Motion Detection

import cv2
import numpy as np

def frame_difference_motion(video_source=0, threshold=30, min_area=500):
    """
    Detect motion by comparing consecutive frames.
    Highlights regions where pixels changed significantly.
    """
    cap = cv2.VideoCapture(video_source)
    if not cap.isOpened():
        print("Could not open video source")
        return

    ret, prev_frame = cap.read()
    if not ret:
        return

    prev_gray = cv2.cvtColor(prev_frame, cv2.COLOR_BGR2GRAY)
    prev_gray = cv2.GaussianBlur(prev_gray, (21, 21), 0)

    frame_count = 0
    motion_events = 0

    while frame_count < 500:  # Process 500 frames
        ret, frame = cap.read()
        if not ret:
            break

        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        gray = cv2.GaussianBlur(gray, (21, 21), 0)

        # Absolute difference between current and previous frame
        diff = cv2.absdiff(prev_gray, gray)

        # Threshold the difference image
        _, motion_mask = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)

        # Clean up mask
        motion_mask = cv2.dilate(motion_mask, None, iterations=2)

        # Find motion contours
        contours, _ = cv2.findContours(
            motion_mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
        )

        motion_detected = False
        result = frame.copy()

        for contour in contours:
            if cv2.contourArea(contour) < min_area:
                continue
            x, y, w, h = cv2.boundingRect(contour)
            cv2.rectangle(result, (x, y), (x+w, y+h), (0, 255, 0), 2)
            motion_detected = True

        if motion_detected:
            motion_events += 1
            cv2.putText(result, "MOTION DETECTED", (10, 30),
                       cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 3)

        prev_gray = gray
        frame_count += 1

    cap.release()
    print(f"Processed {frame_count} frames, motion events: {motion_events}")

frame_difference_motion(video_source="security_camera.mp4")