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https://github.com/THU-MIG/yolov10.git
synced 2025-05-23 13:34:23 +08:00
Fixed model calling method in app.py
This commit is contained in:
Steven Chen
parent
ea93d4f379
commit
93632efa4a
113
app.py
113
app.py
@ -2,13 +2,48 @@ import gradio as gr
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import cv2
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import tempfile
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from ultralytics import YOLOv10
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import supervision as sv
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from huggingface_hub import hf_hub_download
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def yolov10_inference(image, video, model_id, image_size, conf_threshold):
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model = YOLOv10.from_pretrained(f'jameslahm/{model_id}')
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def download_models(model_id):
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hf_hub_download("kadirnar/Yolov10", filename=f"{model_id}", local_dir=f"./")
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return f"./{model_id}"
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box_annotator = sv.BoxAnnotator()
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category_dict = {
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0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
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6: 'train', 7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant',
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11: 'stop sign', 12: 'parking meter', 13: 'bench', 14: 'bird', 15: 'cat',
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16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow', 20: 'elephant', 21: 'bear',
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22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
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27: 'tie', 28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard',
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32: 'sports ball', 33: 'kite', 34: 'baseball bat', 35: 'baseball glove',
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36: 'skateboard', 37: 'surfboard', 38: 'tennis racket', 39: 'bottle',
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40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon', 45: 'bowl',
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46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli',
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51: 'carrot', 52: 'hot dog', 53: 'pizza', 54: 'donut', 55: 'cake',
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56: 'chair', 57: 'couch', 58: 'potted plant', 59: 'bed', 60: 'dining table',
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61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote', 66: 'keyboard',
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67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
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72: 'refrigerator', 73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors',
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77: 'teddy bear', 78: 'hair drier', 79: 'toothbrush'
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}
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def yolov10_inference(image, video, model_id, image_size, conf_threshold, iou_threshold):
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model_path = download_models(model_id)
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model = YOLOv10(model_path)
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if image:
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results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)
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annotated_image = results[0].plot()
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results = model(source=image, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0]
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detections = sv.Detections.from_ultralytics(results)
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labels = [
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f"{category_dict[class_id]} {confidence:.2f}"
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for class_id, confidence in zip(detections.class_id, detections.confidence)
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]
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annotated_image = box_annotator.annotate(image, detections=detections, labels=labels)
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return annotated_image[:, :, ::-1], None
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else:
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video_path = tempfile.mktemp(suffix=".webm")
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@ -29,8 +64,14 @@ def yolov10_inference(image, video, model_id, image_size, conf_threshold):
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if not ret:
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break
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results = model.predict(source=frame, imgsz=image_size, conf=conf_threshold)
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annotated_frame = results[0].plot()
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results = model(source=frame, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0]
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detections = sv.Detections.from_ultralytics(results)
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labels = [
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f"{category_dict[class_id]} {confidence:.2f}"
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for class_id, confidence in zip(detections.class_id, detections.confidence)
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]
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annotated_frame = box_annotator.annotate(frame, detections=detections, labels=labels)
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out.write(annotated_frame)
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cap.release()
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@ -39,8 +80,8 @@ def yolov10_inference(image, video, model_id, image_size, conf_threshold):
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return None, output_video_path
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def yolov10_inference_for_examples(image, model_path, image_size, conf_threshold):
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annotated_image, _ = yolov10_inference(image, None, model_path, image_size, conf_threshold)
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def yolov10_inference_for_examples(image, model_id, image_size, conf_threshold, iou_threshold):
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annotated_image, _ = yolov10_inference(image, None, model_id, image_size, conf_threshold, iou_threshold)
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return annotated_image
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@ -58,14 +99,14 @@ def app():
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model_id = gr.Dropdown(
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label="Model",
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choices=[
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"yolov10n",
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"yolov10s",
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"yolov10m",
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"yolov10b",
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"yolov10l",
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"yolov10x",
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"yolov10n.pt",
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"yolov10s.pt",
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"yolov10m.pt",
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"yolov10b.pt",
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"yolov10l.pt",
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"yolov10x.pt",
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],
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value="yolov10m",
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value="yolov10m.pt",
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)
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image_size = gr.Slider(
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label="Image Size",
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@ -76,11 +117,18 @@ def app():
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)
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conf_threshold = gr.Slider(
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label="Confidence Threshold",
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minimum=0.0,
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minimum=0.1,
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maximum=1.0,
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step=0.05,
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step=0.1,
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value=0.25,
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)
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iou_threshold = gr.Slider(
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label="IoU Threshold",
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minimum=0.1,
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maximum=1.0,
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step=0.1,
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value=0.45,
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)
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yolov10_infer = gr.Button(value="Detect Objects")
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with gr.Column():
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@ -88,12 +136,13 @@ def app():
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output_video = gr.Video(label="Annotated Video", visible=False)
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def update_visibility(input_type):
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image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
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video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
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output_image = gr.update(visible=True) if input_type == "Image" else gr.update(visible=False)
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output_video = gr.update(visible=False) if input_type == "Image" else gr.update(visible=True)
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return image, video, output_image, output_video
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image_visibility = input_type == "Image"
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return (
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gr.update(visible=image_visibility),
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gr.update(visible=not image_visibility),
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gr.update(visible=image_visibility),
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gr.update(visible=not image_visibility),
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)
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input_type.change(
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fn=update_visibility,
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@ -101,16 +150,15 @@ def app():
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outputs=[image, video, output_image, output_video],
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)
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def run_inference(image, video, model_id, image_size, conf_threshold, input_type):
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def run_inference(image, video, model_id, image_size, conf_threshold, iou_threshold, input_type):
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if input_type == "Image":
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return yolov10_inference(image, None, model_id, image_size, conf_threshold)
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return yolov10_inference(image, None, model_id, image_size, conf_threshold, iou_threshold)
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else:
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return yolov10_inference(None, video, model_id, image_size, conf_threshold)
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return yolov10_inference(None, video, model_id, image_size, conf_threshold, iou_threshold)
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yolov10_infer.click(
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fn=run_inference,
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inputs=[image, video, model_id, image_size, conf_threshold, input_type],
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inputs=[image, video, model_id, image_size, conf_threshold, iou_threshold, input_type],
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outputs=[output_image, output_video],
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)
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@ -118,15 +166,17 @@ def app():
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examples=[
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[
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"ultralytics/assets/bus.jpg",
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"yolov10s",
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"yolov10s.pt",
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640,
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0.25,
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0.45,
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],
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[
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"ultralytics/assets/zidane.jpg",
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"yolov10s",
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"yolov10s.pt",
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640,
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0.25,
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0.45,
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],
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],
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fn=yolov10_inference_for_examples,
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@ -135,11 +185,13 @@ def app():
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model_id,
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image_size,
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conf_threshold,
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iou_threshold,
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],
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outputs=[output_image],
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cache_examples='lazy',
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)
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gradio_app = gr.Blocks()
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with gradio_app:
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gr.HTML(
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@ -157,5 +209,6 @@ with gradio_app:
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with gr.Row():
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with gr.Column():
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app()
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if __name__ == '__main__':
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gradio_app.launch()
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