Add basic motion detection script

Introduces motion_detection.py with frame differencing and background subtraction (MOG2) for motion detection using OpenCV. The script provides real-time motion mask and annotated frame display, with adjustable parameters for contour filtering and morphology.

Author: shivamkb17

computer_vision/motion_detection.py

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+import cv2
+import numpy as np
+
+"""
+Basic Motion Detection using frame differencing and background subtraction (MOG2).
+
+Usage:
+    - Set VIDEO_SOURCE to a video file path or an integer (e.g., 0) for webcam.
+    - The script shows two windows: motion mask and annotated frame.
+    - Press 'q' to quit.
+
+Notes:
+    - Requires OpenCV (opencv-python) and NumPy.
+    - This example focuses on clarity and educational value, not production tuning.
+"""
+
+
+# Parameters
+VIDEO_SOURCE = 0  # use integer for webcam (e.g., 0) or string path for video file
+MIN_CONTOUR_AREA = 500  # pixels; filter tiny motions/noise
+MORPH_KERNEL_SIZE = (5, 5)  # kernel for opening/closing
+DISPLAY_SCALE = 1.0  # resize factor for display
+
+
+def create_background_subtractor() -> cv2.BackgroundSubtractor:
+    """
+    Create and return a MOG2 background subtractor with sensible defaults.
+    """
+    # history=500, varThreshold=16 are common defaults; detectShadows adds robustness
+    return cv2.createBackgroundSubtractorMOG2(history=500, varThreshold=16, detectShadows=True)
+
+
+def preprocess_frame(frame: cv2.Mat) -> cv2.Mat:
+    """
+    Convert to grayscale and apply Gaussian blur to suppress noise.
+    """
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    return blurred
+
+
+def frame_difference(prev_gray: cv2.Mat, curr_gray: cv2.Mat) -> cv2.Mat:
+    """
+    Compute absolute difference between consecutive grayscale frames.
+    Returns a binary motion mask after thresholding and morphology.
+    """
+    diff = cv2.absdiff(prev_gray, curr_gray)
+    _, thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, MORPH_KERNEL_SIZE)
+    opened = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
+    closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, kernel, iterations=1)
+    return closed
+
+
+def background_subtraction_mask(subtractor: cv2.BackgroundSubtractor, frame: cv2.Mat) -> cv2.Mat:
+    """
+    Apply background subtraction to obtain a motion mask. Includes morphology.
+    """
+    fg_mask = subtractor.apply(frame)
+    # Remove shadows if present (MOG2 shadows are typically 127)
+    _, fg_mask = cv2.threshold(fg_mask, 200, 255, cv2.THRESH_BINARY)
+    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, MORPH_KERNEL_SIZE)
+    opened = cv2.morphologyEx(fg_mask, cv2.MORPH_OPEN, kernel, iterations=1)
+    closed = cv2.morphologyEx(opened, cv2.MORPH_CLOSE, kernel, iterations=1)
+    return closed
+
+
+def annotate_motion(frame: cv2.Mat, motion_mask: cv2.Mat) -> cv2.Mat:
+    """
+    Find contours on the motion mask and draw bounding boxes on the frame.
+    """
+    contours, _ = cv2.findContours(motion_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    annotated = frame.copy()
+    for contour in contours:
+        if cv2.contourArea(contour) < MIN_CONTOUR_AREA:
+            continue
+        x, y, w, h = cv2.boundingRect(contour)
+        cv2.rectangle(annotated, (x, y), (x + w, y + h), (0, 255, 0), 2)
+    return annotated
+
+
+def main() -> None:
+    cap = cv2.VideoCapture(VIDEO_SOURCE)
+    if not cap.isOpened():
+        raise RuntimeError("Unable to open video source. Set VIDEO_SOURCE correctly.")
+
+    subtractor = create_background_subtractor()
+
+    ret, prev_frame = cap.read()
+    if not ret:
+        cap.release()
+        raise RuntimeError("Failed to read initial frame from source.")
+
+    prev_gray = preprocess_frame(prev_frame)
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Optionally resize for display/performance
+        if DISPLAY_SCALE != 1.0:
+            frame = cv2.resize(frame, None, fx=DISPLAY_SCALE, fy=DISPLAY_SCALE)
+
+        curr_gray = preprocess_frame(frame)
+
+        # Frame differencing motion mask
+        diff_mask = frame_difference(prev_gray, curr_gray)
+
+        # Background subtraction motion mask
+        bs_mask = background_subtraction_mask(subtractor, frame)
+
+        # Combine masks to be more robust (logical OR)
+        combined_mask = cv2.bitwise_or(diff_mask, bs_mask)
+
+        annotated = annotate_motion(frame, combined_mask)
+
+        cv2.imshow("Motion Mask", combined_mask)
+        cv2.imshow("Motion Detection", annotated)
+
+        prev_gray = curr_gray
+
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord("q"):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()
+    print("DONE ✅")
+
+