Rewrite court detection: find ALL court lines, not just 4 corners

- Detect white lines via color threshold + Canny + Hough
- Group/merge nearby parallel segments into court lines
- Classify as 'across' (baseline, kitchen) and 'along' (sidelines, service)
- Match detected lines to known court geometry for solvePnP
- Debug image shows: raw segments (gray), merged across (yellow), along (magenta)
- Show line counts + matched points in calibration UI

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

jetson/main.py

@@ -35,9 +35,12 @@ _args = None
 
 
 def auto_calibrate():
-    """One-click calibration: detect court rectangle from current frames,
+    """Detect all visible court lines, identify them, compute camera 3D position.
-    compute camera 3D position via solvePnP using known court dimensions.
+
-    Returns debug images showing detected court quad + computed camera position.
+    Each camera sees one half of the pickleball court from the net.
+    We detect all white lines, classify them by angle and position,
+    try to match them to known court lines, then use solvePnP.
+    Returns debug images showing every detected line + camera position.
     """
     results = {}
 
@@ -51,137 +54,318 @@ def auto_calibrate():
         side = 'left' if sensor_id == 0 else 'right'
         debug_frame = frame.copy()
 
-        # Detect court rectangle — find 4 corners of the court
+        # Detect all court lines
-        detection = _detect_court_corners(frame, side)
+        detection = _detect_court_lines(frame)
-        corners_pixel = detection.get('corners') if detection else None
+        all_segments = detection['segments']
+        grouped = detection['grouped']
+
+        # Draw ALL detected line segments on debug frame
+        for seg in all_segments:
+            x1, y1, x2, y2 = seg
+            cv2.line(debug_frame, (x1, y1), (x2, y2), (80, 80, 80), 1)
+
+        # Draw grouped/merged lines with colors
+        # "across" lines (roughly horizontal in image = baseline, kitchen, net)
+        across_colors = [(0, 255, 255), (0, 200, 200), (0, 150, 150),
+                         (0, 100, 200), (0, 80, 160)]
+        for i, line in enumerate(grouped.get('across', [])):
+            x1, y1, x2, y2 = line['endpoints']
+            color = across_colors[i % len(across_colors)]
+            cv2.line(debug_frame, (x1, y1), (x2, y2), color, 3)
+            cv2.putText(debug_frame, f"across#{i} ({line['count']}seg)",
+                        (x1, max(y1 - 8, 15)),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)
+
+        # "along" lines (roughly vertical in image = sidelines, center service)
+        along_colors = [(255, 0, 255), (200, 0, 200), (150, 0, 150),
+                        (200, 0, 100), (160, 0, 80)]
+        for i, line in enumerate(grouped.get('along', [])):
+            x1, y1, x2, y2 = line['endpoints']
+            color = along_colors[i % len(along_colors)]
+            cv2.line(debug_frame, (x1, y1), (x2, y2), color, 3)
+            cv2.putText(debug_frame, f"along#{i} ({line['count']}seg)",
+                        (x1 + 5, (y1 + y2) // 2),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.4, color, 1)
+
+        # Summary text
+        n_across = len(grouped.get('across', []))
+        n_along = len(grouped.get('along', []))
+        cv2.putText(debug_frame,
+                    f"Lines: {len(all_segments)} raw, {n_across} across, {n_along} along",
+                    (10, h - 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+
+        # Try to match lines to known court geometry and calibrate
+        match = _match_court_lines(grouped, side, w, h)
+
+        if match['points_2d'] is not None and len(match['points_2d']) >= 4:
+            # We have enough correspondences — calibrate
+            cal = CameraCalibrator()
+            cal.calibrate(
+                np.array(match['points_2d'], dtype=np.float32),
+                np.array(match['points_3d'], dtype=np.float32),
+                w, h
+            )
+
+            cam_pos = (-cal.rotation_matrix.T @ cal.translation_vec).flatten()
+
+            # Draw matched points
+            for i, (px, py) in enumerate(match['points_2d']):
+                cv2.circle(debug_frame, (int(px), int(py)), 6, (0, 255, 0), -1)
+
+            cv2.putText(debug_frame,
+                        f"CAM{sensor_id}: X={cam_pos[0]:.2f} Y={cam_pos[1]:.2f} Z={cam_pos[2]:.2f}m",
+                        (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+            cv2.putText(debug_frame,
+                        f"Matched {len(match['points_2d'])} points, {side} half",
+                        (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+
+            # Save
+            cal_path = os.path.join(_args.calibration_dir,
+                                    f'cam{sensor_id}_calibration.json')
+            os.makedirs(os.path.dirname(cal_path), exist_ok=True)
+            cal.save(cal_path)
+            state['calibrators'][sensor_id] = cal
+
+            _, jpeg = cv2.imencode('.jpg', debug_frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
+            results[str(sensor_id)] = {
+                'ok': True,
+                'camera_position': cam_pos.tolist(),
+                'debug_image': base64.b64encode(jpeg.tobytes()).decode('ascii'),
+                'lines_detected': {'across': n_across, 'along': n_along},
+                'points_matched': len(match['points_2d']),
+            }
+            print(f"[CAM {sensor_id}] Calibrated! Camera at "
+                  f"({cam_pos[0]:.2f}, {cam_pos[1]:.2f}, {cam_pos[2]:.2f}), "
+                  f"{len(match['points_2d'])} correspondences")
+        else:
+            cv2.putText(debug_frame,
+                        f"Not enough correspondences: {match.get('error', 'unknown')}",
+                        (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
 
-        if corners_pixel is None:
-            error_detail = detection.get('error', 'Unknown') if detection else 'No court detected'
-            # Draw error on debug frame
-            cv2.putText(debug_frame, f"FAILED: {error_detail}", (10, 30),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
             _, jpeg = cv2.imencode('.jpg', debug_frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
             results[str(sensor_id)] = {
                 'ok': False,
-                'error': f'CAM {sensor_id}: {error_detail}',
+                'error': f"CAM {sensor_id}: {match.get('error', 'Not enough line correspondences')}",
                 'debug_image': base64.b64encode(jpeg.tobytes()).decode('ascii'),
+                'lines_detected': {'across': n_across, 'along': n_along},
             }
-            continue
-
-        # Draw detected court quadrilateral on debug frame
-        pts = corners_pixel.astype(int)
-        for i in range(4):
-            p1 = tuple(pts[i])
-            p2 = tuple(pts[(i + 1) % 4])
-            cv2.line(debug_frame, p1, p2, (0, 255, 0), 3)
-            cv2.circle(debug_frame, p1, 8, (0, 0, 255), -1)
-
-        # Get known 3D coordinates for this half-court
-        corners_3d = get_half_court_3d_points(side)
-
-        # Calibrate — errors propagate
-        cal = CameraCalibrator()
-        cal.calibrate(
-            np.array(corners_pixel, dtype=np.float32),
-            corners_3d, w, h
-        )
-
-        # Camera position in world coordinates
-        cam_pos = (-cal.rotation_matrix.T @ cal.translation_vec).flatten()
-
-        # Draw camera position on debug frame
-        cv2.putText(debug_frame,
-                    f"Camera: X={cam_pos[0]:.2f}m Y={cam_pos[1]:.2f}m Z={cam_pos[2]:.2f}m",
-                    (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
-        cv2.putText(debug_frame, f"Court: {side} half",
-                    (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
-
-        _, jpeg = cv2.imencode('.jpg', debug_frame, [cv2.IMWRITE_JPEG_QUALITY, 85])
-        debug_b64 = base64.b64encode(jpeg.tobytes()).decode('ascii')
-
-        # Save calibration
-        cal_path = os.path.join(_args.calibration_dir,
-                                f'cam{sensor_id}_calibration.json')
-        os.makedirs(os.path.dirname(cal_path), exist_ok=True)
-        cal.save(cal_path)
-
-        state['calibrators'][sensor_id] = cal
-
-        results[str(sensor_id)] = {
-            'ok': True,
-            'camera_position': cam_pos.tolist(),
-            'debug_image': debug_b64,
-        }
-        print(f"[CAM {sensor_id}] Calibrated! Camera at "
-              f"({cam_pos[0]:.2f}, {cam_pos[1]:.2f}, {cam_pos[2]:.2f})")
 
     return results
 
 
-def _detect_court_corners(frame, side):
+def _detect_court_lines(frame):
-    """Detect 4 corners of the court rectangle from camera frame.
+    """Detect all white line segments in the frame.
 
-    Uses edge detection + Hough lines to find the court boundaries,
+    Returns:
-    then finds 4 intersection points forming the court quadrilateral.
+        segments: list of [x1, y1, x2, y2] raw segments
-
+        grouped: dict with 'across' and 'along' merged line groups
-    Returns dict with:
-        corners: 4x2 numpy array (TL, TR, BR, BL) or None
-        error: description string if detection failed
     """
+    # White line detection via color thresholding + edges
     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # White lines are bright — threshold to isolate them
+    _, white_mask = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY)
+
+    # Also use edge detection
     blur = cv2.GaussianBlur(gray, (5, 5), 0)
     edges = cv2.Canny(blur, 50, 150)
 
-    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=80,
+    # Combine: edges that are also near white regions
-                            minLineLength=100, maxLineGap=20)
+    dilated_white = cv2.dilate(white_mask, np.ones((5, 5), np.uint8))
+    combined = cv2.bitwise_and(edges, dilated_white)
 
-    if lines is None or len(lines) < 4:
+    # Hough line detection on combined mask
-        n = 0 if lines is None else len(lines)
+    lines = cv2.HoughLinesP(combined, 1, np.pi / 180, threshold=50,
-        return {'corners': None, 'error': f'Found {n} lines, need at least 4'}
+                            minLineLength=60, maxLineGap=30)
 
-    # Classify into horizontal / vertical
+    if lines is None:
-    horizontals = []
+        return {'segments': [], 'grouped': {'across': [], 'along': []}}
-    verticals = []
 
-    for line in lines:
+    segments = [line[0].tolist() for line in lines]
-        x1, y1, x2, y2 = line[0]
-        angle = abs(np.arctan2(y2 - y1, x2 - x1) * 180 / np.pi)
-        if angle < 30 or angle > 150:
-            horizontals.append(line[0])
-        elif 60 < angle < 120:
-            verticals.append(line[0])
 
-    if len(horizontals) < 2 or len(verticals) < 2:
+    # Classify by angle and group nearby parallel lines
-        return {'corners': None,
+    across_lines = []  # roughly horizontal (court width direction)
-                'error': f'{len(horizontals)} horiz + {len(verticals)} vert lines, need 2+ each'}
+    along_lines = []   # roughly vertical (court length direction)
 
-    # Take outermost lines as court boundaries
+    for seg in segments:
-    h_sorted = sorted(horizontals, key=lambda l: (l[1] + l[3]) / 2)
+        x1, y1, x2, y2 = seg
-    v_sorted = sorted(verticals, key=lambda l: (l[0] + l[2]) / 2)
+        angle = np.arctan2(y2 - y1, x2 - x1) * 180 / np.pi
+        length = np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
 
-    top, bottom = h_sorted[0], h_sorted[-1]
+        if length < 40:
-    left, right = v_sorted[0], v_sorted[-1]
+            continue
 
-    def intersect(l1, l2):
+        abs_angle = abs(angle)
-        x1, y1, x2, y2 = l1
+        if abs_angle < 40 or abs_angle > 140:
-        x3, y3, x4, y4 = l2
+            across_lines.append(seg)
+        elif 50 < abs_angle < 130:
+            along_lines.append(seg)
+
+    def merge_nearby(segs, position_key, threshold=30):
+        """Merge line segments that are close together (same court line)."""
+        if not segs:
+            return []
+
+        # Sort by mid-position perpendicular to the line direction
+        def get_pos(s):
+            if position_key == 'y':
+                return (s[1] + s[3]) / 2
+            return (s[0] + s[2]) / 2
+
+        sorted_segs = sorted(segs, key=get_pos)
+        groups = []
+        current_group = [sorted_segs[0]]
+
+        for seg in sorted_segs[1:]:
+            if abs(get_pos(seg) - get_pos(current_group[-1])) < threshold:
+                current_group.append(seg)
+            else:
+                groups.append(current_group)
+                current_group = [seg]
+        groups.append(current_group)
+
+        # Merge each group into one representative line
+        merged = []
+        for group in groups:
+            all_pts = []
+            for s in group:
+                all_pts.extend([(s[0], s[1]), (s[2], s[3])])
+            all_pts = np.array(all_pts)
+
+            # Fit a line through all points
+            if len(all_pts) >= 2:
+                vx, vy, cx, cy = cv2.fitLine(all_pts, cv2.DIST_L2, 0, 0.01, 0.01).flatten()
+                # Extend line across frame
+                t_max = max(np.sqrt((p[0] - cx) ** 2 + (p[1] - cy) ** 2) for p in all_pts)
+                x1, y1 = int(cx - vx * t_max), int(cy - vy * t_max)
+                x2, y2 = int(cx + vx * t_max), int(cy + vy * t_max)
+                mid_pos = get_pos(group[0])
+                merged.append({
+                    'endpoints': [x1, y1, x2, y2],
+                    'mid_pos': mid_pos,
+                    'count': len(group),
+                })
+
+        return merged
+
+    grouped = {
+        'across': merge_nearby(across_lines, 'y'),
+        'along': merge_nearby(along_lines, 'x'),
+    }
+
+    return {'segments': segments, 'grouped': grouped}
+
+
+def _match_court_lines(grouped, side, frame_w, frame_h):
+    """Try to match detected line groups to known court lines.
+
+    For a camera at the net looking at one half:
+    - 'across' lines map to: baseline, kitchen line (and possibly net line)
+    - 'along' lines map to: left sideline, right sideline, center service line
+
+    Returns dict with points_2d, points_3d for solvePnP, or error.
+    """
+    across = grouped.get('across', [])
+    along = grouped.get('along', [])
+
+    if len(across) < 2 or len(along) < 2:
+        return {
+            'points_2d': None, 'points_3d': None,
+            'error': f'{len(across)} across + {len(along)} along lines (need 2+ each)',
+        }
+
+    # Sort across lines by vertical position (top of frame = far from camera = baseline)
+    across_sorted = sorted(across, key=lambda l: l['mid_pos'])
+
+    # Sort along lines by horizontal position
+    along_sorted = sorted(along, key=lambda l: l['mid_pos'])
+
+    # Known court geometry for this half
+    # Court: 13.4m x 6.1m, half = 6.7m
+    # Kitchen (NVZ) = 2.13m from net
+    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)]
 
-    corners = [
+    points_2d = []
-        intersect(top, left),
+    points_3d = []
-        intersect(top, right),
-        intersect(bottom, right),
-        intersect(bottom, left),
-    ]
 
-    if any(c is None for c in corners):
+    # Baseline x left sideline
-        return {'corners': None, 'error': 'Court lines are parallel, cannot find intersections'}
+    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])
 
-    return {'corners': np.array(corners, dtype=np.float32), 'error': None}
+    # 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])
+
+    if len(points_2d) < 4:
+        return {
+            'points_2d': None, 'points_3d': None,
+            'error': f'Only {len(points_2d)} intersection points found (need >= 4)',
+        }
+
+    return {'points_2d': points_2d, 'points_3d': points_3d, 'error': None}
 
 
 

src/web/templates/index.html

@@ -509,16 +509,23 @@ function doCalibrate() {
                 errEl.style.display = 'none';
                 updateCalibrationStatus();
 
-                // Show computed camera positions
+                // Show computed camera positions and line stats
                 var posEl = document.getElementById('calPositions');
                 if (posEl && data.result) {
                     posEl.style.display = 'block';
                     for (var sid in data.result) {
                         var r = data.result[sid];
+                        var el = document.getElementById('calPos' + sid);
+                        if (!el) continue;
                         if (r.ok && r.camera_position) {
                             var p = r.camera_position;
-                            var el = document.getElementById('calPos' + sid);
+                            var lines = r.lines_detected || {};
-                            if (el) el.textContent = 'CAM' + sid + ': X=' + p[0].toFixed(2) + ' Y=' + p[1].toFixed(2) + ' Z=' + p[2].toFixed(2) + 'm';
+                            el.textContent = 'CAM' + sid + ': X=' + p[0].toFixed(2) + ' Y=' + p[1].toFixed(2) + ' Z=' + p[2].toFixed(2) + 'm'
+                                + ' (' + (r.points_matched || 0) + 'pts, ' + (lines.across || 0) + 'a+' + (lines.along || 0) + 'l)';
+                        } else if (r.lines_detected) {
+                            var lines = r.lines_detected;
+                            el.textContent = 'CAM' + sid + ': ' + (lines.across || 0) + ' across + ' + (lines.along || 0) + ' along lines';
+                            el.style.color = '#ff4444';
                         }
                     }
                 }