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import json |
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import gradio as gr |
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import yolov5 |
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from PIL import Image |
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from huggingface_hub import hf_hub_download |
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app_title = "License Plate Object Detection" |
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models_ids = ['keremberke/yolov5n-license-plate', 'keremberke/yolov5s-license-plate', 'keremberke/yolov5m-license-plate'] |
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article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>model</a> | <a href='https://huggingface.co/keremberke/license-plate-object-detection'>dataset</a> | <a href='https://github.com/keremberke/awesome-yolov5-models'>awesome-yolov5-models</a> </p>" |
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current_model_id = models_ids[-1] |
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model = yolov5.load(current_model_id) |
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examples = [['test_images/CarLongPlate686_jpg.rf.97172961f3f90ae6e4b0ef1edfa24b98.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['test_images/CarLongPlate834_jpg.rf.c6da1db4c7c6ce9d9d864a90bb46ff1d.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['test_images/CarLongPlateGen3663_jpg.rf.26f54b241dbee94a3faabc9a08fd638a.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['test_images/CarLongPlateGen570_jpg.rf.305252bdd2798c370af7f1d702c0dd97.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['test_images/xemay1024_jpg.rf.1d25cb47787faa4e72967cf4c356af2a.jpg', 0.25, 'keremberke/yolov5m-license-plate'], ['test_images/xemay1349_jpg.rf.759edbd383937d1fdc243203450a1823.jpg', 0.25, 'keremberke/yolov5m-license-plate']] |
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def predict(image, threshold=0.25, model_id=None): |
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global current_model_id |
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global model |
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if model_id != current_model_id: |
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model = yolov5.load(model_id) |
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current_model_id = model_id |
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config_path = hf_hub_download(repo_id=model_id, filename="config.json") |
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with open(config_path, "r") as f: |
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config = json.load(f) |
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input_size = config["input_size"] |
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model.conf = threshold |
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results = model(image, size=input_size) |
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numpy_image = results.render()[0] |
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output_image = Image.fromarray(numpy_image) |
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return output_image |
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gr.Interface( |
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title=app_title, |
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description="Created by 'keremberke'", |
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article=article, |
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fn=predict, |
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inputs=[ |
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gr.Image(type="pil"), |
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gr.Slider(maximum=1, step=0.01, value=0.25), |
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gr.Dropdown(models_ids, value=models_ids[-1]), |
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], |
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outputs=gr.Image(type="pil"), |
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examples=examples, |
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cache_examples=True if examples else False, |
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).launch(enable_queue=True) |
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