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import gradio as gr |
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import torch |
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import json |
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import yolov5 |
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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/WongKinYiu/yolov7/main/inference/images/image3.jpg', 'image3.jpg') |
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torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt','yolov5s.pt') |
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model_path = "yolov5x.pt" |
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image_size = 640, |
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conf_threshold = 0.25, |
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iou_threshold = 0.45, |
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model = yolov5.load(model_path, device="cpu") |
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def yolov5_inference( |
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image: gr.inputs.Image = None, |
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): |
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""" |
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YOLOv5 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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image_size: Image size |
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conf_threshold: Confidence threshold |
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iou_threshold: IOU threshold |
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Returns: |
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Rendered image |
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""" |
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results = model([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 results.pred is not None: |
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for i, element in enumerate(results.pred[0]): |
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object = {} |
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itemclass = round(element[5].item()) |
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object["classe"] = itemclass |
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object["nome"] = results.names[itemclass] |
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object["score"] = element[4].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="pil", label="Input Image"), |
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] |
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outputs = gr.outputs.Image(type="filepath", label="Output Image") |
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title = "YOLOv5" |
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description = "YOLOv5 is a family of object detection models pretrained on COCO dataset. This model is a pip implementation of the original YOLOv5 model." |
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examples = [['zidane.jpg'], ['image3.jpg']] |
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demo_app = gr.Interface( |
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fn=yolov5_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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) |
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demo_app.launch(debug=True, server_name="192.168.0.153", server_port=8080, enable_queue=True) |
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demo_app.launch(debug=True, enable_queue=True) |
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