Rewrite calibration: green court surface segmentation instead of line angles
- Detect green court surface via HSV color thresholding - Find court boundary as quadrilateral (contour approximation) - Detect white line segments within the green area only - Debug image: green overlay + court quad + white lines + camera XYZ - Much more robust than angle-based line classification Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Ruslan Bakiev
461
jetson/main.py
461
jetson/main.py
@@ -35,12 +35,14 @@ _args = None
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def auto_calibrate():
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"""Detect all visible court lines, identify them, compute camera 3D position.
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"""Detect the green court surface, find its boundary quadrilateral,
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map to known court dimensions, compute camera 3D position via solvePnP.
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Each camera sees one half of the pickleball court from the net.
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We detect all white lines, classify them by angle and position,
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try to match them to known court lines, then use solvePnP.
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Returns debug images showing every detected line + camera position.
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Debug images show:
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- Green mask overlay (what the system sees as court)
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- Detected quadrilateral (court boundary)
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- White line segments found on court
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- Computed camera position
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"""
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results = {}
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@@ -54,69 +56,62 @@ def auto_calibrate():
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side = 'left' if sensor_id == 0 else 'right'
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debug_frame = frame.copy()
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# Detect all court lines
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detection = _detect_court_lines(frame)
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all_segments = detection['segments']
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grouped = detection['grouped']
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# Step 1: Detect green court surface
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court = _detect_court_surface(frame)
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# Draw ALL detected line segments on debug frame
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for seg in all_segments:
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# Draw green mask as semi-transparent overlay
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if court['green_mask'] is not None:
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green_overlay = np.zeros_like(debug_frame)
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green_overlay[court['green_mask'] > 0] = (0, 80, 0)
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debug_frame = cv2.addWeighted(debug_frame, 1.0, green_overlay, 0.3, 0)
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if court['quad'] is None:
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cv2.putText(debug_frame, f"FAILED: {court['error']}", (10, 30),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
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_, jpeg = cv2.imencode('.jpg', debug_frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
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results[str(sensor_id)] = {
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'ok': False,
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'error': f"CAM {sensor_id}: {court['error']}",
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'debug_image': base64.b64encode(jpeg.tobytes()).decode('ascii'),
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}
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continue
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quad = court['quad']
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# Draw detected court quadrilateral
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pts = quad.astype(int)
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for i in range(4):
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p1 = tuple(pts[i])
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p2 = tuple(pts[(i + 1) % 4])
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cv2.line(debug_frame, p1, p2, (0, 255, 0), 3)
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cv2.circle(debug_frame, p1, 8, (0, 0, 255), -1)
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cv2.putText(debug_frame, f"C{i}", (p1[0] + 10, p1[1] - 5),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
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# Step 2: Detect white lines on court surface
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white_lines = _detect_white_lines_on_court(frame, court['green_mask'])
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for seg in white_lines:
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x1, y1, x2, y2 = seg
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cv2.line(debug_frame, (x1, y1), (x2, y2), (80, 80, 80), 1)
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cv2.line(debug_frame, (x1, y1), (x2, y2), (255, 255, 0), 1)
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# Draw grouped/merged lines with colors
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# "across" lines (roughly horizontal in image = baseline, kitchen, net)
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across_colors = [(0, 255, 255), (0, 200, 200), (0, 150, 150),
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(0, 100, 200), (0, 80, 160)]
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for i, line in enumerate(grouped.get('across', [])):
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x1, y1, x2, y2 = line['endpoints']
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color = across_colors[i % len(across_colors)]
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cv2.line(debug_frame, (x1, y1), (x2, y2), color, 3)
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cv2.putText(debug_frame, f"across#{i} ({line['count']}seg)",
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(x1, max(y1 - 8, 15)),
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cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)
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# Step 3: Map quad corners to 3D court coordinates
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# Quad corners are ordered: TL, TR, BR, BL
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# For camera at net looking at one half-court:
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# TL = far-left, TR = far-right (baseline)
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# BL = near-left, BR = near-right (near net/camera)
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corners_3d = get_half_court_3d_points(side)
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# "along" lines (roughly vertical in image = sidelines, center service)
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along_colors = [(255, 0, 255), (200, 0, 200), (150, 0, 150),
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(200, 0, 100), (160, 0, 80)]
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for i, line in enumerate(grouped.get('along', [])):
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x1, y1, x2, y2 = line['endpoints']
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color = along_colors[i % len(along_colors)]
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cv2.line(debug_frame, (x1, y1), (x2, y2), color, 3)
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cv2.putText(debug_frame, f"along#{i} ({line['count']}seg)",
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(x1 + 5, (y1 + y2) // 2),
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cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)
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# Summary text
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n_across = len(grouped.get('across', []))
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n_along = len(grouped.get('along', []))
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cv2.putText(debug_frame,
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f"Lines: {len(all_segments)} raw, {n_across} across, {n_along} along",
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(10, h - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
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# Try to match lines to known court geometry and calibrate
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match = _match_court_lines(grouped, side, w, h)
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if match['points_2d'] is not None and len(match['points_2d']) >= 4:
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# We have enough correspondences — calibrate
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# Calibrate
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cal = CameraCalibrator()
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cal.calibrate(
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np.array(match['points_2d'], dtype=np.float32),
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np.array(match['points_3d'], dtype=np.float32),
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w, h
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)
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cal.calibrate(quad, corners_3d, w, h)
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cam_pos = (-cal.rotation_matrix.T @ cal.translation_vec).flatten()
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# Draw matched points
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for i, (px, py) in enumerate(match['points_2d']):
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cv2.circle(debug_frame, (int(px), int(py)), 6, (0, 255, 0), -1)
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cv2.putText(debug_frame,
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f"CAM{sensor_id}: X={cam_pos[0]:.2f} Y={cam_pos[1]:.2f} Z={cam_pos[2]:.2f}m",
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(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
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cv2.putText(debug_frame,
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f"Matched {len(match['points_2d'])} points, {side} half",
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f"{side} half | {len(white_lines)} white line segments",
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(10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
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# Save
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@@ -127,284 +122,122 @@ def auto_calibrate():
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state['calibrators'][sensor_id] = cal
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_, jpeg = cv2.imencode('.jpg', debug_frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
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# Court lines in 3D for visualization
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matched_lines_3d = _get_half_court_lines_3d(side)
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results[str(sensor_id)] = {
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'ok': True,
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'camera_position': cam_pos.tolist(),
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'debug_image': base64.b64encode(jpeg.tobytes()).decode('ascii'),
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'lines_detected': {'across': n_across, 'along': n_along},
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'points_matched': len(match['points_2d']),
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'matched_lines_3d': match.get('matched_lines_3d', []),
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'matched_lines_3d': matched_lines_3d,
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}
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print(f"[CAM {sensor_id}] Calibrated! Camera at "
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f"({cam_pos[0]:.2f}, {cam_pos[1]:.2f}, {cam_pos[2]:.2f}), "
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f"{len(match['points_2d'])} correspondences")
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else:
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cv2.putText(debug_frame,
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f"Not enough correspondences: {match.get('error', 'unknown')}",
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(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
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_, jpeg = cv2.imencode('.jpg', debug_frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
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results[str(sensor_id)] = {
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'ok': False,
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'error': f"CAM {sensor_id}: {match.get('error', 'Not enough line correspondences')}",
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'debug_image': base64.b64encode(jpeg.tobytes()).decode('ascii'),
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'lines_detected': {'across': n_across, 'along': n_along},
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}
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f"({cam_pos[0]:.2f}, {cam_pos[1]:.2f}, {cam_pos[2]:.2f})")
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return results
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def _detect_court_lines(frame):
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"""Detect all white line segments in the frame.
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def _get_half_court_lines_3d(side):
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"""Return 3D line segments for all lines of a half-court."""
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if side == 'left':
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bx, kx = 0, 4.57 # baseline, kitchen
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else:
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bx, kx = 13.4, 8.83
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lines = [
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{'name': 'baseline', 'from': [bx, 0, 0], 'to': [bx, 6.1, 0]},
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{'name': 'kitchen', 'from': [kx, 0, 0], 'to': [kx, 6.1, 0]},
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{'name': 'sideline_near', 'from': [bx, 0, 0], 'to': [kx, 0, 0]},
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{'name': 'sideline_far', 'from': [bx, 6.1, 0], 'to': [kx, 6.1, 0]},
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{'name': 'center_service', 'from': [bx, 3.05, 0], 'to': [kx, 3.05, 0]},
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]
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return lines
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Returns:
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segments: list of [x1, y1, x2, y2] raw segments
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grouped: dict with 'across' and 'along' merged line groups
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def _detect_court_surface(frame):
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"""Detect the green court surface and find its boundary as a quadrilateral.
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The court surface is distinctly green against pink/gray surroundings.
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We segment by color, find the largest contour, and approximate with 4 corners.
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Returns dict with:
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quad: 4x2 numpy array of court boundary corners in image, or None
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green_mask: binary mask of detected green surface
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error: description if detection failed
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"""
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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# Green court surface — relatively broad range to handle lighting
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lower_green = np.array([30, 30, 50])
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upper_green = np.array([90, 255, 220])
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green_mask = cv2.inRange(hsv, lower_green, upper_green)
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# Clean up noise
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kernel = np.ones((7, 7), np.uint8)
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green_mask = cv2.morphologyEx(green_mask, cv2.MORPH_CLOSE, kernel)
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green_mask = cv2.morphologyEx(green_mask, cv2.MORPH_OPEN, kernel)
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# Find contours
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contours, _ = cv2.findContours(green_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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if not contours:
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return {'quad': None, 'green_mask': green_mask, 'error': 'No green surface detected'}
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# Take largest contour (should be the court)
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largest = max(contours, key=cv2.contourArea)
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area = cv2.contourArea(largest)
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frame_area = frame.shape[0] * frame.shape[1]
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if area < frame_area * 0.05:
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return {'quad': None, 'green_mask': green_mask,
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'error': f'Green area too small ({area / frame_area * 100:.0f}% of frame)'}
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# Approximate contour with polygon
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epsilon = 0.02 * cv2.arcLength(largest, True)
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approx = cv2.approxPolyDP(largest, epsilon, True)
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# We want 4 corners — if we got more, use convex hull + min area rect
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if len(approx) == 4:
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quad = approx.reshape(4, 2).astype(np.float32)
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else:
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# Use minimum area rectangle as fallback
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rect = cv2.minAreaRect(largest)
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quad = cv2.boxPoints(rect).astype(np.float32)
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# Order corners: top-left, top-right, bottom-right, bottom-left
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# Sort by y first to get top/bottom pairs, then by x within each pair
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sorted_by_y = quad[np.argsort(quad[:, 1])]
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top_pair = sorted_by_y[:2]
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bottom_pair = sorted_by_y[2:]
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top_pair = top_pair[np.argsort(top_pair[:, 0])]
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bottom_pair = bottom_pair[np.argsort(bottom_pair[:, 0])]
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quad = np.array([top_pair[0], top_pair[1], bottom_pair[1], bottom_pair[0]], dtype=np.float32)
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return {'quad': quad, 'green_mask': green_mask, 'error': None}
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def _detect_white_lines_on_court(frame, green_mask):
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"""Detect white lines within the green court area.
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Returns list of line segments [x1, y1, x2, y2] that are on the court surface.
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"""
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# White line detection via color thresholding + edges
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# White lines are bright — threshold to isolate them
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_, white_mask = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
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# White lines are bright pixels on the green surface
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_, white = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
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# Also use edge detection
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blur = cv2.GaussianBlur(gray, (5, 5), 0)
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edges = cv2.Canny(blur, 50, 150)
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# Only keep white pixels within/near the court
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dilated_court = cv2.dilate(green_mask, np.ones((15, 15), np.uint8))
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white_on_court = cv2.bitwise_and(white, dilated_court)
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# Combine: edges that are also near white regions
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dilated_white = cv2.dilate(white_mask, np.ones((5, 5), np.uint8))
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combined = cv2.bitwise_and(edges, dilated_white)
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# Hough line detection on combined mask
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lines = cv2.HoughLinesP(combined, 1, np.pi / 180, threshold=50,
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minLineLength=60, maxLineGap=30)
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# Hough line detection
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lines = cv2.HoughLinesP(white_on_court, 1, np.pi / 180, threshold=40,
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minLineLength=50, maxLineGap=25)
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if lines is None:
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return {'segments': [], 'grouped': {'across': [], 'along': []}}
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segments = [line[0].tolist() for line in lines]
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# Classify by angle and group nearby parallel lines
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across_lines = [] # roughly horizontal (court width direction)
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along_lines = [] # roughly vertical (court length direction)
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for seg in segments:
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x1, y1, x2, y2 = seg
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angle = np.arctan2(y2 - y1, x2 - x1) * 180 / np.pi
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length = np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
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if length < 40:
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continue
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abs_angle = abs(angle)
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if abs_angle < 40 or abs_angle > 140:
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across_lines.append(seg)
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elif 50 < abs_angle < 130:
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along_lines.append(seg)
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def merge_nearby(segs, position_key, threshold=30):
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"""Merge line segments that are close together (same court line)."""
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if not segs:
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return []
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# Sort by mid-position perpendicular to the line direction
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def get_pos(s):
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if position_key == 'y':
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return (s[1] + s[3]) / 2
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return (s[0] + s[2]) / 2
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sorted_segs = sorted(segs, key=get_pos)
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groups = []
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current_group = [sorted_segs[0]]
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for seg in sorted_segs[1:]:
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if abs(get_pos(seg) - get_pos(current_group[-1])) < threshold:
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current_group.append(seg)
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else:
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groups.append(current_group)
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current_group = [seg]
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groups.append(current_group)
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# Merge each group into one representative line
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merged = []
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for group in groups:
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all_pts = []
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for s in group:
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all_pts.extend([(s[0], s[1]), (s[2], s[3])])
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all_pts = np.array(all_pts)
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# Fit a line through all points
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if len(all_pts) >= 2:
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vx, vy, cx, cy = cv2.fitLine(all_pts, cv2.DIST_L2, 0, 0.01, 0.01).flatten()
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# Extend line across frame
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t_max = max(np.sqrt((p[0] - cx) ** 2 + (p[1] - cy) ** 2) for p in all_pts)
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x1, y1 = int(cx - vx * t_max), int(cy - vy * t_max)
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x2, y2 = int(cx + vx * t_max), int(cy + vy * t_max)
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mid_pos = get_pos(group[0])
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merged.append({
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'endpoints': [x1, y1, x2, y2],
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'mid_pos': mid_pos,
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'count': len(group),
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})
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return merged
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grouped = {
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'across': merge_nearby(across_lines, 'y'),
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'along': merge_nearby(along_lines, 'x'),
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}
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return {'segments': segments, 'grouped': grouped}
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def _match_court_lines(grouped, side, frame_w, frame_h):
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"""Try to match detected line groups to known court lines.
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For a camera at the net looking at one half:
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- 'across' lines map to: baseline, kitchen line (and possibly net line)
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- 'along' lines map to: left sideline, right sideline, center service line
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Returns dict with points_2d, points_3d for solvePnP, or error.
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"""
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across = grouped.get('across', [])
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along = grouped.get('along', [])
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if len(across) < 2 or len(along) < 2:
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return {
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'points_2d': None, 'points_3d': None,
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'error': f'{len(across)} across + {len(along)} along lines (need 2+ each)',
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}
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# Sort across lines by vertical position (top of frame = far from camera = baseline)
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across_sorted = sorted(across, key=lambda l: l['mid_pos'])
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# Sort along lines by horizontal position
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along_sorted = sorted(along, key=lambda l: l['mid_pos'])
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# Known court geometry for this half
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# Court: 13.4m x 6.1m, half = 6.7m
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# Kitchen (NVZ) = 2.13m from net
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||||
if side == 'left':
|
||||
# Camera at net (X=6.7) looking toward baseline (X=0)
|
||||
# "across" lines (perpendicular to camera view):
|
||||
# - baseline at X=0
|
||||
# - kitchen at X=6.7-2.13=4.57
|
||||
# "along" lines (parallel to camera view):
|
||||
# - near sideline at Y=0
|
||||
# - far sideline at Y=6.1
|
||||
# - center service at Y=3.05
|
||||
across_3d_x = [0, 4.57] # baseline, kitchen
|
||||
along_3d_y = [0, 6.1] # near sideline, far sideline
|
||||
center_service_y = 3.05
|
||||
else:
|
||||
# Camera at net (X=6.7) looking toward baseline (X=13.4)
|
||||
across_3d_x = [13.4, 6.7 + 2.13] # baseline (far), kitchen
|
||||
along_3d_y = [0, 6.1]
|
||||
center_service_y = 3.05
|
||||
|
||||
# Match: take outermost across lines as baseline (farthest) and kitchen (closest)
|
||||
# In image: top across line = far = baseline, bottom = near = kitchen
|
||||
baseline_px = across_sorted[0] # topmost = farthest = baseline
|
||||
kitchen_px = across_sorted[-1] # bottommost = nearest = kitchen
|
||||
|
||||
# Take outermost along lines as sidelines
|
||||
left_sideline_px = along_sorted[0]
|
||||
right_sideline_px = along_sorted[-1]
|
||||
|
||||
# Find intersection points as 2D-3D correspondences
|
||||
def line_intersect(l1_ep, l2_ep):
|
||||
x1, y1, x2, y2 = l1_ep
|
||||
x3, y3, x4, y4 = l2_ep
|
||||
denom = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
|
||||
if abs(denom) < 1e-6:
|
||||
return None
|
||||
t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / denom
|
||||
return [x1 + t * (x2 - x1), y1 + t * (y2 - y1)]
|
||||
|
||||
points_2d = []
|
||||
points_3d = []
|
||||
|
||||
# Baseline x left sideline
|
||||
pt = line_intersect(baseline_px['endpoints'], left_sideline_px['endpoints'])
|
||||
if pt:
|
||||
points_2d.append(pt)
|
||||
points_3d.append([across_3d_x[0], along_3d_y[0], 0])
|
||||
|
||||
# Baseline x right sideline
|
||||
pt = line_intersect(baseline_px['endpoints'], right_sideline_px['endpoints'])
|
||||
if pt:
|
||||
points_2d.append(pt)
|
||||
points_3d.append([across_3d_x[0], along_3d_y[1], 0])
|
||||
|
||||
# Kitchen x right sideline
|
||||
pt = line_intersect(kitchen_px['endpoints'], right_sideline_px['endpoints'])
|
||||
if pt:
|
||||
points_2d.append(pt)
|
||||
points_3d.append([across_3d_x[1], along_3d_y[1], 0])
|
||||
|
||||
# Kitchen x left sideline
|
||||
pt = line_intersect(kitchen_px['endpoints'], left_sideline_px['endpoints'])
|
||||
if pt:
|
||||
points_2d.append(pt)
|
||||
points_3d.append([across_3d_x[1], along_3d_y[0], 0])
|
||||
|
||||
# If we have a center service line, add more correspondences
|
||||
if len(along_sorted) >= 3:
|
||||
center_px = along_sorted[len(along_sorted) // 2]
|
||||
pt = line_intersect(baseline_px['endpoints'], center_px['endpoints'])
|
||||
if pt:
|
||||
points_2d.append(pt)
|
||||
points_3d.append([across_3d_x[0], center_service_y, 0])
|
||||
pt = line_intersect(kitchen_px['endpoints'], center_px['endpoints'])
|
||||
if pt:
|
||||
points_2d.append(pt)
|
||||
points_3d.append([across_3d_x[1], center_service_y, 0])
|
||||
|
||||
# Build list of matched 3D court lines for visualization
|
||||
matched_lines_3d = []
|
||||
# Baseline
|
||||
matched_lines_3d.append({
|
||||
'name': 'baseline',
|
||||
'from': [across_3d_x[0], along_3d_y[0], 0],
|
||||
'to': [across_3d_x[0], along_3d_y[1], 0],
|
||||
})
|
||||
# Kitchen
|
||||
matched_lines_3d.append({
|
||||
'name': 'kitchen',
|
||||
'from': [across_3d_x[1], along_3d_y[0], 0],
|
||||
'to': [across_3d_x[1], along_3d_y[1], 0],
|
||||
})
|
||||
# Left sideline
|
||||
matched_lines_3d.append({
|
||||
'name': 'sideline_near',
|
||||
'from': [across_3d_x[0], along_3d_y[0], 0],
|
||||
'to': [across_3d_x[1], along_3d_y[0], 0],
|
||||
})
|
||||
# Right sideline
|
||||
matched_lines_3d.append({
|
||||
'name': 'sideline_far',
|
||||
'from': [across_3d_x[0], along_3d_y[1], 0],
|
||||
'to': [across_3d_x[1], along_3d_y[1], 0],
|
||||
})
|
||||
# Center service (if detected)
|
||||
if len(along_sorted) >= 3:
|
||||
matched_lines_3d.append({
|
||||
'name': 'center_service',
|
||||
'from': [across_3d_x[0], center_service_y, 0],
|
||||
'to': [across_3d_x[1], center_service_y, 0],
|
||||
})
|
||||
|
||||
if len(points_2d) < 4:
|
||||
return {
|
||||
'points_2d': None, 'points_3d': None,
|
||||
'matched_lines_3d': matched_lines_3d,
|
||||
'error': f'Only {len(points_2d)} intersection points found (need >= 4)',
|
||||
}
|
||||
|
||||
return {
|
||||
'points_2d': points_2d, 'points_3d': points_3d,
|
||||
'matched_lines_3d': matched_lines_3d, 'error': None,
|
||||
}
|
||||
return [line[0].tolist() for line in lines]
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user