traffic-monitoring-new/app6.py

import cv2
import numpy as np
import pymysql
from datetime import datetime

# DB Connection
conn = pymysql.connect(
    host='localhost',
    user='root',
    password='',
    database='db_traffic2'
)
cursor = conn.cursor()

# RTSP or video
cap = cv2.VideoCapture('rtsp://20.20.20.33:8554/')
ret, frame = cap.read()
if not ret:
    print("Failed to read from source.")
    exit()

height2, width2, channels = frame.shape

fgbg = cv2.createBackgroundSubtractorMOG2()

vehicle_id_counter = 0
tracker_dict = {}

# Speed lines
speed_line_y1 = 230
speed_line_y2 = 340
real_world_distance_m = 0.25  # Distance between lines in meters
min_speed_kmh = 10  # Speed threshold

last_vehicle_ids_in_zone = set()

while True:
    ret, frame = cap.read()
    if not ret:
        break

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    fgmask = fgbg.apply(gray)

    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
    closing = cv2.morphologyEx(fgmask, cv2.MORPH_CLOSE, kernel)
    opening = cv2.morphologyEx(closing, cv2.MORPH_OPEN, kernel)
    dilation = cv2.dilate(opening, kernel)
    _, bins = cv2.threshold(dilation, 220, 255, cv2.THRESH_BINARY)

    contours, _ = cv2.findContours(bins, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]
    cv2.drawContours(frame, [cv2.convexHull(c) for c in contours], -1, (0, 255, 0), 2)

    min_area = 400
    max_area = 40000

    vehicle_ids_in_zone = set()

    for cnt in contours:
        area = cv2.contourArea(cnt)
        if not (min_area < area < max_area):
            continue

        M = cv2.moments(cnt)
        if M['m00'] == 0:
            continue
        cx = int(M['m10'] / M['m00'])
        cy = int(M['m01'] / M['m00'])

        x, y, w, h = cv2.boundingRect(cnt)
        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)
        cv2.drawMarker(frame, (cx, cy), (0, 0, 255), cv2.MARKER_CROSS, 10, 1)

        matched_id = None
        for vid, info in tracker_dict.items():
            old_cx, old_cy = info['pos']
            if abs(cx - old_cx) < 30 and abs(cy - old_cy) < 30:
                matched_id = vid
                break

        if matched_id is None:
            matched_id = vehicle_id_counter
            vehicle_id_counter += 1
            tracker_dict[matched_id] = {
                'pos': (cx, cy),
                'last_cy': cy,
                't1': None,
                't2': None,
                'direction': None,
                'logged': False
            }
        else:
            tracker_dict[matched_id]['pos'] = (cx, cy)

        vehicle = tracker_dict[matched_id]
        last_cy = vehicle['last_cy']

        # Detect downward movement
        if not vehicle['t1'] and last_cy < speed_line_y1 <= cy:
            vehicle['t1'] = datetime.now()
        elif vehicle['t1'] and not vehicle['t2'] and last_cy < speed_line_y2 <= cy:
            vehicle['t2'] = datetime.now()
            vehicle['direction'] = 'down'

        # Detect upward movement
        elif not vehicle['t1'] and last_cy > speed_line_y2 >= cy:
            vehicle['t1'] = datetime.now()
        elif vehicle['t1'] and not vehicle['t2'] and last_cy > speed_line_y1 >= cy:
            vehicle['t2'] = datetime.now()
            vehicle['direction'] = 'up'

        vehicle['last_cy'] = cy

        # Speed and database log
        if vehicle['t1'] and vehicle['t2'] and not vehicle['logged']:
            delta_time = (vehicle['t2'] - vehicle['t1']).total_seconds()
            if delta_time > 0:
                speed_kmh = (real_world_distance_m / delta_time) * 3.6
                if speed_kmh >= min_speed_kmh:
                    print(f"ID {matched_id} SPEED: {speed_kmh:.2f} km/h DIRECTION: {vehicle['direction']}")
                    cursor.execute(
                        "INSERT INTO vehicle_log (vehicle_id, direction, timestamp, speed) VALUES (%s, %s, %s, %s)",
                        (matched_id, vehicle['direction'], datetime.now(), round(speed_kmh, 2))
                    )
                    conn.commit()
                else:
                    print(f"ID {matched_id} IGNORED (too slow): {speed_kmh:.2f} km/h")
                vehicle['logged'] = True

        # Label
        cv2.putText(frame, f"ID:{matched_id}", (x, y - 5),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 255), 1)

        if vehicle['t1'] and vehicle['t2']:
            delta_time = (vehicle['t2'] - vehicle['t1']).total_seconds()
            if delta_time > 0:
                speed_kmh = (real_world_distance_m / delta_time) * 3.6
                cv2.putText(frame, f"{speed_kmh:.1f} km/h", (x, y + h + 15),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 0), 2)

        if speed_line_y1 <= cy <= speed_line_y2:
            vehicle_ids_in_zone.add(matched_id)

    # Store vehicle count in zone
    if vehicle_ids_in_zone != last_vehicle_ids_in_zone:
        last_vehicle_ids_in_zone = vehicle_ids_in_zone.copy()
        count = len(vehicle_ids_in_zone)
        cursor.execute("INSERT INTO total_vehicle (count, timestamp) VALUES (%s, %s)", (count, datetime.now()))
        conn.commit()
        print(f"🚗 Vehicles in zone: {count}")

    # Draw lines
    cv2.line(frame, (0, speed_line_y1), (width2, speed_line_y1), (0, 255, 255), 2)
    cv2.line(frame, (0, speed_line_y2), (width2, speed_line_y2), (255, 0, 255), 2)

    cv2.imshow("Vehicle Detection", frame)
    if cv2.waitKey(1) & 0xFF == 27:
        break

# Cleanup
cap.release()
cursor.close()
conn.close()
cv2.destroyAllWindows()