first commit

app6.py

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+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()