952 lines
40 KiB
Python
952 lines
40 KiB
Python
print("🟡 [CROSSWALK_UTILS] This is d:/Downloads/finale6/Khatam final/khatam/qt_app_pyside/utils/crosswalk_utils.py LOADED")
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import cv2
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import numpy as np
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from typing import Tuple, Optional
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def detect_crosswalk_and_violation_line(frame: np.ndarray, traffic_light_position: Optional[Tuple[int, int]] = None):
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"""
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Detects crosswalk (zebra crossing) or fallback stop line in a traffic scene using classical CV.
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Args:
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frame: BGR image frame from video feed
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traffic_light_position: Optional (x, y) of traffic light in frame
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Returns:
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crosswalk_bbox: (x, y, w, h) or None if fallback used
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violation_line_y: int (y position for violation check)
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debug_info: dict (for visualization/debugging)
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"""
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debug_info = {}
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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h, w = gray.shape
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# --- Preprocessing for zebra crossing ---
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# Enhance contrast for night/low-light
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if np.mean(gray) < 80:
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gray = cv2.equalizeHist(gray)
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debug_info['hist_eq'] = True
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else:
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debug_info['hist_eq'] = False
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# Adaptive threshold to isolate white stripes
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thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C,
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cv2.THRESH_BINARY, 19, 7)
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# Morphology to connect stripes
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 3))
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morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
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# Find contours
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contours, _ = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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zebra_rects = []
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for cnt in contours:
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x, y, rw, rh = cv2.boundingRect(cnt)
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area = rw * rh
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aspect = rw / rh if rh > 0 else 0
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# Heuristic: long, thin, bright, horizontal stripes
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if area > 500 and 2 < aspect < 15 and rh < h * 0.15:
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zebra_rects.append((x, y, rw, rh))
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debug_info['zebra_rects'] = zebra_rects
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# Group rectangles that are aligned horizontally (zebra crossing)
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crosswalk_bbox = None
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violation_line_y = None
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if len(zebra_rects) >= 3:
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# Sort by y, then group by proximity
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zebra_rects = sorted(zebra_rects, key=lambda r: r[1])
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groups = []
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group = [zebra_rects[0]]
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for rect in zebra_rects[1:]:
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if abs(rect[1] - group[-1][1]) < 40: # 40px vertical tolerance
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group.append(rect)
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else:
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if len(group) >= 3:
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groups.append(group)
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group = [rect]
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if len(group) >= 3:
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groups.append(group)
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# Pick group closest to traffic light (if provided), else lowest in frame
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def group_center_y(g):
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return np.mean([r[1] + r[3] // 2 for r in g])
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if groups:
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if traffic_light_position:
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tx, ty = traffic_light_position
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best_group = min(groups, key=lambda g: abs(group_center_y(g) - ty))
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else:
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best_group = max(groups, key=group_center_y)
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# Union bbox
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xs = [r[0] for r in best_group] + [r[0] + r[2] for r in best_group]
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ys = [r[1] for r in best_group] + [r[1] + r[3] for r in best_group]
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x1, x2 = min(xs), max(xs)
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y1, y2 = min(ys), max(ys)
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crosswalk_bbox = (x1, y1, x2 - x1, y2 - y1)
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# Violation line: just before crosswalk starts (bottom of bbox - margin)
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violation_line_y = y2 - 5
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debug_info['crosswalk_group'] = best_group
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# --- Fallback: Stop line detection ---
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if crosswalk_bbox is None:
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edges = cv2.Canny(gray, 80, 200)
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lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=80, minLineLength=60, maxLineGap=20)
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stop_lines = []
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if lines is not None:
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for l in lines:
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x1, y1, x2, y2 = l[0]
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angle = np.degrees(np.arctan2(y2 - y1, x2 - x1))
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if abs(angle) < 20 or abs(angle) > 160: # horizontal
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if y1 > h // 2 or y2 > h // 2: # lower half
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stop_lines.append((x1, y1, x2, y2))
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debug_info['stop_lines'] = stop_lines
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if stop_lines:
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# Pick the lowest (closest to bottom or traffic light)
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if traffic_light_position:
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tx, ty = traffic_light_position
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best_line = min(stop_lines, key=lambda l: abs(((l[1]+l[3])//2) - ty))
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else:
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best_line = max(stop_lines, key=lambda l: max(l[1], l[3]))
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x1, y1, x2, y2 = best_line
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crosswalk_bbox = None
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violation_line_y = min(y1, y2) - 5
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debug_info['stop_line'] = best_line
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return crosswalk_bbox, violation_line_y, debug_info
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# Example usage:
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# bbox, vline, dbg = detect_crosswalk_and_violation_line(frame, (tl_x, tl_y))
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print("🟡 [CROSSWALK_UTILS] This is d:/Downloads/finale6/Khatam final/khatam/qt_app_pyside/utils/crosswalk_utils.py LOADED")
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import cv2
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import numpy as np
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from typing import Dict, List, Tuple, Optional, Any
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import math
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# --- DeepLabV3+ Crosswalk Segmentation Integration ---
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import sys
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import os
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sys.path.append(r'D:\Downloads\finale6\Khatam final\khatam\qt_app_pyside\DeepLabV3Plus-Pytorch')
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import torch
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import torch.nn as nn
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from PIL import Image
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from torchvision import transforms as T
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def detect_crosswalk(frame: np.ndarray, roi_height_percentage: float = 0.4) -> Optional[List[int]]:
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"""
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[DEPRECATED] Use detect_and_draw_crosswalk for advanced visualization and analytics.
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This function is kept for backward compatibility but will print a warning.
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"""
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print("[WARN] detect_crosswalk is deprecated. Use detect_and_draw_crosswalk instead.")
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try:
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height, width = frame.shape[:2]
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roi_height = int(height * roi_height_percentage)
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roi_y = height - roi_height
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# Extract ROI
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roi = frame[roi_y:height, 0:width]
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# Convert to grayscale
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gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
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# Apply adaptive thresholding
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binary = cv2.adaptiveThreshold(
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gray,
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255,
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cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY,
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19,
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2
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)
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# Apply morphological operations to clean up the binary image
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 3))
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binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
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binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
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# Find contours
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contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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# Filter contours by shape and aspect ratio
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potential_stripes = []
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for contour in contours:
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x, y, w, h = cv2.boundingRect(contour)
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aspect_ratio = w / h if h > 0 else 0
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area = cv2.contourArea(contour)
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# Stripe criteria: Rectangular, wide, not too tall
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if area > 100 and aspect_ratio >= 3 and aspect_ratio <= 20:
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potential_stripes.append((x, y + roi_y, w, h))
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# Group nearby stripes into crosswalk
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if len(potential_stripes) >= 3:
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# Sort by y-coordinate (top to bottom)
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potential_stripes.sort(key=lambda s: s[1])
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# Find groups of stripes with similar y-positions
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stripe_groups = []
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current_group = [potential_stripes[0]]
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for i in range(1, len(potential_stripes)):
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# If this stripe is close to the previous one in y-direction
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if abs(potential_stripes[i][1] - current_group[-1][1]) < 50:
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current_group.append(potential_stripes[i])
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else:
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# Start a new group
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if len(current_group) >= 3:
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stripe_groups.append(current_group)
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current_group = [potential_stripes[i]]
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# Add the last group if it has enough stripes
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if len(current_group) >= 3:
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stripe_groups.append(current_group)
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# Find the largest group
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if stripe_groups:
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largest_group = max(stripe_groups, key=len)
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# Compute bounding box for the crosswalk
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min_x = min(stripe[0] for stripe in largest_group)
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min_y = min(stripe[1] for stripe in largest_group)
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max_x = max(stripe[0] + stripe[2] for stripe in largest_group)
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max_y = max(stripe[1] + stripe[3] for stripe in largest_group)
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return [min_x, min_y, max_x, max_y]
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return None
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except Exception as e:
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print(f"Error detecting crosswalk: {e}")
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return None
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def detect_stop_line(frame: np.ndarray) -> Optional[int]:
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"""
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Detect stop line in a frame using edge detection and Hough Line Transform.
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Args:
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frame: Input video frame
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Returns:
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Y-coordinate of the stop line or None if not detected
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"""
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try:
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height, width = frame.shape[:2]
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# Define ROI - bottom 30% of the frame
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roi_height = int(height * 0.3)
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roi_y = height - roi_height
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roi = frame[roi_y:height, 0:width].copy()
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# Convert to grayscale
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gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
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# Apply Gaussian blur to reduce noise
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blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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# Apply Canny edge detection
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edges = cv2.Canny(blurred, 50, 150)
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# Apply Hough Line Transform
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lines = cv2.HoughLinesP(
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edges,
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rho=1,
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theta=np.pi/180,
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threshold=80,
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minLineLength=width//3, # Lines should be at least 1/3 of image width
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maxLineGap=50
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)
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if lines is None or len(lines) == 0:
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return None
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# Filter horizontal lines (slope close to 0)
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horizontal_lines = []
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for line in lines:
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x1, y1, x2, y2 = line[0]
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if x2 - x1 == 0: # Avoid division by zero
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continue
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slope = abs((y2 - y1) / (x2 - x1))
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# Horizontal line has slope close to 0
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if slope < 0.2:
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horizontal_lines.append((x1, y1, x2, y2, slope))
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if not horizontal_lines:
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return None
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# Sort by y-coordinate (bottom to top)
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horizontal_lines.sort(key=lambda line: max(line[1], line[3]), reverse=True)
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# Get the uppermost horizontal line
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if horizontal_lines:
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x1, y1, x2, y2, _ = horizontal_lines[0]
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stop_line_y = roi_y + max(y1, y2)
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return stop_line_y
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return None
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except Exception as e:
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print(f"Error detecting stop line: {e}")
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return None
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def draw_violation_line(frame: np.ndarray, y_coord: int, color: Tuple[int, int, int] = (0, 0, 255),
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label: str = "VIOLATION LINE", thickness: int = 2) -> np.ndarray:
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"""
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Draw a violation line on the frame with customizable label.
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Args:
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frame: Input video frame
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y_coord: Y-coordinate for the line
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color: Line color (BGR)
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label: Custom label text to display
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thickness: Line thickness
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Returns:
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Frame with the violation line drawn
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"""
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height, width = frame.shape[:2]
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cv2.line(frame, (0, y_coord), (width, y_coord), color, thickness)
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# Add label with transparent background for better visibility
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text_size, _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.6, 2)
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text_x = width // 2 - text_size[0] // 2
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text_y = y_coord - 10
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# Draw semi-transparent background
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overlay = frame.copy()
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cv2.rectangle(
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overlay,
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(text_x - 5, text_y - text_size[1] - 5),
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(text_x + text_size[0] + 5, text_y + 5),
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(0, 0, 0),
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-1
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)
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cv2.addWeighted(overlay, 0.6, frame, 0.4, 0, frame)
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# Add label
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cv2.putText(
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frame,
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label,
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(text_x, text_y),
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cv2.FONT_HERSHEY_SIMPLEX,
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0.6,
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color,
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2
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)
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return frame
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def check_vehicle_violation(vehicle_bbox: List[int], violation_line_y: int) -> bool:
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"""
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Check if a vehicle has crossed the violation line.
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Args:
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vehicle_bbox: Vehicle bounding box [x1, y1, x2, y2]
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violation_line_y: Y-coordinate of the violation line
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Returns:
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True if violation detected, False otherwise
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"""
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# Get the bottom-center point of the vehicle
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x1, y1, x2, y2 = vehicle_bbox
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vehicle_bottom = y2
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vehicle_center_y = (y1 + y2) / 2
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# Calculate how much of the vehicle is below the violation line
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height = y2 - y1
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if height <= 0: # Avoid division by zero
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return False
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# A vehicle is considered in violation if either:
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# 1. Its bottom edge is below the violation line
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# 2. Its center is below the violation line (for large vehicles)
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is_violation = (vehicle_bottom > violation_line_y) or (vehicle_center_y > violation_line_y)
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if is_violation:
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print(f"🚨 Vehicle crossing violation line! Vehicle bottom: {vehicle_bottom}, Line: {violation_line_y}")
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return is_violation
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def get_deeplab_model(weights_path, device='cpu', model_name='deeplabv3plus_mobilenet', num_classes=21, output_stride=8):
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"""
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Loads DeepLabV3+ model and weights for crosswalk segmentation.
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"""
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print(f"[DEBUG] get_deeplab_model called with weights_path={weights_path}, device={device}, model_name={model_name}")
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import network # DeepLabV3Plus-Pytorch/network/__init__.py
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model = network.modeling.__dict__[model_name](num_classes=num_classes, output_stride=output_stride)
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if weights_path is not None and os.path.isfile(weights_path):
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print(f"[DEBUG] Loading weights from: {weights_path}")
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checkpoint = torch.load(weights_path, map_location=torch.device(device))
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model.load_state_dict(checkpoint["model_state"])
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else:
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print(f"[DEBUG] Weights file not found: {weights_path}")
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model = nn.DataParallel(model)
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model.to(device)
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model.eval()
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print(f"[DEBUG] Model loaded and moved to {device}")
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return model
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def run_inference(model, frame, device='cpu'):
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"""
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Preprocesses frame and runs DeepLabV3+ model to get mask.
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"""
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# frame: np.ndarray (H, W, 3) in BGR
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img_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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pil_img = Image.fromarray(img_rgb)
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transform = T.Compose([
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T.ToTensor(),
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T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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input_tensor = transform(pil_img).unsqueeze(0).to(device)
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with torch.no_grad():
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output = model(input_tensor)
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if isinstance(output, dict):
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output = output["out"] if "out" in output else list(output.values())[0]
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mask = output.argmax(1).squeeze().cpu().numpy().astype(np.uint8)
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return mask
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def detect_and_draw_crosswalk(frame: np.ndarray, roi_height_percentage: float = 0.4, use_deeplab: bool = True) -> Tuple[np.ndarray, Optional[List[int]], Optional[List]]:
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"""
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Advanced crosswalk detection with DeepLabV3+ segmentation (if enabled),
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otherwise falls back to Hough Transform + line clustering.
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Args:
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frame: Input video frame
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roi_height_percentage: Percentage of the frame height to use as ROI
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use_deeplab: If True, use DeepLabV3+ segmentation for crosswalk detection
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Returns:
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Tuple containing:
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- Annotated frame with crosswalk visualization
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- Crosswalk bounding box [x, y, w, h] or None if not detected
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- List of detected crosswalk contours or lines or None
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"""
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try:
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height, width = frame.shape[:2]
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annotated_frame = frame.copy()
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print(f"[DEBUG] detect_and_draw_crosswalk called, use_deeplab={use_deeplab}")
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# --- DeepLabV3+ Segmentation Path ---
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if use_deeplab:
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# Load model only once (cache in function attribute)
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if not hasattr(detect_and_draw_crosswalk, '_deeplab_model'):
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weights_path = os.path.join(os.path.dirname(__file__), '../DeepLabV3Plus-Pytorch/best_crosswalk.pth')
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print(f"[DEBUG] Loading DeepLabV3+ model from: {weights_path}")
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detect_and_draw_crosswalk._deeplab_model = get_deeplab_model(weights_path, device='cpu')
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model = detect_and_draw_crosswalk._deeplab_model
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# Run inference
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mask = run_inference(model, frame)
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print(f"[DEBUG] DeepLabV3+ mask shape: {mask.shape}, unique values: {np.unique(mask)}")
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# Assume crosswalk class index is 12 (change if needed)
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crosswalk_class = 12
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crosswalk_mask = (mask == crosswalk_class).astype(np.uint8) * 255
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print(f"[DEBUG] crosswalk_mask unique values: {np.unique(crosswalk_mask)}")
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# Find contours in mask
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contours, _ = cv2.findContours(crosswalk_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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print(f"[DEBUG] DeepLabV3+ found {len(contours)} contours")
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if not contours:
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print("[DEBUG] No contours found in DeepLabV3+ mask, falling back to classic method.")
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# Fallback to classic method if nothing found
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return detect_and_draw_crosswalk(frame, roi_height_percentage, use_deeplab=False)
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# Draw all crosswalk contours
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x_min, y_min, x_max, y_max = width, height, 0, 0
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for cnt in contours:
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x, y, w, h = cv2.boundingRect(cnt)
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x_min = min(x_min, x)
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y_min = min(y_min, y)
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x_max = max(x_max, x + w)
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y_max = max(y_max, y + h)
|
|
cv2.drawContours(annotated_frame, [cnt], -1, (0, 255, 255), 3)
|
|
# Clamp bbox to frame and ensure non-negative values
|
|
x_min = max(0, min(x_min, width - 1))
|
|
y_min = max(0, min(y_min, height - 1))
|
|
x_max = max(0, min(x_max, width - 1))
|
|
y_max = max(0, min(y_max, height - 1))
|
|
w = max(0, x_max - x_min)
|
|
h = max(0, y_max - y_min)
|
|
crosswalk_bbox = [x_min, y_min, w, h]
|
|
# Ignore invalid bboxes
|
|
if w <= 0 or h <= 0:
|
|
print("[DEBUG] Ignoring invalid crosswalk_bbox (zero or negative size)")
|
|
return annotated_frame, None, contours
|
|
# TODO: Mask out detected vehicles before running crosswalk detection to reduce false positives.
|
|
cv2.rectangle(
|
|
annotated_frame,
|
|
(crosswalk_bbox[0], crosswalk_bbox[1]),
|
|
(crosswalk_bbox[0] + crosswalk_bbox[2], crosswalk_bbox[1] + crosswalk_bbox[3]),
|
|
(0, 255, 255), 2
|
|
)
|
|
cv2.putText(
|
|
annotated_frame,
|
|
"CROSSWALK",
|
|
(crosswalk_bbox[0], crosswalk_bbox[1] - 10),
|
|
cv2.FONT_HERSHEY_SIMPLEX,
|
|
0.7,
|
|
(0, 255, 255),
|
|
2
|
|
)
|
|
print(f"[DEBUG] DeepLabV3+ crosswalk_bbox: {crosswalk_bbox}")
|
|
return annotated_frame, crosswalk_bbox, contours
|
|
# --- Classic Hough Transform Fallback ---
|
|
print("[DEBUG] Using classic Hough Transform fallback method.")
|
|
height, width = frame.shape[:2]
|
|
roi_height = int(height * roi_height_percentage)
|
|
roi_y = height - roi_height
|
|
roi = frame[roi_y:height, 0:width]
|
|
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
|
|
blurred = cv2.GaussianBlur(gray, (5, 5), 0)
|
|
edges = cv2.Canny(blurred, 50, 150, apertureSize=3)
|
|
lines = cv2.HoughLinesP(edges, 1, np.pi/180, threshold=60, minLineLength=40, maxLineGap=30)
|
|
print(f"[DEBUG] HoughLinesP found {0 if lines is None else len(lines)} lines")
|
|
if lines is None:
|
|
return frame, None, None
|
|
angle_threshold = 12 # degrees
|
|
parallel_lines = []
|
|
for line in lines:
|
|
x1, y1, x2, y2 = line[0]
|
|
angle = math.degrees(math.atan2(y2 - y1, x2 - x1))
|
|
if -angle_threshold <= angle <= angle_threshold or 80 <= abs(angle) <= 100:
|
|
parallel_lines.append((x1, y1, x2, y2, angle))
|
|
print(f"[DEBUG] {len(parallel_lines)} parallel lines after angle filtering")
|
|
if len(parallel_lines) < 3:
|
|
return frame, None, None
|
|
parallel_lines = sorted(parallel_lines, key=lambda l: min(l[1], l[3]))
|
|
clusters = []
|
|
cluster = [parallel_lines[0]]
|
|
min_spacing = 10
|
|
max_spacing = 60
|
|
for i in range(1, len(parallel_lines)):
|
|
prev_y = min(cluster[-1][1], cluster[-1][3])
|
|
curr_y = min(parallel_lines[i][1], parallel_lines[i][3])
|
|
spacing = abs(curr_y - prev_y)
|
|
if min_spacing < spacing < max_spacing:
|
|
cluster.append(parallel_lines[i])
|
|
else:
|
|
if len(cluster) >= 3:
|
|
clusters.append(cluster)
|
|
cluster = [parallel_lines[i]]
|
|
if len(cluster) >= 3:
|
|
clusters.append(cluster)
|
|
print(f"[DEBUG] {len(clusters)} clusters found")
|
|
if not clusters:
|
|
return frame, None, None
|
|
best_cluster = max(clusters, key=len)
|
|
x_min = width
|
|
y_min = roi_height
|
|
x_max = 0
|
|
y_max = 0
|
|
for x1, y1, x2, y2, angle in best_cluster:
|
|
cv2.line(annotated_frame, (x1, y1 + roi_y), (x2, y2 + roi_y), (0, 255, 255), 3)
|
|
x_min = min(x_min, x1, x2)
|
|
y_min = min(y_min, y1, y2)
|
|
x_max = max(x_max, x1, x2)
|
|
y_max = max(y_max, y1, y2)
|
|
crosswalk_bbox = [x_min, y_min + roi_y, x_max - x_min, y_max - y_min]
|
|
cv2.rectangle(
|
|
annotated_frame,
|
|
(crosswalk_bbox[0], crosswalk_bbox[1]),
|
|
(crosswalk_bbox[0] + crosswalk_bbox[2], crosswalk_bbox[1] + crosswalk_bbox[3]),
|
|
(0, 255, 255), 2
|
|
)
|
|
cv2.putText(
|
|
annotated_frame,
|
|
"CROSSWALK",
|
|
(crosswalk_bbox[0], crosswalk_bbox[1] - 10),
|
|
cv2.FONT_HERSHEY_SIMPLEX,
|
|
0.7,
|
|
(0, 255, 255),
|
|
2
|
|
)
|
|
print(f"[DEBUG] Classic method crosswalk_bbox: {crosswalk_bbox}")
|
|
return annotated_frame, crosswalk_bbox, best_cluster
|
|
except Exception as e:
|
|
print(f"Error in detect_and_draw_crosswalk: {str(e)}")
|
|
import traceback
|
|
traceback.print_exc()
|
|
return frame, None, None
|
|
|
|
|
|
#working
|
|
print("🟡 [CROSSWALK_UTILS] This is d:/Downloads/finale6/Khatam final/khatam/qt_app_pyside/utils/crosswalk_utils.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)
|
|
"""
|
|
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. White Color Filtering (relaxed)
|
|
mask_white = cv2.inRange(frame, (160, 160, 160), (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
|
|
# 5. Adaptive threshold (tuned)
|
|
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
|
|
cv2.THRESH_BINARY, 15, 5)
|
|
# Combine with color mask
|
|
combined = cv2.bitwise_and(thresh, mask_white)
|
|
# 2. Morphology (tuned)
|
|
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (25, 3))
|
|
morph = cv2.morphologyEx(combined, cv2.MORPH_CLOSE, kernel, iterations=1)
|
|
# Find contours
|
|
contours, _ = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
zebra_rects = []
|
|
for cnt in contours:
|
|
x, y, w, h = cv2.boundingRect(cnt)
|
|
aspect_ratio = w / max(h, 1)
|
|
area = w * h
|
|
angle = 0 # For simplicity, assume horizontal stripes
|
|
# Heuristic: wide, short, and not too small
|
|
if aspect_ratio > 3 and 1000 < area < 0.5 * frame.shape[0] * frame.shape[1] and h < 60:
|
|
zebra_rects.append((x, y, w, h, angle))
|
|
cv2.rectangle(orig_frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
|
|
# --- Overlay drawing for debugging: draw all zebra candidates ---
|
|
for r in zebra_rects:
|
|
x, y, rw, rh, _ = r
|
|
cv2.rectangle(orig_frame, (x, y), (x+rw, y+rh), (0, 255, 0), 2)
|
|
# Draw all zebra candidate rectangles for debugging (no saving)
|
|
for r in zebra_rects:
|
|
x, y, rw, rh, _ = r
|
|
cv2.rectangle(orig_frame, (x, y), (x+rw, y+rh), (0, 255, 0), 2)
|
|
# --- Probabilistic Scoring for Groups ---
|
|
def group_score(group):
|
|
if len(group) < 3:
|
|
return 0
|
|
heights = [r[3] for r in group]
|
|
x_centers = [r[0] + r[2]//2 for r in group]
|
|
angles = [r[4] for r in group]
|
|
# Stripe count (normalized)
|
|
count_score = min(len(group) / 6, 1.0)
|
|
# Height consistency
|
|
height_score = 1.0 - min(np.std(heights) / (np.mean(heights) + 1e-6), 1.0)
|
|
# X-center alignment
|
|
x_score = 1.0 - min(np.std(x_centers) / (w * 0.2), 1.0)
|
|
# Angle consistency (prefer near 0 or 90)
|
|
mean_angle = np.mean([abs(a) for a in angles])
|
|
angle_score = 1.0 - min(np.std(angles) / 10.0, 1.0)
|
|
# Whiteness (mean mask_white in group area)
|
|
whiteness = 0
|
|
for r in group:
|
|
x, y, rw, rh, _ = r
|
|
whiteness += np.mean(mask_white[y:y+rh, x:x+rw]) / 255
|
|
whiteness_score = whiteness / len(group)
|
|
# Final score (weighted sum)
|
|
score = 0.25*count_score + 0.2*height_score + 0.2*x_score + 0.15*angle_score + 0.2*whiteness_score
|
|
return score
|
|
# 4. Dynamic grouping tolerance
|
|
y_tolerance = int(h * 0.05)
|
|
crosswalk_bbox = None
|
|
violation_line_y = None
|
|
best_score = 0
|
|
best_group = None
|
|
if len(zebra_rects) >= 3:
|
|
zebra_rects = sorted(zebra_rects, key=lambda r: r[1])
|
|
groups = []
|
|
group = [zebra_rects[0]]
|
|
for rect in zebra_rects[1:]:
|
|
if abs(rect[1] - group[-1][1]) < y_tolerance:
|
|
group.append(rect)
|
|
else:
|
|
if len(group) >= 3:
|
|
groups.append(group)
|
|
group = [rect]
|
|
if len(group) >= 3:
|
|
groups.append(group)
|
|
# Score all groups
|
|
scored_groups = [(group_score(g), g) for g in groups if group_score(g) > 0.1]
|
|
print(f"[CROSSWALK DEBUG] scored_groups: {[s 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("Best group score:", best_score)
|
|
# Visualization for debugging
|
|
debug_vis = orig_frame.copy()
|
|
for r in zebra_rects:
|
|
x, y, rw, rh, _ = r
|
|
cv2.rectangle(debug_vis, (x, y), (x+rw, y+rh), (255, 0, 255), 2)
|
|
for r in best_group:
|
|
x, y, rw, rh, _ = r
|
|
cv2.rectangle(debug_vis, (x, y), (x+rw, y+rh), (0, 255, 255), 3)
|
|
cv2.imwrite(f"debug_crosswalk_group.png", debug_vis)
|
|
# Optionally, filter by vanishing point as before
|
|
# ...existing vanishing point code...
|
|
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)
|
|
violation_line_y = y2 - 5
|
|
debug_info['crosswalk_group'] = best_group
|
|
debug_info['crosswalk_score'] = best_score
|
|
debug_info['crosswalk_angles'] = [r[4] for r in best_group]
|
|
# --- Fallback: Stop line detection ---
|
|
if crosswalk_bbox is None:
|
|
edges = cv2.Canny(gray, 80, 200)
|
|
lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=80, minLineLength=60, maxLineGap=20)
|
|
stop_lines = []
|
|
if lines is not None:
|
|
for l in lines:
|
|
x1, y1, x2, y2 = l[0]
|
|
angle = np.degrees(np.arctan2(y2 - y1, x2 - x1))
|
|
if abs(angle) < 20 or abs(angle) > 160: # horizontal
|
|
if y1 > h // 2 or y2 > h // 2: # lower half
|
|
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:
|
|
if traffic_light_position:
|
|
tx, ty = traffic_light_position
|
|
best_line = min(stop_lines, key=lambda l: abs(((l[1]+l[3])//2) - ty))
|
|
else:
|
|
best_line = max(stop_lines, key=lambda l: max(l[1], l[3]))
|
|
x1, y1, x2, y2 = best_line
|
|
crosswalk_bbox = None
|
|
violation_line_y = min(y1, y2) - 5
|
|
debug_info['stop_line'] = best_line
|
|
print(f"[CROSSWALK DEBUG] using stop_line: {best_line}")
|
|
# Draw fallback violation line overlay for debugging (no saving)
|
|
if crosswalk_bbox is None and violation_line_y is not None:
|
|
print(f"[DEBUG] Drawing violation line at y={violation_line_y} (frame height={orig_frame.shape[0]})")
|
|
if 0 <= violation_line_y < orig_frame.shape[0]:
|
|
orig_frame = draw_violation_line(orig_frame, violation_line_y, color=(0, 255, 255), thickness=8, style='solid', label='Fallback Stop Line')
|
|
else:
|
|
print(f"[WARNING] Invalid violation line position: {violation_line_y}")
|
|
# --- Manual overlay for visualization pipeline test ---
|
|
# Removed fake overlays that could overwrite the real violation line
|
|
print(f"[CROSSWALK DEBUG] crosswalk_bbox: {crosswalk_bbox}, violation_line_y: {violation_line_y}")
|
|
return orig_frame, crosswalk_bbox, violation_line_y, debug_info
|
|
|
|
def draw_violation_line(frame: np.ndarray, y: int, color=(0, 255, 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)
|
|
# 4. Fallback
|
|
return int(height * 0.75)
|
|
|
|
# Example usage:
|
|
# bbox, vline, dbg = detect_crosswalk_and_violation_line(frame, (tl_x, tl_y), perspective_M)
|
|
##working
|
|
print("🟡 [CROSSWALK_UTILS] This is d:/Downloads/finale6/Khatam final/khatam/qt_app_pyside/utils/crosswalk_utils.py LOADED")
|
|
import cv2
|
|
import numpy as np
|
|
from sklearn import linear_model
|
|
|
|
def detect_crosswalk_and_violation_line(frame, traffic_light_position=None, debug=False):
|
|
"""
|
|
Robust crosswalk and violation line detection for red-light violation system.
|
|
Returns:
|
|
frame_with_overlays, crosswalk_bbox, violation_line_y, debug_info
|
|
"""
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frame_out = frame.copy()
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h, w = frame.shape[:2]
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debug_info = {}
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|
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# === Step 1: Robust white color mask (HSV) ===
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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lower_white = np.array([0, 0, 180])
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upper_white = np.array([180, 80, 255])
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mask = cv2.inRange(hsv, lower_white, upper_white)
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|
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# === Step 2: Morphological filtering ===
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (7, 3))
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mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
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mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
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|
|
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# === Step 3: Contour extraction and filtering ===
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contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
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crosswalk_bars = []
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for cnt in contours:
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x, y, cw, ch = cv2.boundingRect(cnt)
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if cw > w * 0.05 and ch < h * 0.15:
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crosswalk_bars.append((x, y, cw, ch))
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|
|
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# === Step 4: Draw detected bars for debug ===
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for (x, y, cw, ch) in crosswalk_bars:
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cv2.rectangle(frame_out, (x, y), (x + cw, y + ch), (0, 255, 255), 2) # yellow
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|
|
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# === Step 5: Violation line placement at bottom of bars ===
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ys = np.array([y for (x, y, w, h) in crosswalk_bars])
|
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hs = np.array([h for (x, y, w, h) in crosswalk_bars])
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if len(ys) >= 3:
|
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bottom_edges = ys + hs
|
|
violation_line_y = int(np.max(bottom_edges)) + 5 # +5 offset
|
|
violation_line_y = min(violation_line_y, h - 1)
|
|
crosswalk_bbox = (0, int(np.min(ys)), w, int(np.max(bottom_edges)) - int(np.min(ys)))
|
|
# Draw semi-transparent crosswalk region
|
|
overlay = frame_out.copy()
|
|
cv2.rectangle(overlay, (0, int(np.min(ys))), (w, int(np.max(bottom_edges))), (0, 255, 0), -1)
|
|
frame_out = cv2.addWeighted(overlay, 0.2, frame_out, 0.8, 0)
|
|
cv2.rectangle(frame_out, (0, int(np.min(ys))), (w, int(np.max(bottom_edges))), (0, 255, 0), 2)
|
|
cv2.putText(frame_out, "Crosswalk", (10, int(np.min(ys)) - 10),
|
|
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
|
|
else:
|
|
violation_line_y = int(h * 0.65)
|
|
crosswalk_bbox = None
|
|
|
|
# === Draw violation line ===
|
|
cv2.line(frame_out, (0, violation_line_y), (w, violation_line_y), (0, 0, 255), 3)
|
|
cv2.putText(frame_out, "Violation Line", (10, violation_line_y - 10),
|
|
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
|
|
|
|
debug_info['crosswalk_bars'] = crosswalk_bars
|
|
debug_info['violation_line_y'] = violation_line_y
|
|
debug_info['crosswalk_bbox'] = crosswalk_bbox
|
|
|
|
return frame_out, crosswalk_bbox, violation_line_y, debug_info
|
|
|
|
def draw_violation_line(frame: np.ndarray, y: int, color=(0, 0, 255), thickness=4, style='solid', label='Violation Line'):
|
|
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)
|
|
cv2.addWeighted(overlay, 0.7, frame, 0.3, 0, frame)
|
|
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)
|
|
# 4. Fallback
|
|
return int(height * 0.75)
|
|
|
|
# Example usage:
|
|
# bbox, vline, dbg = detect_crosswalk_and_violation_line(frame, (tl_x, tl_y), perspective_M)
|