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| import cv2 | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| # input_video = 'car.mp4' | |
| # video Inference | |
| def vid_inf(vid_path): | |
| # 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() | |
| # 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) | |
| # cv2.imshow('Frame_bg', fg_mask) | |
| # apply global threshol to remove shadows | |
| retval, mask_thresh = cv2.threshold( | |
| fg_mask, 180, 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) | |
| # 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 = 1000 # 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, (x, y), (x+w, y+h), (0, 0, 200), 3) | |
| frame_out_display = cv2.cvtColor(frame_out, cv2.COLOR_BGR2RGB) | |
| vid = out.write(frame_out) | |
| # Display the resulting frame | |
| # cv2.imshow('Frame_final', frame_out) | |
| # update the count every frame and display every 12th frame | |
| if not count % 12: | |
| yield frame_out_display, 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") | |
| output_frames = gr.Image(label="Output Frames") | |
| output_video_file = gr.Video(label="Output video") | |
| # sample_video=r'sample/car.mp4' | |
| app = gr.Interface( | |
| fn=vid_inf, | |
| inputs=[input_video], | |
| outputs=[output_frames, output_video_file], | |
| title=f"MotionScope", | |
| 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"]], | |
| ) | |
| app.queue().launch() |