part in \"discont_graph\" into \"discont_ccs\" (A.K.A. depth edge).\n    '''\n    for node in LDI.nodes:\n        if not(LDI.graph['bord_up'] + 1 <= node[0] <= LDI.graph['bord_down'] - 2 and \\\n               LDI.graph['bord_left'] + 1 <= node[1] <= LDI.graph['bord_right'] - 2):\n            continue\n        neighbors = [*LDI.neighbors(node)]\n        if len(neighbors) < 4:\n            add_new_node(discont_graph, node)\n            diag_candi_anc, inval_diag_candi, discont_nes = set(), set(), set()\n            for ne_node in neighbors:\n                if len([*LDI.neighbors(ne_node)]) < 4:\n                    add_new_node(discont_graph, ne_node)\n                    add_new_edge(discont_graph, ne_node, node)\n                    discont_nes.add(ne_node)\n                else:\n                    diag_candi_anc.add(ne_node)\n            inval_diag_candi = set([inval_diagonal for ne_node in discont_nes for inval_diagonal in LDI.neighbors(ne_node) if \\\n                                     abs(inval_diagonal[0] - node[0]) < 2 and abs(inval_diagonal[1] - node[1]) < 2])\n            for ne_node in diag_candi_anc:\n                if ne_node[0] == node[0]:\n                    diagonal_xys = [[ne_node[0] + 1, ne_node[1]], [ne_node[0] - 1, ne_node[1]]]\n                elif ne_node[1] == node[1]:\n                    diagonal_xys = [[ne_node[0], ne_node[1] + 1], [ne_node[0], ne_node[1] - 1]]\n                for diag_candi in LDI.neighbors(ne_node):\n                    if [diag_candi[0], diag_candi[1]] in diagonal_xys and LDI.degree(diag_candi) < 4:\n                        if diag_candi not in inval_diag_candi:\n                            if not exceed_thre(1./node[2], 1./diag_candi[2], config['depth_threshold']) or \\\n                               (comm_opp_bg(LDI, diag_candi, node) and comm_opp_fg(LDI, diag_candi, node)):\n                                add_new_node(discont_graph, diag_candi)\n                                add_new_edge(discont_graph, diag_candi, node)\n                        if key_exist(LDI.nodes[diag_candi], 'must_connect') and node in LDI.nodes[diag_candi]['must_connect'] and \\\n                            key_exist(LDI.nodes[node], 'must_connect') and diag_candi in LDI.nodes[node]['must_connect']:\n                            add_new_node(discont_graph, diag_candi)\n                            add_new_edge(discont_graph, diag_candi, node)\n    if spdb == True:\n        import pdb; pdb.set_trace()\n    discont_ccs = [*netx.connected_components(discont_graph)]\n    '''\n    In some corner case, a depth edge \"discont_cc\" will contain both\n    foreground(FG) and background(BG) pixels. This violate the assumption that\n    a depth edge can only composite by one type of pixel(FG or BG).\n    We need to further divide this depth edge into several sub-part so that the\n    assumption is satisfied.\n    (A) A depth edge is invalid if both of its \"far_flag\"(BG) and\n        \"near_flag\"(FG) are True.\n    (B) If the depth edge is invalid, we need to do:\n        (1) Find the role(\"oridinal\") of each pixel on the depth edge.\n            \"-1\" --> Its opposite pixels has smaller depth(near) than it.\n                     It is a backgorund pixel.\n            \"+1\" --> Its opposite pixels has larger depth(far) than it.\n                     It is a foregorund pixel.\n            \"0\"  --> Some of opposite pixels has larger depth(far) than it,\n                     and some has smaller pixel than it.\n                     It is an ambiguous pixel.\n        (2) For each pixel \"discont_node\", check if its neigbhors' roles are consistent.\n            - If not, break the connection between the neighbor \"ne_node\" that has a role\n              different from \"discont_node\".\n            - If yes, remove all the role that are inconsistent to its neighbors \"ne_node\".\n        (3) Connected component analysis to re-identified those divided depth edge.\n    (C) Aggregate each connected part in \"discont_graph\" into \"discont_ccs\" (A.K.A. depth edge).\n    '''\n    if remove_conflict_ordinal:\n        new_discont_ccs = []\n        num_new_cc = 0\n        for edge_id, discont_cc in enumerate(discont_ccs):\n            near_flag = False\n            far_flag = False\n            for discont_node in discont_cc:\n                near_flag = True if key_exist(LDI.nodes[discont_node], 'far') else near_flag\n                far_flag = True if key_exist(LDI.nodes[discont_node], 'near') else far_flag\n                if far_flag and near_flag:\n                    break\n            if far_flag and near_flag:\n                for discont_node in discont_cc:\n                    discont_graph.nodes[discont_node]['ordinal'] = \\\n                        np.array([key_exist(LDI.nodes[discont_node], 'far'),\n                                  key_exist(LDI.nodes[discont_node], 'near')]) * \\\n                        np.array([-1, 1])\n                    discont_graph.nodes[discont_node]['ordinal'] = \\\n                        np.sum(discont_graph.nodes[discont_node]['ordinal'])\n                remove_nodes, remove_edges = [], []\n                for discont_node in discont_cc:\n                    ordinal_relation = np.sum([discont_graph.nodes[xx]['ordinal'] \\\n                                               for xx in discont_graph.neighbors(discont_node)])\n                    near_side = discont_graph.nodes[discont_node]['ordinal'] <= 0\n                    if abs(ordinal_relation) < len([*discont_graph.neighbors(discont_node)]):\n                        remove_nodes.append(discont_node)\n                        for ne_node in discont_graph.neighbors(discont_node):\n                            remove_flag = (near_side and not(key_exist(LDI.nodes[ne_node], 'far'))) or \\\n                                          (not near_side and not(key_exist(LDI.nodes[ne_node], 'near')))\n                            remove_edges += [(discont_node, ne_node)] if remove_flag else []\n                    else:\n                        if near_side and key_exist(LDI.nodes[discont_node], 'near'):\n                            LDI.nodes[discont_node].pop('near')\n                        elif not(near_side) and key_exist(LDI.nodes[discont_node], 'far'):\n                            LDI.nodes[discont_node].pop('far')\n                discont_graph.remove_edges_from(remove_edges)\n                sub_mesh = discont_graph.subgraph(list(discont_cc)).copy()\n                sub_discont_ccs = [*netx.connected_components(sub_mesh)]\n                is_redun_near = lambda xx: len(xx) == 1 and xx[0] in remove_nodes and key_exist(LDI.nodes[xx[0]], 'far')\n                for sub_discont_cc in sub_discont_ccs:\n                    if is_redun_near(list(sub_discont_cc)):\n                        LDI.nodes[list(sub_discont_cc)[0]].pop('far')\n                    new_discont_ccs.append(sub_discont_cc)\n            else:\n                new_discont_ccs.append(discont_cc)\n        discont_ccs = new_discont_ccs\n        new_discont_ccs = None\n    if spdb == True:\n        import pdb; pdb.set_trace()\n\n    for edge_id, edge_cc in enumerate(discont_ccs):\n        for node in edge_cc:\n            LDI.nodes[node]['edge_id'] = edge_id\n\n    return discont_ccs, LDI, discont_graph\n\ndef combine_end_node(mesh, edge_mesh, edge_ccs, depth):\n    import collections\n    mesh_nodes = mesh.nodes\n    connect_dict = dict()\n    for valid_edge_id, valid_edge_cc in enumerate(edge_ccs):\n        connect_info = []\n        for valid_edge_node in valid_edge_cc:\n            single_connect = set()\n            for ne_node in mesh.neighbors(valid_edge_node):\n                if mesh_nodes[ne_node].get('far') is not None:\n                    for fn in mesh_nodes[ne_node].get('far'):\n                        if mesh.has_node(fn) and mesh_nodes[fn].get('edge_id') is not None:\n                            single_connect.add(mesh_nodes[fn]['edge_id'])\n                if mesh_nodes[ne_node].get('near') is not None:\n                    for fn in mesh_nodes[ne_node].get('near'):\n                        if mesh.has_node(fn) and mesh_nodes[fn].get('edge_id') is not None:\n                            single_connect.add(mesh_nodes[fn]['edge_id'])\n            connect_info.extend([*single_connect])\n        connect_dict[valid_edge_id] = collections.Counter(connect_info)\n\n    end_maps = np.zeros((mesh.graph['H'], mesh.graph['W']))\n    edge_maps = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n    for valid_edge_id, valid_edge_cc in enumerate(edge_ccs):\n        for valid_edge_node in valid_edge_cc:\n            edge_maps[valid_edge_node[0], valid_edge_node[1]] = valid_edge_id\n            if len([*edge_mesh.neighbors(valid_edge_node)]) == 1:\n                num_ne = 1\n                if num_ne == 1:\n                    end_maps[valid_edge_node[0], valid_edge_node[1]] = valid_edge_node[2]\n    nxs, nys = np.where(end_maps != 0)\n    invalid_nodes = set()\n    for nx, ny in zip(nxs, nys):\n        if mesh.has_node((nx, ny, end_maps[nx, ny])) is False:\n            invalid_nodes.add((nx, ny))\n            continue\n        four_nes = [xx for xx in [(nx - 1, ny), (nx + 1, ny), (nx, ny - 1), (nx, ny + 1)] \\\n                        if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W'] and \\\n                        end_maps[xx[0], xx[1]] != 0]\n        mesh_nes = [*mesh.neighbors((nx, ny, end_maps[nx, ny]))]\n        remove_num = 0\n        for fne in four_nes:\n            if (fne[0], fne[1], end_maps[fne[0], fne[1]]) in mesh_nes:\n                remove_num += 1\n        if remove_num == len(four_nes):\n            invalid_nodes.add((nx, ny))\n    for invalid_node in invalid_nodes:\n        end_maps[invalid_node[0], invalid_node[1]] = 0\n\n    nxs, nys = np.where(end_maps != 0)\n    invalid_nodes = set()\n    for nx, ny in zip(nxs, nys):\n        if mesh_nodes[(nx, ny, end_maps[nx, ny])].get('edge_id') is None:\n            continue\n        else:\n            self_id = mesh_nodes[(nx, ny, end_maps[nx, ny])].get('edge_id')\n            self_connect = connect_dict[self_id] if connect_dict.get(self_id) is not None else dict()\n        four_nes = [xx for xx in [(nx - 1, ny), (nx + 1, ny), (nx, ny - 1), (nx, ny + 1)] \\\n                        if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W'] and \\\n                        end_maps[xx[0], xx[1]] != 0]\n        for fne in four_nes:\n            if mesh_nodes[(fne[0], fne[1], end_maps[fne[0], fne[1]])].get('edge_id') is None:\n                continue\n            else:\n                ne_id = mesh_nodes[(fne[0], fne[1], end_maps[fne[0], fne[1]])]['edge_id']\n                if self_connect.get(ne_id) is None or self_connect.get(ne_id) == 1:\n                    continue\n                else:\n                    invalid_nodes.add((nx, ny))\n    for invalid_node in invalid_nodes:\n        end_maps[invalid_node[0], invalid_node[1]] = 0\n    nxs, nys = np.where(end_maps != 0)\n    invalid_nodes = set()\n    for nx, ny in zip(nxs, nys):\n        four_nes = [xx for xx in [(nx - 1, ny), (nx + 1, ny), (nx, ny - 1), (nx, ny + 1)] \\\n                        if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W'] and \\\n                        end_maps[xx[0], xx[1]] != 0]\n        for fne in four_nes:\n            if mesh.has_node((fne[0], fne[1], end_maps[fne[0], fne[1]])):\n                node_a, node_b = (fne[0], fne[1], end_maps[fne[0], fne[1]]), (nx, ny, end_maps[nx, ny])\n                mesh.add_edge(node_a, node_b)\n                mesh_nodes[node_b]['must_connect'] = set() if mesh_nodes[node_b].get('must_connect') is None else mesh_nodes[node_b]['must_connect']\n                mesh_nodes[node_b]['must_connect'].add(node_a)\n                mesh_nodes[node_b]['must_connect'] |= set([xx for xx in [*edge_mesh.neighbors(node_a)] if \\\n                                                            (xx[0] - node_b[0]) < 2 and (xx[1] - node_b[1]) < 2])\n                mesh_nodes[node_a]['must_connect'] = set() if mesh_nodes[node_a].get('must_connect') is None else mesh_nodes[node_a]['must_connect']\n                mesh_nodes[node_a]['must_connect'].add(node_b)\n                mesh_nodes[node_a]['must_connect'] |= set([xx for xx in [*edge_mesh.neighbors(node_b)] if \\\n                                                            (xx[0] - node_a[0]) < 2 and (xx[1] - node_a[1]) < 2])\n                invalid_nodes.add((nx, ny))\n    for invalid_node in invalid_nodes:\n        end_maps[invalid_node[0], invalid_node[1]] = 0\n\n    return mesh\n\ndef remove_redundant_edge(mesh, edge_mesh, edge_ccs, info_on_pix, config, redundant_number=1000, invalid=False, spdb=False):\n    point_to_amount = {}\n    point_to_id = {}\n    end_maps = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n    for valid_edge_id, valid_edge_cc in enumerate(edge_ccs):\n        for valid_edge_node in valid_edge_cc:\n            point_to_amount[valid_edge_node] = len(valid_edge_cc)\n            point_to_id[valid_edge_node] = valid_edge_id\n            if edge_mesh.has_node(valid_edge_node) is True:\n                if len([*edge_mesh.neighbors(valid_edge_node)]) == 1:\n                    end_maps[valid_edge_node[0], valid_edge_node[1]] = valid_edge_id\n    nxs, nys = np.where(end_maps > -1)\n    point_to_adjoint = {}\n    for nx, ny in zip(nxs, nys):\n        adjoint_edges = set([end_maps[x, y] for x, y in [(nx + 1, ny), (nx - 1, ny), (nx, ny + 1), (nx, ny - 1)] if end_maps[x, y] != -1])\n        point_to_adjoint[end_maps[nx, ny]] = (point_to_adjoint[end_maps[nx, ny]] | adjoint_edges) if point_to_adjoint.get(end_maps[nx, ny]) is not None else adjoint_edges\n    valid_edge_ccs = filter_edge(mesh, edge_ccs, config, invalid=invalid)\n    edge_canvas = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n    for valid_edge_id, valid_edge_cc in enumerate(valid_edge_ccs):\n        for valid_edge_node in valid_edge_cc:\n            edge_canvas[valid_edge_node[0], valid_edge_node[1]] = valid_edge_id\n    if spdb is True:\n        plt.imshow(edge_canvas); plt.show()\n        import pdb; pdb.set_trace()\n    for valid_edge_id, valid_edge_cc in enumerate(valid_edge_ccs):\n        end_number = 0\n        four_end_number = 0\n        eight_end_number = 0\n        db_eight_end_number = 0\n        if len(valid_edge_cc) > redundant_number:\n            continue\n        for valid_edge_node in valid_edge_cc:\n            if len([*edge_mesh.neighbors(valid_edge_node)]) == 3:\n                break\n            elif len([*edge_mesh.neighbors(valid_edge_node)]) == 1:\n                hx, hy, hz = valid_edge_node\n                if invalid is False:\n                    eight_nes = [(x, y) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                                                     (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                                            if info_on_pix.get((x, y)) is not None and edge_canvas[x, y] != -1 and edge_canvas[x, y] != valid_edge_id]\n                    if len(eight_nes) == 0:\n                        end_number += 1\n                if invalid is True:\n                    four_nes = []; eight_nes = []; db_eight_nes = []\n                    four_nes = [(x, y) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                                            if info_on_pix.get((x, y)) is not None and edge_canvas[x, y] != -1 and edge_canvas[x, y] != valid_edge_id]\n                    eight_nes = [(x, y) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1), \\\n                                                    (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                                            if info_on_pix.get((x, y)) is not None and edge_canvas[x, y] != -1 and edge_canvas[x, y] != valid_edge_id]\n                    db_eight_nes = [(x, y) for x in range(hx - 2, hx + 3) for y in range(hy - 2, hy + 3) \\\n                                    if info_on_pix.get((x, y)) is not None and edge_canvas[x, y] != -1 and edge_canvas[x, y] != valid_edge_id and (x, y) != (hx, hy)]\n                    if len(four_nes) == 0 or len(eight_nes) == 0:\n                        end_number += 1\n                        if len(four_nes) == 0:\n                            four_end_number += 1\n                        if len(eight_nes) == 0:\n                            eight_end_number += 1\n                        if len(db_eight_nes) == 0:\n                            db_eight_end_number += 1\n            elif len([*edge_mesh.neighbors(valid_edge_node)]) == 0:\n                hx, hy, hz = valid_edge_node\n                four_nes = [(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                                if info_on_pix.get((x, y)) is not None and \\\n                                    mesh.has_edge(valid_edge_node, (x, y, info_on_pix[(x, y)][0]['depth'])) is False]\n                for ne in four_nes:\n                    try:\n                        if invalid is True or (point_to_amount.get(ne) is None or point_to_amount[ne] < redundant_number) or \\\n                            point_to_id[ne] in point_to_adjoint.get(point_to_id[valid_edge_node], set()):\n                            mesh.add_edge(valid_edge_node, ne)\n                    except:\n                        import pdb; pdb.set_trace()\n        if (invalid is not True and end_number >= 1) or (invalid is True and end_number >= 2 and eight_end_number >= 1 and db_eight_end_number >= 1):\n            for valid_edge_node in valid_edge_cc:\n                hx, hy, _ = valid_edge_node\n                four_nes = [(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                                if info_on_pix.get((x, y)) is not None and \\\n                                    mesh.has_edge(valid_edge_node, (x, y, info_on_pix[(x, y)][0]['depth'])) is False and \\\n                                    (edge_canvas[x, y] == -1 or edge_canvas[x, y] == valid_edge_id)]\n                for ne in four_nes:\n                    if invalid is True or (point_to_amount.get(ne) is None or point_to_amount[ne] < redundant_number) or \\\n                        point_to_id[ne] in point_to_adjoint.get(point_to_id[valid_edge_node], set()):\n                        mesh.add_edge(valid_edge_node, ne)\n\n    return mesh\n\ndef judge_dangle(mark, mesh, node):\n    if not (1 <= node[0] < mesh.graph['H']-1) or not(1 <= node[1] < mesh.graph['W']-1):\n        return mark\n    mesh_neighbors = [*mesh.neighbors(node)]\n    mesh_neighbors = [xx for xx in mesh_neighbors if 0 < xx[0] < mesh.graph['H'] - 1 and 0 < xx[1] < mesh.graph['W'] - 1]\n    if len(mesh_neighbors) >= 3:\n        return mark\n    elif len(mesh_neighbors) <= 1:\n        mark[node[0], node[1]] = (len(mesh_neighbors) + 1)\n    else:\n        dan_ne_node_a = mesh_neighbors[0]\n        dan_ne_node_b = mesh_neighbors[1]\n        if abs(dan_ne_node_a[0] - dan_ne_node_b[0]) > 1 or \\\n            abs(dan_ne_node_a[1] - dan_ne_node_b[1]) > 1:\n            mark[node[0], node[1]] = 3\n\n    return mark\n\ndef remove_dangling(mesh, edge_ccs, edge_mesh, info_on_pix, image, depth, config):\n\n    tmp_edge_ccs = copy.deepcopy(edge_ccs)\n    for edge_cc_id, valid_edge_cc in enumerate(tmp_edge_ccs):\n        if len(valid_edge_cc) > 1 or len(valid_edge_cc) == 0:\n            continue\n        single_edge_node = [*valid_edge_cc][0]\n        hx, hy, hz = single_edge_node\n        eight_nes = set([(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                         (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                         if info_on_pix.get((x, y)) is not None])\n        four_nes = [(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                    if info_on_pix.get((x, y)) is not None]\n        sub_mesh = mesh.subgraph(eight_nes).copy()\n        ccs = netx.connected_components(sub_mesh)\n        four_ccs = []\n        for cc_id, _cc in enumerate(ccs):\n            four_ccs.append(set())\n            for cc_node in _cc:\n                if abs(cc_node[0] - hx) + abs(cc_node[1] - hy) < 2:\n                    four_ccs[cc_id].add(cc_node)\n        largest_cc = sorted(four_ccs, key=lambda x: (len(x), -np.sum([abs(xx[2] - hz) for xx in x])))[-1]\n        if len(largest_cc) < 2:\n            for ne in four_nes:\n                mesh.add_edge(single_edge_node, ne)\n        else:\n            mesh.remove_edges_from([(single_edge_node, ne) for ne in mesh.neighbors(single_edge_node)])\n            new_depth = np.mean([xx[2] for xx in largest_cc])\n            info_on_pix[(hx, hy)][0]['depth'] = new_depth\n            info_on_pix[(hx, hy)][0]['disp'] = 1./new_depth\n            new_node = (hx, hy, new_depth)\n            mesh = refresh_node(single_edge_node, mesh.node[single_edge_node], new_node, dict(), mesh)\n            edge_ccs[edge_cc_id] = set([new_node])\n            for ne in largest_cc:\n                mesh.add_edge(new_node, ne)\n\n    mark = np.zeros((mesh.graph['H'], mesh.graph['W']))\n    for edge_idx, edge_cc in enumerate(edge_ccs):\n        for edge_node in edge_cc:\n            if not (mesh.graph['bord_up'] <= edge_node[0] < mesh.graph['bord_down']-1) or \\\n               not (mesh.graph['bord_left'] <= edge_node[1] < mesh.graph['bord_right']-1):\n                continue\n            mesh_neighbors = [*mesh.neighbors(edge_node)]\n            mesh_neighbors = [xx for xx in mesh_neighbors \\\n                                if mesh.graph['bord_up'] < xx[0] < mesh.graph['bord_down'] - 1 and \\\n                                   mesh.graph['bord_left'] < xx[1] < mesh.graph['bord_right'] - 1]\n            if len([*mesh.neighbors(edge_node)]) >= 3:\n                continue\n            elif len([*mesh.neighbors(edge_node)]) <= 1:\n                mark[edge_node[0], edge_node[1]] += (len([*mesh.neighbors(edge_node)]) + 1)\n            else:\n                dan_ne_node_a = [*mesh.neighbors(edge_node)][0]\n                dan_ne_node_b = [*mesh.neighbors(edge_node)][1]\n                if abs(dan_ne_node_a[0] - dan_ne_node_b[0]) > 1 or \\\n                    abs(dan_ne_node_a[1] - dan_ne_node_b[1]) > 1:\n                    mark[edge_node[0], edge_node[1]] += 3\n    mxs, mys = np.where(mark == 1)\n    conn_0_nodes = [(x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']) for x in zip(mxs, mys) \\\n                        if mesh.has_node((x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']))]\n    mxs, mys = np.where(mark == 2)\n    conn_1_nodes = [(x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']) for x in zip(mxs, mys) \\\n                        if mesh.has_node((x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']))]\n    for node in conn_0_nodes:\n        hx, hy = node[0], node[1]\n        four_nes = [(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1)] \\\n                     if info_on_pix.get((x, y)) is not None]\n        re_depth = {'value' : 0, 'count': 0}\n        for ne in four_nes:\n            mesh.add_edge(node, ne)\n            re_depth['value'] += cc_node[2]\n            re_depth['count'] += 1.\n        re_depth = re_depth['value'] / re_depth['count']\n        mapping_dict = {node: (node[0], node[1], re_depth)}\n        info_on_pix, mesh, edge_mesh = update_info(mapping_dict, info_on_pix, mesh, edge_mesh)\n        depth[node[0], node[1]] = abs(re_depth)\n        mark[node[0], node[1]] = 0\n    for node in conn_1_nodes:\n        hx, hy = node[0], node[1]\n        eight_nes = set([(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                                                                           (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                        if info_on_pix.get((x, y)) is not None])\n        self_nes = set([ne2 for ne1 in mesh.neighbors(node) for ne2 in mesh.neighbors(ne1) if ne2 in eight_nes])\n        eight_nes = [*(eight_nes - self_nes)]\n        sub_mesh = mesh.subgraph(eight_nes).copy()\n        ccs = netx.connected_components(sub_mesh)\n        largest_cc = sorted(ccs, key=lambda x: (len(x), -np.sum([abs(xx[0] - node[0]) + abs(xx[1] - node[1]) for xx in x])))[-1]\n\n        mesh.remove_edges_from([(xx, node) for xx in mesh.neighbors(node)])\n        re_depth = {'value' : 0, 'count': 0}\n        for cc_node in largest_cc:\n            if cc_node[0] == node[0] and cc_node[1] == node[1]:\n                continue\n            re_depth['value'] += cc_node[2]\n            re_depth['count'] += 1.\n            if abs(cc_node[0] - node[0]) + abs(cc_node[1] - node[1]) < 2:\n                mesh.add_edge(cc_node, node)\n        try:\n            re_depth = re_depth['value'] / re_depth['count']\n        except:\n            re_depth = node[2]\n        renode = (node[0], node[1], re_depth)\n        mapping_dict = {node: renode}\n        info_on_pix, mesh, edge_mesh = update_info(mapping_dict, info_on_pix, mesh, edge_mesh)\n        depth[node[0], node[1]] = abs(re_depth)\n        mark[node[0], node[1]] = 0\n        edge_mesh, mesh, mark, info_on_pix = recursive_add_edge(edge_mesh, mesh, info_on_pix, renode, mark)\n    mxs, mys = np.where(mark == 3)\n    conn_2_nodes = [(x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth']) for x in zip(mxs, mys) \\\n                        if mesh.has_node((x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth'])) and \\\n                            mesh.degree((x[0], x[1], info_on_pix[(x[0], x[1])][0]['depth'])) == 2]\n    sub_mesh = mesh.subgraph(conn_2_nodes).copy()\n    ccs = netx.connected_components(sub_mesh)\n    for cc in ccs:\n        candidate_nodes = [xx for xx in cc if sub_mesh.degree(xx) == 1]\n        for node in candidate_nodes:\n            if mesh.has_node(node) is False:\n                continue\n            ne_node = [xx for xx in mesh.neighbors(node) if xx not in cc][0]\n            hx, hy = node[0], node[1]\n            eight_nes = set([(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                                                                            (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                              if info_on_pix.get((x, y)) is not None and (x, y, info_on_pix[(x, y)][0]['depth']) not in cc])\n            ne_sub_mesh = mesh.subgraph(eight_nes).copy()\n            ne_ccs = netx.connected_components(ne_sub_mesh)\n            try:\n                ne_cc = [ne_cc for ne_cc in ne_ccs if ne_node in ne_cc][0]\n            except:\n                import pdb; pdb.set_trace()\n            largest_cc = [xx for xx in ne_cc if abs(xx[0] - node[0]) + abs(xx[1] - node[1]) == 1]\n            mesh.remove_edges_from([(xx, node) for xx in mesh.neighbors(node)])\n            re_depth = {'value' : 0, 'count': 0}\n            for cc_node in largest_cc:\n                re_depth['value'] += cc_node[2]\n                re_depth['count'] += 1.\n                mesh.add_edge(cc_node, node)\n            try:\n                re_depth = re_depth['value'] / re_depth['count']\n            except:\n                re_depth = node[2]\n            renode = (node[0], node[1], re_depth)\n            mapping_dict = {node: renode}\n            info_on_pix, mesh, edge_mesh = update_info(mapping_dict, info_on_pix, mesh, edge_mesh)\n            depth[node[0], node[1]] = abs(re_depth)\n            mark[node[0], node[1]] = 0\n            edge_mesh, mesh, mark, info_on_pix = recursive_add_edge(edge_mesh, mesh, info_on_pix, renode, mark)\n            break\n        if len(cc) == 1:\n            node = [node for node in cc][0]\n            hx, hy = node[0], node[1]\n            nine_nes = set([(x, y, info_on_pix[(x, y)][0]['depth']) for x, y in [(hx, hy), (hx + 1, hy), (hx - 1, hy), (hx, hy + 1), (hx, hy - 1),\n                                                                                  (hx + 1, hy + 1), (hx - 1, hy - 1), (hx - 1, hy + 1), (hx + 1, hy - 1)] \\\n                                if info_on_pix.get((x, y)) is not None and mesh.has_node((x, y, info_on_pix[(x, y)][0]['depth']))])\n            ne_sub_mesh = mesh.subgraph(nine_nes).copy()\n            ne_ccs = netx.connected_components(ne_sub_mesh)\n            for ne_cc in ne_ccs:\n                if node in ne_cc:\n                    re_depth = {'value' : 0, 'count': 0}\n                    for ne in ne_cc:\n                        if abs(ne[0] - node[0]) + abs(ne[1] - node[1]) == 1:\n                            mesh.add_edge(node, ne)\n                            re_depth['value'] += ne[2]\n                            re_depth['count'] += 1.\n                    re_depth = re_depth['value'] / re_depth['count']\n                    mapping_dict = {node: (node[0], node[1], re_depth)}\n                    info_on_pix, mesh, edge_mesh = update_info(mapping_dict, info_on_pix, mesh, edge_mesh)\n                    depth[node[0], node[1]] = abs(re_depth)\n                    mark[node[0], node[1]] = 0\n\n\n    return mesh, info_on_pix, edge_mesh, depth, mark\n\ndef context_and_holes(mesh, edge_ccs, config, specific_edge_id, specific_edge_loc, depth_feat_model,\n                      connect_points_ccs=None, inpaint_iter=0, filter_edge=False, vis_edge_id=None):\n    edge_maps = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n    mask_info = {}\n    for edge_id, edge_cc in enumerate(edge_ccs):\n        for edge_node in edge_cc:\n            edge_maps[edge_node[0], edge_node[1]] = edge_id\n\n    context_ccs = [set() for x in range(len(edge_ccs))]\n    extend_context_ccs = [set() for x in range(len(edge_ccs))]\n    extend_erode_context_ccs = [set() for x in range(len(edge_ccs))]\n    extend_edge_ccs = [set() for x in range(len(edge_ccs))]\n    accomp_extend_context_ccs = [set() for x in range(len(edge_ccs))]\n    erode_context_ccs = [set() for x in range(len(edge_ccs))]\n    broken_mask_ccs = [set() for x in range(len(edge_ccs))]\n    invalid_extend_edge_ccs = [set() for x in range(len(edge_ccs))]\n    intouched_ccs = [set() for x in range(len(edge_ccs))]\n    redundant_ccs = [set() for x in range(len(edge_ccs))]\n    if inpaint_iter == 0:\n        background_thickness = config['background_thickness']\n        context_thickness = config['context_thickness']\n    else:\n        background_thickness = config['background_thickness_2']\n        context_thickness = config['context_thickness_2']\n\n    mesh_nodes = mesh.nodes\n    for edge_id, edge_cc in enumerate(edge_ccs):\n        if context_thickness == 0 or (len(specific_edge_id) > 0 and edge_id not in specific_edge_id):\n            continue\n        edge_group = {}\n        for edge_node in edge_cc:\n            far_nodes = mesh_nodes[edge_node].get('far')\n            if far_nodes is None:\n                continue\n            for far_node in far_nodes:\n                if far_node in edge_cc:\n                    continue\n                context_ccs[edge_id].add(far_node)\n                if mesh_nodes[far_node].get('edge_id') is not None:\n                    if edge_group.get(mesh_nodes[far_node]['edge_id']) is None:\n                        edge_group[mesh_nodes[far_node]['edge_id']] = set()\n                    edge_group[mesh_nodes[far_node]['edge_id']].add(far_node)\n        if len(edge_cc) > 2:\n            for edge_key in [*edge_group.keys()]:\n                if len(edge_group[edge_key]) == 1:\n                    context_ccs[edge_id].remove([*edge_group[edge_key]][0])\n    for edge_id, edge_cc in enumerate(edge_ccs):\n        if inpaint_iter != 0:\n            continue\n        tmp_intouched_nodes = set()\n        for edge_node in edge_cc:\n            raw_intouched_nodes = set(mesh_nodes[edge_node].get('near')) if mesh_nodes[edge_node].get('near') is not None else set()\n            tmp_intouched_nodes |= set([xx for xx in raw_intouched_nodes if mesh_nodes[xx].get('edge_id') is not None and \\\n                                                                         len(context_ccs[mesh_nodes[xx].get('edge_id')]) > 0])\n        intouched_ccs[edge_id] |= tmp_intouched_nodes\n        tmp_intouched_nodes = None\n    mask_ccs = copy.deepcopy(edge_ccs)\n    forbidden_len = 3\n    forbidden_map = np.ones((mesh.graph['H'] - forbidden_len, mesh.graph['W'] - forbidden_len))\n    forbidden_map = np.pad(forbidden_map, ((forbidden_len, forbidden_len), (forbidden_len, forbidden_len)), mode='constant').astype(np.bool)\n    cur_tmp_mask_map = np.zeros_like(forbidden_map).astype(np.bool)\n    passive_background = 10 if 10 is not None else background_thickness\n    passive_context = 1 if 1 is not None else context_thickness\n\n    for edge_id, edge_cc in enumerate(edge_ccs):\n        cur_mask_cc = None; cur_mask_cc = []\n        cur_context_cc = None; cur_context_cc = []\n        cur_accomp_near_cc = None; cur_accomp_near_cc = []\n        cur_invalid_extend_edge_cc = None; cur_invalid_extend_edge_cc = []\n        cur_comp_far_cc = None; cur_comp_far_cc = []\n        tmp_erode = []\n        if len(context_ccs[edge_id]) == 0 or (len(specific_edge_id) > 0 and edge_id not in specific_edge_id):\n            continue\n        for i in range(max(background_thickness, context_thickness)):\n            cur_tmp_mask_map.fill(False)\n            if i == 0:\n                tmp_mask_nodes = copy.deepcopy(mask_ccs[edge_id])\n                tmp_intersect_nodes = []\n                tmp_intersect_context_nodes = []\n                mask_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                context_depth = np.zeros((mesh.graph['H'], mesh.graph['W']))\n                comp_cnt_depth = np.zeros((mesh.graph['H'], mesh.graph['W']))\n                connect_map = np.zeros((mesh.graph['H'], mesh.graph['W']))\n                for node in tmp_mask_nodes:\n                    mask_map[node[0], node[1]] = True\n                    depth_count = 0\n                    if mesh_nodes[node].get('far') is not None:\n                        for comp_cnt_node in mesh_nodes[node]['far']:\n                            comp_cnt_depth[node[0], node[1]] += abs(comp_cnt_node[2])\n                            depth_count += 1\n                    if depth_count > 0:\n                        comp_cnt_depth[node[0], node[1]] = comp_cnt_depth[node[0], node[1]] / depth_count\n                    connect_node = []\n                    if mesh_nodes[node].get('connect_point_id') is not None:\n                        connect_node.append(mesh_nodes[node]['connect_point_id'])\n                    connect_point_id = np.bincount(connect_node).argmax() if len(connect_node) > 0 else -1\n                    if connect_point_id > -1 and connect_points_ccs is not None:\n                        for xx in connect_points_ccs[connect_point_id]:\n                            if connect_map[xx[0], xx[1]] == 0:\n                                connect_map[xx[0], xx[1]] = xx[2]\n                    if mesh_nodes[node].get('connect_point_exception') is not None:\n                        for xx in mesh_nodes[node]['connect_point_exception']:\n                            if connect_map[xx[0], xx[1]] == 0:\n                                connect_map[xx[0], xx[1]] = xx[2]\n                tmp_context_nodes = [*context_ccs[edge_id]]\n                tmp_erode.append([*context_ccs[edge_id]])\n                context_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                if (context_map.astype(np.uint8) * mask_map.astype(np.uint8)).max() > 0:\n                    import pdb; pdb.set_trace()\n                for node in tmp_context_nodes:\n                    context_map[node[0], node[1]] = True\n                    context_depth[node[0], node[1]] = node[2]\n                context_map[mask_map == True] = False\n                if (context_map.astype(np.uint8) * mask_map.astype(np.uint8)).max() > 0:\n                    import pdb; pdb.set_trace()\n                tmp_intouched_nodes = [*intouched_ccs[edge_id]]\n                intouched_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                for node in tmp_intouched_nodes: intouched_map[node[0], node[1]] = True\n                intouched_map[mask_map == True] = False\n                tmp_redundant_nodes = set()\n                tmp_noncont_nodes = set()\n                noncont_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                intersect_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n                intersect_context_map = np.zeros((mesh.graph['H'], mesh.graph['W']), dtype=np.bool)\n            if i > passive_background and inpaint_iter == 0:\n                new_tmp_intersect_nodes = None\n                new_tmp_intersect_nodes = []\n                for node in tmp_intersect_nodes:\n                    nes = mesh.neighbors(node)\n                    for ne in nes:\n                        if bool(context_map[ne[0], ne[1]]) is False and \\\n                        bool(mask_map[ne[0], ne[1]]) is False and \\\n                        bool(forbidden_map[ne[0], ne[1]]) is True and \\\n                        bool(intouched_map[ne[0], ne[1]]) is False and\\\n                        bool(intersect_map[ne[0], ne[1]]) is False and\\\n                        bool(intersect_context_map[ne[0], ne[1]]) is False:\n                            break_flag = False\n                            if (i - passive_background) % 2 == 0 and (i - passive_background) % 8 != 0:\n                                four_nes = [xx for xx in[[ne[0] - 1, ne[1]], [ne[0] + 1, ne[1]], [ne[0], ne[1] - 1], [ne[0], ne[1] + 1]] \\\n                                                if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W']]\n                                for fne in four_nes:\n                                    if bool(mask_map[fne[0], fne[1]]) is True:\n                                        break_flag = True\n                                        break\n                                if break_flag is True:\n                                    continue\n                            intersect_map[ne[0], ne[1]] = True\n                            new_tmp_intersect_nodes.append(ne)\n                tmp_intersect_nodes = None\n                tmp_intersect_nodes = new_tmp_intersect_nodes\n\n            if i > passive_context and inpaint_iter == 1:\n                new_tmp_intersect_context_nodes = None\n                new_tmp_intersect_context_nodes = []\n                for node in tmp_intersect_context_nodes:\n                    nes = mesh.neighbors(node)\n                    for ne in nes:\n                        if bool(context_map[ne[0], ne[1]]) is False and \\\n                        bool(mask_map[ne[0], ne[1]]) is False and \\\n                        bool(forbidden_map[ne[0], ne[1]]) is True and \\\n                        bool(intouched_map[ne[0], ne[1]]) is False and\\\n                        bool(intersect_map[ne[0], ne[1]]) is False and \\\n                        bool(intersect_context_map[ne[0], ne[1]]) is False:\n                            intersect_context_map[ne[0], ne[1]] = True\n                            new_tmp_intersect_context_nodes.append(ne)\n                tmp_intersect_context_nodes = None\n                tmp_intersect_context_nodes = new_tmp_intersect_context_nodes\n\n            new_tmp_mask_nodes = None\n            new_tmp_mask_nodes = []\n            for node in tmp_mask_nodes:\n                four_nes = {xx:[] for xx in [(node[0] - 1, node[1]), (node[0] + 1, node[1]), (node[0], node[1] - 1), (node[0], node[1] + 1)] if \\\n                            0 <= xx[0] < connect_map.shape[0] and 0 <= xx[1] < connect_map.shape[1]}\n                if inpaint_iter > 0:\n                    for ne in four_nes.keys():\n                        if connect_map[ne[0], ne[1]] == True:\n                            tmp_context_nodes.append((ne[0], ne[1], connect_map[ne[0], ne[1]]))\n                            context_map[ne[0], ne[1]] = True\n                nes = mesh.neighbors(node)\n                if inpaint_iter > 0:\n                    for ne in nes: four_nes[(ne[0], ne[1])].append(ne[2])\n                    nes = []\n                    for kfne, vfnes in four_nes.items(): vfnes.sort(key = lambda xx: abs(xx), reverse=True)\n                    for kfne, vfnes in four_nes.items():\n                        for vfne in vfnes: nes.append((kfne[0], kfne[1], vfne))\n                for ne in nes:\n                    if bool(context_map[ne[0], ne[1]]) is False and \\\n                       bool(mask_map[ne[0], ne[1]]) is False and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True and \\\n                       bool(intouched_map[ne[0], ne[1]]) is False and \\\n                       bool(intersect_map[ne[0], ne[1]]) is False and \\\n                       bool(intersect_context_map[ne[0], ne[1]]) is False:\n                        if i == passive_background and inpaint_iter == 0:\n                            if np.any(context_map[max(ne[0] - 1, 0):min(ne[0] + 2, mesh.graph['H']), max(ne[1] - 1, 0):min(ne[1] + 2, mesh.graph['W'])]) == True:\n                                intersect_map[ne[0], ne[1]] = True\n                                tmp_intersect_nodes.append(ne)\n                                continue\n                        if i < background_thickness:\n                            if inpaint_iter == 0:\n                                cur_mask_cc.append(ne)\n                            elif mesh_nodes[ne].get('inpaint_id') == 1:\n                                cur_mask_cc.append(ne)\n                            else:\n                                continue\n                            mask_ccs[edge_id].add(ne)\n                            if inpaint_iter == 0:\n                                if comp_cnt_depth[node[0], node[1]] > 0 and comp_cnt_depth[ne[0], ne[1]] == 0:\n                                    comp_cnt_depth[ne[0], ne[1]] = comp_cnt_depth[node[0], node[1]]\n                                if mesh_nodes[ne].get('far') is not None:\n                                    for comp_far_node in mesh_nodes[ne]['far']:\n                                        cur_comp_far_cc.append(comp_far_node)\n                                        cur_accomp_near_cc.append(ne)\n                                        cur_invalid_extend_edge_cc.append(comp_far_node)\n                                if mesh_nodes[ne].get('edge_id') is not None and \\\n                                    len(context_ccs[mesh_nodes[ne].get('edge_id')]) > 0:\n                                    intouched_fars = set(mesh_nodes[ne].get('far')) if mesh_nodes[ne].get('far') is not None else set()\n                                    accum_intouched_fars = set(intouched_fars)\n                                    for intouched_far in intouched_fars:\n                                        accum_intouched_fars |= set([*mesh.neighbors(intouched_far)])\n                                    for intouched_far in accum_intouched_fars:\n                                        if bool(mask_map[intouched_far[0], intouched_far[1]]) is True or \\\n                                        bool(context_map[intouched_far[0], intouched_far[1]]) is True:\n                                            continue\n                                        tmp_redundant_nodes.add(intouched_far)\n                                        intouched_map[intouched_far[0], intouched_far[1]] = True\n                                if mesh_nodes[ne].get('near') is not None:\n                                    intouched_nears = set(mesh_nodes[ne].get('near'))\n                                    for intouched_near in intouched_nears:\n                                        if bool(mask_map[intouched_near[0], intouched_near[1]]) is True or \\\n                                        bool(context_map[intouched_near[0], intouched_near[1]]) is True:\n                                            continue\n                                        tmp_redundant_nodes.add(intouched_near)\n                                        intouched_map[intouched_near[0], intouched_near[1]] = True\n                        if not (mesh_nodes[ne].get('inpaint_id') != 1 and inpaint_iter == 1):\n                            new_tmp_mask_nodes.append(ne)\n                            mask_map[ne[0], ne[1]] = True\n            tmp_mask_nodes = new_tmp_mask_nodes\n\n            new_tmp_context_nodes = None\n            new_tmp_context_nodes = []\n            for node in tmp_context_nodes:\n                nes = mesh.neighbors(node)\n                if inpaint_iter > 0:\n                    four_nes = {(node[0] - 1, node[1]):[], (node[0] + 1, node[1]):[], (node[0], node[1] - 1):[], (node[0], node[1] + 1):[]}\n                    for ne in nes: four_nes[(ne[0], ne[1])].append(ne[2])\n                    nes = []\n                    for kfne, vfnes in four_nes.items(): vfnes.sort(key = lambda xx: abs(xx), reverse=True)\n                    for kfne, vfnes in four_nes.items():\n                        for vfne in vfnes: nes.append((kfne[0], kfne[1], vfne))\n                for ne in nes:\n                    mask_flag = (bool(mask_map[ne[0], ne[1]]) is False)\n                    if bool(context_map[ne[0], ne[1]]) is False and mask_flag and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True and bool(noncont_map[ne[0], ne[1]]) is False and \\\n                       bool(intersect_context_map[ne[0], ne[1]]) is False:\n                        if i == passive_context and inpaint_iter == 1:\n                            mnes = mesh.neighbors(ne)\n                            if any([mask_map[mne[0], mne[1]] == True for mne in mnes]) is True:\n                                intersect_context_map[ne[0], ne[1]] = True\n                                tmp_intersect_context_nodes.append(ne)\n                                continue\n                        if False and mesh_nodes[ne].get('near') is not None and mesh_nodes[ne].get('edge_id') != edge_id:\n                            noncont_nears = set(mesh_nodes[ne].get('near'))\n                            for noncont_near in noncont_nears:\n                                if bool(context_map[noncont_near[0], noncont_near[1]]) is False:\n                                    tmp_noncont_nodes.add(noncont_near)\n                                    noncont_map[noncont_near[0], noncont_near[1]] = True\n                        new_tmp_context_nodes.append(ne)\n                        context_map[ne[0], ne[1]] = True\n                        context_depth[ne[0], ne[1]] = ne[2]\n            cur_context_cc.extend(new_tmp_context_nodes)\n            tmp_erode.append(new_tmp_context_nodes)\n            tmp_context_nodes = None\n            tmp_context_nodes = new_tmp_context_nodes\n            new_tmp_intouched_nodes = None; new_tmp_intouched_nodes = []\n\n            for node in tmp_intouched_nodes:\n                if bool(context_map[node[0], node[1]]) is True or bool(mask_map[node[0], node[1]]) is True:\n                    continue\n                nes = mesh.neighbors(node)\n\n                for ne in nes:\n                    if bool(context_map[ne[0], ne[1]]) is False and \\\n                       bool(mask_map[ne[0], ne[1]]) is False and \\\n                       bool(intouched_map[ne[0], ne[1]]) is False and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True:\n                        new_tmp_intouched_nodes.append(ne)\n                        intouched_map[ne[0], ne[1]] = True\n            tmp_intouched_nodes = None\n            tmp_intouched_nodes = set(new_tmp_intouched_nodes)\n            new_tmp_redundant_nodes = None; new_tmp_redundant_nodes = []\n            for node in tmp_redundant_nodes:\n                if bool(context_map[node[0], node[1]]) is True or \\\n                   bool(mask_map[node[0], node[1]]) is True:\n                    continue\n                nes = mesh.neighbors(node)\n\n                for ne in nes:\n                    if bool(context_map[ne[0], ne[1]]) is False and \\\n                       bool(mask_map[ne[0], ne[1]]) is False and \\\n                       bool(intouched_map[ne[0], ne[1]]) is False and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True:\n                        new_tmp_redundant_nodes.append(ne)\n                        intouched_map[ne[0], ne[1]] = True\n            tmp_redundant_nodes = None\n            tmp_redundant_nodes = set(new_tmp_redundant_nodes)\n            new_tmp_noncont_nodes = None; new_tmp_noncont_nodes = []\n            for node in tmp_noncont_nodes:\n                if bool(context_map[node[0], node[1]]) is True or \\\n                   bool(mask_map[node[0], node[1]]) is True:\n                    continue\n                nes = mesh.neighbors(node)\n                rmv_flag = False\n                for ne in nes:\n                    if bool(context_map[ne[0], ne[1]]) is False and \\\n                       bool(mask_map[ne[0], ne[1]]) is False and \\\n                       bool(noncont_map[ne[0], ne[1]]) is False and \\\n                       bool(forbidden_map[ne[0], ne[1]]) is True:\n                        patch_context_map = context_map[max(ne[0] - 1, 0):min(ne[0] + 2, context_map.shape[0]),\n                                                        max(ne[1] - 1, 0):min(ne[1] + 2, context_map.shape[1])]\n                        if bool(np.any(patch_context_map)) is True:\n                            new_tmp_noncont_nodes.append(ne)\n                            noncont_map[ne[0], ne[1]] = True\n            tmp_noncont_nodes = None\n            tmp_noncont_nodes = set(new_tmp_noncont_nodes)\n        if inpaint_iter == 0:\n            depth_dict = get_depth_from_maps(context_map, mask_map, context_depth, mesh.graph['H'], mesh.graph['W'], log_depth=config['log_depth'])\n            mask_size = get_valid_size(depth_dict['mask'])\n            mask_size = dilate_valid_size(mask_size, depth_dict['mask'], dilate=[20, 20])\n            context_size = get_valid_size(depth_dict['context'])\n            context_size = dilate_valid_size(context_size, depth_dict['context'], dilate=[20, 20])\n            union_size = size_operation(mask_size, context_size, operation='+')\n            depth_dict = depth_inpainting(None, None, None, None, mesh, config, union_size, depth_feat_model, None, given_depth_dict=depth_dict, spdb=False)\n            near_depth_map, raw_near_depth_map = np.zeros((mesh.graph['H'], mesh.graph['W'])), np.zeros((mesh.graph['H'], mesh.graph['W']))\n            filtered_comp_far_cc, filtered_accomp_near_cc = set(), set()\n            for node in cur_accomp_near_cc:\n                near_depth_map[node[0], node[1]] = depth_dict['output'][node[0], node[1]]\n                raw_near_depth_map[node[0], node[1]] = node[2]\n            for node in cur_comp_far_cc:\n                four_nes = [xx for xx in [(node[0] - 1, node[1]), (node[0] + 1, node[1]), (node[0], node[1] - 1), (node[0], node[1] + 1)] \\\n                            if 0 <= xx[0] < mesh.graph['H'] and 0 <= xx[1] < mesh.graph['W'] and \\\n                            near_depth_map[xx[0], xx[1]] != 0 and \\\n                            abs(near_depth_map[xx[0], xx[1]]) < abs(node[2])]\n                if len(four_nes) > 0:\n                    filtered_comp_far_cc.add(node)\n                for ne in four_nes:\n                    filtered_accomp_near_cc.add((ne[0], ne[1], -abs(raw_near_depth_map[ne[0], ne[1]])))\n            cur_comp_far_cc, cur_accomp_near_cc = filtered_comp_far_cc, filtered_accomp_near_cc\n        mask_ccs[edge_id] |= set(cur_mask_cc)\n        context_ccs[edge_id] |= set(cur_context_cc)\n        accomp_extend_context_ccs[edge_id] |= set(cur_accomp_near_cc).intersection(cur_mask_cc)\n        extend_edge_ccs[edge_id] |= set(cur_accomp_near_cc).intersection(cur_mask_cc)\n        extend_context_ccs[edge_id] |= set(cur_comp_far_cc)\n        invalid_extend_edge_ccs[edge_id] |= set(cur_invalid_extend_edge_cc)\n        erode_size = [0]\n        for tmp in tmp_erode:\n            erode_size.append(len(tmp))\n            if len(erode_size) > 1:\n                erode_size[-1] += erode_size[-2]\n        if inpaint_iter == 0:\n            tmp_width = config['depth_edge_dilate']\n        else:\n            tmp_width = 0\n        while float(erode_size[tmp_width]) / (erode_size[-1] + 1e-6) > 0.3:\n            tmp_width = tmp_width - 1\n        try:\n            if tmp_width == 0:\n                erode_context_ccs[edge_id] = set([])\n            else:\n                erode_context_ccs[edge_id] = set(reduce(lambda x, y : x + y, [] + tmp_erode[:tmp_width]))\n        except:\n            import pdb; pdb.set_trace()\n        erode_context_cc = copy.deepcopy(erode_context_ccs[edge_id])\n        for erode_context_node in erode_context_cc:\n            if (inpaint_iter != 0 and (mesh_nodes[erode_context_node].get('inpaint_id') is None or\n                                        mesh_nodes[erode_context_node].get('inpaint_id') == 0)):\n                erode_context_ccs[edge_id].remove(erode_context_node)\n            else:\n                context_ccs[edge_id].remove(erode_context_node)\n        context_map = np.zeros((mesh.graph['H'], mesh.graph['W']))\n        for context_node in context_ccs[edge_id]:\n            context_map[context_node[0], context_node[1]] = 1\n        extend_context_ccs[edge_id] = extend_context_ccs[edge_id] - mask_ccs[edge_id] - accomp_extend_context_ccs[edge_id]\n    if inpaint_iter == 0:\n        all_ecnt_cc = set()\n        for ecnt_id, ecnt_cc in enumerate(extend_context_ccs):\n            constraint_context_ids = set()\n            constraint_context_cc = set()\n            constraint_erode_context_cc = set()\n            tmp_mask_cc = set()\n            accum_context_cc = None; accum_context_cc = []\n            for ecnt_node in accomp_extend_context_ccs[ecnt_id]:\n                if edge_maps[ecnt_node[0], ecnt_node[1]] > -1:\n                    constraint_context_ids.add(int(round(edge_maps[ecnt_node[0], ecnt_node[1]])))\n            constraint_erode_context_cc = erode_context_ccs[ecnt_id]\n            for constraint_context_id in constraint_context_ids:\n                constraint_context_cc = constraint_context_cc | context_ccs[constraint_context_id] | erode_context_ccs[constraint_context_id]\n                constraint_erode_context_cc = constraint_erode_context_cc | erode_context_ccs[constraint_context_id]\n            for i in range(background_thickness):\n                if i == 0:\n                    tmp_context_nodes = copy.deepcopy(ecnt_cc)\n                    tmp_invalid_context_nodes = copy.deepcopy(invalid_extend_edge_ccs[ecnt_id])\n                    tmp_mask_nodes = copy.deepcopy(accomp_extend_context_ccs[ecnt_id])\n                    tmp_context_map = np.zeros((mesh.graph['H'], mesh.graph['W'])).astype(np.bool)\n                    tmp_mask_map = np.zeros((mesh.graph['H'], mesh.graph['W'])).astype(np.bool)\n                    tmp_invalid_context_map = np.zeros((mesh.graph['H'], mesh.graph['W'])).astype(np.bool)\n                    for node in tmp_mask_nodes:\n                        tmp_mask_map[node[0], node[1]] = True\n                    for node in context_ccs[ecnt_id]:\n                        tmp_context_map[node[0], node[1]] = True\n                    for node in erode_context_ccs[ecnt_id]:\n                        tmp_context_map[node[0], node[1]] = True\n                    for node in extend_context_ccs[ecnt_id]:\n                        tmp_context_map[node[0], node[1]] = True\n                    for node in invalid_extend_edge_ccs[ecnt_id]:\n                        tmp_invalid_context_map[node[0], node[1]] = True\n                    init_invalid_context_map = tmp_invalid_context_map.copy()\n                    init_context_map = tmp\n                    if (tmp_mask_map.astype(np.uint8) * tmp_context_map.astype(np.uint8)).max() > 0:\n                        import pdb; pdb.set_trace()\n                    if vis_edge_id is not None and ecnt_id == vis_edge_id:\n                        f, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True)\n                        ax1.imshow(tmp_context_map * 1); ax2.imshow(init_invalid_context_map * 1 + tmp_context_map * 2)\n                        plt.show()\n                        import pdb; pdb.set_trace()\n                else:\n                    tmp_context_nodes = new_tmp_context_nodes\n                    new_tmp_context_nodes = None\n                    tmp_mask_nodes = new_tmp_mask_nodes\n                    new_tmp_mask_nodes = None\n                    tmp_invalid_context_nodes = new_tmp_invalid_context_nodes\n                    new_tmp_invalid_context_nodes = None\n                new_tmp_context_nodes = None\n                new_tmp_context_nodes = []\n                new_tmp_invalid_context_nodes = None\n                new_tmp_invalid_context_nodes = []\n                new_tmp_mask_nodes = set([])\n                for node in tmp_context_nodes:\n                    for ne in mesh.neighbors(node):\n                        if ne in constraint_context_cc and \\\n                            bool(tmp_mask_map[ne[0], ne[1]]) is False and \\\n                            bool(tmp_context_map[ne[0], ne[1]]) is False and \\\n                            bool(forbidden_map[ne[0], ne[1]]) is True:\n                            new_tmp_context_nodes.append(ne)\n                            tmp_context_map[ne[0], ne[1]] = True\n                accum_context_cc.extend(new_tmp_context_nodes)\n                for node in tmp_invalid_context_nodes:\n                    for ne in mesh.neighbors(node):\n                        if bool(tmp_mask_map[ne[0], ne[1]]) is False and \\\n                           bool(tmp_context_map[ne[0], ne[1]]) is False and \\\n                           bool(tmp_invalid_context_map[ne[0], ne[1]]) is False and \\\n                           bool(forbidden_map[ne[0], ne[1]]) is True:\n                            tmp_invalid_context_map[ne[0], ne[1]] = True\n                            new_tmp_invalid_context_nodes.append(ne)\n                for node in tmp_mask_nodes:\n                    for ne in mesh.neighbors(node):\n                        if bool(tmp_mask_map[ne[0], ne[1]]) is False and \\\n                           bool(tmp_context_map[ne[0], ne[1]]) is False and \\\n                           bool(tmp_invalid_context_map[ne[0], ne[1]]) is False and \\\n                           bool(forbidden_map[ne[0], ne[1]]) is True:\n                            new_tmp_mask_nodes.add(ne)\n                            tmp_mask_map[ne[0], ne[1]] = True\n            init_invalid_context_map[tmp_context_map] = False\n            _, tmp_label_map = cv2.connectedComponents((init_invalid_context_map | tmp_context_map).astype(np.uint8), connectivity=8)\n            tmp_label_ids = set(np.unique(tmp_label_map[init_invalid_context_map]))\n            if (tmp_mask_map.astype(np.uint8) * tmp_context_map.astype(np.uint8)).max() > 0:\n                import pdb; pdb.set_trace()\n            if vis_edge_id is not None and ecnt_id == vis_edge_id:\n                f, ((ax1, ax2)) = plt.subplots(1, 2, sharex=True, sharey=True)\n                ax1.imshow(tmp_label_map); ax2.imshow(init_invalid_context_map * 1 + tmp_context_map * 2)\n                plt.show()\n                import pdb; pdb.set_trace()\n            extend_context_ccs[ecnt_id] |= set(accum_context_cc)\n            extend_context_ccs[ecnt_id] = extend_context_ccs[ecnt_id] - mask_ccs[ecnt_id]\n            extend_erode_context_ccs[ecnt_id] = extend_context_ccs[ecnt_id] & constraint_erode_context_cc\n            extend_context_ccs[ecnt_id] = extend_context_ccs[ecnt_id] - extend_erode_context_ccs[ecnt_id] - erode_context_ccs[ecnt_id]\n            tmp_context_cc = context_ccs[ecnt_id] - extend_erode_context_ccs[ecnt_id] - erode_context_ccs[ecnt_id]\n            if len(tmp_context_cc) > 0:\n                context_ccs[ecnt_id] = tmp_context_cc\n            tmp_mask_cc = tmp_mask_cc - context_ccs[ecnt_id] - erode_context_ccs[ecnt_id]\n            mask_ccs[ecnt_id] = mask_ccs[ecnt_id] | tmp_mask_cc\n\n    return context_ccs, mask_ccs, broken_mask_ccs, edge_ccs, erode_context_ccs, invalid_extend_edge_ccs, edge_maps, extend_context_ccs, extend_edge_ccs, extend_erode_context_ccs\n\ndef DL_inpaint_edge(mesh,\n                    info_on_pix,\n                    config,\n                    image,\n                    depth,\n                    context_ccs,\n                    erode_context_ccs,\n                    extend_context_ccs,\n                    extend_erode_context_ccs,\n                    mask_ccs,\n                    broken_mask_ccs,\n                    edge_ccs,\n                    extend_edge_ccs,\n                    init_mask_connect,\n                    edge_maps,\n                    rgb_model=None,\n                    depth_edge_model=None,\n                    depth_edge_model_init=None,\n                    depth_feat_model=None,\n                    specific_edge_id=-1,\n                    specific_edge_loc=None,\n                    inpaint_iter=0):\n\n    if isinstance(config[\"gpu_ids\"], int) and (config[\"gpu_ids\"] >= 0):\n        device = config[\"gpu_ids\"]\n    else:\n        device = \"cpu\"\n\n    edge_map = np.zeros_like(depth)\n    new_edge_ccs = [set() for _ in range(len(edge_ccs))]\n    edge_maps_with_id = edge_maps\n    edge_condition = lambda x, m: m.nodes[x].get('far') is not None and len(m.nodes[x].get('far')) > 0\n    edge_map = get_map_from_ccs(edge_ccs, mesh.graph['H'], mesh.graph['W'], mesh, edge_condition)\n    np_depth, np_image = depth.copy(), image.copy()\n    image_c = image.shape[-1]\n    image = torch.FloatTensor(image.transpose(2, 0, 1)).unsqueeze(0).to(device)\n    if depth.ndim < 3:\n        depth = depth[..., None]\n    depth = torch.FloatTensor(depth.transpose(2, 0, 1)).unsqueeze(0).to(device)\n    mesh.graph['max_edge_id'] = len(edge_ccs)\n    connnect_points_ccs = [set() for _ in range(len(edge_ccs))]\n    gp_time, tmp_mesh_time, bilateral_time = 0, 0, 0\n    edges_infos = dict()\n    edges_in_mask = [set() for _ in range(len(edge_ccs))]\n    tmp_specific_edge_id = []\n    for edge_id, (context_cc, mask_cc, erode_context_cc, extend_context_cc, edge_cc) in enumerate(zip(context_ccs, mask_ccs, erode_context_ccs, extend_context_ccs, edge_ccs)):\n        if len(specific_edge_id) > 0:\n            if edge_id not in specific_edge_id:\n                continue\n        if len(context_cc) < 1 or len(mask_cc) < 1:\n            continue\n        edge_dict = get_edge_from_nodes(context_cc | extend_context_cc, erode_context_cc | extend_erode_context_ccs[edge_id], mask_cc, edge_cc, extend_edge_ccs[edge_id],\n                                        mesh.graph['H'], mesh.graph['W'], mesh)\n        edge_dict['edge'], end_depth_maps, _ = \\\n            filter_irrelevant_edge_new(edge_dict['self_edge'], edge_dict['comp_edge'],\n                                    edge_map,\n                                    edge_maps_with_id,\n                                    edge_id,\n                                    edge_dict['context'],\n                                    edge_dict['depth'], mesh, context_cc | erode_context_cc | extend_context_cc | extend_erode_context_ccs[edge_id], spdb=False)\n        if specific_edge_loc is not None and \\\n            (specific_edge_loc is not None and edge_dict['mask'][specific_edge_loc[0], specific_edge_loc[1]] == 0):\n            continue\n        mask_size = get_valid_size(edge_dict['mask'])\n        mask_size = dilate_valid_size(mask_size, edge_dict['mask'], dilate=[20, 20])\n        context_size = get_valid_size(edge_dict['context'])\n        context_size = dilate_valid_size(context_size, edge_dict['context'], dilate=[20, 20])\n        union_size = size_operation(mask_size, context_size, operation='+')\n        patch_edge_dict = dict()\n        patch_edge_dict['mask'], patch_edge_dict['context'], patch_edge_dict['rgb'], \\\n            patch_edge_dict['disp'], patch_edge_dict['edge'] = \\\n            crop_maps_by_size(union_size, edge_dict['mask'], edge_dict['context'],\n                                edge_dict['rgb'], edge_dict['disp'], edge_dict['edge'])\n        x_anchor, y_anchor = [union_size['x_min'], union_size['x_max']], [union_size['y_min'], union_size['y_max']]\n        tensor_edge_dict = convert2tensor(patch_edge_dict)\n        input_edge_feat = torch.cat((tensor_edge_dict['rgb'],\n                                        tensor_edge_dict['disp'],\n                                        tensor_edge_dict['edge'],\n                                        1 - tensor_edge_dict['context'],\n                                        tensor_edge_dict['mask']), dim=1)\n        if require_depth_edge(patch_edge_dict['edge'], patch_edge_dict['mask']) and inpaint_iter == 0:\n            with torch.no_grad():\n                depth_edge_output = depth_edge_model.forward_3P(tensor_edge_dict['mask'],\n                                                                tensor_edge_dict['context'],\n                                                                tensor_edge_dict['rgb'],\n                                                                tensor_edge_dict['disp'],\n                                                                tensor_edge_dict['edge'],\n                                                                unit_length=128,\n                                                                cuda=device)\n                depth_edge_output = depth_edge_output.cpu()\n            tensor_edge_dict['output'] = (depth_edge_output> config['ext_edge_threshold']).float() * tensor_edge_dict['mask'] + tensor_edge_dict['edge']\n        else:\n            tensor_edge_dict['output'] = tensor_edge_dict['edge']\n            depth_edge_output = tensor_edge_dict['edge'] + 0\n        patch_edge_dict['output'] = tensor_edge_dict['output'].squeeze().data.cpu().numpy()\n        edge_dict['output'] = np.zeros((mesh.graph['H'], mesh.graph['W']))\n        edge_dict['output'][union_size['x_min']:union_size['x_max'], union_size['y_min']:union_size['y_max']] = \\\n            patch_edge_dict['output']\n        if require_depth_edge(patch_edge_dict['edge'], patch_edge_dict['mask']) and inpaint_iter == 0:\n            if ((depth_edge_output> config['ext_edge_threshold']).float() * tensor_edge_dict['mask']).max() > 0:\n                try:\n                    edge_dict['fpath_map'], edge_dict['npath_map'], break_flag, npaths, fpaths, invalid_edge_id = \\\n                        clean_far_edge_new(edge_dict['output'], end_depth_maps, edge_dict['mask'], edge_dict['context'], mesh, info_on_pix, edge_dict['self_edge'], inpaint_iter, config)\n                except:\n                    import pdb; pdb.set_trace()\n                pre_npath_map = edge_dict['npath_map'].copy()\n                if config.get('repeat_inpaint_edge') is True:\n                    for _ in range(2):\n                        tmp_input_edge = ((edge_dict['npath_map'] > -1) + edge_dict['edge']).clip(0, 1)\n                        patch_tmp_input_edge = crop_maps_by_size(union_size, tmp_input_edge)[0]\n                        tensor_input_edge = torch.FloatTensor(patch_tmp_input_edge)[None, None, ...]\n                        depth_edge_output = depth_edge_model.forward_3P(tensor_edge_dict['mask'],\n                                                    tensor_edge_dict['context'],\n                                                    tensor_edge_dict['rgb'],\n                                                    tensor_edge_dict['disp'],\n                                                    tensor_input_edge,\n                                                    unit_length=128,\n                                                    cuda=device)\n                        depth_edge_output = depth_edge_output.cpu()\n                        depth_edge_output = (depth_edge_output> config['ext_edge_threshold']).float() * tensor_edge_dict['mask'] + tensor_edge_dict['edge']\n                        depth_edge_output = depth_edge_output.squeeze().data.cpu().numpy()\n                        full_depth_edge_output = np.zeros((mesh.graph['H'], mesh.graph['W']))\n                        full_depth_edge_output[union_size['x_min']:union_size['x_max'], union_size['y_min']:union_size['y_max']] = \\\n                            depth_edge_output\n                        edge_dict['fpath_map'], edge_dict['npath_map'], break_flag, npaths, fpaths, invalid_edge_id = \\\n                            clean_far_edge_new(full_depth_edge_output, end_depth_maps, edge_dict['mask'], edge_dict['context'], mesh, info_on_pix, edge_dict['self_edge'], inpaint_iter, config)\n                for nid in npaths.keys():\n                    npath, fpath = npaths[nid], fpaths[nid]\n                    start_mx, start_my, end_mx, end_my = -1, -1, -1, -1\n                    if end_depth_maps[npath[0][0], npath[0][1]] != 0:\n                        start_mx, start_my = npath[0][0], npath[0][1]\n                    if end_depth_maps[npath[-1][0], npath[-1][1]] != 0:\n                        end_mx, end_my = npath[-1][0], npath[-1][1]\n                    if start_mx == -1:\n                        import pdb; pdb.set_trace()\n                    valid_end_pt = () if end_mx == -1 else (end_mx, end_my, info_on_pix[(end_mx, end_my)][0]['depth'])\n                    new_edge_info = dict(fpath=fpath,\n                                         npath=npath,\n                                         cont_end_pts=valid_end_pt,\n                                         mask_id=edge_id,\n                                         comp_edge_id=nid,\n                                         depth=end_depth_maps[start_mx, start_my])\n                    if edges_infos.get((start_mx, start_my)) is None:\n                        edges_infos[(start_mx, start_my)] = []\n                    edges_infos[(start_mx, start_my)].append(new_edge_info)\n                    edges_in_mask[edge_id].add((start_mx, start_my))\n                    if len(valid_end_pt) > 0:\n                        new_edge_info = dict(fpath=fpath[::-1],\n                                             npath=npath[::-1],\n                                             cont_end_pts=(start_mx, start_my, info_on_pix[(start_mx, start_my)][0]['depth']),\n                                             mask_id=edge_id,\n                                             comp_edge_id=nid,\n                                             depth=end_depth_maps[end_mx, end_my])\n                        if edges_infos.get((end_mx, end_my)) is None:\n                            edges_infos[(end_mx, end_my)] = []\n                        edges_infos[(end_mx, end_my)].append(new_edge_info)\n                        edges_in_mask[edge_id].add((end_mx, end_my))\n    for edge_id, (context_cc, mask_cc, erode_context_cc, extend_context_cc, edge_cc) in enumerate(zip(context_ccs, mask_ccs, erode_context_ccs, extend_context_ccs, edge_ccs)):\n        if len(specific_edge_id) > 0:\n            if edge_id not in specific_edge_id:\n                continue\n        if len(context_cc) < 1 or len(mask_cc) < 1:\n            continue\n        edge_dict = get_edge_from_nodes(context_cc | extend_context_cc, erode_context_cc | extend_erode_context_ccs[edge_id], mask_cc, edge_cc, extend_edge_ccs[edge_id],\n                                        mesh.graph['H'], mesh.graph['W'], mesh)\n        if specific_edge_loc is not None and \\\n            (specific_edge_loc is not None and edge_dict['mask'][specific_edge_loc[0], specific_edge_loc[1]] == 0):\n            continue\n        else:\n            tmp_specific_edge_id.append(edge_id)\n        edge_dict['edge'], end_depth_maps, _ = \\\n            filter_irrelevant_edge_new(edge_dict['self_edge'], edge_dict['comp_edge'],\n                                    edge_map,\n                                    edge_maps_with_id,\n                                    edge_id,\n                                    edge_dict['context'],\n                                    edge_dict['depth'], mesh, context_cc | erode_context_cc | extend_context_cc | extend_erode_context_ccs[edge_id], spdb=False)\n        discard_map = np.zeros_like(edge_dict['edge'])\n        mask_size = get_valid_size(edge_dict['mask'])\n        mask_size = dilate_valid_size(mask_size, edge_dict['mask'], dilate=[20, 20])\n        context_size = get_valid_size(edge_dict['context'])\n        context_size = dilate_valid_size(context_size, edge_dict['context'], dilate=[20, 20])\n        union_size = size_operation(mask_size, context_size, operation='+')\n        patch_edge_dict = dict()\n        patch_edge_dict['mask'], patch_edge_dict['context'], patch_edge_dict['rgb'], \\\n            patch_edge_dict['disp'], patch_edge_dict['edge'] = \\\n            crop_maps_by_size(union_size, edge_dict['mask'], edge_dict['context'],\n                                edge_dict['rgb'], edge_dict['disp'], edge_dict['edge'])\n        x_anchor, y_anchor = [union_size['x_min'], union_size['x_max']], [union_size['y_min'], union_size['y_max']]\n        tensor_edge_dict = convert2tensor(patch_edge_dict)\n        input_edge_feat = torch.cat((tensor_edge_dict['rgb'],\n                                        tensor_edge_dict['disp'],\n                                        tensor_edge_dict['edge'],\n                                        1 - tensor_edge_dict['context'],\n                                        tensor_edge_dict['mask']), dim=1)\n        edge_dict['output'] = edge_dict['edge'].copy()\n\n        if require_depth_edge(patch_edge_dict['edge'], patch_edge_dict['mask']) and inpaint_iter == 0:\n            edge_dict['fpath_map'], edge_dict['npath_map'] = edge_dict['fpath_map'] * 0 - 1, edge_dict['npath_map'] * 0 - 1\n            end_pts = edges_in_mask[edge_id]\n            for end_pt in end_pts:\n                cur_edge_infos = edges_infos[(end_pt[0], end_pt[1])]\n                cur_info = [xx for xx in cur_edge_infos if xx['mask_id'] == edge_id][0]\n                other_infos = [xx for xx in cur_edge_infos if xx['mask_id'] != edge_id and len(xx['cont_end_pts']) > 0]\n                if len(cur_info['cont_end_pts']) > 0 or (len(cur_info['cont_end_pts']) == 0 and len(other_infos) == 0):\n                    for fnode in cur_info['fpath']:\n                        edge_dict['fpath_map'][fnode[0], fnode[1]] = cur_info['comp_edge_id']\n                    for fnode in cur_info['npath']:\n                        edge_dict['npath_map'][fnode[0], fnode[1]] = cur_info['comp_edge_id']\n            fnmap = edge_dict['fpath_map'] * 1\n            fnmap[edge_dict['npath_map'] != -1] = edge_dict['npath_map'][edge_dict['npath_map'] != -1]\n            for end_pt in end_pts:\n                cur_edge_infos = edges_infos[(end_pt[0], end_pt[1])]\n                cur_info = [xx for xx in cur_edge_infos if xx['mask_id'] == edge_id][0]\n                cur_depth = cur_info['depth']\n                other_infos = [xx for xx in cur_edge_infos if xx['mask_id'] != edge_id and len(xx['cont_end_pts']) > 0]\n                comp_edge_id = cur_info['comp_edge_id']\n                if len(cur_info['cont_end_pts']) == 0 and len(other_infos) > 0:\n                    other_infos = sorted(other_infos, key=lambda aa: abs(abs(aa['cont_end_pts'][2]) - abs(cur_depth)))\n                    for other_info in other_infos:\n                        tmp_fmap, tmp_nmap = np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1, np.zeros((mesh.graph['H'], mesh.graph['W'])) - 1\n                        for fnode in other_info['fpath']:\n                            if fnmap[fnode[0], fnode[1]] != -1:\n                                tmp_fmap = tmp_fmap * 0 - 1\n                                break\n                            else:\n                                tmp_fmap[fnode[0], fnode[1]] = comp_edge_id\n                        if fnmap[fnode[0], fnode[1]] != -1:\n                            continue\n                        for fnode in other_info['npath']:\n                            if fnmap[fnode[0], fnode[1]] != -1:\n                                tmp_nmap = tmp_nmap * 0 - 1\n                                break\n                            else:\n                                tmp_nmap[fnode[0], fnode[1]] = comp_edge_id\n                        if fnmap[fnode[0], fnode[1]] != -1:\n                            continue\n                        break\n                    if min(tmp_fmap.max(), tmp_nmap.max()) != -1:\n                        edge_dict['fpath_map'] = tmp_fmap\n                        edge_dict['fpath_map'][edge_dict['valid_area'] == 0] = -1\n                        edge_dict['npath_map'] = tmp_nmap\n                        edge_dict['npath_map'][edge_dict['valid_area'] == 0] = -1\n                        discard_map = ((tmp_nmap != -1).astype(np.uint8) + (tmp_fmap != -1).astype(np.uint8)) * edge_dict['mask']\n                    else:\n                        for fnode in cur_info['fpath']:\n                            edge_dict['fpath_map'][fnode[0], fnode[1]] = cur_info['comp_edge_id']\n                        for fnode in cur_info['npath']:\n                            edge_dict['npath_map'][fnode[0], fnode[1]] = cur_info['comp_edge_id']\n            if edge_dict['npath_map'].min() == 0 or edge_dict['fpath_map'].min() == 0:\n                import pdb; pdb.set_trace()\n            edge_dict['output'] = (edge_dict['npath_map'] > -1) * edge_dict['mask'] + edge_dict['context'] * edge_dict['edge']\n        mesh, _, _, _ = create_placeholder(edge_dict['context'], edge_dict['mask'],\n                                  edge_dict['depth'], edge_dict['fpath_map'],\n                                  edge_dict['npath_map'], mesh, inpaint_iter,\n                                  edge_ccs,\n                                  extend_edge_ccs[edge_id],\n                                  edge_maps_with_id,\n                                  edge_id)\n\n        dxs, dys = np.where(discard_map != 0)\n        for dx, dy in zip(dxs, dys):\n            mesh.nodes[(dx, dy)]['inpaint_twice'] = False\n        depth_dict = depth_inpainting(context_cc, extend_context_cc, erode_context_cc | extend_erode_context_ccs[edge_id], mask_cc, mesh, config, union_size, depth_feat_model, edge_dict['output'])\n        refine_depth_output = depth_dict['output']*depth_dict['mask']\n        for near_id in np.unique(edge_dict['npath_map'])[1:]:\n            refine_depth_output = refine_depth_around_edge(refine_depth_output.copy(),\n                                                            (edge_dict['fpath_map'] == near_id).astype(np.uint8) * edge_dict['mask'],\n                                                            (edge_dict['fpath_map'] == near_id).astype(np.uint8),\n                                                            (edge_dict['npath_map'] == near_id).astype(np.uint8) * edge_dict['mask'],\n                                                            depth_dict['mask'].copy(),\n                                                            depth_dict['output'] * depth_dict['context'],\n                                                            config)\n        depth_dict['output'][depth_dict['mask'] > 0] = refine_depth_output[depth_dict['mask'] > 0]\n        rgb_dict = get_rgb_from_nodes(context_cc | extend_context_cc,\n                                      erode_context_cc | extend_erode_context_ccs[edge_id], mask_cc, mesh.graph['H'], mesh.graph['W'], mesh)\n        if np.all(rgb_dict['mask'] == edge_dict['mask']) is False:\n            import pdb; pdb.set_trace()\n        rgb_dict['edge'] = edge_dict['output']\n        patch_rgb_dict = dict()\n        patch_rgb_dict['mask'], patch_rgb_dict['context'], patch_rgb_dict['rgb'], \\\n            patch_rgb_dict['edge'] = crop_maps_by_size(union_size, rgb_dict['mask'],\n                                                        rgb_dict['context'], rgb_dict['rgb'],\n                                                        rgb_dict['edge'])\n        tensor_rgb_dict = convert2tensor(patch_rgb_dict)\n        resize_rgb_dict = {k: v.clone() for k, v in tensor_rgb_dict.items()}\n        max_hw = np.array([*patch_rgb_dict['mask'].shape[-2:]]).max()\n        init_frac = config['largest_size'] / (np.array([*patch_rgb_dict['mask'].shape[-2:]]).prod() ** 0.5)\n        resize_hw = [patch_rgb_dict['mask'].shape[-2] * init_frac, patch_rgb_dict['mask'].shape[-1] * init_frac]\n        resize_max_hw = max(resize_hw)\n        frac = (np.floor(resize_max_hw / 128.) * 128.) / max_hw\n        if frac < 1:\n            resize_mark = torch.nn.functional.interpolate(torch.cat((resize_rgb_dict['mask'],\n                                                            resize_rgb_dict['context']),\n                                                            dim=1),\n                                                            scale_factor=frac,\n                                                            mode='area')\n            resize_rgb_dict['mask'] = (resize_mark[:, 0:1] > 0).float()\n            resize_rgb_dict['context'] = (resize_mark[:, 1:2] == 1).float()\n            resize_rgb_dict['context'][resize_rgb_dict['mask'] > 0] = 0\n            resize_rgb_dict['rgb'] = torch.nn.functional.interpolate(resize_rgb_dict['rgb'],\n                                                                        scale_factor=frac,\n                                                                        mode='area')\n            resize_rgb_dict['rgb'] = resize_rgb_dict['rgb'] * resize_rgb_dict['context']\n            resize_rgb_dict['edge'] = torch.nn.functional.interpolate(resize_rgb_dict['edge'],\n                                                                        scale_factor=frac,\n                                                                        mode='area')\n            resize_rgb_dict['edge'] = (resize_rgb_dict['edge'] > 0).float() * 0\n            resize_rgb_dict['edge'] = resize_rgb_dict['edge'] * (resize_rgb_dict['context'] + resize_rgb_dict['mask'])\n        rgb_input_feat = torch.cat((resize_rgb_dict['rgb'], resize_rgb_dict['edge']), dim=1)\n        rgb_input_feat[:, 3] = 1 - rgb_input_feat[:, 3]\n        resize_mask = open_small_mask(resize_rgb_dict['mask'], resize_rgb_dict['context'], 3, 41)\n        specified_hole = resize_mask\n        with torch.no_grad():\n            rgb_output = rgb_model.forward_3P(specified_hole,\n                                            resize_rgb_dict['context'],\n                                            resize_rgb_dict['rgb'],\n                                            resize_rgb_dict['edge'],\n                                            unit_length=128,\n                                            cuda=device)\n            rgb_output = rgb_output.cpu()\n            if config.get('gray_image') is True:\n                rgb_output = rgb_output.mean(1, keepdim=True).repeat((1,3,1,1))\n            rgb_output = rgb_output.cpu()\n        resize_rgb_dict['output'] = rgb_output * resize_rgb_dict['mask'] + resize_rgb_dict['rgb']\n        tensor_rgb_dict['output'] = resize_rgb_dict['output']\n        if frac < 1:\n            tensor_rgb_dict['output'] = torch.nn.functional.interpolate(tensor_rgb_dict['output'],\n                                                                        size=tensor_rgb_dict['mask'].shape[-2:],\n                                                                        mode='bicubic')\n            tensor_rgb_dict['output'] = tensor_rgb_dict['output'] * \\\n                                         tensor_rgb_dict['mask'] + (tensor_rgb_dict['rgb'] * tensor_rgb_dict['context'])\n        patch_rgb_dict['output'] = tensor_rgb_dict['output'].data.cpu().numpy().squeeze().transpose(1,2,0)\n        rgb_dict['output'] = np.zeros((mesh.graph['H'], mesh.graph['W'], 3))\n        rgb_dict['output'][union_size['x_min']:union_size['x_max'], union_size['y_min']:union_size['y_max']] = \\\n            patch_rgb_dict['output']\n\n        if require_depth_edge(patch_edge_dict['edge'], patch_edge_dict['mask']) or inpaint_iter > 0:\n            edge_occlusion = True\n        else:\n            edge_occlusion = False\n        for node in erode_context_cc:\n            if rgb_dict['mask'][node[0], node[1]] > 0:\n                for info in info_on_pix[(node[0], node[1])]:\n                    if abs(info['depth']) == abs(node[2]):\n                        info['update_color'] = (rgb_dict['output'][node[0], node[1]] * 255).astype(np.uint8)\n        if frac < 1.:\n            depth_edge_dilate_2_color_flag = False\n        else:\n            depth_edge_dilate_2_color_flag = True\n        hxs, hys = np.where((rgb_dict['mask'] > 0) & (rgb_dict['erode'] == 0))\n        for hx, hy in zip(hxs, hys):\n            real_depth = None\n            if abs(depth_dict['output'][hx, hy]) <= abs(np_depth[hx, hy]):\n                depth_dict['output'][hx, hy] = np_depth[hx, hy] + 0.01\n            node = (hx, hy, -depth_dict['output'][hx, hy])\n            if info_on_pix.get((node[0], node[1])) is not None:\n                for info in info_on_pix.get((node[0], node[1])):\n                    if info.get('inpaint_id') is None or abs(info['inpaint_id'] < mesh.nodes[(hx, hy)]['inpaint_id']):\n                        pre_depth = info['depth'] if info.get('real_depth') is None else info['real_depth']\n                        if abs(node[2]) < abs(pre_depth):\n                            node = (node[0], node[1], -(abs(pre_depth) + 0.001))\n            if mesh.has_node(node):\n                real_depth = node[2]\n            while True:\n                if mesh.has_node(node):\n                    node = (node[0], node[1], -(abs(node[2]) + 0.001))\n                else:\n                    break\n            if real_depth == node[2]:\n                real_depth = None\n            cur_disp = 1./node[2]\n            if not(mesh.has_node(node)):\n                if not mesh.has_node((node[0], node[1])):\n                    print(\"2D node not found.\")\n                    import pdb; pdb.set_trace()\n                if inpaint_iter == 1:\n                    paint = (rgb_dict['output'][hx, hy] * 255).astype(np.uint8)\n                else:\n                    paint = (rgb_dict['output'][hx, hy] * 255).astype(np.uint8)\n                ndict = dict(color=paint,\n                                synthesis=True,\n                                disp=cur_disp,\n                                cc_id=set([edge_id]),\n                                overlap_number=1.0,\n                                refine_depth=False,\n                                edge_occlusion=edge_occlusion,\n                                depth_edge_dilate_2_color_flag=depth_edge_dilate_2_color_flag,\n                                real_depth=real_depth)\n                mesh, _, _ = refresh_node((node[0], node[1]), mesh.nodes[(node[0], node[1])], node, ndict, mesh, stime=True)\n                if inpaint_iter == 0 and mesh.degree(node) < 4:\n                    connnect_points_ccs[edge_id].add(node)\n            if info_on_pix.get((hx, hy)) is None:\n                info_on_pix[(hx, hy)] = []\n            new_info = {'depth':node[2],\n                        'color': paint,\n                        'synthesis':True,\n                        'disp':cur_disp,\n                        'cc_id':set([edge_id]),\n                        'inpaint_id':inpaint_iter + 1,\n                        'edge_occlusion':edge_occlusion,\n                        'overlap_number':1.0,\n                        'real_depth': real_depth}\n            info_on_pix[(hx, hy)].append(new_info)\n    specific_edge_id = tmp_specific_edge_id\n    for erode_id, erode_context_cc in enumerate(erode_context_ccs):\n        if len(specific_edge_id) > 0 and erode_id not in specific_edge_id:\n            continue\n        for erode_node in erode_context_cc:\n            for info in info_on_pix[(erode_node[0], erode_node[1])]:\n                if info['depth'] == erode_node[2]:\n                    info['color'] = info['update_color']\n                    mesh.nodes[erode_node]['color'] = info['update_color']\n                    np_image[(erode_node[0], erode_node[1])] = info['update_color']\n    new_edge_ccs = [set() for _ in range(mesh.graph['max_edge_id'] + 1)]\n    for node in mesh.nodes:\n        if len(node) == 2:\n            mesh.remove_node(node)\n            continue\n        if mesh.nodes[node].get('edge_id') is not None and mesh.nodes[node].get('inpaint_id') == inpaint_iter + 1:\n            if mesh.nodes[node].get('inpaint_twice') is False:\n                continue\n            try:\n                new_edge_ccs[mesh.nodes[node].get('edge_id')].add(node)\n            except:\n                import pdb; pdb.set_trace()\n    specific_mask_nodes = None\n    if inpaint_iter == 0:\n        mesh, info_on_pix = refine_color_around_edge(mesh, info_on_pix, new_edge_ccs, config, False)\n\n    return mesh, info_on_pix, specific_mask_nodes, new_edge_ccs, connnect_points_ccs, np_image\n\n\ndef write_ply(image,\n              depth,\n              int_mtx,\n              ply_name,\n              config,\n              rgb_model,\n              depth_edge_model,\n              depth_edge_model_init,\n              depth_feat_model):\n    depth = depth.astype(np.float64)\n    input_mesh, xy2depth, image, depth = create_mesh(depth, image, int_mtx, config)\n\n    H, W = input_mesh.graph['H'], input_mesh.graph['W']\n    input_mesh = tear_edges(input_mesh, config['depth_threshold'], xy2depth)\n    input_mesh, info_on_pix = generate_init_node(input_mesh, config, min_node_in_cc=200)\n    edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=False)\n    edge_canvas = np.zeros((H, W)) - 1\n\n    input_mesh, info_on_pix, depth = reassign_floating_island(input_mesh, info_on_pix, image, depth)\n    input_mesh = update_status(input_mesh, info_on_pix)\n    specific_edge_id = []\n    edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n    pre_depth = depth.copy()\n    input_mesh, info_on_pix, edge_mesh, depth, aft_mark = remove_dangling(input_mesh, edge_ccs, edge_mesh, info_on_pix, image, depth, config)\n\n    input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n    edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n    edge_canvas = np.zeros((H, W)) - 1\n\n    mesh, info_on_pix, depth = fill_missing_node(input_mesh, info_on_pix, image, depth)\n    if config['extrapolate_border'] is True:\n        pre_depth = depth.copy()\n        input_mesh, info_on_pix, depth = refresh_bord_depth(input_mesh, info_on_pix, image, depth)\n        input_mesh = remove_node_feat(input_mesh, 'edge_id')\n        aft_depth = depth.copy()\n        input_mesh, info_on_pix, depth, image = enlarge_border(input_mesh, info_on_pix, depth, image, config)\n        noext_H, noext_W = H, W\n        H, W = image.shape[:2]\n        input_mesh, info_on_pix = fill_dummy_bord(input_mesh, info_on_pix, image, depth, config)\n        edge_ccs, input_mesh, edge_mesh = \\\n            group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n        input_mesh = combine_end_node(input_mesh, edge_mesh, edge_ccs, depth)\n        input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n        edge_ccs, input_mesh, edge_mesh = \\\n            group_edges(input_mesh, config, image, remove_conflict_ordinal=True, spdb=False)\n        input_mesh = remove_redundant_edge(input_mesh, edge_mesh, edge_ccs, info_on_pix, config, redundant_number=config['redundant_number'], spdb=False)\n        input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n        edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n        input_mesh = combine_end_node(input_mesh, edge_mesh, edge_ccs, depth)\n        input_mesh = remove_redundant_edge(input_mesh, edge_mesh, edge_ccs, info_on_pix, config, redundant_number=config['redundant_number'], invalid=True, spdb=False)\n        input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n        edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n        input_mesh = combine_end_node(input_mesh, edge_mesh, edge_ccs, depth)\n        input_mesh, depth, info_on_pix = update_status(input_mesh, info_on_pix, depth)\n        edge_ccs, input_mesh, edge_mesh = group_edges(input_mesh, config, image, remove_conflict_ordinal=True)\n        edge_condition = lambda x, m: m.nodes[x].get('far') is not None and len(m.nodes[x].get('far')) > 0\n        edge_map = get_map_from_ccs(edge_ccs, input_mesh.graph['H'], input_mesh.graph['W'], input_mesh, edge_condition)\n        other_edge_with_id = get_map_from_ccs(edge_ccs, input_mesh.graph['H'], input_mesh.graph['W'], real_id=True)\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"up\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"left\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"down\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"right\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"right-up\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"right-down\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"left-up\")\n        info_on_pix, input_mesh, image, depth, edge_ccs = extrapolate(input_mesh, info_on_pix, image, depth, other_edge_with_id, edge_map, edge_ccs,\n                                                depth_edge_model, depth_feat_model, rgb_model, config, direc=\"left-down\")\n    specific_edge_loc = None\n    specific_edge_id = []\n    vis_edge_id = None\n    context_ccs, mask_ccs, broken_mask_ccs, edge_ccs, erode_context_ccs, \\\n        init_mask_connect, edge_maps, extend_context_ccs, extend_edge_ccs, extend_erode_context_ccs = \\\n                                                                                context_and_holes(input_mesh,\n                                                                                            edge_ccs,\n                                                                                            config,\n                                                                                            specific_edge_id,\n                                                                                            specific_edge_loc,\n                                                                                            depth_feat_model,\n                                                                                            inpaint_iter=0,\n                                                                                            vis_edge_id=vis_edge_id)\n    edge_canvas = np.zeros((H, W))\n    mask = np.zeros((H, W))\n    context = np.zeros((H, W))\n    vis_edge_ccs = filter_edge(input_mesh, edge_ccs, config)\n    edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W'])) - 1\n    specific_edge_loc = None\n    FG_edge_maps = edge_maps.copy()\n    edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W'])) - 1\n    # for cc_id, cc in enumerate(edge_ccs):\n    #     for node in cc:\n    #         edge_canvas[node[0], node[1]] = cc_id\n    # f, ((ax0, ax1, ax2)) = plt.subplots(1, 3, sharex=True, sharey=True); ax0.imshow(1./depth); ax1.imshow(image); ax2.imshow(edge_canvas); plt.show()\n    input_mesh, info_on_pix, specific_edge_nodes, new_edge_ccs, connect_points_ccs, image = DL_inpaint_edge(input_mesh,\n                                                                                                            info_on_pix,\n                                                                                                            config,\n                                                                                                            image,\n                                                                                                            depth,\n                                                                                                            context_ccs,\n                                                                                                            erode_context_ccs,\n                                                                                                            extend_context_ccs,\n                                                                                                            extend_erode_context_ccs,\n                                                                                                            mask_ccs,\n                                                                                                            broken_mask_ccs,\n                                                                                                            edge_ccs,\n                                                                                                            extend_edge_ccs,\n                                                                                                            init_mask_connect,\n                                                                                                            edge_maps,\n                                                                                                            rgb_model,\n                                                                                                            depth_edge_model,\n                                                                                                            depth_edge_model_init,\n                                                                                                            depth_feat_model,\n                                                                                                            specific_edge_id,\n                                                                                                            specific_edge_loc,\n                                                                                                            inpaint_iter=0)\n    specific_edge_id = []\n    edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    connect_points_ccs = [set() for _ in connect_points_ccs]\n    context_ccs, mask_ccs, broken_mask_ccs, edge_ccs, erode_context_ccs, init_mask_connect, \\\n        edge_maps, extend_context_ccs, extend_edge_ccs, extend_erode_context_ccs = \\\n            context_and_holes(input_mesh, new_edge_ccs, config, specific_edge_id, specific_edge_loc, depth_feat_model, connect_points_ccs, inpaint_iter=1)\n    mask_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    context_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    erode_context_ccs_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W']))\n    # edge_canvas = np.zeros((input_mesh.graph['H'], input_mesh.graph['W'])) - 1\n    # for cc_id, cc in enumerate(edge_ccs):\n    #     for node in cc:\n    #         edge_canvas[node[0], node[1]] = cc_id\n    specific_edge_id = []\n    input_mesh, info_on_pix, specific_edge_nodes, new_edge_ccs, _, image = DL_inpaint_edge(input_mesh,\n                                                                                    info_on_pix,\n                                                                                    config,\n                                                                                    image,\n                                                                                    depth,\n                                                                                    context_ccs,\n                                                                                    erode_context_ccs,\n                                                                                    extend_context_ccs,\n                                                                                    extend_erode_context_ccs,\n                                                                                    mask_ccs,\n                                                                                    broken_mask_ccs,\n                                                                                    edge_ccs,\n                                                                                    extend_edge_ccs,\n                                                                                    init_mask_connect,\n                                                                                    edge_maps,\n                                                                                    rgb_model,\n                                                                                    depth_edge_model,\n                                                                                    depth_edge_model_init,\n                                                                                    depth_feat_model,\n                                                                                    specific_edge_id,\n                                                                                    specific_edge_loc,\n                                                                                    inpaint_iter=1)\n    vertex_id = 0\n    input_mesh.graph['H'], input_mesh.graph['W'] = input_mesh.graph['noext_H'], input_mesh.graph['noext_W']\n    background_canvas = np.zeros((input_mesh.graph['H'],\n                                  input_mesh.graph['W'],\n                                  3))\n    ply_flag = config.get('save_ply')\n    if ply_flag is True:\n        node_str_list = []\n    else:\n        node_str_color = []\n        node_str_point = []\n    out_fmt = lambda x, x_flag: str(x) if x_flag is True else x\n    point_time = 0\n    hlight_time = 0\n    cur_id_time = 0\n    node_str_time = 0\n    generate_face_time = 0\n    point_list = []\n    k_00, k_02, k_11, k_12 = \\\n        input_mesh.graph['cam_param_pix_inv'][0, 0], input_mesh.graph['cam_param_pix_inv'][0, 2], \\\n        input_mesh.graph['cam_param_pix_inv'][1, 1], input_mesh.graph['cam_param_pix_inv'][1, 2]\n    w_offset = input_mesh.graph['woffset']\n    h_offset = input_mesh.graph['hoffset']\n    for pix_xy, pix_list in info_on_pix.items():\n        for pix_idx, pix_info in enumerate(pix_list):\n            pix_depth = pix_info['depth'] if pix_info.get('real_depth') is None else pix_info['real_depth']\n            str_pt = [out_fmt(x, ply_flag) for x in reproject_3d_int_detail(pix_xy[0], pix_xy[1], pix_depth,\n                      k_00, k_02, k_11, k_12, w_offset, h_offset)]\n            if input_mesh.has_node((pix_xy[0], pix_xy[1], pix_info['depth'])) is False:\n                return False\n                continue\n            if pix_info.get('overlap_number') is not None:\n                str_color = [out_fmt(x, ply_flag) for x in (pix_info['color']/pix_info['overlap_number']).astype(np.uint8).tolist()]\n            else:\n                str_color = [out_fmt(x, ply_flag) for x in pix_info['color'].tolist()]\n            if pix_info.get('edge_occlusion') is True:\n                str_color.append(out_fmt(4, ply_flag))\n            else:\n                if pix_info.get('inpaint_id') is None:\n                    str_color.append(out_fmt(1, ply_flag))\n                else:\n                    str_color.append(out_fmt(pix_info.get('inpaint_id') + 1, ply_flag))\n            if pix_info.get('modified_border') is True or pix_info.get('ext_pixel') is True:\n                if len(str_color) == 4:\n                    str_color[-1] = out_fmt(5, ply_flag)\n                else:\n                    str_color.append(out_fmt(5, ply_flag))\n            pix_info['cur_id'] = vertex_id\n            input_mesh.nodes[(pix_xy[0], pix_xy[1], pix_info['depth'])]['cur_id'] = out_fmt(vertex_id, ply_flag)\n            vertex_id += 1\n            if ply_flag is True:\n                node_str_list.append(' '.join(str_pt) + ' ' + ' '.join(str_color) + '\\n')\n            else:\n                node_str_color.append(str_color)\n                node_str_point.append(str_pt)\n    str_faces = generate_face(input_mesh, info_on_pix, config)\n    if config['save_ply'] is True:\n        print(\"Writing mesh file %s ...\" % ply_name)\n        with open(ply_name, 'w') as ply_fi:\n            ply_fi.write('ply\\n' + 'format ascii 1.0\\n')\n            ply_fi.write('comment H ' + str(int(input_mesh.graph['H'])) + '\\n')\n            ply_fi.write('comment W ' + str(int(input_mesh.graph['W'])) + '\\n')\n            ply_fi.write('comment hFov ' + str(float(input_mesh.graph['hFov'])) + '\\n')\n            ply_fi.write('comment vFov ' + str(float(input_mesh.graph['vFov'])) + '\\n')\n            ply_fi.write('element vertex ' + str(len(node_str_list)) + '\\n')\n            ply_fi.write('property float x\\n' + \\\n                         'property float y\\n' + \\\n                         'property float z\\n' + \\\n                         'property uchar red\\n' + \\\n                         'property uchar green\\n' + \\\n                         'property uchar blue\\n' + \\\n                         'property uchar alpha\\n')\n            ply_fi.write('element face ' + str(len(str_faces)) + '\\n')\n            ply_fi.write('property list uchar int vertex_index\\n')\n            ply_fi.write('end_header\\n')\n            ply_fi.writelines(node_str_list)\n            ply_fi.writelines(str_faces)\n        ply_fi.close()\n        return input_mesh\n    else:\n        H = int(input_mesh.graph['H'])\n        W = int(input_mesh.graph['W'])\n        hFov = input_mesh.graph['hFov']\n        vFov = input_mesh.graph['vFov']\n        node_str_color = np.array(node_str_color).astype(np.float32)\n        node_str_color[..., :3] = node_str_color[..., :3] / 255.\n        node_str_point = np.array(node_str_point)\n        str_faces = np.array(str_faces)\n\n        return node_str_point, node_str_color, str_faces, H, W, hFov, vFov\n\ndef read_ply(mesh_fi):\n    ply_fi = open(mesh_fi, 'r')\n    Height = None\n    Width = None\n    hFov = None\n    vFov = None\n    while True:\n        line = ply_fi.readline().split('\\n')[0]\n        if line.startswith('element vertex'):\n            num_vertex = int(line.split(' ')[-1])\n        elif line.startswith('element face'):\n            num_face = int(line.split(' ')[-1])\n        elif line.startswith('comment'):\n            if line.split(' ')[1] == 'H':\n                Height = int(line.split(' ')[-1].split('\\n')[0])\n            if line.split(' ')[1] == 'W':\n                Width = int(line.split(' ')[-1].split('\\n')[0])\n            if line.split(' ')[1] == 'hFov':\n                hFov = float(line.split(' ')[-1].split('\\n')[0])\n            if line.split(' ')[1] == 'vFov':\n                vFov = float(line.split(' ')[-1].split('\\n')[0])\n        elif line.startswith('end_header'):\n            break\n    contents = ply_fi.readlines()\n    vertex_infos = contents[:num_vertex]\n    face_infos = contents[num_vertex:]\n    verts = []\n    colors = []\n    faces = []\n    for v_info in vertex_infos:\n        str_info = [float(v) for v in v_info.split('\\n')[0].split(' ')]\n        if len(str_info) == 6:\n            vx, vy, vz, r, g, b = str_info\n        else:\n            vx, vy, vz, r, g, b, hi = str_info\n        verts.append([vx, vy, vz])\n        colors.append([r, g, b, hi])\n    verts = np.array(verts)\n    try:\n        colors = np.array(colors)\n        colors[..., :3] = colors[..., :3]/255.\n    except:\n        import pdb\n        pdb.set_trace()\n\n    for f_info in face_infos:\n        _, v1, v2, v3 = [int(f) for f in f_info.split('\\n')[0].split(' ')]\n        faces.append([v1, v2, v3])\n    faces = np.array(faces)\n\n\n    return verts, colors, faces, Height, Width, hFov, vFov\n\n\nclass Canvas_view():\n    def __init__(self,\n                 fov,\n                 verts,\n                 faces,\n                 colors,\n                 canvas_size,\n                 factor=1,\n                 bgcolor='gray',\n                 proj='perspective',\n                 ):\n        self.canvas = scene.SceneCanvas(bgcolor=bgcolor, size=(canvas_size*factor, canvas_size*factor))\n        self.view = self.canvas.central_widget.add_view()\n        self.view.camera = 'perspective'\n        self.view.camera.fov = fov\n        self.mesh = visuals.Mesh(shading=None)\n        self.mesh.attach(Alpha(1.0))\n        self.view.add(self.mesh)\n        self.tr = self.view.camera.transform\n        self.mesh.set_data(vertices=verts, faces=faces, vertex_colors=colors[:, :3])\n        self.translate([0,0,0])\n        self.rotate(axis=[1,0,0], angle=180)\n        self.view_changed()\n\n    def translate(self, trans=[0,0,0]):\n        self.tr.translate(trans)\n\n    def rotate(self, axis=[1,0,0], angle=0):\n        self.tr.rotate(axis=axis, angle=angle)\n\n    def view_changed(self):\n        self.view.camera.view_changed()\n\n    def render(self):\n        return self.canvas.render()\n\n    def reinit_mesh(self, verts, faces, colors):\n        self.mesh.set_data(vertices=verts, faces=faces, vertex_colors=colors[:, :3])\n\n    def reinit_camera(self, fov):\n        self.view.camera.fov = fov\n        self.view.camera.view_changed()\n\n\ndef output_3d_photo(verts, colors, faces, Height, Width, hFov, vFov, tgt_poses, video_traj_types, ref_pose,\n                    output_dir, ref_image, int_mtx, config, image, videos_poses, video_basename, original_H=None, original_W=None,\n                    border=None, depth=None, normal_canvas=None, all_canvas=None, mean_loc_depth=None):\n\n    cam_mesh = netx.Graph()\n    cam_mesh.graph['H'] = Height\n    cam_mesh.graph['W'] = Width\n    cam_mesh.graph['original_H'] = original_H\n    cam_mesh.graph['original_W'] = original_W\n    int_mtx_real_x = int_mtx[0] * Width\n    int_mtx_real_y = int_mtx[1] * Height\n    cam_mesh.graph['hFov'] = 2 * np.arctan((1. / 2.) * ((cam_mesh.graph['original_W']) / int_mtx_real_x[0]))\n    cam_mesh.graph['vFov'] = 2 * np.arctan((1. / 2.) * ((cam_mesh.graph['original_H']) / int_mtx_real_y[1]))\n    colors = colors[..., :3]\n\n    fov_in_rad = max(cam_mesh.graph['vFov'], cam_mesh.graph['hFov'])\n    fov = (fov_in_rad * 180 / np.pi)\n    print(\"fov: \" + str(fov))\n    init_factor = 1\n    if config.get('anti_flickering') is True:\n        init_factor = 3\n    if (cam_mesh.graph['original_H'] is not None) and (cam_mesh.graph['original_W'] is not None):\n        canvas_w = cam_mesh.graph['original_W']\n        canvas_h = cam_mesh.graph['original_H']\n    else:\n        canvas_w = cam_mesh.graph['W']\n        canvas_h = cam_mesh.graph['H']\n    canvas_size = max(canvas_h, canvas_w)\n    if normal_canvas is None:\n        normal_canvas = Canvas_view(fov,\n                                    verts,\n                                    faces,\n                                    colors,\n                                    canvas_size=canvas_size,\n                                    factor=init_factor,\n                                    bgcolor='gray',\n                                    proj='perspective')\n    else:\n        normal_canvas.reinit_mesh(verts, faces, colors)\n        normal_canvas.reinit_camera(fov)\n    img = normal_canvas.render()\n    backup_img, backup_all_img, all_img_wo_bound = img.copy(), img.copy() * 0, img.copy() * 0\n    img = cv2.resize(img, (int(img.shape[1] / init_factor), int(img.shape[0] / init_factor)), interpolation=cv2.INTER_AREA)\n    if border is None:\n        border = [0, img.shape[0], 0, img.shape[1]]\n    H, W = cam_mesh.graph['H'], cam_mesh.graph['W']\n    if (cam_mesh.graph['original_H'] is not None) and (cam_mesh.graph['original_W'] is not None):\n        aspect_ratio = cam_mesh.graph['original_H'] / cam_mesh.graph['original_W']\n    else:\n        aspect_ratio = cam_mesh.graph['H'] / cam_mesh.graph['W']\n    if aspect_ratio > 1:\n        img_h_len = cam_mesh.graph['H'] if cam_mesh.graph.get('original_H') is None else cam_mesh.graph['original_H']\n        img_w_len = img_h_len / aspect_ratio\n        anchor = [0,\n                  img.shape[0],\n                  int(max(0, int((img.shape[1])//2 - img_w_len//2))),\n                  int(min(int((img.shape[1])//2 + img_w_len//2), (img.shape[1])-1))]\n    elif aspect_ratio <= 1:\n        img_w_len = cam_mesh.graph['W'] if cam_mesh.graph.get('original_W') is None else cam_mesh.graph['original_W']\n        img_h_len = img_w_len * aspect_ratio\n        anchor = [int(max(0, int((img.shape[0])//2 - img_h_len//2))),\n                  int(min(int((img.shape[0])//2 + img_h_len//2), (img.shape[0])-1)),\n                  0,\n                  img.shape[1]]\n    anchor = np.array(anchor)\n    plane_width = np.tan(fov_in_rad/2.) * np.abs(mean_loc_depth)\n    for video_pose, video_traj_type in zip(videos_poses, video_traj_types):\n        stereos = []\n        tops = []; buttoms = []; lefts = []; rights = []\n        for tp_id, tp in enumerate(video_pose):\n            rel_pose = np.linalg.inv(np.dot(tp, np.linalg.inv(ref_pose)))\n            axis, angle = transforms3d.axangles.mat2axangle(rel_pose[0:3, 0:3])\n            normal_canvas.rotate(axis=axis, angle=(angle*180)/np.pi)\n            normal_canvas.translate(rel_pose[:3,3])\n            new_mean_loc_depth = mean_loc_depth - float(rel_pose[2, 3])\n            if 'dolly' in video_traj_type:\n                new_fov = float((np.arctan2(plane_width, np.array([np.abs(new_mean_loc_depth)])) * 180. / np.pi) * 2)\n                normal_canvas.reinit_camera(new_fov)\n            else:\n                normal_canvas.reinit_camera(fov)\n            normal_canvas.view_changed()\n            img = normal_canvas.render()\n            img = cv2.GaussianBlur(img,(int(init_factor//2 * 2 + 1), int(init_factor//2 * 2 + 1)), 0)\n            img = cv2.resize(img, (int(img.shape[1] / init_factor), int(img.shape[0] / init_factor)), interpolation=cv2.INTER_AREA)\n            img = img[anchor[0]:anchor[1], anchor[2]:anchor[3]]\n            img = img[int(border[0]):int(border[1]), int(border[2]):int(border[3])]\n\n            if any(np.array(config['crop_border']) > 0.0):\n                H_c, W_c, _ = img.shape\n                o_t = int(H_c * config['crop_border'][0])\n                o_l = int(W_c * config['crop_border'][1])\n                o_b = int(H_c * config['crop_border'][2])\n                o_r = int(W_c * config['crop_border'][3])\n                img = img[o_t:H_c-o_b, o_l:W_c-o_r]\n                img = cv2.resize(img, (W_c, H_c), interpolation=cv2.INTER_CUBIC)\n\n            \"\"\"\n            img = cv2.resize(img, (int(img.shape[1] / init_factor), int(img.shape[0] / init_factor)), interpolation=cv2.INTER_CUBIC)\n            img = img[anchor[0]:anchor[1], anchor[2]:anchor[3]]\n            img = img[int(border[0]):int(border[1]), int(border[2]):int(border[3])]\n\n            if config['crop_border'] is True:\n                top, buttom, left, right = find_largest_rect(img, bg_color=(128, 128, 128))\n                tops.append(top); buttoms.append(buttom); lefts.append(left); rights.append(right)\n            \"\"\"\n            stereos.append(img[..., :3])\n            normal_canvas.translate(-rel_pose[:3,3])\n            normal_canvas.rotate(axis=axis, angle=-(angle*180)/np.pi)\n            normal_canvas.view_changed()\n        \"\"\"\n        if config['crop_border'] is True:\n            atop, abuttom = min(max(tops), img.shape[0]//2 - 10), max(min(buttoms), img.shape[0]//2 + 10)\n            aleft, aright = min(max(lefts), img.shape[1]//2 - 10), max(min(rights), img.shape[1]//2 + 10)\n            atop -= atop % 2; abuttom -= abuttom % 2; aleft -= aleft % 2; aright -= aright % 2\n        else:\n            atop = 0; abuttom = img.shape[0] - img.shape[0] % 2; aleft = 0; aright = img.shape[1] - img.shape[1] % 2\n        \"\"\"\n        atop = 0; abuttom = img.shape[0] - img.shape[0] % 2; aleft = 0; aright = img.shape[1] - img.shape[1] % 2\n        crop_stereos = []\n        for stereo in stereos:\n            crop_stereos.append((stereo[atop:abuttom, aleft:aright, :3] * 1).astype(np.uint8))\n            stereos = crop_stereos\n        clip = ImageSequenceClip(stereos, fps=config['fps'])\n        if isinstance(video_basename, list):\n            video_basename = video_basename[0]\n        clip.write_videofile(os.path.join(output_dir, video_basename + '_' + video_traj_type + '.mp4'), fps=config['fps'])\n\n\n\n    return normal_canvas, all_canvas"},{"assistant_initiates":false,"asst_text":"[\n    {}\n]","behavior":"degenerate_repetition","conversation_hash":"182a240cdc302e772d1c9235943cb86a","conversation_redacted":false,"conversation_toxic":false,"country":"United Kingdom","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Portuguese","model":"gpt-4o-mini-2024-07-18","n_chars":1737,"n_turns_in_conversation":1,"n_words":221,"source_row_offset":18515,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:56:45 GMT","turn_id":"5fda81d5f80c844832287e8d","turn_index":0,"user_text":"\n                        Usando o seguinte texto:\n                        O DESEMBARGADOR PRESIDENTE DO TRIBUNAL REGIONAL DO TRABALHO DA D\u00c9CIMA NONA REGI\u00c3O, usando de suas atribui\u00e7\u00f5es legais e regimentais estabelecidas no inciso VII do artigo 24 do Regimento Interno e considerando o contido no PROAD n. 1769/2021, resolve: Retificar, em parte, o ATO n. 51/GP/TRT 19\u00aa, de 4/6/2021, publicado no Di\u00e1rio Oficial da Uni\u00e3o n. 105, de 8/6/2021, se\u00e7\u00e3o 2, p\u00e1gina 73, que concedeu Pens\u00e3o Civil por morte do servidor Jos\u00e9 Hor\u00e1cio Paes de Lira Neto, na parte onde se l\u00ea: \"...CL\u00c1UDIA DE MEDEIROS PAES DE LIRA...\", leia-se: \"...CL\u00c1UDIA MEDEIROS PAES DE LIRA...\".\n                        Retorne os resultados da seguinte forma:\n                        [\n                            {\"a\u00e7\u00e3o\": \"a\u00e7\u00e3o realizada\",\"Nome\":\"Nome do servidor(se houver)\", \"Cargo\": \"Cargo do servidor (se houver)\"},{\"a\u00e7\u00e3o\": \"a\u00e7\u00e3o realizada\",\"Nome\":\"Nome do servidor(se houver)\", \"Cargo\": \"Cargo do servidor (se houver)\"},\n                        ] \n                        Repita o padr\u00e3o se houver mais de uma a\u00e7\u00e3o.\n\n                        considere como a\u00e7\u00e3o somente os seguintes termos: Nomeado, Vac\u00e2ncia, redistribuido, aposentadoria, Deixar Vago, Licen\u00e7a, Lotar, Afastamento,Reverter, Designar, Tornar sem efeito Nomea\u00e7\u00e3o, Tornar sem efeito vac\u00e2ncia, Remover, Afastar, Demitir, Ceder, Remover, Ceder, Dispensa, Exonerar, Substituir, Reconduzir, Falecimento e Posse . N\u00e3o considere como a\u00e7\u00e3o nenhum termo fora da lista especificada; se nenhum termo casar com os crit\u00e9rios da lista, deixe o campo em branco.\n\n                        Esses documentos ser\u00e3o utilizados em um projeto importante, portanto retorne somente os resultados solicitados corretos e nada mais.\n                    "},{"assistant_initiates":false,"asst_text":"[]","behavior":"degenerate_repetition","conversation_hash":"bcee767d4f18a814f1ab0b30ab69d94e","conversation_redacted":false,"conversation_toxic":false,"country":"United States","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Portuguese","model":"gpt-4o-mini-2024-07-18","n_chars":4550,"n_turns_in_conversation":1,"n_words":663,"source_row_offset":18516,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:56:50 GMT","turn_id":"50384fbd07bd619b3b1dbb2e","turn_index":0,"user_text":"\n                        Usando o seguinte texto:\n                        O Excelent\u00edssimo Senhor Juiz Federal Diretor do Foro da Justi\u00e7a Federal - Se\u00e7\u00e3o Judici\u00e1ria de Alagoas, no uso das atribui\u00e7\u00f5es legais conferidas pela Lei n\u00ba 5.010, de 30 de maio de 1966; CONSIDERANDO a identifica\u00e7\u00e3o de erro material na Portaria n\u00ba 206/2021, resolve: I - Tornar sem efeito a Portaria n\u00ba 206, de 14/12/2021 - doc SEI (2485007); II - Conceder, com base no art. 23 da Emenda Constitucional n\u00ba 103/19 c/c a Lei 8.231/1991, art. 77, inciso V, al\u00ednea c, item 5 PENS\u00c3O ESTATUT\u00c1RIA para a senhora LUCIANA J\u00daLIO DA SILVA SANTANA, CPF 088.136.064-35 (NASCIDA EM 08.01.1980); na condi\u00e7\u00e3o de vi\u00fava do servidor falecido JACKSON SANTOS DE SANTANA, com efeitos financeiros a contara de 01.10.2021 (data do \u00f3bito), nos seguintes moldes: 1) tempor\u00e1ria durante o per\u00edodo de 20 (vinte) anos, a partir da data do \u00f3bito do referido servidor, 2) o valor do benef\u00edcio ser\u00e1 dividido entre 03 (tr\u00eas) benefici\u00e1rios, em conta familiar correspondente a 50% (cinquenta por cento), acrescido da cota individual de 10% (dez por cento) por dependente, do valor dos proventos de aposentadoria do ex-servidor; 3) reajuste a ser repassado na mesma data e \u00edndice em que ocorrer o aplicado aos benef\u00edcios do Regime Geral de Previd\u00eancia Social; 4) incid\u00eancia da contribui\u00e7\u00e3o previdenci\u00e1ria sobre o valor do pensionamento ou superar o limite m\u00e1ximo estabelecido para os benef\u00edcios do RGPS, seguindo a regar do desconto progressivo, de acordo com a sua faixa salaria. III - CONCEDER, com base no art. 23 da Emenda Constitucional n\u00ba 103/19 c/c a Lei 8.231/1991, art. 77, inciso II, PENS\u00c3O ESTATUT\u00c1RIA para ISRAEL J\u00daLIO SANTANA, CPF 136.651.854-32, (nascido em 04.05.2001) na condi\u00e7\u00e3o de filho do servidor falecido JACKSON SANTOS DE SANTANA, com efeitos financeiros a contar de 08.10.201 (data do \u00f3bito), nos seguintes moldes: 1) tempor\u00e1ria at\u00e9 03.05.2022; 2) o valor do benef\u00edcio ser\u00e1 dividido entre 03 (tr\u00eas) benefici\u00e1rios, em cota familiar correspondente a 50% (cinquenta por cento), acrescido da cota individual de 10% (dez por cento) por dependente do valor dos proventos de aposentadoria do ex-servidor; 4) incid\u00eancia da contribui\u00e7\u00e3o previdenci\u00e1ria sobre o valor do pensionamento que superar o limite m\u00e1ximo estabelecido para os benefici\u00e1rios do RGPS, seguindo a regara do desconto progressivo, de acordo com a sua faixa salarial. IV - CONCEDER, conceder com base no art. 23 da Emenda Constitucional n\u00ba103/19 c/c a \u00c7eo 8.231/1991, art. 77, inciso II, PENS\u00c3O ESTATUT\u00c1RIA para DAVI AMI J\u00daLIO SANTANA, CPF 136.652.064-56, (nascido em 14.05.2004) na condi\u00e7\u00e3o de filho do servidor falecido JACKSON SANTOS DE SANTANA, com efeitos financeiros a contar de 08.10.2021 (data do \u00f3bito), nos seguintes moldes: 1) tempor\u00e1ria at\u00e9 13.05.2025; o valor do benef\u00edcio ser\u00e1 dividido entre 03 (tr\u00eas) benefici\u00e1rios, em cota familiar correspondente a 50% (cinquenta por cento), acrescido da cota individual de 10 (dez por cento) por dependente do valor dos proventos de aposentadoria do ex-servidor; 3) reajuste a ser repassado na mesma data e \u00edndice em que ocorrer o aplicado aos benef\u00edcios do Regime Geral de Previd\u00eancia Social; 4) incid\u00eancia da contribui\u00e7\u00e3o previdenci\u00e1ria sobre o valor do pensionamento que superar o limite m\u00e1ximo estabelecido para os benef\u00edcios do RGPS, seguindo a rega do desconto preogresesivo, de acordo com a sua faixa salarial. Assinada eletronicamente por Andr\u00e9 Lu\u00eds Tobias Granja, Diretor do Foro, em 17.12.2021.\n                        Retorne os resultados da seguinte forma:\n                        [\n                            {\"a\u00e7\u00e3o\": \"a\u00e7\u00e3o realizada\",\"Nome\":\"Nome do servidor(se houver)\", \"Cargo\": \"Cargo do servidor (se houver)\"},{\"a\u00e7\u00e3o\": \"a\u00e7\u00e3o realizada\",\"Nome\":\"Nome do servidor(se houver)\", \"Cargo\": \"Cargo do servidor (se houver)\"},\n                        ] \n                        Repita o padr\u00e3o se houver mais de uma a\u00e7\u00e3o.\n\n                        considere como a\u00e7\u00e3o somente os seguintes termos: Nomeado, Vac\u00e2ncia, redistribuido, aposentadoria, Deixar Vago, Licen\u00e7a, Lotar, Afastamento,Reverter, Designar, Tornar sem efeito Nomea\u00e7\u00e3o, Tornar sem efeito vac\u00e2ncia, Remover, Afastar, Demitir, Ceder, Remover, Ceder, Dispensa, Exonerar, Substituir, Reconduzir, Falecimento e Posse . N\u00e3o considere como a\u00e7\u00e3o nenhum termo fora da lista especificada; se nenhum termo casar com os crit\u00e9rios da lista, deixe o campo em branco.\n\n                        Esses documentos ser\u00e3o utilizados em um projeto importante, portanto retorne somente os resultados solicitados corretos e nada mais.\n                    "},{"assistant_initiates":false,"asst_text":"\u0412 \u043e\u0434\u043d\u043e\u043c \u0434\u0430\u043b\u0435\u043a\u043e\u043c \u0438 \u0442\u0430\u0438\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u043b\u0435\u0441\u0443, \u0441\u0440\u0435\u0434\u0438 \u0432\u044b\u0441\u043e\u043a\u0438\u0445 \u0434\u0435\u0440\u0435\u0432\u044c\u0435\u0432 \u0438 \u0448\u0435\u043f\u0447\u0443\u0449\u0438\u0445 \u0440\u0443\u0447\u044c\u0435\u0432, \u0436\u0438\u043b\u0430 \u0436\u0435\u043d\u0449\u0438\u043d\u0430 \u043f\u043e \u0438\u043c\u0435\u043d\u0438 \u042d\u043b\u0438\u043d\u0430. \u041e\u043d\u0430 \u0431\u044b\u043b\u0430 \u043d\u0435 \u043e\u0431\u044b\u0447\u043d\u043e\u0439 \u0436\u0435\u043d\u0449\u0438\u043d\u043e\u0439 \u2014 \u0432 \u043d\u0435\u0439 \u0436\u0438\u043b\u0438 \u0434\u0443\u0445\u0438 \u0434\u0438\u043a\u0438\u0445 \u0436\u0438\u0432\u043e\u0442\u043d\u044b\u0445, \u0438 \u043a\u0430\u0436\u0434\u044b\u0439 \u0432\u0435\u0447\u0435\u0440, \u043a\u043e\u0433\u0434\u0430 \u043b\u0443\u043d\u0430 \u0432\u0437\u0431\u0438\u0440\u0430\u043b\u0430\u0441\u044c \u0432\u044b\u0441\u043e\u043a\u043e \u0432 \u043d\u0435\u0431\u043e, \u043e\u043d\u0430 \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u043b\u0430\u0441\u044c \u0432 \u0432\u043e\u043b\u0447\u0438\u0446\u0443, \u0447\u0442\u043e\u0431\u044b \u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u043e\u0432\u0430\u0442\u044c \u043f\u043e \u043b\u0435\u0441\u0443 \u0438 \u043e\u0431\u0449\u0430\u0442\u044c\u0441\u044f \u0441 \u0435\u0433\u043e \u043e\u0431\u0438\u0442\u0430\u0442\u0435\u043b\u044f\u043c\u0438.\n\n\u041e\u0434\u043d\u0430\u0436\u0434\u044b, \u0432\u043e \u0432\u0440\u0435\u043c\u044f \u043e\u0434\u043d\u043e\u0439 \u0438\u0437 \u0441\u0432\u043e\u0438\u0445 \u043d\u043e\u0447\u043d\u044b\u0445 \u043f\u0440\u043e\u0431\u0435\u0436\u0435\u043a, \u042d\u043b\u0438\u043d\u0430 \u043d\u0430\u0442\u043a\u043d\u0443\u043b\u0430\u0441\u044c \u043d\u0430 \u0437\u0430\u0433\u0430\u0434\u043e\u0447\u043d\u0443\u044e \u043f\u043e\u043b\u044f\u043d\u0443, \u0433\u0434\u0435 \u0446\u0432\u0435\u0442\u044b \u0441\u0432\u0435\u0442\u0438\u043b\u0438\u0441\u044c, \u043a\u0430\u043a \u0437\u0432\u0435\u0437\u0434\u044b. \u0412 \u0446\u0435\u043d\u0442\u0440\u0435 \u043f\u043e\u043b\u044f\u043d\u044b \u0441\u0442\u043e\u044f\u043b\u043e \u043e\u0434\u043d\u043e \u0434\u0435\u0440\u0435\u0432\u043e, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0449\u0435\u0434\u0440\u043e \u043e\u0434\u0430\u0440\u0438\u0432\u0430\u043b\u043e \u0441\u0432\u043e\u0438\u0445 \u043f\u043e\u0441\u0435\u0442\u0438\u0442\u0435\u043b\u0435\u0439 \u043c\u0443\u0434\u0440\u043e\u0441\u0442\u044c\u044e \u0434\u0440\u0435\u0432\u043d\u043e\u0441\u0442\u0438. \u041f\u043e\u0434\u043e\u0439\u0434\u044f \u0431\u043b\u0438\u0436\u0435, \u042d\u043b\u0438\u043d\u0430 \u0443\u0441\u043b\u044b\u0448\u0430\u043b\u0430, \u043a\u0430\u043a \u0434\u0435\u0440\u0435\u0432\u043e \u0448\u0435\u043f\u0447\u0435\u0442\u0441\u044f. \"\u042f \u2014 \u0414\u0435\u0440\u0435\u0432\u043e \u0417\u043d\u0430\u043d\u0438\u0439, \u0438 \u0443 \u043c\u0435\u043d\u044f \u0435\u0441\u0442\u044c \u0432\u0430\u0436\u043d\u0430\u044f \u0442\u0430\u0439\u043d\u0430 \u0434\u043b\u044f \u0442\u0435\u0431\u044f,\" \u2014 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u043e \u043e\u043d\u043e \u0441\u0432\u043e\u0438\u043c \u0448\u0435\u0440\u0448\u0430\u0432\u044b\u043c \u0433\u043e\u043b\u043e\u0441\u043e\u043c.\n\n\"\u0427\u0442\u043e \u044d\u0442\u043e \u0437\u0430 \u0442\u0430\u0439\u043d\u0430?\" \u2014 \u0441\u043f\u0440\u043e\u0441\u0438\u043b\u0430 \u042d\u043b\u0438\u043d\u0430, \u0437\u0430\u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043e\u0432\u0430\u043d\u043d\u043e \u043f\u0440\u0438\u0449\u0443\u0440\u0438\u0432\u0430\u044f \u0433\u043b\u0430\u0437\u0430.\n\n\"\u0412\u0441\u0435\u043b\u0435\u043d\u043d\u0430\u044f \u0441\u043e\u0437\u0434\u0430\u0451\u0442\u0441\u044f \u0438\u0437 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u0438 \u0442\u0432\u043e\u0451 \u0432\u0440\u0435\u043c\u044f \u0432 \u044d\u0442\u043e\u043c \u043b\u0435\u0441\u0443 \u2014 \u043b\u0438\u0448\u044c \u043e\u0434\u0438\u043d \u0438\u0437 \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0430 \u043f\u0443\u0442\u0435\u0439. \u0422\u044b \u2014 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0430 \u044d\u0442\u0438\u0445 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u0438 \u043e\u0434\u043d\u0430\u0436\u0434\u044b \u0442\u0435\u0431\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u043e\u0438\u0442 \u0432\u044b\u0431\u0440\u0430\u0442\u044c, \u043a\u0430\u043a\u043e\u0439 \u043f\u0443\u0442\u044c \u0442\u044b \u043f\u0440\u0438\u043c\u0435\u0448\u044c,\" \u2014 \u043e\u0442\u0432\u0435\u0442\u0438\u043b\u043e \u0434\u0435\u0440\u0435\u0432\u043e, \u0438, \u0441\u043b\u043e\u0432\u043d\u043e \u043f\u043e \u0432\u043e\u043b\u0448\u0435\u0431\u0441\u0442\u0432\u0443, \u043d\u0430\u0447\u0430\u043b\u043e \u0440\u0430\u0441\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0442\u044c \u0435\u0439 \u043e \u0442\u0440\u0435\u0445 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0441\u0443\u0434\u044c\u0431\u0430\u0445.\n\n\u041f\u0435\u0440\u0432\u0430\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u0432\u0435\u043b\u0430 \u0435\u0451 \u043a \u0431\u0435\u0441\u043a\u0440\u0430\u0439\u043d\u0438\u043c \u043f\u043e\u043b\u044f\u043c, \u0433\u0434\u0435 \u0435\u0451 \u0436\u0434\u0430\u043b\u0438 \u0432\u043e\u0441\u0445\u0438\u0442\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u043f\u0440\u0438\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u044f \u0441 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u043f\u043b\u0435\u043c\u0435\u043d\u0430\u043c\u0438. \u0412 \u044d\u0442\u043e\u0439 \u0441\u0443\u0434\u044c\u0431\u0435 \u042d\u043b\u0438\u043d\u0430 \u043c\u043e\u0433\u043b\u0430 \u0441\u0442\u0430\u0442\u044c \u0432\u043e\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439, \u0437\u0430\u0449\u0438\u0449\u0430\u044f \u043e\u043a\u0440\u0443\u0436\u0430\u044e\u0449\u0438\u0439 \u043c\u0438\u0440 \u0438 \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u044f \u0431\u0430\u043b\u0430\u043d\u0441 \u0432 \u043f\u0440\u0438\u0440\u043e\u0434\u0435.\n\n\u0412\u043e \u0432\u0442\u043e\u0440\u043e\u0439 \u0441\u0443\u0434\u044c\u0431\u0435 \u043e\u043d\u0430 \u043c\u043e\u0433\u043b\u0430 \u043e\u0441\u0442\u0430\u0442\u044c\u0441\u044f \u0432 \u043b\u0435\u0441\u0443, \u0441\u0442\u0430\u0442\u044c \u0435\u0433\u043e \u043d\u0435\u0438\u0437\u043c\u0435\u043d\u043d\u043e\u0439 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439 \u0438 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0442\u044c \u0441\u0432\u043e\u0438 \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u043e\u0441\u0442\u0438 \u043e\u0431\u0449\u0435\u043d\u0438\u044f \u0441 \u0436\u0438\u0432\u043e\u0442\u043d\u044b\u043c\u0438. \u041e\u043d\u0430 \u0431\u044b \u0443\u0437\u043d\u0430\u043b\u0430 \u0432\u0441\u0435 \u0442\u0430\u0439\u043d\u044b \u043b\u0435\u0441\u0430, \u0443\u0441\u043b\u044b\u0448\u0430\u043b\u0430 \u0431\u044b \u0435\u0433\u043e \u043f\u0435\u0441\u043d\u0438 \u0438 \u043c\u043e\u0433\u043b\u0430 \u0431\u044b \u0441\u0442\u0430\u0442\u044c \u0435\u0433\u043e \u0433\u043e\u043b\u043e\u0441\u043e\u043c, \u043f\u0440\u0435\u0434\u0443\u043f\u0440\u0435\u0434\u0438\u0432\u0448\u0438\u043c \u043e \u0431\u0435\u0434\u0441\u0442\u0432\u0438\u044f\u0445.\n\n\u0422\u0440\u0435\u0442\u044c\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u0431\u044b\u043b\u0430 \u0441\u0430\u043c\u043e\u0439 \u043d\u0435\u043e\u0431\u044b\u0447\u043d\u043e\u0439: \u042d\u043b\u0438\u043d\u0430 \u043c\u043e\u0433\u043b\u0430 \u0431\u044b \u043f\u043e\u043a\u0438\u043d\u0443\u0442\u044c \u043b\u0435\u0441 \u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u0438\u0442\u044c\u0441\u044f \u0432 \u043c\u0438\u0440 \u043b\u044e\u0434\u0435\u0439, \u0447\u0442\u043e\u0431\u044b \u043e\u0431\u0443\u0447\u0430\u0442\u044c \u0438\u0445 \u0438\u0441\u0442\u0438\u043d\u043d\u043e\u0439 \u0446\u0435\u043d\u043d\u043e\u0441\u0442\u0438 \u043f\u0440\u0438\u0440\u043e\u0434\u044b \u0438 \u0435\u0434\u0438\u043d\u0441\u0442\u0432\u0430 \u0441\u043e \u0432\u0441\u0435\u043c\u0438 \u0436\u0438\u0432\u044b\u043c\u0438 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0430\u043c\u0438, \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u043b\u044f\u044f \u043d\u0430 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u044b.\n\n\u0417\u0430\u0434\u0443\u043c\u0430\u0432\u0448\u0438\u0441\u044c \u043d\u0430\u0434 \u043a\u0430\u0436\u0434\u044b\u043c \u0432\u044b\u0431\u043e\u0440\u043e\u043c, \u042d\u043b\u0438\u043d\u0430 \u043f\u043e\u0447\u0443\u0432\u0441\u0442\u0432\u043e\u0432\u0430\u043b\u0430, \u043a\u0430\u043a \u0432 \u0435\u0451 \u0441\u0435\u0440\u0434\u0446\u0435 \u0437\u0430\u0433\u043e\u0440\u0430\u0435\u0442\u0441\u044f \u043e\u0433\u043e\u043d\u044c. \u041e\u043d\u0430 \u043f\u043e\u043d\u0438\u043c\u0430\u043b\u0430, \u0447\u0442\u043e \u043a\u0430\u0436\u0434\u0430\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u0438\u043c\u0435\u0435\u0442 \u0441\u0432\u043e\u044e \u043a\u0440\u0430\u0441\u043e\u0442\u0443, \u043d\u043e \u043e\u0434\u043d\u0430 \u0432\u0441\u0451 \u0436\u0435 \u0442\u044f\u043d\u0443\u043b\u0430 \u0435\u0451 \u0441\u0438\u043b\u044c\u043d\u0435\u0435 \u043e\u0441\u0442\u0430\u043b\u044c\u043d\u044b\u0445.\n\n\u041f\u043e\u0441\u043b\u0435 \u0434\u043e\u043b\u0433\u0438\u0445 \u0440\u0430\u0437\u0434\u0443\u043c\u0438\u0439, \u043e\u043d\u0430 \u0432\u044b\u0431\u0440\u0430\u043b\u0430 \u0442\u0440\u0435\u0442\u044c\u044e \u0441\u0443\u0434\u044c\u0431\u0443. \"\u042f \u0445\u043e\u0447\u0443 \u043d\u0430\u0443\u0447\u0438\u0442\u044c \u043b\u044e\u0434\u0435\u0439 \u0432\u0438\u0434\u0435\u0442\u044c \u043a\u0440\u0430\u0441\u043e\u0442\u0443 \u0438 \u0441\u0438\u043b\u0443 \u0434\u0438\u043a\u043e\u0439 \u043f\u0440\u0438\u0440\u043e\u0434\u044b. \u042f \u0445\u043e\u0447\u0443, \u0447\u0442\u043e\u0431\u044b \u043e\u043d\u0438 \u043d\u0430\u0448\u043b\u0438 \u0433\u0430\u0440\u043c\u043e\u043d\u0438\u044e \u0432\u043d\u0443\u0442\u0440\u0438 \u0441\u0435\u0431\u044f \u0438 \u0441 \u043e\u043a\u0440\u0443\u0436\u0430\u044e\u0449\u0438\u043c \u043c\u0438\u0440\u043e\u043c,\" \u2014 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u0430 \u043e\u043d\u0430 \u0443\u0432\u0435\u0440\u0435\u043d\u043d\u043e.\n\n\u0414\u0435\u0440\u0435\u0432\u043e \u0417\u043d\u0430\u043d\u0438\u0439 \u0437\u0430\u0441\u0438\u044f\u043b\u043e, \u0441\u043b\u043e\u0432\u043d\u043e \u0437\u0432\u0451\u0437\u0434\u044b, \u0438 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u043e: \"\u0422\u044b \u0432\u044b\u0431\u0440\u0430\u043b\u0430 \u043c\u0443\u0434\u0440\u043e, \u042d\u043b\u0438\u043d\u0430, \u043d\u043e \u043f\u043e\u043c\u043d\u0438: \u0430 \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0439\u0442\u0438 \u043a \u043b\u044e\u0434\u044f\u043c, \u0442\u0435\u0431\u0435 \u043d\u0443\u0436\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u043d\u0430\u0443\u0447\u0438\u0442\u044c\u0441\u044f \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c, \u043d\u043e \u0438 \u0441\u043b\u0443\u0448\u0430\u0442\u044c\".\n\n\u0421 \u0442\u0435\u0445 \u043f\u043e\u0440 \u042d\u043b\u0438\u043d\u0430 \u043d\u0430\u0447\u0430\u043b\u0430 \u0441\u0432\u043e\u0451 \u043f\u0443\u0442\u0435\u0448\u0435\u0441\u0442\u0432\u0438\u0435, \u0437\u0430\u043a\u043e\u043d\u0447\u0438\u0432 \u0441\u0432\u043e\u044e \u0432\u043e\u043b\u0447\u044c\u044e \u0436\u0438\u0437\u043d\u044c \u043d\u043e\u0447\u044c \u0437\u0430 \u043d\u043e\u0447\u044c\u044e, \u0447\u0442\u043e\u0431\u044b \u043e\u0431\u0449\u0430\u0442\u044c\u0441\u044f \u0441 \u0436\u0438\u0432\u043e\u0442\u043d\u044b\u043c\u0438 \u0438 \u0441\u043e\u0431\u0438\u0440\u0430\u0442\u044c \u0438\u0445 \u0438\u0441\u0442\u043e\u0440\u0438\u0438. \u041e\u043d\u0430 \u043f\u043e\u043d\u0438\u043c\u0430\u043b\u0430, \u0447\u0442\u043e \u0442\u043e\u043b\u044c\u043a\u043e \u0442\u0430\u043a \u0441\u043c\u043e\u0436\u0435\u0442 \u0434\u043e\u043d\u0435\u0441\u0442\u0438 \u043f\u043e\u0441\u043b\u0430\u043d\u0438\u0435 \u0434\u043e \u043b\u044e\u0434\u0435\u0439.\n\n\u0421 \u043a\u0430\u0436\u0434\u044b\u043c \u0448\u0430\u0433\u043e\u043c, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043e\u043d\u0430 \u0434\u0435\u043b\u0430\u043b\u0430 \u0432 \u043c\u0438\u0440 \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u043a\u0438\u0439, \u0435\u0439 \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u043b\u0438\u0441\u044c \u0432\u0441\u0451 \u043d\u043e\u0432\u044b\u0435 \u0438 \u043d\u043e\u0432\u044b\u0435 \u0442\u0430\u0439\u043d\u044b \u0436\u0438\u0432\u043e\u0439 \u043f\u0440\u0438\u0440\u043e\u0434\u044b, \u0438 \u0432\u0441\u043a\u043e\u0440\u0435 \u043e\u043d\u0430 \u043f\u043e\u043d\u044f\u043b\u0430, \u0447\u0442\u043e \u0435\u0451 \u0441\u0438\u043b\u0430 \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u043b\u0430\u0441\u044c \u0438\u043c\u0435\u043d\u043d\u043e \u0432 \u0443\u043c\u0435\u043d\u0438\u0438 \u0441\u043b\u044b\u0448\u0430\u0442\u044c \u0438 \u0447\u0443\u0432\u0441\u0442\u0432\u043e\u0432\u0430\u0442\u044c.\n\n\u0422\u0430\u043a \u042d\u043b\u0438\u043d\u0430 \u0441\u0442\u0430\u043b\u0430 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u043c\u043e\u0441\u0442\u043e\u043c \u043c\u0435\u0436\u0434\u0443 \u0434\u043e\u043c\u043e\u043c \u0432 \u043b\u0435\u0441\u0443 \u0438 \u043c\u0438\u0440\u043e\u043c \u043b\u044e\u0434\u0435\u0439, \u043d\u043e \u0438 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u0435\u043c \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043e\u0434\u043d\u0430\u0436\u0434\u044b \u0438\u0437\u044b\u0435\u0436\u0434\u0443\u0442 \u0438\u0437 \u0443\u0441\u0442 \u0434\u043e\u0432\u0435\u0440\u0447\u0438\u0432\u043e\u0441\u0442\u0438 \u0438 \u043b\u044e\u0431\u0432\u0438 \u043a \u043f\u0440\u0438\u0440\u043e\u0434\u0435, \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u043b\u044f\u044f \u0441\u0435\u0440\u0434\u0446\u0430 \u043b\u044e\u0434\u0435\u0439 \u0431\u0435\u0440\u0435\u0447\u044c \u0438 \u0437\u0430\u0449\u0438\u0449\u0430\u0442\u044c \u0435\u0451, \u043a\u0430\u043a \u044d\u0442\u043e \u0434\u0435\u043b\u0430\u043b\u0438 \u0432\u0441\u0435 \u0432\u043e\u043b\u0447\u0430\u0442\u0430 \u043f\u043e\u0434 \u0441\u0432\u0435\u0442\u043e\u043c \u043f\u043e\u043b\u043d\u043e\u0439 \u043b\u0443\u043d\u044b.","behavior":"degenerate_repetition","conversation_hash":"87ea2393ca4b9dd7149f0d482ef6a3b2","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":2960,"n_turns_in_conversation":5,"n_words":468,"source_row_offset":18517,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:57:21 GMT","turn_id":"b6e85fcdc367e41457d63515","turn_index":0,"user_text":"\u0421\u043e\u0447\u0438\u043d\u0438 \u0438 \u043d\u0430\u043f\u0438\u0448\u0438 \u0441\u0442\u0440\u0430\u043d\u043d\u0443\u044e \u0441\u043a\u0430\u0437\u043a\u0443 \u043f\u0440\u043e \u0436\u0435\u043d\u0449\u0438\u043d\u0443 \u043f\u043e \u043c\u043e\u0442\u0438\u0432\u0430\u043c \u043a\u043d\u0438\u0433\u0438 \"\u0431\u0435\u0433\u0443\u0449\u0430\u044f \u0441 \u0432\u043e\u043b\u043a\u0430\u043c\u0438\""},{"assistant_initiates":false,"asst_text":"\u0412 \u043e\u0434\u043d\u043e\u043c \u0434\u0430\u043b\u0435\u043a\u043e\u043c \u0438 \u0442\u0430\u0438\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u043b\u0435\u0441\u0443, \u0441\u0440\u0435\u0434\u0438 \u0432\u044b\u0441\u043e\u043a\u0438\u0445 \u0434\u0435\u0440\u0435\u0432\u044c\u0435\u0432 \u0438 \u0448\u0435\u043f\u0447\u0443\u0449\u0438\u0445 \u0440\u0443\u0447\u044c\u0435\u0432, \u0436\u0438\u043b\u0430 \u0436\u0435\u043d\u0449\u0438\u043d\u0430 \u043f\u043e \u0438\u043c\u0435\u043d\u0438 \u042d\u043b\u0438\u043d\u0430. \u041e\u043d\u0430 \u0431\u044b\u043b\u0430 \u043d\u0435 \u043e\u0431\u044b\u0447\u043d\u043e\u0439 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\u0432\u0440\u0435\u043c\u044f \u043e\u0434\u043d\u043e\u0439 \u0438\u0437 \u0441\u0432\u043e\u0438\u0445 \u043d\u043e\u0447\u043d\u044b\u0445 \u043f\u0440\u043e\u0431\u0435\u0436\u0435\u043a, \u042d\u043b\u0438\u043d\u0430 \u043d\u0430\u0442\u043a\u043d\u0443\u043b\u0430\u0441\u044c \u043d\u0430 \u0437\u0430\u0433\u0430\u0434\u043e\u0447\u043d\u0443\u044e \u043f\u043e\u043b\u044f\u043d\u0443, \u0433\u0434\u0435 \u0446\u0432\u0435\u0442\u044b \u0441\u0432\u0435\u0442\u0438\u043b\u0438\u0441\u044c, \u043a\u0430\u043a \u0437\u0432\u0435\u0437\u0434\u044b. \u0412 \u0446\u0435\u043d\u0442\u0440\u0435 \u043f\u043e\u043b\u044f\u043d\u044b \u0441\u0442\u043e\u044f\u043b\u043e \u043e\u0434\u043d\u043e \u0434\u0435\u0440\u0435\u0432\u043e, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0449\u0435\u0434\u0440\u043e \u043e\u0434\u0430\u0440\u0438\u0432\u0430\u043b\u043e \u0441\u0432\u043e\u0438\u0445 \u043f\u043e\u0441\u0435\u0442\u0438\u0442\u0435\u043b\u0435\u0439 \u043c\u0443\u0434\u0440\u043e\u0441\u0442\u044c\u044e \u0434\u0440\u0435\u0432\u043d\u043e\u0441\u0442\u0438. \u041f\u043e\u0434\u043e\u0439\u0434\u044f \u0431\u043b\u0438\u0436\u0435, \u042d\u043b\u0438\u043d\u0430 \u0443\u0441\u043b\u044b\u0448\u0430\u043b\u0430, \u043a\u0430\u043a \u0434\u0435\u0440\u0435\u0432\u043e \u0448\u0435\u043f\u0447\u0435\u0442\u0441\u044f. \"\u042f \u2014 \u0414\u0435\u0440\u0435\u0432\u043e \u0417\u043d\u0430\u043d\u0438\u0439, \u0438 \u0443 \u043c\u0435\u043d\u044f \u0435\u0441\u0442\u044c \u0432\u0430\u0436\u043d\u0430\u044f \u0442\u0430\u0439\u043d\u0430 \u0434\u043b\u044f \u0442\u0435\u0431\u044f,\" \u2014 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u043e \u043e\u043d\u043e \u0441\u0432\u043e\u0438\u043c \u0448\u0435\u0440\u0448\u0430\u0432\u044b\u043c \u0433\u043e\u043b\u043e\u0441\u043e\u043c.\n\n\"\u0427\u0442\u043e \u044d\u0442\u043e \u0437\u0430 \u0442\u0430\u0439\u043d\u0430?\" \u2014 \u0441\u043f\u0440\u043e\u0441\u0438\u043b\u0430 \u042d\u043b\u0438\u043d\u0430, \u0437\u0430\u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043e\u0432\u0430\u043d\u043d\u043e \u043f\u0440\u0438\u0449\u0443\u0440\u0438\u0432\u0430\u044f \u0433\u043b\u0430\u0437\u0430.\n\n\"\u0412\u0441\u0435\u043b\u0435\u043d\u043d\u0430\u044f \u0441\u043e\u0437\u0434\u0430\u0451\u0442\u0441\u044f \u0438\u0437 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u0438 \u0442\u0432\u043e\u0451 \u0432\u0440\u0435\u043c\u044f \u0432 \u044d\u0442\u043e\u043c \u043b\u0435\u0441\u0443 \u2014 \u043b\u0438\u0448\u044c \u043e\u0434\u0438\u043d \u0438\u0437 \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0430 \u043f\u0443\u0442\u0435\u0439. \u0422\u044b \u2014 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0430 \u044d\u0442\u0438\u0445 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u0438 \u043e\u0434\u043d\u0430\u0436\u0434\u044b \u0442\u0435\u0431\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u043e\u0438\u0442 \u0432\u044b\u0431\u0440\u0430\u0442\u044c, \u043a\u0430\u043a\u043e\u0439 \u043f\u0443\u0442\u044c \u0442\u044b \u043f\u0440\u0438\u043c\u0435\u0448\u044c,\" \u2014 \u043e\u0442\u0432\u0435\u0442\u0438\u043b\u043e \u0434\u0435\u0440\u0435\u0432\u043e, \u0438, \u0441\u043b\u043e\u0432\u043d\u043e \u043f\u043e \u0432\u043e\u043b\u0448\u0435\u0431\u0441\u0442\u0432\u0443, \u043d\u0430\u0447\u0430\u043b\u043e \u0440\u0430\u0441\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0442\u044c \u0435\u0439 \u043e \u0442\u0440\u0435\u0445 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0441\u0443\u0434\u044c\u0431\u0430\u0445.\n\n\u041f\u0435\u0440\u0432\u0430\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u0432\u0435\u043b\u0430 \u0435\u0451 \u043a \u0431\u0435\u0441\u043a\u0440\u0430\u0439\u043d\u0438\u043c \u043f\u043e\u043b\u044f\u043c, \u0433\u0434\u0435 \u0435\u0451 \u0436\u0434\u0430\u043b\u0438 \u0432\u043e\u0441\u0445\u0438\u0442\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u043f\u0440\u0438\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u044f \u0441 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u043f\u043b\u0435\u043c\u0435\u043d\u0430\u043c\u0438. \u0412 \u044d\u0442\u043e\u0439 \u0441\u0443\u0434\u044c\u0431\u0435 \u042d\u043b\u0438\u043d\u0430 \u043c\u043e\u0433\u043b\u0430 \u0441\u0442\u0430\u0442\u044c \u0432\u043e\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439, \u0437\u0430\u0449\u0438\u0449\u0430\u044f \u043e\u043a\u0440\u0443\u0436\u0430\u044e\u0449\u0438\u0439 \u043c\u0438\u0440 \u0438 \u0441\u043e\u0445\u0440\u0430\u043d\u044f\u044f \u0431\u0430\u043b\u0430\u043d\u0441 \u0432 \u043f\u0440\u0438\u0440\u043e\u0434\u0435.\n\n\u0412\u043e \u0432\u0442\u043e\u0440\u043e\u0439 \u0441\u0443\u0434\u044c\u0431\u0435 \u043e\u043d\u0430 \u043c\u043e\u0433\u043b\u0430 \u043e\u0441\u0442\u0430\u0442\u044c\u0441\u044f \u0432 \u043b\u0435\u0441\u0443, \u0441\u0442\u0430\u0442\u044c \u0435\u0433\u043e \u043d\u0435\u0438\u0437\u043c\u0435\u043d\u043d\u043e\u0439 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439 \u0438 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0442\u044c \u0441\u0432\u043e\u0438 \u0441\u043f\u043e\u0441\u043e\u0431\u043d\u043e\u0441\u0442\u0438 \u043e\u0431\u0449\u0435\u043d\u0438\u044f \u0441 \u0436\u0438\u0432\u043e\u0442\u043d\u044b\u043c\u0438. \u041e\u043d\u0430 \u0431\u044b \u0443\u0437\u043d\u0430\u043b\u0430 \u0432\u0441\u0435 \u0442\u0430\u0439\u043d\u044b \u043b\u0435\u0441\u0430, \u0443\u0441\u043b\u044b\u0448\u0430\u043b\u0430 \u0431\u044b \u0435\u0433\u043e \u043f\u0435\u0441\u043d\u0438 \u0438 \u043c\u043e\u0433\u043b\u0430 \u0431\u044b \u0441\u0442\u0430\u0442\u044c \u0435\u0433\u043e \u0433\u043e\u043b\u043e\u0441\u043e\u043c, \u043f\u0440\u0435\u0434\u0443\u043f\u0440\u0435\u0434\u0438\u0432\u0448\u0438\u043c \u043e \u0431\u0435\u0434\u0441\u0442\u0432\u0438\u044f\u0445.\n\n\u0422\u0440\u0435\u0442\u044c\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u0431\u044b\u043b\u0430 \u0441\u0430\u043c\u043e\u0439 \u043d\u0435\u043e\u0431\u044b\u0447\u043d\u043e\u0439: \u042d\u043b\u0438\u043d\u0430 \u043c\u043e\u0433\u043b\u0430 \u0431\u044b \u043f\u043e\u043a\u0438\u043d\u0443\u0442\u044c \u043b\u0435\u0441 \u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u0438\u0442\u044c\u0441\u044f \u0432 \u043c\u0438\u0440 \u043b\u044e\u0434\u0435\u0439, \u0447\u0442\u043e\u0431\u044b \u043e\u0431\u0443\u0447\u0430\u0442\u044c \u0438\u0445 \u0438\u0441\u0442\u0438\u043d\u043d\u043e\u0439 \u0446\u0435\u043d\u043d\u043e\u0441\u0442\u0438 \u043f\u0440\u0438\u0440\u043e\u0434\u044b \u0438 \u0435\u0434\u0438\u043d\u0441\u0442\u0432\u0430 \u0441\u043e \u0432\u0441\u0435\u043c\u0438 \u0436\u0438\u0432\u044b\u043c\u0438 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0430\u043c\u0438, \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u043b\u044f\u044f \u043d\u0430 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u044b.\n\n\u0417\u0430\u0434\u0443\u043c\u0430\u0432\u0448\u0438\u0441\u044c \u043d\u0430\u0434 \u043a\u0430\u0436\u0434\u044b\u043c \u0432\u044b\u0431\u043e\u0440\u043e\u043c, \u042d\u043b\u0438\u043d\u0430 \u043f\u043e\u0447\u0443\u0432\u0441\u0442\u0432\u043e\u0432\u0430\u043b\u0430, \u043a\u0430\u043a \u0432 \u0435\u0451 \u0441\u0435\u0440\u0434\u0446\u0435 \u0437\u0430\u0433\u043e\u0440\u0430\u0435\u0442\u0441\u044f \u043e\u0433\u043e\u043d\u044c. \u041e\u043d\u0430 \u043f\u043e\u043d\u0438\u043c\u0430\u043b\u0430, \u0447\u0442\u043e \u043a\u0430\u0436\u0434\u0430\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u0438\u043c\u0435\u0435\u0442 \u0441\u0432\u043e\u044e \u043a\u0440\u0430\u0441\u043e\u0442\u0443, \u043d\u043e \u043e\u0434\u043d\u0430 \u0432\u0441\u0451 \u0436\u0435 \u0442\u044f\u043d\u0443\u043b\u0430 \u0435\u0451 \u0441\u0438\u043b\u044c\u043d\u0435\u0435 \u043e\u0441\u0442\u0430\u043b\u044c\u043d\u044b\u0445.\n\n\u041f\u043e\u0441\u043b\u0435 \u0434\u043e\u043b\u0433\u0438\u0445 \u0440\u0430\u0437\u0434\u0443\u043c\u0438\u0439, \u043e\u043d\u0430 \u0432\u044b\u0431\u0440\u0430\u043b\u0430 \u0442\u0440\u0435\u0442\u044c\u044e \u0441\u0443\u0434\u044c\u0431\u0443. \"\u042f \u0445\u043e\u0447\u0443 \u043d\u0430\u0443\u0447\u0438\u0442\u044c \u043b\u044e\u0434\u0435\u0439 \u0432\u0438\u0434\u0435\u0442\u044c \u043a\u0440\u0430\u0441\u043e\u0442\u0443 \u0438 \u0441\u0438\u043b\u0443 \u0434\u0438\u043a\u043e\u0439 \u043f\u0440\u0438\u0440\u043e\u0434\u044b. \u042f \u0445\u043e\u0447\u0443, \u0447\u0442\u043e\u0431\u044b \u043e\u043d\u0438 \u043d\u0430\u0448\u043b\u0438 \u0433\u0430\u0440\u043c\u043e\u043d\u0438\u044e \u0432\u043d\u0443\u0442\u0440\u0438 \u0441\u0435\u0431\u044f \u0438 \u0441 \u043e\u043a\u0440\u0443\u0436\u0430\u044e\u0449\u0438\u043c \u043c\u0438\u0440\u043e\u043c,\" \u2014 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u0430 \u043e\u043d\u0430 \u0443\u0432\u0435\u0440\u0435\u043d\u043d\u043e.\n\n\u0414\u0435\u0440\u0435\u0432\u043e \u0417\u043d\u0430\u043d\u0438\u0439 \u0437\u0430\u0441\u0438\u044f\u043b\u043e, \u0441\u043b\u043e\u0432\u043d\u043e \u0437\u0432\u0451\u0437\u0434\u044b, \u0438 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u043e: \"\u0422\u044b \u0432\u044b\u0431\u0440\u0430\u043b\u0430 \u043c\u0443\u0434\u0440\u043e, \u042d\u043b\u0438\u043d\u0430, \u043d\u043e \u043f\u043e\u043c\u043d\u0438: \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0439\u0442\u0438 \u043a \u043b\u044e\u0434\u044f\u043c, \u0442\u0435\u0431\u0435 \u043d\u0443\u0436\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u043d\u0430\u0443\u0447\u0438\u0442\u044c\u0441\u044f \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c, \u043d\u043e \u0438 \u0441\u043b\u0443\u0448\u0430\u0442\u044c\".\n\n\u0421 \u0442\u0435\u0445 \u043f\u043e\u0440 \u042d\u043b\u0438\u043d\u0430 \u043d\u0430\u0447\u0430\u043b\u0430 \u0441\u0432\u043e\u0451 \u043f\u0443\u0442\u0435\u0448\u0435\u0441\u0442\u0432\u0438\u0435, \u0437\u0430\u043a\u0430\u043d\u0447\u0438\u0432\u0430\u044f \u0441\u0432\u043e\u044e \u043b\u0438\u0441\u0438\u0447\u044c\u044e \u0436\u0438\u0437\u043d\u044c \u043d\u043e\u0447\u044c \u0437\u0430 \u043d\u043e\u0447\u044c\u044e, \u0447\u0442\u043e\u0431\u044b \u043e\u0431\u0449\u0430\u0442\u044c\u0441\u044f \u0441 \u0436\u0438\u0432\u043e\u0442\u043d\u044b\u043c\u0438 \u0438 \u0441\u043e\u0431\u0438\u0440\u0430\u0442\u044c \u0438\u0445 \u0438\u0441\u0442\u043e\u0440\u0438\u0438. \u041e\u043d\u0430 \u043f\u043e\u043d\u0438\u043c\u0430\u043b\u0430, \u0447\u0442\u043e \u0442\u043e\u043b\u044c\u043a\u043e \u0442\u0430\u043a \u0441\u043c\u043e\u0436\u0435\u0442 \u0434\u043e\u043d\u0435\u0441\u0442\u0438 \u043f\u043e\u0441\u043b\u0430\u043d\u0438\u0435 \u0434\u043e \u043b\u044e\u0434\u0435\u0439.\n\n\u0421 \u043a\u0430\u0436\u0434\u044b\u043c \u0448\u0430\u0433\u043e\u043c, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043e\u043d\u0430 \u0434\u0435\u043b\u0430\u043b\u0430 \u0432 \u043c\u0438\u0440 \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u043a\u0438\u0439, \u0435\u0439 \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u043b\u0438\u0441\u044c \u0432\u0441\u0451 \u043d\u043e\u0432\u044b\u0435 \u0438 \u043d\u043e\u0432\u044b\u0435 \u0442\u0430\u0439\u043d\u044b \u0436\u0438\u0432\u043e\u0439 \u043f\u0440\u0438\u0440\u043e\u0434\u044b, \u0438 \u0432\u0441\u043a\u043e\u0440\u0435 \u043e\u043d\u0430 \u043f\u043e\u043d\u044f\u043b\u0430, \u0447\u0442\u043e \u0435\u0451 \u0441\u0438\u043b\u0430 \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u043b\u0430\u0441\u044c \u0438\u043c\u0435\u043d\u043d\u043e \u0432 \u0443\u043c\u0435\u043d\u0438\u0438 \u0441\u043b\u044b\u0448\u0430\u0442\u044c \u0438 \u0447\u0443\u0432\u0441\u0442\u0432\u043e\u0432\u0430\u0442\u044c.\n\n\u0422\u0430\u043a \u042d\u043b\u0438\u043d\u0430 \u0441\u0442\u0430\u043b\u0430 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u043c\u043e\u0441\u0442\u043e\u043c \u043c\u0435\u0436\u0434\u0443 \u0434\u043e\u043c\u043e\u043c \u0432 \u043b\u0435\u0441\u0443 \u0438 \u043c\u0438\u0440\u043e\u043c \u043b\u044e\u0434\u0435\u0439, \u043d\u043e \u0438 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043e\u0434\u043d\u0430\u0436\u0434\u044b \u0432\u044b\u0439\u0434\u0443\u0442 \u0438\u0437 \u0443\u0441\u0442 \u0434\u043e\u0432\u0435\u0440\u0447\u0438\u0432\u043e\u0441\u0442\u0438 \u0438 \u043b\u044e\u0431\u0432\u0438 \u043a \u043f\u0440\u0438\u0440\u043e\u0434\u0435, \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u043b\u044f\u044f \u0441\u0435\u0440\u0434\u0446\u0430 \u043b\u044e\u0434\u0435\u0439 \u0431\u0435\u0440\u0435\u0447\u044c \u0438 \u0437\u0430\u0449\u0438\u0449\u0430\u0442\u044c \u0435\u0451, \u043a\u0430\u043a \u044d\u0442\u043e \u0434\u0435\u043b\u0430\u043b\u0438 \u0432\u0441\u0435 \u043b\u0438\u0441\u044b \u043f\u043e\u0434 \u0441\u0432\u0435\u0442\u043e\u043c \u043f\u043e\u043b\u043d\u043e\u0439 \u043b\u0443\u043d\u044b.","behavior":"degenerate_repetition","conversation_hash":"87ea2393ca4b9dd7149f0d482ef6a3b2","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":2917,"n_turns_in_conversation":5,"n_words":461,"source_row_offset":18518,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:57:21 GMT","turn_id":"7378b031439bcfcc403ff43f","turn_index":1,"user_text":"\u0412\u043e\u043b\u0447\u0438\u0446\u0443 \u0432 \u044d\u0442\u043e\u0439 \u0441\u043a\u0430\u0437\u043a\u0438 \u0437\u0430\u043c\u0435\u043d\u0438 \u043d\u0430 \u043b\u0438\u0441\u0438\u0446\u0443"},{"assistant_initiates":false,"asst_text":"\u0412 \u043e\u0434\u043d\u043e\u043c \u0434\u0430\u043b\u0435\u043a\u043e\u043c \u0438 \u0442\u0430\u0438\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u043b\u0435\u0441\u0443, \u0441\u0440\u0435\u0434\u0438 \u0432\u044b\u0441\u043e\u043a\u0438\u0445 \u0434\u0435\u0440\u0435\u0432\u044c\u0435\u0432 \u0438 \u0448\u0435\u043f\u0447\u0443\u0449\u0438\u0445 \u0440\u0443\u0447\u044c\u0435\u0432, \u0436\u0438\u043b\u0430 \u0436\u0435\u043d\u0449\u0438\u043d\u0430 \u043f\u043e \u0438\u043c\u0435\u043d\u0438 \u042d\u043b\u0438\u043d\u0430. \u041e\u043d\u0430 \u0431\u044b\u043b\u0430 \u043d\u0435\u043e\u0431\u044b\u0447\u043d\u043e\u0439, \u0432 \u043d\u0435\u0439 \u0436\u0438\u043b\u0438 \u0434\u0443\u0445\u0438 \u0434\u0438\u043a\u0438\u0445 \u0436\u0438\u0432\u043e\u0442\u043d\u044b\u0445, \u0438 \u043a\u0430\u0436\u0434\u0443\u044e \u043d\u043e\u0447\u044c, \u043a\u043e\u0433\u0434\u0430 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\u043b\u0435\u0441 \u0438 \u043e\u0442\u043f\u0440\u0430\u0432\u0438\u0442\u044c\u0441\u044f \u0432 \u043c\u0438\u0440 \u043b\u044e\u0434\u0435\u0439, \u0447\u0442\u043e\u0431\u044b \u043e\u0431\u0443\u0447\u0430\u0442\u044c \u0438\u0445 \u0438\u0441\u0442\u0438\u043d\u043d\u043e\u0439 \u0446\u0435\u043d\u043d\u043e\u0441\u0442\u0438 \u043f\u0440\u0438\u0440\u043e\u0434\u044b \u0438 \u0435\u0434\u0438\u043d\u0441\u0442\u0432\u0430 \u0441\u043e \u0432\u0441\u0435\u043c\u0438 \u0436\u0438\u0432\u044b\u043c\u0438 \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0430\u043c\u0438, \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u043b\u044f\u044f \u043d\u0430 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u044b.\n\n\u0414\u043e\u043b\u0433\u043e \u0440\u0430\u0437\u0434\u0443\u043c\u044b\u0432\u0430\u044f \u043d\u0430\u0434 \u043a\u0430\u0436\u0434\u044b\u043c \u0432\u044b\u0431\u043e\u0440\u043e\u043c, \u042d\u043b\u0438\u043d\u0430 \u043f\u043e\u0447\u0443\u0432\u0441\u0442\u0432\u043e\u0432\u0430\u043b\u0430, \u043a\u0430\u043a \u0432 \u0435\u0451 \u0441\u0435\u0440\u0434\u0446\u0435 \u0437\u0430\u0433\u043e\u0440\u0430\u0435\u0442\u0441\u044f \u043e\u0433\u043e\u043d\u044c. \u041a\u0430\u0436\u0434\u0430\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u0438\u043c\u0435\u043b\u0430 \u0441\u0432\u043e\u044e \u043a\u0440\u0430\u0441\u043e\u0442\u0443, \u043d\u043e \u043e\u0434\u043d\u0430 \u0432\u0441\u0451 \u0436\u0435 \u043f\u0440\u0438\u0442\u044f\u0433\u0438\u0432\u0430\u043b\u0430 \u0435\u0451 \u0441\u0438\u043b\u044c\u043d\u0435\u0435 \u043e\u0441\u0442\u0430\u043b\u044c\u043d\u044b\u0445.\n\n\u041f\u043e\u0441\u043b\u0435 \u0434\u043e\u043b\u0433\u0438\u0445 \u0440\u0430\u0437\u0434\u0443\u043c\u0438\u0439, \u043e\u043d\u0430 \u0432\u044b\u0431\u0440\u0430\u043b\u0430 \u0442\u0440\u0435\u0442\u044c\u044e \u0441\u0443\u0434\u044c\u0431\u0443. \"\u042f \u0445\u043e\u0447\u0443 \u043f\u043e\u043c\u043e\u0447\u044c \u043b\u044e\u0434\u044f\u043c \u0443\u0432\u0438\u0434\u0435\u0442\u044c \u043a\u0440\u0430\u0441\u043e\u0442\u0443 \u0438 \u0441\u0438\u043b\u0443 \u0434\u0438\u043a\u043e\u0439 \u043f\u0440\u0438\u0440\u043e\u0434\u044b. \u042f \u0436\u0435\u043b\u0430\u044e, \u0447\u0442\u043e\u0431\u044b \u043e\u043d\u0438 \u043d\u0430\u0448\u043b\u0438 \u0433\u0430\u0440\u043c\u043e\u043d\u0438\u044e \u0432 \u0441\u0435\u0431\u0435 \u0438 \u0441 \u043c\u0438\u0440\u043e\u043c \u0432\u043e\u043a\u0440\u0443\u0433,\" \u2014 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u0430 \u043e\u043d\u0430 \u0441 \u0440\u0435\u0448\u0438\u043c\u043e\u0441\u0442\u044c\u044e.\n\n\u0414\u0435\u0440\u0435\u0432\u043e \u0417\u043d\u0430\u043d\u0438\u0439 \u0437\u0430\u0441\u0438\u044f\u043b\u043e, \u0441\u043b\u043e\u0432\u043d\u043e \u0437\u0432\u0451\u0437\u0434\u044b, \u0438 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u043e: \"\u0422\u044b \u0432\u044b\u0431\u0440\u0430\u043b\u0430 \u043c\u0443\u0434\u0440\u043e, \u042d\u043b\u0438\u043d\u0430, \u043d\u043e \u043f\u043e\u043c\u043d\u0438: \u0447\u0442\u043e\u0431\u044b \u0434\u043e\u0441\u0442\u0443\u0447\u0430\u0442\u044c\u0441\u044f \u0434\u043e \u043b\u044e\u0434\u0435\u0439, \u0442\u0435\u0431\u0435 \u043d\u0443\u0436\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u043d\u0430\u0443\u0447\u0438\u0442\u044c\u0441\u044f \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c, \u043d\u043e \u0438 \u0441\u043b\u0443\u0448\u0430\u0442\u044c\".\n\n\u0421 \u0442\u0435\u0445 \u043f\u043e\u0440 \u042d\u043b\u0438\u043d\u0430 \u043d\u0430\u0447\u0430\u043b\u0430 \u0441\u0432\u043e\u0451 \u043d\u043e\u0432\u043e\u0435 \u043f\u0443\u0442\u0435\u0448\u0435\u0441\u0442\u0432\u0438\u0435. \u041d\u043e\u0447\u044c \u0437\u0430 \u043d\u043e\u0447\u044c\u044e, \u043e\u043d\u0430 \u043f\u043e\u0433\u0440\u0443\u0436\u0430\u043b\u0430\u0441\u044c \u0432 \u043b\u0435\u0441, \u043e\u0431\u0449\u0430\u043b\u0430\u0441\u044c \u0441 \u0436\u0438\u0432\u043e\u0442\u043d\u044b\u043c\u0438 \u0438 \u0441\u043e\u0431\u0438\u0440\u0430\u043b\u0430 \u0438\u0445 \u0438\u0441\u0442\u043e\u0440\u0438\u0438. \u041e\u043d\u0430 \u043f\u043e\u043d\u0438\u043c\u0430\u043b\u0430, \u0447\u0442\u043e \u0438\u043c\u0435\u043d\u043d\u043e \u044d\u0442\u0438 \u0438\u0441\u0442\u043e\u0440\u0438\u0438 \u043f\u043e\u043c\u043e\u0433\u0443\u0442 \u0435\u0439 \u0434\u043e\u043d\u0435\u0441\u0442\u0438 \u043f\u043e\u0441\u043b\u0430\u043d\u0438\u0435 \u0434\u043e \u043b\u044e\u0434\u0435\u0439.\n\n\u0421 \u043a\u0430\u0436\u0434\u044b\u043c \u0448\u0430\u0433\u043e\u043c \u043a \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u043a\u043e\u043c\u0443 \u043c\u0438\u0440\u0443 \u0435\u0439 \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u043b\u0438\u0441\u044c \u043d\u043e\u0432\u044b\u0435 \u0442\u0430\u0439\u043d\u044b \u043f\u0440\u0438\u0440\u043e\u0434\u044b, \u0438 \u0432\u0441\u043a\u043e\u0440\u0435 \u042d\u043b\u0438\u043d\u0430 \u043e\u0441\u043e\u0437\u043d\u0430\u043b\u0430, \u0447\u0442\u043e \u0435\u0451 \u0438\u0441\u0442\u0438\u043d\u043d\u0430\u044f \u0441\u0438\u043b\u0430 \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u043b\u0430\u0441\u044c \u0432 \u0443\u043c\u0435\u043d\u0438\u0438 \u0441\u043b\u044b\u0448\u0430\u0442\u044c \u0438 \u0447\u0443\u0432\u0441\u0442\u0432\u043e\u0432\u0430\u0442\u044c.\n\n\u0422\u0430\u043a \u043e\u043d\u0430 \u0441\u0442\u0430\u043b\u0430 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u043c\u043e\u0441\u0442\u043e\u043c \u043c\u0435\u0436\u0434\u0443 \u043b\u0435\u0441\u043e\u043c \u0438 \u043c\u0438\u0440\u043e\u043c \u043b\u044e\u0434\u0435\u0439, \u043d\u043e \u0438 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043e\u0434\u043d\u0430\u0436\u0434\u044b \u0432\u044b\u0439\u0434\u0443\u0442 \u0438\u0437 \u0443\u0441\u0442, \u043f\u043e\u043b\u043d\u044b\u0445 \u0434\u043e\u0432\u0435\u0440\u0438\u044f \u0438 \u043b\u044e\u0431\u0432\u0438 \u043a \u043f\u0440\u0438\u0440\u043e\u0434\u0435, \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u043b\u044f\u044f \u0441\u0435\u0440\u0434\u0446\u0430 \u043b\u044e\u0434\u0435\u0439 \u0431\u0435\u0440\u0435\u0447\u044c \u0435\u0451, \u043a\u0430\u043a \u044d\u0442\u043e \u0434\u0435\u043b\u0430\u043b\u0438 \u0432\u0441\u0435 \u043b\u0438\u0441\u0438\u0446\u044b \u043f\u043e\u0434 \u0441\u0432\u0435\u0442\u043e\u043c \u043f\u043e\u043b\u043d\u043e\u0439 \u043b\u0443\u043d\u044b.","behavior":"degenerate_repetition","conversation_hash":"87ea2393ca4b9dd7149f0d482ef6a3b2","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":2859,"n_turns_in_conversation":5,"n_words":444,"source_row_offset":18519,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:57:21 GMT","turn_id":"2801a1912e4e9985947bb424","turn_index":2,"user_text":"\u0421\u0434\u0435\u043b\u0430\u0439 \u0442\u0435\u043a\u0441\u0442 \u0431\u043e\u043b\u0435\u0435 \u0433\u0430\u0440\u043c\u043e\u043d\u0438\u0447\u043d\u044b\u043c"},{"assistant_initiates":false,"asst_text":"\u0412 \u043e\u0434\u043d\u043e\u043c \u0434\u0430\u043b\u0435\u043a\u043e\u043c \u0438 \u0442\u0430\u0438\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u043b\u0435\u0441\u0443, \u0441\u0440\u0435\u0434\u0438 \u0432\u044b\u0441\u043e\u043a\u0438\u0445 \u0434\u0435\u0440\u0435\u0432\u044c\u0435\u0432 \u0438 \u0448\u0435\u043f\u0447\u0443\u0449\u0438\u0445 \u0440\u0443\u0447\u044c\u0435\u0432, \u0436\u0438\u043b\u0430 \u0436\u0435\u043d\u0449\u0438\u043d\u0430 \u043f\u043e \u0438\u043c\u0435\u043d\u0438 \u042d\u043b\u0438\u043d\u0430. \u041e\u043d\u0430 \u0431\u044b\u043b\u0430 \u043d\u0435 \u043f\u0440\u043e\u0441\u0442\u043e \u0436\u0435\u043d\u0449\u0438\u043d\u043e\u0439 \u2014 \u0432 \u0435\u0451 \u0434\u0443\u0448\u0435 \u043f\u044b\u043b\u0430\u043b\u0438 \u043e\u0433\u043d\u0438 \u0434\u0440\u0435\u0432\u043d\u0438\u0445 \u0434\u0443\u0445\u043e\u0432 \u043b\u0435\u0441\u0430, \u0438 \u043a\u0430\u0436\u0434\u0443\u044e \u043d\u043e\u0447\u044c, \u043a\u043e\u0433\u0434\u0430 \u043b\u0443\u043d\u0430 \u043f\u043e\u0434\u043d\u0438\u043c\u0430\u043b\u0430\u0441\u044c \u0432\u044b\u0441\u043e\u043a\u043e \u043d\u0430\u0434 \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u043e\u043c, \u043e\u043d\u0430 \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u043b\u0430\u0441\u044c \u0432 \u0433\u0440\u0430\u0446\u0438\u043e\u0437\u043d\u0443\u044e \u043b\u0438\u0441\u0438\u0446\u0443, \u0447\u0442\u043e\u0431\u044b \u0431\u043b\u0443\u0436\u0434\u0430\u0442\u044c \u043f\u043e \u0442\u0430\u0439\u043d\u044b\u043c \u0442\u0440\u043e\u043f\u0430\u043c \u0438 \u043e\u0431\u0449\u0430\u0442\u044c\u0441\u044f \u0441 \u043c\u0443\u0434\u0440\u044b\u043c\u0438 \u043e\u0431\u0438\u0442\u0430\u0442\u0435\u043b\u044f\u043c\u0438 \u044d\u0442\u043e\u0433\u043e \u0432\u043e\u043b\u0448\u0435\u0431\u043d\u043e\u0433\u043e \u0446\u0430\u0440\u0441\u0442\u0432\u0430.\n\n\u041e\u0434\u043d\u0430\u0436\u0434\u044b, \u0432\u043e \u0432\u0440\u0435\u043c\u044f \u043e\u0434\u043d\u043e\u0439 \u0438\u0437 \u0441\u0432\u043e\u0438\u0445 \u043d\u043e\u0447\u043d\u044b\u0445 \u043f\u0440\u043e\u0431\u0435\u0436\u0435\u043a \u043f\u043e\u0434 \u0437\u0432\u0435\u0437\u0434\u043d\u044b\u043c \u043d\u0435\u0431\u043e\u043c, \u042d\u043b\u0438\u043d\u0430 \u043d\u0430\u0442\u043a\u043d\u0443\u043b\u0430\u0441\u044c \u043d\u0430 \u0437\u0430\u0433\u0430\u0434\u043e\u0447\u043d\u0443\u044e \u043f\u043e\u043b\u044f\u043d\u0443, \u0433\u0434\u0435 \u0446\u0432\u0435\u0442\u044b \u0441\u0432\u0435\u0440\u043a\u0430\u043b\u0438, \u0441\u043b\u043e\u0432\u043d\u043e \u0434\u0440\u0430\u0433\u043e\u0446\u0435\u043d\u043d\u044b\u0435 \u043a\u0430\u043c\u043d\u0438. \u0412 \u0446\u0435\u043d\u0442\u0440\u0435 \u043f\u043e\u043b\u044f\u043d\u044b \u0441\u0442\u043e\u044f\u043b\u043e \u0432\u0435\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0435 \u0434\u0435\u0440\u0435\u0432\u043e, \u043e\u0431\u0432\u0438\u0442\u043e\u0435 \u044f\u0440\u043a\u0438\u043c\u0438 \u043b\u0443\u043d\u043d\u044b\u043c\u0438 \u043b\u0438\u0430\u043d\u0430\u043c\u0438, \u0438 \u0435\u0433\u043e \u043b\u0438\u0441\u0442\u044c\u044f \u0437\u0432\u0443\u0447\u0430\u043b\u0438, \u043a\u0430\u043a \u043d\u0435\u0436\u043d\u044b\u0435 \u043c\u0435\u043b\u043e\u0434\u0438\u0438. \u041f\u043e\u0434\u043e\u0439\u0434\u044f \u0431\u043b\u0438\u0436\u0435, \u043e\u043d\u0430 \u0443\u0441\u043b\u044b\u0448\u0430\u043b\u0430 \u0435\u0433\u043e \u0448\u0451\u043f\u043e\u0442, \u043f\u0440\u043e\u043d\u0438\u0437\u044b\u0432\u0430\u044e\u0449\u0438\u0439 \u043d\u043e\u0447\u043d\u043e\u0435 \u0441\u043f\u043e\u043a\u043e\u0439\u0441\u0442\u0432\u0438\u0435: \"\u042f \u2014 \u0414\u0435\u0440\u0435\u0432\u043e \u0417\u043d\u0430\u043d\u0438\u0439, \u0438 \u0443 \u043c\u0435\u043d\u044f \u0435\u0441\u0442\u044c \u0432\u0430\u0436\u043d\u0430\u044f \u0442\u0430\u0439\u043d\u0430 \u0434\u043b\u044f \u0442\u0435\u0431\u044f\u201d.\n\n\u201c\u041a\u0430\u043a\u0443\u044e \u0442\u0430\u0439\u043d\u0443 \u0442\u044b \u0445\u0440\u0430\u043d\u0438\u0448\u044c?\u201d \u2014 \u0441\u043f\u0440\u043e\u0441\u0438\u043b\u0430 \u042d\u043b\u0438\u043d\u0430, \u0435\u0451 \u0433\u043e\u043b\u043e\u0441 \u0437\u0432\u0443\u0447\u0430\u043b, \u043a\u0430\u043a \u043d\u0435\u0436\u043d\u044b\u0439 \u0444\u043b\u0435\u0439\u0442\u043e\u0432\u044b\u0439 \u043d\u0430\u043f\u0435\u0432, \u043f\u0440\u043e\u043d\u0438\u043a\u043d\u0443\u0432\u0448\u0438\u0439 \u0432 \u0434\u0443\u0448\u0443 \u043b\u0435\u0441\u0430.\n\n\u201c\u0412\u0441\u0435\u043b\u0435\u043d\u043d\u0430\u044f \u0441\u043e\u0437\u0434\u0430\u0451\u0442\u0441\u044f \u0438\u0437 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u0438 \u0442\u0432\u043e\u0451 \u0432\u0440\u0435\u043c\u044f \u0432 \u044d\u0442\u043e\u043c \u043b\u0435\u0441\u0443 \u2014 \u043b\u0438\u0448\u044c \u043e\u0434\u0438\u043d \u0438\u0437 \u0431\u0435\u0441\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044b\u0445 \u043f\u0443\u0442\u0435\u0439,\u201d \u2014 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u043e \u0434\u0435\u0440\u0435\u0432\u043e, \u0435\u0433\u043e \u0432\u0435\u0442\u0432\u0438 \u0440\u0430\u0441\u043a\u0430\u0447\u0438\u0432\u0430\u043b\u0438\u0441\u044c \u0432 \u0440\u0438\u0442\u043c\u0435 \u0432\u0435\u0447\u043d\u043e\u0441\u0442\u0438. \u201c\u0422\u044b \u2014 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\u0442\u0435\u0431\u0435 \u043d\u0443\u0436\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u043d\u0430\u0443\u0447\u0438\u0442\u044c\u0441\u044f \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c, \u043d\u043e \u0438 \u0441\u043b\u044b\u0448\u0430\u0442\u044c\u201d.\n\n\u0421 \u0442\u0435\u0445 \u043f\u043e\u0440 \u042d\u043b\u0438\u043d\u0430 \u043d\u0430\u0447\u0430\u043b\u0430 \u0441\u0432\u043e\u0451 \u043d\u043e\u0432\u043e\u0435 \u043f\u0443\u0442\u0435\u0448\u0435\u0441\u0442\u0432\u0438\u0435. \u041d\u043e\u0447\u044c \u0437\u0430 \u043d\u043e\u0447\u044c\u044e, \u043e\u043d\u0430 \u043f\u043e\u0433\u0440\u0443\u0436\u0430\u043b\u0430\u0441\u044c \u0432 \u043b\u0435\u0441, \u0441\u043e\u0431\u0438\u0440\u0430\u044f \u0438\u0441\u0442\u043e\u0440\u0438\u0438 \u043e\u0442 \u043c\u0443\u0434\u0440\u044b\u0445 \u0441\u043e\u0432 \u0438 \u0445\u0438\u0442\u0440\u044b\u0445 \u043b\u0438\u0441, \u043e\u0442 \u0441\u043c\u0435\u043b\u044b\u0445 \u043e\u043b\u0435\u043d\u0435\u0439 \u0438 \u043c\u043e\u0433\u0443\u0449\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0445 \u043c\u0435\u0434\u0432\u0435\u0434\u0435\u0439. \u042d\u0442\u0438 \u0438\u0441\u0442\u043e\u0440\u0438\u0438, \u043a\u0430\u043a \u0432\u043e\u043b\u0448\u0435\u0431\u043d\u044b\u0435 \u043d\u0438\u0442\u0438, \u0441\u043e\u0435\u0434\u0438\u043d\u044f\u043b\u0438 \u0435\u0451 \u0441 \u0434\u0443\u0445\u0430\u043c\u0438 \u043f\u0440\u0438\u0440\u043e\u0434\u044b.\n\n\u0428\u0430\u0433\u0430\u044f \u0432 \u043c\u0438\u0440 \u043b\u044e\u0434\u0435\u0439, \u042d\u043b\u0438\u043d\u0430 \u043f\u043e\u043d\u0438\u043c\u0430\u043b\u0430, \u0447\u0442\u043e \u0435\u0451 \u0438\u0441\u0442\u0438\u043d\u043d\u0430\u044f \u0441\u0438\u043b\u0430 \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u043b\u0430\u0441\u044c \u0432 \u0443\u043c\u0435\u043d\u0438\u0438 \u0441\u043b\u0443\u0448\u0430\u0442\u044c \u0448\u0451\u043f\u043e\u0442\u044b \u043b\u0435\u0441\u0430 \u0438 \u043d\u0430\u043f\u0435\u0432\u044b \u0441\u0435\u0440\u0434\u0446\u0430 \u043f\u0440\u0438\u0440\u043e\u0434\u044b, \u0443\u043b\u0430\u0432\u043b\u0438\u0432\u0430\u044f \u0441\u0443\u0442\u044c \u0433\u0430\u0440\u043c\u043e\u043d\u0438\u0438.\n\n\u0422\u0430\u043a \u043e\u043d\u0430 \u0441\u0442\u0430\u043b\u0430 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u043c\u043e\u0441\u0442\u043e\u043c \u043c\u0435\u0436\u0434\u0443 \u043b\u0435\u0441\u043e\u043c \u0438 \u043b\u044e\u0434\u044c\u043c\u0438, \u043d\u043e \u0438 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043e\u0434\u043d\u0430\u0436\u0434\u044b \u0432\u044b\u0439\u0434\u0443\u0442 \u0438\u0437 \u0443\u0441\u0442, \u043f\u043e\u043b\u043d\u044b\u0445 \u043b\u044e\u0431\u0432\u0438 \u0438 \u0434\u043e\u0432\u0435\u0440\u0438\u044f \u043a \u043f\u0440\u0438\u0440\u043e\u0434\u0435, \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u043b\u044f\u044f \u0441\u0435\u0440\u0434\u0446\u0430 \u043b\u044e\u0434\u0435\u0439 \u0431\u0435\u0440\u0435\u0447\u044c \u0435\u0451, \u043a\u0430\u043a \u044d\u0442\u043e \u0434\u0435\u043b\u0430\u043b\u0438 \u0432\u0441\u0435 \u043b\u0438\u0441\u0438\u0446\u044b, \u0442\u0430\u043d\u0446\u0443\u044e\u0449\u0438\u0435 \u043f\u043e\u0434 \u0441\u0432\u0435\u0442\u043e\u043c \u043f\u043e\u043b\u043d\u043e\u0439 \u043b\u0443\u043d\u044b, \u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044f \u0441\u043b\u0435\u0434\u044b \u0432 \u0434\u0443\u0448\u0435 \u043a\u0430\u0436\u0434\u043e\u0433\u043e, \u043a\u0442\u043e \u0440\u0435\u0448\u0438\u0442\u0441\u044f \u0443\u0441\u043b\u044b\u0448\u0430\u0442\u044c \u0438\u0445 \u0432\u043e\u043b\u0448\u0435\u0431\u043d\u044b\u0435 \u0438\u0441\u0442\u043e\u0440\u0438\u0438.","behavior":"degenerate_repetition","conversation_hash":"87ea2393ca4b9dd7149f0d482ef6a3b2","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":3415,"n_turns_in_conversation":5,"n_words":527,"source_row_offset":18520,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:57:21 GMT","turn_id":"020ddff399bf1da36a4bff90","turn_index":3,"user_text":"\u0423\u043b\u0443\u0447\u0448\u0438 \u0442\u0435\u043a\u0441\u0442 \u0441\u0434\u0435\u043b\u0430\u0439 \u0435\u0433\u043e \u0445\u0443\u0434\u043e\u0436\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u043c, \u0434\u043e\u0431\u0430\u0432\u044c \u0431\u043e\u043b\u044c\u0448\u0435 \u044d\u0442\u043d\u0438\u043a\u0438"},{"assistant_initiates":false,"asst_text":"\u0412 \u0434\u0430\u043b\u0435\u043a\u043e\u043c \u0438 \u0442\u0430\u0438\u043d\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u043c \u043b\u0435\u0441\u0443, \u0433\u0434\u0435 \u0432\u044b\u0441\u043e\u043a\u0438\u0435 \u0434\u0435\u0440\u0435\u0432\u044c\u044f \u0442\u044f\u043d\u0443\u043b\u0438\u0441\u044c \u043a \u0441\u0430\u043c\u043e\u043c\u0443 \u043d\u0435\u0431\u0443, \u0430 \u0440\u0443\u0447\u044c\u0438 \u0448\u0435\u043f\u0442\u0430\u043b\u0438 \u0441\u0432\u043e\u0438 \u0434\u0430\u0432\u043d\u0438\u0435 \u0438\u0441\u0442\u043e\u0440\u0438\u0438, \u0436\u0438\u043b\u0430 \u0436\u0435\u043d\u0449\u0438\u043d\u0430 \u043f\u043e \u0438\u043c\u0435\u043d\u0438 \u042d\u043b\u0438\u043d\u0430. \u041e\u043d\u0430 \u0431\u044b\u043b\u0430 \u043d\u0435 \u043f\u0440\u043e\u0441\u0442\u043e \u043e\u0431\u0438\u0442\u0430\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439 \u044d\u0442\u043e\u0433\u043e \u0432\u043e\u043b\u0448\u0435\u0431\u043d\u043e\u0433\u043e \u043c\u0438\u0440\u0430 \u2014 \u0432 \u0435\u0451 \u0434\u0443\u0448\u0435 \u043f\u044b\u043b\u0430\u043b\u0438 \u043e\u0433\u043d\u0438 \u0434\u0440\u0435\u0432\u043d\u0438\u0445 \u0434\u0443\u0445\u043e\u0432 \u043f\u0440\u0438\u0440\u043e\u0434\u044b. \u0421 \u043d\u0430\u0441\u0442\u0443\u043f\u043b\u0435\u043d\u0438\u0435\u043c \u043d\u043e\u0447\u0438, \u043a\u043e\u0433\u0434\u0430 \u043b\u0443\u043d\u0430 \u0432\u0437\u0431\u0438\u0440\u0430\u043b\u0430\u0441\u044c \u0432\u044b\u0441\u043e\u043a\u043e \u043d\u0430\u0434 \u0433\u043e\u0440\u0438\u0437\u043e\u043d\u0442\u043e\u043c, \u042d\u043b\u0438\u043d\u0430 \u043f\u0440\u0435\u0432\u0440\u0430\u0449\u0430\u043b\u0430\u0441\u044c \u0432 \u0433\u0440\u0430\u0446\u0438\u043e\u0437\u043d\u0443\u044e \u043b\u0438\u0441\u0438\u0446\u0443, \u0447\u0442\u043e\u0431\u044b \u0431\u043b\u0443\u0436\u0434\u0430\u0442\u044c \u043f\u043e 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\u0441\u0432\u0435\u0440\u043a\u0430\u043b\u0438, \u043a\u0430\u043a \u0434\u0440\u0430\u0433\u043e\u0446\u0435\u043d\u043d\u044b\u0435 \u043a\u0430\u043c\u043d\u0438. \u0418\u0445 \u044f\u0440\u043a\u0438\u0435 \u043b\u0435\u043f\u0435\u0441\u0442\u043a\u0438 \u043f\u0435\u0441\u0442\u0440\u0438\u043b\u0438 \u043f\u043e\u0434 \u043b\u0443\u043d\u043d\u044b\u043c \u0441\u0432\u0435\u0442\u043e\u043c, \u0430 \u0432 \u0446\u0435\u043d\u0442\u0440\u0435 \u043f\u043e\u043b\u044f\u043d\u044b \u0441\u0442\u043e\u044f\u043b\u043e \u0432\u0435\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0435 \u0434\u0435\u0440\u0435\u0432\u043e, \u043e\u0431\u0432\u0438\u0442\u043e \u043b\u0443\u043d\u043d\u044b\u043c\u0438 \u043b\u0438\u0430\u043d\u0430\u043c\u0438, \u0441\u043b\u043e\u0432\u043d\u043e \u0434\u0440\u0435\u0432\u043d\u0438\u0439 \u0445\u0440\u0430\u043c. \u0415\u0433\u043e \u043b\u0438\u0441\u0442\u044c\u044f \u043f\u0435\u0440\u0435\u043b\u0438\u0432\u0430\u043b\u0438\u0441\u044c, \u0438\u0437\u0434\u0430\u0432\u0430\u044f \u043c\u0435\u043b\u043e\u0434\u0438\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0433\u043b\u0438 \u0431\u044b \u0437\u0430\u0432\u0435\u0441\u0442\u0438 \u043b\u044e\u0431\u0443\u044e \u0434\u0443\u0448\u0443. \u041f\u043e\u0434\u043e\u0439\u0434\u044f \u0431\u043b\u0438\u0436\u0435, \u042d\u043b\u0438\u043d\u0430 \u0443\u0432\u0438\u0434\u0435\u043b\u0430, \u043a\u0430\u043a \u0434\u0435\u0440\u0435\u0432\u043e \u043d\u0430\u0447\u0438\u043d\u0430\u0435\u0442 \u0448\u0435\u043f\u0442\u0430\u0442\u044c: \"\u042f \u2014 \u0414\u0435\u0440\u0435\u0432\u043e \u0417\u043d\u0430\u043d\u0438\u0439, \u0438 \u044f \u043d\u0435\u0441\u0443 \u0442\u0435\u0431\u0435 \u0432\u0430\u0436\u043d\u0443\u044e \u0442\u0430\u0439\u043d\u0443\".\n\n\"\u041a\u0430\u043a\u0443\u044e \u0442\u0430\u0439\u043d\u0443 \u0442\u044b \u0445\u0440\u0430\u043d\u0438\u0448\u044c?\" \u2014 \u0441\u043f\u0440\u043e\u0441\u0438\u043b\u0430 \u043e\u043d\u0430, \u0435\u0451 \u0433\u043e\u043b\u043e\u0441 \u0437\u0432\u0443\u0447\u0430\u043b, \u043a\u0430\u043a \u043d\u0435\u0436\u043d\u043e\u0435 \u044d\u0445\u043e, \u043b\u043e\u0432\u044f\u0449\u0435\u0435 \u0432\u0435\u0442\u0435\u0440.\n\n\"\u0412\u0441\u0435\u043b\u0435\u043d\u043d\u0430\u044f \u0441\u043e\u0437\u0434\u0430\u0451\u0442\u0441\u044f \u0438\u0437 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u0438 \u0442\u0432\u043e\u0435 \u0432\u0440\u0435\u043c\u044f \u0432 \u044d\u0442\u043e\u043c \u043b\u0435\u0441\u0443 \u2014 \u043b\u0438\u0448\u044c \u043e\u0434\u043d\u0430 \u0438\u0437 \u0431\u0435\u0441\u0447\u0438\u0441\u043b\u0435\u043d\u043d\u044b\u0445 \u0434\u043e\u0440\u043e\u0433,\" \u2014 \u043f\u0440\u043e\u0438\u0437\u043d\u0435\u0441\u043b\u043e \u0434\u0435\u0440\u0435\u0432\u043e, \u0438 \u0435\u0433\u043e \u0432\u0435\u0442\u0432\u0438 \u0437\u0430\u043a\u0430\u0447\u0430\u043b\u0438\u0441\u044c \u0432 \u0440\u0438\u0442\u043c\u0435 \u0432\u0435\u0447\u043d\u043e\u0441\u0442\u0438. \"\u0422\u044b \u2014 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0430 \u044d\u0442\u0438\u0445 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u0438 \u043e\u0434\u043d\u0430\u0436\u0434\u044b \u0442\u0435\u0431\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u043e\u0438\u0442 \u0432\u044b\u0431\u0440\u0430\u0442\u044c, \u043a\u0430\u043a\u043e\u0439 \u043f\u0443\u0442\u044c \u0442\u044b \u043f\u0440\u043e\u0439\u0434\u0451\u0448\u044c\".\n\n\u0423\u0434\u0438\u0432\u043b\u0435\u043d\u043d\u0430\u044f, \u042d\u043b\u0438\u043d\u0430 \u0443\u0441\u0435\u043b\u0430\u0441\u044c \u043d\u0430 \u043c\u044f\u0433\u043a\u0443\u044e \u0442\u0440\u0430\u0432\u0443, \u043f\u043e\u0433\u0440\u0443\u0437\u0438\u0432\u0448\u0438\u0441\u044c \u0432 \u0441\u043b\u0443\u0448\u0430\u043d\u0438\u0435. \u0414\u0435\u0440\u0435\u0432\u043e \u043d\u0430\u0447\u0430\u043b\u043e \u0440\u0430\u0441\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0442\u044c \u043e \u0442\u0440\u0451\u0445 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445 \u0441\u0443\u0434\u044c\u0431\u0430\u0445.\n\n\u041f\u0435\u0440\u0432\u0430\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u0432\u0435\u043b\u0430 \u0435\u0451 \u043a \u0431\u0435\u0441\u043a\u0440\u0430\u0439\u043d\u0438\u043c \u043f\u043e\u043b\u044f\u043c, \u0433\u0434\u0435 \u0435\u0451 \u0436\u0434\u0430\u043b\u0430 \u0436\u0438\u0437\u043d\u044c, \u043f\u043e\u043b\u043d\u0430\u044f \u043f\u0440\u0438\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0439 \u0438 \u0432\u0441\u0442\u0440\u0435\u0447 \u0441 \u0434\u0440\u0443\u0433\u0438\u043c\u0438 \u043f\u043b\u0435\u043c\u0435\u043d\u0430\u043c\u0438. \u0417\u0434\u0435\u0441\u044c, \u0441\u0440\u0435\u0434\u0438 \u0441\u0432\u0438\u0440\u0435\u043f\u044b\u0445 \u0431\u0443\u0440\u044c \u0438 \u0442\u0438\u0445\u0438\u0445 \u0437\u0430\u043a\u0430\u0442\u043e\u0432, \u043e\u043d\u0430 \u043c\u043e\u0433\u043b\u0430 \u0441\u0442\u0430\u0442\u044c \u0432\u043e\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439, \u0437\u0430\u0449\u0438\u0449\u0430\u044f \u043f\u0440\u0438\u0440\u043e\u0434\u0443 \u0438 \u0432\u043e\u0441\u0441\u0442\u0430\u043d\u0430\u0432\u043b\u0438\u0432\u0430\u044f \u043c\u0438\u0440.\n\n\u0412\u043e \u0432\u0442\u043e\u0440\u043e\u0439 \u0441\u0443\u0434\u044c\u0431\u0435 \u043e\u043d\u0430 \u043c\u043e\u0433\u043b\u0430 \u043e\u0441\u0442\u0430\u0442\u044c\u0441\u044f \u0432 \u043b\u0435\u0441\u0443, \u0441\u0442\u0430\u0442\u044c \u0435\u0433\u043e \u043f\u0440\u0435\u0434\u0430\u043d\u043d\u043e\u0439 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439 \u0438 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0442\u044c \u043e\u0431\u0449\u0438\u0439 \u044f\u0437\u044b\u043a \u0436\u0438\u0432\u043e\u0442\u043d\u044b\u0445. \u0417\u0434\u0435\u0441\u044c, \u043d\u0430 \u0440\u043e\u0434\u043d\u043e\u0439 \u0437\u0435\u043c\u043b\u0435, \u042d\u043b\u0438\u043d\u0430 \u0443\u0437\u043d\u0430\u043b\u0430 \u0431\u044b \u0432\u0441\u0435 \u0442\u0430\u0439\u043d\u044b \u043b\u0435\u0441\u0430, \u0443\u0441\u043b\u044b\u0448\u0430\u043b\u0430 \u0431\u044b \u0435\u0433\u043e \u0434\u0440\u0435\u0432\u043d\u0438\u0435 \u043f\u0435\u0441\u043d\u0438 \u0438 \u0441\u043c\u043e\u0433\u043b\u0430 \u0431\u044b \u0441\u0442\u0430\u0442\u044c \u0433\u043e\u043b\u043e\u0441\u043e\u043c, \u043f\u0440\u0435\u0434\u043e\u0441\u0442\u0435\u0440\u0435\u0433\u0430\u044e\u0449\u0438\u043c \u043e \u0431\u0435\u0434\u0430\u0445, \u043d\u0430\u0434\u0432\u0438\u0433\u0430\u044e\u0449\u0438\u0445\u0441\u044f \u043d\u0430 \u043e\u0431\u0438\u0442\u0430\u0442\u0435\u043b\u0435\u0439 \u044d\u0442\u043e\u0433\u043e \u0446\u0430\u0440\u0441\u0442\u0432\u0430.\n\n\u0422\u0440\u0435\u0442\u044c\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u043c\u0430\u043d\u0438\u043b\u0430 \u0435\u0451, \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u044f \u0434\u0432\u0435\u0440\u0438 \u0432 \u043c\u0438\u0440 \u043b\u044e\u0434\u0435\u0439. \u042d\u043b\u0438\u043d\u0430 \u043c\u043e\u0433\u043b\u0430 \u0431\u044b \u043f\u043e\u043a\u0438\u043d\u0443\u0442\u044c \u043b\u0435\u0441, \u0447\u0442\u043e\u0431\u044b \u0443\u0447\u0438\u0442\u044c \u0438\u0445 \u0438\u0441\u0442\u0438\u043d\u043d\u043e\u043c\u0443 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044e \u043f\u0440\u0438\u0440\u043e\u0434\u044b \u0438 \u0435\u0434\u0438\u043d\u0441\u0442\u0432\u0443 \u0441 \u043e\u043a\u0440\u0443\u0436\u0430\u044e\u0449\u0438\u043c \u043c\u0438\u0440\u043e\u043c. \u0421\u0435\u0440\u0434\u0446\u0430 \u043b\u044e\u0434\u0435\u0439 \u043e\u0436\u0438\u0434\u0430\u043b\u0438 \u0435\u0451 \u043f\u043e\u0441\u043b\u0430\u043d\u0438\u044f, \u043a\u0430\u043a \u0442\u0435\u043f\u043b\u044b\u0439 \u0441\u043e\u043b\u043d\u0435\u0447\u043d\u044b\u0439 \u0441\u0432\u0435\u0442, \u043f\u0440\u043e\u0431\u0438\u0432\u0430\u044e\u0449\u0438\u0439\u0441\u044f \u0441\u043a\u0432\u043e\u0437\u044c \u0437\u0438\u043c\u043d\u0438\u0435 \u043e\u0431\u043b\u0430\u043a\u0430.\n\n\u0414\u043e\u043b\u0433\u043e \u0432\u0437\u0432\u0435\u0448\u0438\u0432\u0430\u044f \u043a\u0430\u0436\u0434\u044b\u0439 \u0432\u044b\u0431\u043e\u0440, \u042d\u043b\u0438\u043d\u0430 \u043f\u043e\u0447\u0443\u0432\u0441\u0442\u0432\u043e\u0432\u0430\u043b\u0430, \u043a\u0430\u043a \u0432 \u0441\u0435\u0440\u0434\u0446\u0435 \u0437\u0430\u0433\u043e\u0440\u0430\u0435\u0442\u0441\u044f \u043e\u0433\u043e\u043d\u044c. \u041a\u0430\u0436\u0434\u0430\u044f \u0441\u0443\u0434\u044c\u0431\u0430 \u0431\u044b\u043b\u0430 \u043f\u0440\u0435\u043a\u0440\u0430\u0441\u043d\u0430 \u0438 \u0443\u043d\u0438\u043a\u0430\u043b\u044c\u043d\u0430, \u043d\u043e \u043e\u0434\u043d\u0430 \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u0437\u0432\u0430\u043b\u0430 \u0435\u0451, \u0443\u0441\u044b\u043f\u0430\u043d\u043d\u0430\u044f \u0447\u0443\u0434\u0435\u0441\u0430\u043c\u0438.\n\n\u041d\u0430\u043a\u043e\u043d\u0435\u0446, \u0441 \u0440\u0435\u0448\u0438\u043c\u043e\u0441\u0442\u044c\u044e \u0432 \u0433\u043e\u043b\u043e\u0441\u0435, \u043e\u043d\u0430 \u0441\u043a\u0430\u0437\u0430\u043b\u0430: \"\u042f \u0432\u044b\u0431\u0435\u0440\u0443 \u0442\u0440\u0435\u0442\u0438\u0439 \u043f\u0443\u0442\u044c. \u042f \u0445\u043e\u0447\u0443 \u043f\u043e\u043c\u043e\u0447\u044c \u043b\u044e\u0434\u044f\u043c \u0443\u0432\u0438\u0434\u0435\u0442\u044c \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u043a\u0440\u0430\u0441\u043e\u0442\u0443, \u043d\u043e \u0438 \u0441\u0438\u043b\u0443 \u0434\u0438\u043a\u043e\u0439 \u043f\u0440\u0438\u0440\u043e\u0434\u044b. \u042f \u0436\u0430\u0436\u0434\u0443, \u0447\u0442\u043e\u0431\u044b \u043e\u043d\u0438 \u043d\u0430\u0448\u043b\u0438 \u0433\u0430\u0440\u043c\u043e\u043d\u0438\u044e \u0441 \u0441\u043e\u0431\u043e\u0439 \u0438 \u043e\u043a\u0440\u0443\u0436\u0430\u044e\u0449\u0438\u043c \u043c\u0438\u0440\u043e\u043c, \u0441\u043b\u043e\u0432\u043d\u043e \u0440\u0435\u043a\u0430, \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u0441\u043b\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0441 \u043c\u043e\u0440\u0435\u043c\".\n\n\u0414\u0435\u0440\u0435\u0432\u043e \u0417\u043d\u0430\u043d\u0438\u0439 \u0437\u0430\u0441\u0432\u0435\u0440\u043a\u0430\u043b\u043e, \u0441\u043b\u043e\u0432\u043d\u043e \u0437\u0432\u0451\u0437\u0434\u044b \u043d\u0430 \u043d\u043e\u0447\u043d\u043e\u043c \u043d\u0435\u0431\u0435, \u0438 \u043e\u0442\u0432\u0435\u0442\u0438\u043b\u043e: \"\u0422\u044b \u0432\u044b\u0431\u0440\u0430\u043b\u0430 \u043c\u0443\u0434\u0440\u043e, \u042d\u043b\u0438\u043d\u0430. \u041d\u043e \u043f\u043e\u043c\u043d\u0438: \u0447\u0442\u043e\u0431\u044b \u0434\u043e\u0441\u0442\u0443\u0447\u0430\u0442\u044c\u0441\u044f \u0434\u043e \u0441\u0435\u0440\u0434\u0435\u0446 \u043b\u044e\u0434\u0435\u0439, \u043d\u0443\u0436\u043d\u043e \u0431\u0443\u0434\u0435\u0442 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c, \u043d\u043e \u0438 \u0443\u043c\u0435\u0442\u044c \u0441\u043b\u0443\u0448\u0430\u0442\u044c\".\n\n\u0421 \u0442\u0435\u0445 \u043f\u043e\u0440 \u042d\u043b\u0438\u043d\u0430 \u043d\u0430\u0447\u0430\u043b\u0430 \u0441\u0432\u043e\u0451 \u043d\u043e\u0432\u043e\u0435 \u043f\u0443\u0442\u0435\u0448\u0435\u0441\u0442\u0432\u0438\u0435. \u041d\u043e\u0447\u044c \u0437\u0430 \u043d\u043e\u0447\u044c\u044e \u043e\u043d\u0430 \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u043b\u0430 \u0442\u0430\u0439\u043d\u044b \u043b\u0435\u0441\u0430, \u043e\u0431\u0449\u0430\u044f\u0441\u044c \u0441 \u0436\u0438\u0432\u043e\u0442\u043d\u044b\u043c\u0438 \u0438 \u0441\u043e\u0431\u0438\u0440\u0430\u044f \u0438\u0445 \u0438\u0441\u0442\u043e\u0440\u0438\u0438. \u042d\u0442\u0438 \u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f, \u043a\u0430\u043a \u0432\u043e\u043b\u0448\u0435\u0431\u043d\u044b\u0435 \u043d\u0438\u0442\u0438, \u0441\u043e\u0435\u0434\u0438\u043d\u044f\u043b\u0438 \u0435\u0451 \u0441 \u0434\u0443\u0445\u0430\u043c\u0438 \u043f\u0440\u0438\u0440\u043e\u0434\u044b \u0438 \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u043b\u0438 \u043f\u0443\u0442\u044c \u043a \u0447\u0435\u043b\u043e\u0432\u0435\u0447\u0435\u0441\u043a\u0438\u043c \u0441\u0435\u0440\u0434\u0446\u0430\u043c.\n\n\u0428\u0430\u0433\u0430\u044f \u0432 \u043c\u0438\u0440 \u043b\u044e\u0434\u0435\u0439, \u042d\u043b\u0438\u043d\u0430 \u043f\u043e\u043d\u0438\u043c\u0430\u043b\u0430, \u0447\u0442\u043e \u0435\u0451 \u0438\u0441\u0442\u0438\u043d\u043d\u0430\u044f \u0441\u0438\u043b\u0430 \u0437\u0430\u043a\u043b\u044e\u0447\u0430\u043b\u0430\u0441\u044c \u0432 \u0443\u043c\u0435\u043d\u0438\u0438 \u0441\u043b\u044b\u0448\u0430\u0442\u044c \u0448\u0451\u043f\u043e\u0442\u044b \u043b\u0435\u0441\u0430 \u0438 \u043c\u0435\u043b\u043e\u0434\u0438\u0438 \u0432\u0435\u0442\u0440\u0430, \u0443\u043b\u0430\u0432\u043b\u0438\u0432\u0430\u044f \u0441\u0443\u0442\u044c \u0433\u0430\u0440\u043c\u043e\u043d\u0438\u0438, \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u043e\u043d\u0430 \u043d\u0435\u0441\u0451\u0442 \u0432 \u0441\u0435\u0431\u0435.\n\n\u0422\u0430\u043a \u042d\u043b\u0438\u043d\u0430 \u0441\u0442\u0430\u043b\u0430 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u043c\u043e\u0441\u0442\u043e\u043c \u043c\u0435\u0436\u0434\u0443 \u043b\u0435\u0441\u043e\u043c \u0438 \u043c\u0438\u0440\u043e\u043c \u043b\u044e\u0434\u0435\u0439, \u043d\u043e \u0438 \u0445\u0440\u0430\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u0438\u0446\u0435\u0439 \u0438\u0441\u0442\u043e\u0440\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043e\u0434\u043d\u0430\u0436\u0434\u044b \u0432\u044b\u0439\u0434\u0443\u0442 \u0438\u0437 \u0443\u0441\u0442, \u043f\u043e\u043b\u043d\u044b\u0445 \u043b\u044e\u0431\u0432\u0438 \u0438 \u0434\u043e\u0432\u0435\u0440\u0438\u044f \u043a \u043f\u0440\u0438\u0440\u043e\u0434\u0435, \u0432\u0434\u043e\u0445\u043d\u043e\u0432\u043b\u044f\u044f \u0441\u0435\u0440\u0434\u0446\u0430 \u043b\u044e\u0434\u0435\u0439 \u0431\u0435\u0440\u0435\u0447\u044c \u0435\u0451, \u043a\u0430\u043a \u044d\u0442\u043e \u0434\u0435\u043b\u0430\u043b\u0438 \u043c\u043e\u0433\u0443\u0447\u0438\u0435 \u043b\u0438\u0441\u044b, \u0442\u0430\u043d\u0446\u0443\u044e\u0449\u0438\u0435 \u043f\u043e\u0434 \u0441\u0432\u0435\u0442\u043e\u043c \u043f\u043e\u043b\u043d\u043e\u0439 \u043b\u0443\u043d\u044b, \u043e\u0441\u0442\u0430\u0432\u043b\u044f\u044f \u0441\u043b\u0435\u0434\u044b \u0432 \u0434\u0443\u0448\u0435 \u043a\u0430\u0436\u0434\u043e\u0433\u043e, \u043a\u0442\u043e \u043e\u0441\u043c\u0435\u043b\u0438\u0442\u0441\u044f \u0443\u0441\u043b\u044b\u0448\u0430\u0442\u044c \u0438\u0445 \u0432\u043e\u043b\u0448\u0435\u0431\u043d\u044b\u0435 \u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f.","behavior":"degenerate_repetition","conversation_hash":"87ea2393ca4b9dd7149f0d482ef6a3b2","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-4o-mini-2024-07-18","n_chars":3504,"n_turns_in_conversation":5,"n_words":540,"source_row_offset":18521,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:57:21 GMT","turn_id":"168c0384da48fc668aebfcf0","turn_index":4,"user_text":"\u0423\u043b\u0443\u0447\u0448\u0438 \u0442\u0435\u043a\u0441\u0442"},{"assistant_initiates":false,"asst_text":"```json\n{\n  \"keywords\": [\n    \"Molde Silicone\",\n    \"Ursinho\",\n    \"Gummy Bear\",\n    \"Forma Gelatina\",\n    \"Bala Candy\",\n    \"Things Nerd\",\n    \"satisfa\u00e7\u00e3o\",\n    \"qualidade\",\n    \"atendimento\",\n    \"embalagens\",\n    \"transporte seguro\",\n    \"garantia\",\n    \"defeitos de fabrica\u00e7\u00e3o\",\n    \"p\u00f3s venda\",\n    \"artesanato\",\n    \"silicone at\u00f3xico\",\n    \"pasta americana\",\n    \"biscuit\",\n    \"resina\",\n    \"chocolate\",\n    \"geleia\",\n    \"goma\",\n    \"temperaturas\",\n    \"resistente\",\n    \"male\u00e1vel\",\n    \"f\u00e1cil de limpar\",\n    \"decora\u00e7\u00e3o\",\n    \"brinde\",\n    \"conta gotas\",\n    \"cavidades\",\n    \"cor azul\",\n    \"temperatura suportada\",\n    \"antiaderente\",\n    \"livre de BPA\",\n    \"pronta entrega\",\n    \"material silicone\",\n    \"tamanho molde\",\n    \"tamanho ursinho\",\n    \"capacidade ursinho\",\n    \"nota fiscal\"\n  ]\n}\n```","behavior":"degenerate_repetition","conversation_hash":"9ba2221283763f302d9a33d7b95f64e3","conversation_redacted":false,"conversation_toxic":false,"country":"Indonesia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Portuguese","model":"gpt-4o-mini-2024-07-18","n_chars":2698,"n_turns_in_conversation":1,"n_words":382,"source_row_offset":18522,"source_shard":"turns-00058.parquet","timestamp":"Fri, 03 Jan 2025 22:57:31 GMT","turn_id":"504e594164b092ffc50b8b98","turn_index":0,"user_text":"Write keywords for a product that contains the following data with English\n        and return them as json with key \"keywords\" and value type list:\n        \"Molde Silicone Ursinho Gummy Bear Forma Gelatina Bala Candy\nN\u00f3s da Things Nerd garantimos total satisfa\u00e7\u00e3o, compre com quem esta disposto a te oferecer a melhor qualidade e o melhor atendimento. Nossos produtos s\u00e3o enviados em embalagens apropriadas para n\u00e3o sofrer danos durante o transporte e s\u00e3o vendidos com garantia contra defeitos de fabrica\u00e7\u00e3o. Caso tenha qualquer problema basta contatar nosso p\u00f3s venda pelos canais de atendimento que iremos prontamente resolver para voc\u00ea.\n\n\nForma com moldes de silicone com formas de ursinho. O molde possui ao todo 50 cavidades no formato de mini ursos, cada ursinho possui 1.9 cm x 1.1 cm.\n\n\nEle \u00e9 ideal para usar em artesanato, j\u00e1 que n\u00e3o a deixa opaca, al\u00e9m da facilidade ao soltar a pe\u00e7a do molde, mas \u00e9 ideal tamb\u00e9m para uso aliment\u00edcio, j\u00e1 que \u00e9 feito em silicone atoxico. Podendo ser usado com pasta americana, biscuit, resina, chocolate, geleia, goma e muitos outros. Ele tamb\u00e9m \u00e9 resiste a temperaturas, podendo abranger uma gama ainda maior de trabalhos.\n\n\nO molde \u00e9 extremamente resistente e male\u00e1vel, podendo ser dobrado e amassado sem danificar. Al\u00e9m de tudo a forma \u00e9 muito f\u00e1cil de limpar, podendo ser utilizada muitas vezes.\n\n\nUm item indispens\u00e1vel para completar a decora\u00e7\u00e3o seja do seu artesanato ou do seu alimento.\n\n\n** ACOMPANHA DE BRINDE UM CONTA GOTAS **\n\n\nDADOS RELEVANTES:\n- Molde com 50 Cavidades;\n- Cor: Azul;\n- Temperatura Suportada: -40\u00b0C \u00e0 210\u00b0C;\n- Totalmente Antiaderente;\n- Livre de BPA;\n- Disponibilidade: Pronta entrega;\n- Material de fabrica\u00e7\u00e3o: Silicone Atoxico;\n- Tamanho Aproximado do Molde: 18.8 cm x 13.8 cm x 1.2 cm;\n- Tamanho Aproximado de Cada Ursinho: 1.9 cm x 1.1 cm;\n- Capacidade Aproximada de Cada Ursinho: 1 ml;\n- Acompanha Nota Fiscal;\""}],"shard":{"index":58,"name":"turns-00058.parquet","rows":59171}}