first commit

this.py

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+import cv2
+import numpy as np
+import pymysql
+from datetime import datetime
+
+# Database connection
+conn = pymysql.connect(
+    host='localhost',
+    user='root',
+    password='',
+    database='db_traffic2'
+)
+cursor = conn.cursor()
+
+# RTSP stream or test video
+cap = cv2.VideoCapture('pagi.mp4')  # Replace with RTSP URL when needed
+
+# Get original width and height
+ret, frame = cap.read()
+if not ret:
+    print("Failed to read from video source.")
+    exit()
+
+height2, width2, channels = frame.shape
+
+# Background subtractor
+fgbg = cv2.createBackgroundSubtractorMOG2()
+
+# Vehicle tracking and speed
+vehicle_id_counter = 0
+tracker_dict = {}
+speed_line_y1 = 245
+speed_line_y2 = 355
+meters_travel_pixel = 0.2
+
+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:]
+    hull = [cv2.convexHull(c) for c in contours]
+    cv2.drawContours(frame, hull, -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 min_area < area < max_area:
+            M = cv2.moments(cnt)
+            if M['m00'] == 0:
+                continue
+            cx = int(M['m10'] / M['m00'])
+            cy = int(M['m01'] / M['m00'])
+
+            if speed_line_y1 <= cy <= speed_line_y2:
+                vehicle_ids_in_zone.add(matched_id)
+
+            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),
+                    't1': None,
+                    't2': None,
+                    'logged': False,
+                    'last_cy': cy,
+                    'direction': None
+                }
+            else:
+                tracker_dict[matched_id]['pos'] = (cx, cy)
+
+            vehicle = tracker_dict[matched_id]
+            last_cy = vehicle['last_cy']
+            vehicle['last_cy'] = cy
+
+            # Downward detection (your original logic)
+            if not vehicle['t1'] and cy >= speed_line_y1:
+                vehicle['t1'] = datetime.now()
+            elif not vehicle['t2'] and cy >= speed_line_y2:
+                vehicle['t2'] = datetime.now()
+                vehicle['direction'] = 'down'
+
+            # Upward detection (added logic)
+            if not vehicle['t1'] and cy <= speed_line_y2:
+                vehicle['t1'] = datetime.now()
+            elif not vehicle['t2'] and cy <= speed_line_y1:
+                vehicle['t2'] = datetime.now()
+                vehicle['direction'] = 'up'
+
+            # Speed calculation
+            if vehicle['t1'] and vehicle['t2'] and not vehicle['logged']:
+                delta_time = (vehicle['t2'] - vehicle['t1']).total_seconds()
+                if delta_time > 0:
+                    speed_kmh = (meters_travel_pixel / delta_time) * 3.6
+                    direction = vehicle['direction'] or 'unknown'
+                    print(f"ID {matched_id} SPEED: {speed_kmh:.2f} km/h DIR: {direction}")
+                    
+                    if speed_kmh >= 10:
+                        cursor.execute(
+                            "INSERT INTO vehicle_log (vehicle_id, direction, timestamp, speed) VALUES (%s, %s, %s, %s)",
+                            (matched_id, direction, datetime.now(), round(speed_kmh, 2))
+                        )
+                        conn.commit()
+                    vehicle['logged'] = True
+
+            # Show ID
+            cv2.putText(frame, f"ID:{matched_id}", (x, y - 5),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 255, 255), 1)
+
+            # Show speed
+            if vehicle['t1'] and vehicle['t2']:
+                delta_time = (vehicle['t2'] - vehicle['t1']).total_seconds()
+                if delta_time > 0:
+                    speed_kmh = (meters_travel_pixel / 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)
+
+    
+    # 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 detection 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
+
+cap.release()
+cursor.close()
+conn.close()
+cv2.destroyAllWindows()