updated app.py

README.md

@@ -4,7 +4,7 @@ emoji: 👁
 colorFrom: yellow
 colorTo: yellow
 sdk: gradio
-sdk_version: 4.42.0
+sdk_version: 5.23.3
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -2,102 +2,70 @@ import cv2
 import gradio as gr
 import numpy as np
 
-# input_video = './sample/car.mp4'
 
-# video Inference
 def vid_inf(vid_path, contour_thresh):
-    # Create a VideoCapture object
+    contour_thresh = int(contour_thresh)
+
     cap = cv2.VideoCapture(vid_path)
+    if not cap.isOpened():
+        print("Error opening video file")
+        yield None, None
+        return
 
-    # 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')
+    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:
+        if not ret:
             break
 
-    # When everything done, release the video capture and writer object
+        fg_mask = backSub.apply(frame)
+
+        _, mask_thresh = cv2.threshold(fg_mask, 200, 255, cv2.THRESH_BINARY)
+
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
+        mask_cleaned = cv2.morphologyEx(mask_thresh, cv2.MORPH_OPEN, kernel)
+
+        contours, _ = cv2.findContours(mask_cleaned, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        large_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > contour_thresh]
+
+        frame_out = frame.copy()
+        for cnt in large_contours:
+            x, y, w, h = cv2.boundingRect(cnt)
+            cv2.rectangle(frame_out, (x, y), (x + w, y + h), (0, 0, 200), 3)
+
+        frame_rgb = cv2.cvtColor(frame_out, cv2.COLOR_BGR2RGB)
+        out.write(frame_out)
+
+        if count % 12 == 0:
+            yield frame_rgb, None
+        count += 1
+
     cap.release()
     out.release()
-    # Closes all the frames
     cv2.destroyAllWindows()
     yield None, output_video
 
-# vid_inf(input_video)
-
 
-# gradio interface
+# 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.")
+contour_thresh = gr.Slider(
+    0,
+    10000,
+    value=1000,
+    label="Contour Threshold",
+    info="Set the minimum size of moving objects to detect (in pixels).",
+)
 output_frames = gr.Image(label="Output Frames")
 output_video_file = gr.Video(label="Output video")
 
@@ -105,10 +73,11 @@ 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", "1000"], ["./sample/motion_test.mp4", "5000"], ["./sample/home.mp4", "4500"]],
+    title="Motion Detection using OpenCV",
+    description="A Gradio app that uses background subtraction and contour detection to highlight moving objects in a video.",
+    flagging_mode="never",
+    examples=[["./sample/car.mp4", 1000], ["./sample/motion_test.mp4", 5000], ["./sample/home.mp4", 4500]],
     cache_examples=False,
 )
-app.queue().launch()
+
+app.queue().launch()

requirements.txt

@@ -1,2 +1,2 @@
 opencv-python==4.10.0.84
-gradio==4.42.0
+gradio==5.23.3