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.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+sample/car.mp4 filter=lfs diff=lfs merge=lfs -text
+sample/home.mp4 filter=lfs diff=lfs merge=lfs -text
+sample/motion_test.mp4 filter=lfs diff=lfs merge=lfs -text

app.py

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+import cv2
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+
+# input_video = './sample/car.mp4'
+
+# video Inference
+def vid_inf(vid_path, contour_thresh):
+    # Create a VideoCapture object
+    cap = cv2.VideoCapture(vid_path)
+
+    # get the video frames' width and height for proper saving of videos
+    frame_width = int(cap.get(3))
+    frame_height = int(cap.get(4))
+    fps = int(cap.get(cv2.CAP_PROP_FPS))
+    frame_size = (frame_width, frame_height)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    output_video = "output_recorded.mp4"
+
+    # create the `VideoWriter()` object
+    out = cv2.VideoWriter(output_video, fourcc, fps, frame_size)
+
+    # Create Background Subtractor MOG2 object
+    backSub = cv2.createBackgroundSubtractorMOG2(history=200, varThreshold=25, detectShadows=True)
+    # print(dir(backSub))
+
+    # Check if camera opened successfully
+    if not cap.isOpened():
+        print("Error opening video file")
+    count = 0
+    # Read until video is completed
+    while cap.isOpened():
+        # Capture frame-by-frame
+        ret, frame = cap.read()
+        # print(frame.shape)
+        if ret:
+            # Apply background subtraction
+            fg_mask = backSub.apply(frame)
+            # print(fg_mask.shape)
+            # fg_mask = cv2.resize(fg_mask, (640,480))
+            # print(fg_mask.shape)
+            # cv2.imshow('Frame_bg', fg_mask)
+
+            # apply global threshol to remove shadows
+            retval, mask_thresh = cv2.threshold(
+                fg_mask, 200, 255, cv2.THRESH_BINARY)
+            # cv2.imshow('frame_thresh', mask_thresh)
+
+            # set the kernal
+            kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+            # Apply erosion
+            mask_eroded = cv2.morphologyEx(mask_thresh, cv2.MORPH_OPEN, kernel)
+            # mask_eroded = cv2.resize(mask_eroded, (640,480)) 
+            # cv2.imshow('frame_erode', mask_eroded)
+
+            # Find contours
+            contours, hierarchy = cv2.findContours(
+                mask_eroded, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            # print(contours)
+
+            min_contour_area = contour_thresh  # Define your minimum area threshold
+            large_contours = [
+                cnt for cnt in contours if cv2.contourArea(cnt) > min_contour_area]
+            # frame_ct = cv2.drawContours(frame, large_contours, -1, (0, 255, 0), 2)
+            frame_out = frame.copy()
+            for cnt in large_contours:
+                # print(cnt.shape)
+                x, y, w, h = cv2.boundingRect(cnt)
+                frame_out = cv2.rectangle(frame_out, (x, y), (x+w, y+h), (0, 0, 200), 3)
+            frame_out_final = cv2.cvtColor(frame_out, cv2.COLOR_BGR2RGB)
+            vid = out.write(frame_out)
+
+            # Display the resulting frame
+            # resized_frame = cv2.resize(frame_out, (640,480)) 
+            # cv2.imshow('Frame_final', frame_out)
+
+            # update the count every frame and display every 12th frame
+            if not count % 12:
+                yield frame_out_final, None
+            count += 1
+
+            # Press Q on keyboard to exit
+            if cv2.waitKey(25) & 0xFF == ord('q'):
+                break
+        else:
+            break
+
+    # When everything done, release the video capture and writer object
+    cap.release()
+    out.release()
+    # Closes all the frames
+    cv2.destroyAllWindows()
+    yield None, output_video
+
+# vid_inf(input_video)
+
+
+# gradio interface
+input_video = gr.Video(label="Input Video")
+contour_thresh = gr.Slider(0, 10000, value=4, label="Contour Threshold", info="Adjust the Countour Threshold according to the object size that you want to detect.")
+output_frames = gr.Image(label="Output Frames")
+output_video_file = gr.Video(label="Output video")
+
+app = gr.Interface(
+    fn=vid_inf,
+    inputs=[input_video, contour_thresh],
+    outputs=[output_frames, output_video_file],
+    title=f"Motion Detection using OpenCV",
+    description=f'A gradio app for dynamic video analysis tool that leverages advanced background subtraction and contour detection techniques to identify and track moving objects in real-time.',
+    allow_flagging="never",
+    examples=[["./sample/car.mp4"], ["./sample/motion_test.mp4"], ["./sample/home.mp4"]],
+)
+app.queue().launch()

requirements.txt

@@ -0,0 +1,3 @@
+opencv-contrib-python
+numpy
+gradio

sample/car.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7f7287faa231cf287c433ef8b2f38949e214cac9c473aea286d75f2bdea2330
+size 4708643

sample/home.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6f269d372f84dcc2bb8317f7be2db05173bcc42a8433e28ede51ffca7f8265d
+size 2853355

sample/motion_test.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20090f4160519b776e1fb855617d2268745e826e07d81f1eab4a588df43628cf
+size 17887070