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import gradio as gr |
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import os |
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os.system("pip -qq install yoloxdetect==0.0.7") |
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import torch |
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from yoloxdetect import YoloxDetector |
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torch.hub.download_url_to_file('https://tochkanews.ru/wp-content/uploads/2020/09/0.jpg', '1.jpg') |
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torch.hub.download_url_to_file('https://s.rdrom.ru/1/pubs/4/35893/1906770.jpg', '2.jpg') |
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torch.hub.download_url_to_file('https://static.mk.ru/upload/entities/2022/04/17/07/articles/detailPicture/5b/39/28/b6/ffb1aa636dd62c30e6ff670f84474f75.jpg', '3.jpg') |
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def yolox_inference( |
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image_path: gr.inputs.Image = None, |
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model_path: gr.inputs.Dropdown = 'kadirnar/yolox_s-v0.1.1', |
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config_path: gr.inputs.Textbox = 'configs.yolox_s', |
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image_size: gr.inputs.Slider = 640 |
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): |
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""" |
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YOLOX inference function |
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Args: |
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image: Input image |
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model_path: Path to the model |
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config_path: Path to the config file |
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image_size: Image size |
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Returns: |
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Rendered image |
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""" |
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model = YoloxDetector(model_path, config_path=config_path, device="cpu", hf_model=True) |
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pred = model.predict(image_path=image_path, image_size=image_size) |
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return pred |
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inputs = [ |
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gr.inputs.Image(type="filepath", label="Input Image"), |
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gr.inputs.Dropdown( |
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label="Model Path", |
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choices=[ |
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"kadirnar/yolox_s-v0.1.1", |
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"kadirnar/yolox_m-v0.1.1", |
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"kadirnar/yolox_tiny-v0.1.1", |
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], |
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default="kadirnar/yolox_s-v0.1.1", |
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), |
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gr.inputs.Dropdown( |
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label="Config Path", |
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choices=[ |
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"configs.yolox_s", |
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"configs.yolox_m", |
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"configs.yolox_tiny", |
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], |
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default="configs.yolox_s", |
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), |
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gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"), |
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] |
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outputs = gr.outputs.Image(type="filepath", label="Output Image") |
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title = "YOLOX is a high-performance anchor-free YOLO." |
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examples = [ |
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["1.jpg", "kadirnar/yolox_m-v0.1.1", "configs.yolox_m", 640], |
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["2.jpg", "kadirnar/yolox_s-v0.1.1", "configs.yolox_s", 640], |
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["3.jpg", "kadirnar/yolox_tiny-v0.1.1", "configs.yolox_tiny", 640], |
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] |
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demo_app = gr.Interface( |
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fn=yolox_inference, |
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inputs=inputs, |
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outputs=outputs, |
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title=title, |
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examples=examples, |
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cache_examples=True, |
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theme='huggingface', |
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) |
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demo_app.launch(debug=True, enable_queue=True) |
