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import gradio as gr |
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from icevision.all import * |
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import PIL |
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class_map = ClassMap(['raccoon']) |
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model = models.torchvision.faster_rcnn.model(backbone=models.torchvision.faster_rcnn.backbones.resnet50_fpn(pretrained=True), num_classes=len(class_map)) |
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state_dict = torch.load('fasterRCNNRaccoonRESNET50.pth') |
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model.load_state_dict(state_dict) |
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size = 384 |
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infer_tfms = tfms.A.Adapter([*tfms.A.resize_and_pad(size),tfms.A.Normalize()]) |
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def predict(img): |
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np.int = int |
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img = PIL.Image.fromarray(img) |
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pred_dict = models.torchvision.faster_rcnn.end2end_detect(img, infer_tfms, model.to("cpu"), class_map=class_map, detection_threshold=0.5) |
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return pred_dict['img'] |
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gr.Interface(fn=predict, inputs=["image"], outputs=["image"], examples=['raccoon/train/images/raccoon-197.jpg','raccoon/train/images/raccoon-177.jpg']).launch(share=True,debug=True) |
