143 lines
4.8 KiB
Python
143 lines
4.8 KiB
Python
import cv2
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import numpy as np
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import pymysql
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from datetime import datetime
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# Database connection
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conn = pymysql.connect(
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host='localhost',
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user='root',
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password='',
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database='db_traffic2'
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)
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cursor = conn.cursor()
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# RTSP stream or test video
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# cap = cv2.VideoCapture('pagi.mp4') # Replace with RTSP URL when needed
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cap = cv2.VideoCapture('rtsp://20.20.20.33:8554/') # Replace with RTSP URL when needed
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# Get original width and height
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ret, frame = cap.read()
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if not ret:
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print("Failed to read from video source.")
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exit()
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height2, width2, channels = frame.shape
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# Background subtractor
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fgbg = cv2.createBackgroundSubtractorMOG2()
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# Vehicle tracking and speed
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vehicle_id_counter = 0
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tracker_dict = {}
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# Line positions (adjust as needed)
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speed_line_y1 = 255
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speed_line_y2 = 390
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pixel_distance = abs(speed_line_y2 - speed_line_y1)
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# Real-world distance between lines in meters (adjust based on real road)
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real_world_distance_m = 0.25 # ← adjust if your line spacing is closer/farther
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while True:
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ret, frame = cap.read()
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if not ret:
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break
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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fgmask = fgbg.apply(gray)
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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
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closing = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)
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opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
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dilation = cv2.dilate(opening, kernel)
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_, bins = cv2.threshold(dilation, 220, 255, cv2.THRESH_BINARY)
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contours, _ = cv2.findContours(bins, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]
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hull = [cv2.convexHull(c) for c in contours]
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cv2.drawContours(frame, hull, -1, (0, 255, 0), 2)
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min_area = 400
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max_area = 40000
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for cnt in contours:
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area = cv2.contourArea(cnt)
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if min_area < area < max_area:
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M = cv2.moments(cnt)
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if M['m00'] == 0:
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continue
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cx = int(M['m10'] / M['m00'])
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cy = int(M['m01'] / M['m00'])
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x, y, w, h = cv2.boundingRect(cnt)
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cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
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cv2.drawMarker(frame, (cx, cy), (0, 0, 255), cv2.MARKER_CROSS, 10, 1)
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matched_id = None
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for vid, info in tracker_dict.items():
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old_cx, old_cy = info['pos']
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if abs(cx - old_cx) < 30 and abs(cy - old_cy) < 30:
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matched_id = vid
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break
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if matched_id is None:
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matched_id = vehicle_id_counter
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vehicle_id_counter += 1
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tracker_dict[matched_id] = {'pos': (cx, cy), 't1': None, 't2': None, 'logged': False}
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else:
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tracker_dict[matched_id]['pos'] = (cx, cy)
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vehicle = tracker_dict[matched_id]
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if not vehicle['t1'] and cy >= speed_line_y1:
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vehicle['t1'] = datetime.now()
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elif not vehicle['t2'] and cy >= speed_line_y2:
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vehicle['t2'] = datetime.now()
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if vehicle['t1'] and vehicle['t2'] and not vehicle['logged']:
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delta_time = (vehicle['t2'] - vehicle['t1']).total_seconds()
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if delta_time > 0:
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speed_kmh = (real_world_distance_m / delta_time) * 3.6
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if speed_kmh >= 10:
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print(f"ID {matched_id} SPEED: {speed_kmh:.2f} km/h")
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# Insert into DB only if speed ≥ 10
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cursor.execute(
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"INSERT INTO vehicle_log (vehicle_id, direction, timestamp, speed) VALUES (%s, %s, %s, %s)",
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(matched_id, 'down', datetime.now(), round(speed_kmh, 2))
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)
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conn.commit()
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else:
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print(f"ID {matched_id} IGNORED (too slow): {speed_kmh:.2f} km/h")
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vehicle['logged'] = True
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# Display ID
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cv2.putText(frame, f"ID:{matched_id}", (x, y - 5),
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cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 255), 1)
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# Show speed on screen
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if vehicle['t1'] and vehicle['t2']:
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delta_time = (vehicle['t2'] - vehicle['t1']).total_seconds()
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if delta_time > 0:
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speed_kmh = (real_world_distance_m / delta_time) * 3.6
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cv2.putText(frame, f"{speed_kmh:.1f} km/h", (x, y + h + 15),
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cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 2)
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# Draw lines
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cv2.line(frame, (0, speed_line_y1), (width2, speed_line_y1), (0, 255, 255), 2)
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cv2.line(frame, (0, speed_line_y2), (width2, speed_line_y2), (255, 0, 255), 2)
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# Show output
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cv2.imshow("Vehicle Detection", frame)
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if cv2.waitKey(1) & 0xFF == 27:
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break
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# Cleanup
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cap.release()
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cursor.close()
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conn.close()
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cv2.destroyAllWindows()
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