| import gradio as gr |
| import cv2 |
| from ultralytics import YOLO |
| import subprocess |
| import os |
|
|
| |
| model = YOLO('yolov8n.pt') |
|
|
| def process_video(video_path): |
| if not video_path: |
| return None |
|
|
| |
| vehicle_classes = ['car', 'motorcycle', 'bus', 'truck'] |
| vehicle_class_ids = [2, 3, 5, 7] |
| vehicle_count = {cls: 0 for cls in vehicle_classes} |
| track_history = {} |
| counted_ids = set() |
|
|
| cap = cv2.VideoCapture(video_path) |
| fps = cap.get(cv2.CAP_PROP_FPS) |
| width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) |
| height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) |
| |
| |
| line_y = int(height * 0.6) |
|
|
| |
| temp_output = 'temp_output.mp4' |
| fourcc = cv2.VideoWriter_fourcc(*'mp4v') |
| out = cv2.VideoWriter(temp_output, fourcc, fps, (width, height)) |
|
|
| while cap.isOpened(): |
| ret, frame = cap.read() |
| if not ret: |
| break |
|
|
| results = model.track(frame, classes=vehicle_class_ids, persist=True, verbose=False) |
|
|
| cv2.line(frame, (0, line_y), (width, line_y), (0, 0, 255), 3) |
| cv2.putText(frame, "Counting Line", (10, line_y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2) |
|
|
| if results[0].boxes.id is not None: |
| boxes = results[0].boxes |
| ids = boxes.id.cpu().numpy().astype(int) |
| classes = boxes.cls.cpu().numpy().astype(int) |
| confs = boxes.conf.cpu().numpy() |
|
|
| for (x1, y1, x2, y2, obj_id, cls_id, conf) in zip( |
| boxes.xyxy[:,0], boxes.xyxy[:,1], boxes.xyxy[:,2], boxes.xyxy[:,3], |
| ids, classes, confs |
| ): |
| label = model.names[cls_id] |
| cx = int((x1 + x2) / 2) |
| cy = int((y1 + y2) / 2) |
|
|
| cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (255, 255, 0), 2) |
| cv2.circle(frame, (cx, cy), 5, (0, 255, 0), -1) |
| cv2.putText(frame, f"ID:{obj_id} {label}", (int(x1), int(y1) - 10), |
| cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 0), 2) |
|
|
| |
| if obj_id in track_history and obj_id not in counted_ids: |
| prev_cy = track_history[obj_id] |
| if (prev_cy < line_y and cy >= line_y) or (prev_cy > line_y and cy <= line_y): |
| vehicle_count[label] += 1 |
| counted_ids.add(obj_id) |
| cv2.line(frame, (0, line_y), (width, line_y), (0, 255, 0), 5) |
| |
| track_history[obj_id] = cy |
|
|
| |
| total = sum(vehicle_count.values()) |
| overlay = frame.copy() |
| box_w, box_h = 250, 160 |
| x0, y0 = width - box_w - 20, 20 |
| cv2.rectangle(overlay, (x0, y0), (x0 + box_w, y0 + box_h), (0, 0, 0), -1) |
| frame = cv2.addWeighted(overlay, 0.6, frame, 0.4, 0) |
|
|
| cv2.putText(frame, f'Total Objects: {total}', (x0 + 10, y0 + 30), |
| cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2) |
|
|
| for i, (cls, cnt) in enumerate(vehicle_count.items()): |
| cv2.putText(frame, f'{cls.capitalize()}: {cnt}', |
| (x0 + 10, y0 + 60 + i * 25), |
| cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2) |
|
|
| out.write(frame) |
|
|
| cap.release() |
| out.release() |
|
|
| |
| final_output = 'result_video.mp4' |
| if os.path.exists(final_output): |
| os.remove(final_output) |
| |
| subprocess.run( |
| ['ffmpeg', '-i', temp_output, '-vcodec', 'libx264', '-crf', '28', final_output], |
| check=True |
| ) |
| |
| return final_output |
|
|
| |
| interface = gr.Interface( |
| fn=process_video, |
| inputs=gr.Video(label="Tải video của bạn lên đây"), |
| outputs=gr.Video(label="Video kết quả đếm xe"), |
| title="Hệ thống đếm xe tự động (YOLOv8)", |
| description="Tải lên một video giao thông để hệ thống tự động nhận diện và đếm số lượng xe cộ qua vạch kẻ ngang.", |
| allow_flagging="never" |
| ) |
|
|
| |
| if __name__ == "__main__": |
| interface.launch() |
|
|
