print("� [CROSSWALK_UTILS2] This is d:/Downloads/finale6/Khatam final/khatam/qt_app_pyside/utils/crosswalk_utils2.py LOADED") import cv2 import numpy as np from typing import Tuple, Optional def detect_crosswalk_and_violation_line(frame: np.ndarray, traffic_light_position: Optional[Tuple[int, int]] = None, perspective_M: Optional[np.ndarray] = None): """ Detects crosswalk (zebra crossing) or fallback stop line in a traffic scene using classical CV. Args: frame: BGR image frame from video feed traffic_light_position: Optional (x, y) of traffic light in frame perspective_M: Optional 3x3 homography matrix for bird's eye view normalization Returns: result_frame: frame with overlays (for visualization) crosswalk_bbox: (x, y, w, h) or None if fallback used violation_line_y: int (y position for violation check) debug_info: dict (for visualization/debugging) """ # --- PROCESS CROSSWALK DETECTION REGARDLESS OF TRAFFIC LIGHT --- print(f"[CROSSWALK DEBUG] Starting crosswalk detection. Traffic light: {traffic_light_position}") if traffic_light_position is None: print("[CROSSWALK DEBUG] No traffic light detected, but proceeding with crosswalk detection") debug_info = {} orig_frame = frame.copy() h, w = frame.shape[:2] # 1. Perspective Normalization (Bird's Eye View) if perspective_M is not None: frame = cv2.warpPerspective(frame, perspective_M, (w, h)) debug_info['perspective_warped'] = True else: debug_info['perspective_warped'] = False # 1. Enhanced White Color Filtering (more permissive for zebra stripes) mask_white = cv2.inRange(frame, (140, 140, 140), (255, 255, 255)) debug_info['mask_white_ratio'] = np.sum(mask_white > 0) / (h * w) # 2. Grayscale for adaptive threshold gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Enhance contrast for night/low-light if np.mean(gray) < 80: gray = cv2.equalizeHist(gray) debug_info['hist_eq'] = True else: debug_info['hist_eq'] = False # 3. Adaptive threshold (more permissive) thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 3) # Combine with color mask combined = cv2.bitwise_and(thresh, mask_white) # 4. Better morphology for zebra stripe detection # Horizontal kernel to connect zebra stripes kernel_h = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 3)) morph = cv2.morphologyEx(combined, cv2.MORPH_CLOSE, kernel_h, iterations=1) # Vertical kernel to separate stripes kernel_v = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 5)) morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel_v, iterations=1) # Find contours contours, _ = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) zebra_rects = [] # Focus on lower half of frame where crosswalks typically are roi_y_start = int(h * 0.4) # Start from 40% down for cnt in contours: x, y, w, h_rect = cv2.boundingRect(cnt) # Skip if in upper part of frame if y < roi_y_start: continue aspect_ratio = w / max(h_rect, 1) area = w * h_rect # More permissive criteria for zebra stripe detection min_area = 300 # Smaller minimum area max_area = 0.3 * frame.shape[0] * frame.shape[1] # Larger max area min_aspect = 2.0 # Lower aspect ratio requirement max_height = 40 # Allow taller stripes if (aspect_ratio > min_aspect and min_area < area < max_area and h_rect < max_height and w > 50): # Minimum width for zebra stripe angle = 0 # For simplicity, assume horizontal stripes zebra_rects.append((x, y, w, h_rect, angle)) print(f"[CROSSWALK DEBUG] Found {len(zebra_rects)} zebra stripe candidates") # --- Enhanced Grouping and Scoring for Crosswalk Detection --- def group_score(group): if len(group) < 2: # Reduced minimum requirement return 0 heights = [r[3] for r in group] x_centers = [r[0] + r[2]//2 for r in group] y_centers = [r[1] + r[3]//2 for r in group] # Stripe count (normalized) - more permissive count_score = min(len(group) / 4, 1.0) # Reduced from 6 to 4 # Height consistency if len(heights) > 1: height_score = 1.0 - min(np.std(heights) / (np.mean(heights) + 1e-6), 1.0) else: height_score = 0.5 # Horizontal alignment (zebra stripes should be roughly aligned) if len(y_centers) > 1: y_score = 1.0 - min(np.std(y_centers) / (h * 0.1), 1.0) else: y_score = 0.5 # Regular spacing between stripes if len(group) >= 3: x_sorted = sorted([r[0] for r in group]) gaps = [x_sorted[i+1] - x_sorted[i] for i in range(len(x_sorted)-1)] gap_consistency = 1.0 - min(np.std(gaps) / (np.mean(gaps) + 1e-6), 1.0) else: gap_consistency = 0.3 # Area coverage (zebra crossing should cover reasonable area) total_area = sum(r[2] * r[3] for r in group) area_score = min(total_area / (w * h * 0.05), 1.0) # At least 5% of frame # Final score (weighted sum) score = (0.3*count_score + 0.2*height_score + 0.2*y_score + 0.15*gap_consistency + 0.15*area_score) return score # 4. More flexible grouping crosswalk_bbox = None violation_line_y = None if len(zebra_rects) >= 2: # Reduced minimum requirement from 3 to 2 # Sort by y-coordinate for grouping zebra_rects = sorted(zebra_rects, key=lambda r: r[1]) # Group stripes that are horizontally aligned y_tolerance = int(h * 0.08) # Increased tolerance to 8% groups = [] if zebra_rects: group = [zebra_rects[0]] for rect in zebra_rects[1:]: # Check if this stripe is roughly at the same y-level as the group group_y_avg = sum(r[1] for r in group) / len(group) if abs(rect[1] - group_y_avg) < y_tolerance: group.append(rect) else: if len(group) >= 2: # Reduced from 3 to 2 groups.append(group) group = [rect] # Don't forget the last group if len(group) >= 2: groups.append(group) # Score all groups scored_groups = [(group_score(g), g) for g in groups] # More permissive threshold scored_groups = [(s, g) for s, g in scored_groups if s > 0.05] # Reduced from 0.1 print(f"[CROSSWALK DEBUG] Found {len(groups)} potential crosswalk groups") print(f"[CROSSWALK DEBUG] scored_groups: {[round(s, 3) for s, _ in scored_groups]}") if scored_groups: scored_groups.sort(reverse=True, key=lambda x: x[0]) best_score, best_group = scored_groups[0] print(f"[CROSSWALK DEBUG] Best crosswalk group score: {best_score:.3f}") print(f"[CROSSWALK DEBUG] Best group has {len(best_group)} stripes") # Calculate crosswalk bounding box xs = [r[0] for r in best_group] + [r[0] + r[2] for r in best_group] ys = [r[1] for r in best_group] + [r[1] + r[3] for r in best_group] x1, x2 = min(xs), max(xs) y1, y2 = min(ys), max(ys) crosswalk_bbox = (x1, y1, x2 - x1, y2 - y1) # Place violation line just before the crosswalk violation_line_y = y1 - 15 # 15 pixels before crosswalk starts debug_info['crosswalk_group'] = best_group debug_info['crosswalk_score'] = best_score debug_info['crosswalk_bbox'] = crosswalk_bbox print(f"[CROSSWALK DEBUG] CROSSWALK DETECTED at bbox: {crosswalk_bbox}") print(f"[CROSSWALK DEBUG] Violation line at y={violation_line_y}") else: print("[CROSSWALK DEBUG] No valid crosswalk groups found") # --- Fallback: Improved Stop line detection --- if crosswalk_bbox is None: # Enhanced edge detection for stop lines edges = cv2.Canny(gray, 50, 150, apertureSize=3) # Focus on lower half of frame where stop lines typically are roi_height = int(h * 0.6) # Lower 60% of frame roi_y = h - roi_height roi_edges = edges[roi_y:h, :] # Detect horizontal lines (stop lines) lines = cv2.HoughLinesP(roi_edges, 1, np.pi / 180, threshold=50, minLineLength=100, maxLineGap=30) stop_lines = [] if lines is not None: for l in lines: x1, y1, x2, y2 = l[0] # Convert back to full frame coordinates y1 += roi_y y2 += roi_y # Check if line is horizontal (stop line characteristic) angle = np.degrees(np.arctan2(y2 - y1, x2 - x1)) line_length = np.sqrt((x2-x1)**2 + (y2-y1)**2) if (abs(angle) < 15 or abs(angle) > 165) and line_length > 80: stop_lines.append((x1, y1, x2, y2)) debug_info['stop_lines'] = stop_lines print(f"[CROSSWALK DEBUG] stop_lines: {len(stop_lines)} found") if stop_lines: # Choose the best stop line based on traffic light position or bottom-most line if traffic_light_position: tx, ty = traffic_light_position # Find line closest to traffic light but below it valid_lines = [l for l in stop_lines if ((l[1]+l[3])//2) > ty + 50] if valid_lines: best_line = min(valid_lines, key=lambda l: abs(((l[1]+l[3])//2) - (ty + 100))) else: best_line = min(stop_lines, key=lambda l: abs(((l[1]+l[3])//2) - ty)) else: # Use the bottom-most horizontal line as stop line best_line = max(stop_lines, key=lambda l: max(l[1], l[3])) x1, y1, x2, y2 = best_line crosswalk_bbox = None # Place violation line slightly above the detected stop line violation_line_y = min(y1, y2) - 10 debug_info['stop_line'] = best_line print(f"[CROSSWALK DEBUG] using stop_line: {best_line}") print(f"[CROSSWALK DEBUG] violation line placed at y={violation_line_y}") # Draw violation line on the frame for visualization result_frame = orig_frame.copy() if violation_line_y is not None: print(f"[CROSSWALK DEBUG] Drawing VIOLATION LINE at y={violation_line_y}") result_frame = draw_violation_line(result_frame, violation_line_y, color=(0, 0, 255), thickness=8, style='solid', label='VIOLATION LINE') return result_frame, crosswalk_bbox, violation_line_y, debug_info def draw_violation_line(frame: np.ndarray, y: int, color=(0, 0, 255), thickness=8, style='solid', label='Violation Line'): """ Draws a thick, optionally dashed, labeled violation line at the given y-coordinate. Args: frame: BGR image y: y-coordinate for the line color: BGR color tuple thickness: line thickness style: 'solid' or 'dashed' label: Optional label to draw above the line Returns: frame with line overlay """ import cv2 h, w = frame.shape[:2] x1, x2 = 0, w overlay = frame.copy() if style == 'dashed': dash_len = 30 gap = 20 for x in range(x1, x2, dash_len + gap): x_end = min(x + dash_len, x2) cv2.line(overlay, (x, y), (x_end, y), color, thickness, lineType=cv2.LINE_AA) else: cv2.line(overlay, (x1, y), (x2, y), color, thickness, lineType=cv2.LINE_AA) # Blend for semi-transparency cv2.addWeighted(overlay, 0.7, frame, 0.3, 0, frame) # Draw label if label: font = cv2.FONT_HERSHEY_SIMPLEX text_size, _ = cv2.getTextSize(label, font, 0.8, 2) text_x = max(10, (w - text_size[0]) // 2) text_y = max(0, y - 12) cv2.rectangle(frame, (text_x - 5, text_y - text_size[1] - 5), (text_x + text_size[0] + 5, text_y + 5), (0,0,0), -1) cv2.putText(frame, label, (text_x, text_y), font, 0.8, color, 2, cv2.LINE_AA) return frame def get_violation_line_y(frame, traffic_light_bbox=None, crosswalk_bbox=None): """ Returns the y-coordinate of the violation line using the following priority: 1. Crosswalk bbox (most accurate) 2. Stop line detection via image processing (CV) 3. Traffic light bbox heuristic 4. Fallback (default) """ height, width = frame.shape[:2] # 1. Crosswalk bbox if crosswalk_bbox is not None and len(crosswalk_bbox) == 4: return int(crosswalk_bbox[1]) - 15 # 2. Stop line detection (CV) roi_height = int(height * 0.4) roi_y = height - roi_height roi = frame[roi_y:height, 0:width] gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY) binary = cv2.adaptiveThreshold( gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 15, -2 ) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 1)) processed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel) contours, _ = cv2.findContours(processed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) stop_line_candidates = [] for cnt in contours: x, y, w, h = cv2.boundingRect(cnt) aspect_ratio = w / max(h, 1) normalized_width = w / width if (aspect_ratio > 5 and normalized_width > 0.3 and h < 15 and y > roi_height * 0.5): abs_y = y + roi_y stop_line_candidates.append((abs_y, w)) if stop_line_candidates: stop_line_candidates.sort(key=lambda x: x[1], reverse=True) return stop_line_candidates[0][0] # 3. Traffic light bbox heuristic if traffic_light_bbox is not None and len(traffic_light_bbox) == 4: traffic_light_bottom = traffic_light_bbox[3] traffic_light_height = traffic_light_bbox[3] - traffic_light_bbox[1] estimated_distance = min(5 * traffic_light_height, height * 0.3) return min(int(traffic_light_bottom + estimated_distance), height - 20) # Example usage: # bbox, vline, dbg = detect_crosswalk_and_violation_line(frame, (tl_x, tl_y), perspective_M)