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import gradio as gr |
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import os |
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os.system("pip -qq install yoloxdetect") |
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import torch |
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import json |
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import yoloxdetect2.helpers as yoloxdetectow |
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torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg') |
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torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg') |
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torch.hub.download_url_to_file('https://raw.githubusercontent.com/Megvii-BaseDetection/YOLOX/main/assets/dog.jpg', 'dog.jpg') |
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model = yoloxdetectow.YoloxDetector2('kadirnar/yolox_s-v0.1.1', 'configs.yolox_s', device="cpu", hf_model=True) |
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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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pred2 = [] |
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if model : |
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model.torchyolo = True |
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pred2 = model.predict(image_path=image_path, image_size=image_size) |
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tensor = { |
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"tensorflow": [ |
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] |
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} |
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if pred2 is not None: |
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for i, element in enumerate(pred2[0]): |
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object = {} |
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itemclass = round(pred2[2][i].item()) |
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object["classe"] = itemclass |
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object["nome"] = pred2[3][itemclass] |
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object["score"] = pred2[1][i].item() |
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object["x"] = element[0].item() |
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object["y"] = element[1].item() |
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object["w"] = element[2].item() |
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object["h"] = element[3].item() |
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tensor["tensorflow"].append(object) |
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text = json.dumps(tensor) |
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return text |
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inputs = [ |
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gr.inputs.Image(type="filepath", label="Input Image"), |
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gr.inputs.Textbox(lines=1, label="Model Path", default="kadirnar/yolox_s-v0.1.1"), |
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gr.inputs.Textbox(lines=1, label="Config Path", default="configs.yolox_s"), |
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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 = "SIMULADOR PARA RECONHECIMENTO DE IMAGEM" |
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examples = [ |
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["small-vehicles1.jpeg", "kadirnar/yolox_m-v0.1.1", "configs.yolox_m", 640], |
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["zidane.jpg", "kadirnar/yolox_s-v0.1.1", "configs.yolox_s", 640], |
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["dog.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=["text"], |
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title=title, |
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examples=examples, |
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cache_examples=True, |
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live=True, |
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theme='huggingface', |
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) |
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demo_app.launch(debug=True, enable_queue=True) |
