import cv2 import gradio as gr import numpy as np from ultralytics import YOLO # Load the trained YOLO model model = YOLO('best.pt') # Replace with your trained model file def detect_packages(frame): results = model(frame) for result in results: for box in result.boxes: x1, y1, x2, y2 = map(int, box.xyxy[0]) cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 0, 255), 3) return frame def vid_inf(vid_path, contour_thresh): cap = cv2.VideoCapture(vid_path) 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" out = cv2.VideoWriter(output_video, fourcc, fps, frame_size) if not cap.isOpened(): print("Error opening video file") return None, None count = 0 while cap.isOpened(): ret, frame = cap.read() if ret: frame_out = detect_packages(frame.copy()) frame_out_final = cv2.cvtColor(frame_out, cv2.COLOR_BGR2RGB) out.write(frame_out) if not count % 12: yield frame_out_final, None count += 1 else: break cap.release() out.release() cv2.destroyAllWindows() yield None, output_video # Gradio Interface input_video = gr.Video(label="Input Video") contour_thresh = gr.Slider(0, 10000, value=4, label="Contour Threshold", info="Adjust the Contour 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="Motion Detection using YOLOv8", description="A Gradio app for dynamic video analysis using YOLOv8 to track labeled packages while ignoring other moving objects.", allow_flagging="never", examples=[["./sample/package_test.mp4", "1000"]], cache_examples=False, ) app.queue().launch()