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import numpy as np |
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import gradio as gr |
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from pytorch_grad_cam import GradCAM |
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from pytorch_grad_cam.utils.image import show_cam_on_image |
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from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget |
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from display import inference, draw_predictions |
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gr.Interface( |
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inference, |
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inputs=[ |
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gr.Image(label="Input Image"), |
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gr.Slider(0, 1, value=0.50, label="IOU Threshold"), |
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gr.Slider(0, 1, value=0.50, label="Threshold"), |
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gr.Checkbox(label="Show GradCam Image"), |
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gr.Slider(0, 1, value=0.5, label="Opacity of GradCAM"), |
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], |
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outputs=gr.Gallery(rows=2, columns=1), |
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title = "Object Detection : YoloV3 on PASCAL VOC Dataset From Scratch (with GradCAM)" |
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,examples=[ |
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["Examples/000001.jpg", 0.75, 0.75, True, 0.5], |
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["Examples/000002.jpg", 0.75, 0.75, True, 0.5], |
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["Examples/000003.jpg", 0.75, 0.75, True, 0.5], |
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["Examples/000004.jpg", 0.75, 0.75, True, 0.5], |
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["Examples/000005.jpg", 0.75, 0.75, True, 0.5], |
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["Examples/000006.jpg", 0.75, 0.75, True, 0.5], |
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["Examples/000007.jpg", 0.75, 0.75, True, 0.5], |
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["Examples/000008.jpg", 0.75, 0.75, True, 0.5], |
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["Examples/000009.jpg", 0.75, 0.75, True, 0.5], |
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["Examples/000010.jpg", 0.75, 0.75, True, 0.5] |
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] |
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, |
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layout="horizontal" |
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).launch() |
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