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| import gradio as gr | |
| import cv2 | |
| import tempfile | |
| from ultralytics import YOLOv10 | |
| import supervision as sv | |
| from huggingface_hub import hf_hub_download | |
| import spaces | |
| def download_models(model_id): | |
| hf_hub_download("kadirnar/Yolov10", filename=f"{model_id}", local_dir=f"./") | |
| return f"./{model_id}" | |
| box_annotator = sv.BoxAnnotator() | |
| category_dict = { | |
| 0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus', | |
| 6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', | |
| 11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat', | |
| 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear', | |
| 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag', | |
| 27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', | |
| 32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove', | |
| 36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle', | |
| 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl', | |
| 46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', | |
| 51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake', | |
| 56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table', | |
| 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard', | |
| 67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink', | |
| 72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', | |
| 77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush' | |
| } | |
| def yolov10_inference(image, video, model_id, image_size, conf_threshold, iou_threshold): | |
| model_path = download_models(model_id) | |
| model = YOLOv10(model_path) | |
| if image: | |
| results = model(source=image, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0] | |
| detections = sv.Detections.from_ultralytics(results) | |
| labels = [ | |
| f"{category_dict[class_id]} {confidence:.2f}" | |
| for class_id, confidence in zip(detections.class_id, detections.confidence) | |
| ] | |
| annotated_image = box_annotator.annotate(image, detections=detections, labels=labels) | |
| return annotated_image[:, :, ::-1], None | |
| else: | |
| video_path = tempfile.mktemp(suffix=".webm") | |
| with open(video_path, "wb") as f: | |
| with open(video, "rb") as g: | |
| f.write(g.read()) | |
| cap = cv2.VideoCapture(video_path) | |
| fps = cap.get(cv2.CAP_PROP_FPS) | |
| frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| output_video_path = tempfile.mktemp(suffix=".webm") | |
| out = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*'vp80'), fps, (frame_width, frame_height)) | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| results = model(source=frame, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0] | |
| detections = sv.Detections.from_ultralytics(results) | |
| labels = [ | |
| f"{category_dict[class_id]} {confidence:.2f}" | |
| for class_id, confidence in zip(detections.class_id, detections.confidence) | |
| ] | |
| annotated_frame = box_annotator.annotate(frame, detections=detections, labels=labels) | |
| out.write(annotated_frame) | |
| cap.release() | |
| out.release() | |
| return None, output_video_path | |
| def yolov10_inference_for_examples(image, model_id, image_size, conf_threshold, iou_threshold): | |
| annotated_image, _ = yolov10_inference(image, None, model_id, image_size, conf_threshold, iou_threshold) | |
| return annotated_image | |
| def app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| image = gr.Image(type="pil", label="Image", visible=True) | |
| video = gr.Video(label="Video", visible=False) | |
| input_type = gr.Radio( | |
| choices=["Image", "Video"], | |
| value="Image", | |
| label="Input Type", | |
| ) | |
| model_id = gr.Dropdown( | |
| label="Model", | |
| choices=[ | |
| "yolov10n.pt", | |
| "yolov10s.pt", | |
| "yolov10m.pt", | |
| "yolov10b.pt", | |
| "yolov10l.pt", | |
| "yolov10x.pt", | |
| ], | |
| value="yolov10m.pt", | |
| ) | |
| image_size = gr.Slider( | |
| label="Image Size", | |
| minimum=320, | |
| maximum=1280, | |
| step=32, | |
| value=640, | |
| ) | |
| conf_threshold = gr.Slider( | |
| label="Confidence Threshold", | |
| minimum=0.1, | |
| maximum=1.0, | |
| step=0.1, | |
| value=0.25, | |
| ) | |
| iou_threshold = gr.Slider( | |
| label="IoU Threshold", | |
| minimum=0.1, | |
| maximum=1.0, | |
| step=0.1, | |
| value=0.45, | |
| ) | |
| yolov10_infer = gr.Button(value="Detect Objects") | |
| with gr.Column(): | |
| output_image = gr.Image(type="numpy", label="Annotated Image", visible=True) | |
| output_video = gr.Video(label="Annotated Video", visible=False) | |
| def update_visibility(input_type): | |
| image_visibility = input_type == "Image" | |
| return ( | |
| gr.update(visible=image_visibility), | |
| gr.update(visible=not image_visibility), | |
| gr.update(visible=image_visibility), | |
| gr.update(visible=not image_visibility), | |
| ) | |
| input_type.change( | |
| fn=update_visibility, | |
| inputs=[input_type], | |
| outputs=[image, video, output_image, output_video], | |
| ) | |
| def run_inference(image, video, model_id, image_size, conf_threshold, iou_threshold, input_type): | |
| if input_type == "Image": | |
| return yolov10_inference(image, None, model_id, image_size, conf_threshold, iou_threshold) | |
| else: | |
| return yolov10_inference(None, video, model_id, image_size, conf_threshold, iou_threshold) | |
| yolov10_infer.click( | |
| fn=run_inference, | |
| inputs=[image, video, model_id, image_size, conf_threshold, iou_threshold, input_type], | |
| outputs=[output_image, output_video], | |
| ) | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| "ultralytics/assets/bus.jpg", | |
| "yolov10s.pt", | |
| 640, | |
| 0.25, | |
| 0.45, | |
| ], | |
| [ | |
| "ultralytics/assets/zidane.jpg", | |
| "yolov10s.pt", | |
| 640, | |
| 0.25, | |
| 0.45, | |
| ], | |
| ], | |
| fn=yolov10_inference_for_examples, | |
| inputs=[ | |
| image, | |
| model_id, | |
| image_size, | |
| conf_threshold, | |
| iou_threshold, | |
| ], | |
| outputs=[output_image], | |
| cache_examples='lazy', | |
| ) | |
| gradio_app = gr.Blocks() | |
| with gradio_app: | |
| gr.HTML( | |
| """ | |
| <h1 style='text-align: center'> | |
| YOLOv10: Real-Time End-to-End Object Detection | |
| </h1> | |
| """) | |
| gr.HTML( | |
| """ | |
| <h3 style='text-align: center'> | |
| <a href='https://arxiv.org/abs/2405.14458' target='_blank'>arXiv</a> | <a href='https://github.com/THU-MIG/yolov10' target='_blank'>github</a> | |
| </h3> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| app() | |
| if __name__ == '__main__': | |
| gradio_app.launch() |