ASSISTANT
To detect the court in real-time, you can use OpenCV's video capture capabilities to capture frames from a camera and apply the same court detection algorithm to each frame. To map the court's lines to the minimap, you can apply the homography transformation to the lines and then draw them on the minimap.
Here's an example code that demonstrates the real-time court detection and minimap mapping:
```python
import cv2
import numpy as np
# Define the corners of the court in a specific order
court_corners = np.float32([
[0, 0],
[0, 25],
[20, 25],
[20, 0],
])
# Create a black image with the same width as the topdown image and height 150
black = np.zeros((150, 400, 3), dtype=np.uint8)
# Stack the black image and topdown image vertically using np.vstack() function
minimap = np.vstack((black, np.zeros((600, 400, 3), dtype=np.uint8)))
# Load the video capture device or the video file to read
cap = cv2.VideoCapture(0) # Change to 1 if you're using an external webcam
while True:
# Read a frame from the video capture device or the video file
ret, frame = cap.read()
if ret:
# Convert the frame to grayscale
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Apply edge detection to the frame
edges = cv2.Canny(gray, 50, 150)
# Find the contours in the frame
contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Loop through the contours and find the one that matches the shape of the pickleball court
for cnt in contours:
# Calculate the perimeter of the contour
perimeter = cv2.arcLength(cnt, True)
# Approximate the contour to a polygon with fewer vertices
approx = cv2.approxPolyDP(cnt, 0.01 * perimeter, True)
# If the polygon has 4 vertices, it is likely the pickleball court
if len(approx) == 4:
# Calculate the homography matrix between the court corners and the frame
M, mask = cv2.findHomography(court_corners, approx)
# Apply the homography transform to the frame to get a top-down view of the court
court_topdown = cv2.warpPerspective(frame, M, (400, 600))
# Draw lines on the court to represent the court boundary and net
court_lines = np.array([
[[0, 0], [0, 600]],
[[0, 0], [400, 0]],
[[400, 600], [0, 600]],
[[400, 600], [400, 0]],
[[200, 0], [200, 600]],
])
# Apply the homography transform to the lines to get them in top-down view
court_lines_topdown = cv2.perspectiveTransform(court_lines.reshape(-1,1,2), M)
# Draw the lines on the minimap
cv2.polylines(minimap, np.int32([court_lines_topdown]), True, (255,0,255), 2)
break
# Display the frame and minimap side by side
cv2.imshow('Pickleball Court Detection', np.hstack([frame, minimap]))
# Exit the loop by pressing 'q'
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release the video capture device or the video file and close all windows
cap.release()
cv2.destroyAllWindows()
```
This code initializes the video capture device using `cv2.VideoCapture` and starts reading frames from the camera using a loop. Inside the loop, it applies the court detection algorithm to each frame using the same algorithm as before. If the court is detected, it applies the homography transformation to the court's boundary lines (`court_lines`) and maps the lines to the minimap. It then displays the frame and minimap side by side.
You can adjust the code as per your requirements such as the position of court in frame, video capture settings, key to exit the loop, line thickness, color etc.