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from huggingface_hub import from_pretrained_fastai |
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import gradio as gr |
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from fastai.vision.all import * |
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import PIL |
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import torchvision.transforms as transforms |
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class SegmentationAlbumentationsTransform(ItemTransform): |
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split_idx = 0 |
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def __init__(self, aug): |
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self.aug = aug |
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def encodes(self, x): |
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img,mask = x |
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aug = self.aug(image=np.array(img), mask=np.array(mask)) |
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return PILImage.create(aug["image"]), PILMask.create(aug["mask"]) |
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class TargetMaskConvertTransform(ItemTransform): |
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def __init__(self): |
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pass |
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def encodes(self, x): |
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img,mask = x |
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mask = np.array(mask) |
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mask[mask==255]=1 |
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mask[mask==150]=2 |
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mask[mask==76]=3 |
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mask[mask==74]=3 |
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mask[mask==29]=4 |
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mask[mask==25]=4 |
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mask = PILMask.create(mask) |
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return img, mask |
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repo_id = "paascorb/practica3_Segmentation" |
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learner = from_pretrained_fastai(repo_id) |
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def transform_image(image): |
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my_transforms = transforms.Compose([transforms.ToTensor(), |
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transforms.Normalize( |
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[0.485, 0.456, 0.406], |
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[0.229, 0.224, 0.225])]) |
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image_aux = image |
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return my_transforms(image_aux).unsqueeze(0).to(device) |
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def predict(img): |
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img = PIL.Image.fromarray(img, "RGB") |
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image = transforms.Resize((480,640))(img) |
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tensor = transform_image(image=image) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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learner.to(device) |
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with torch.no_grad(): |
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outputs = learner(tensor) |
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outputs = torch.argmax(outputs,1) |
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mask = np.array(outputs.cpu()) |
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mask[mask==1]=255 |
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mask[mask==2]=150 |
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mask[mask==3]=76 |
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mask[mask==4]=29 |
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mask=np.reshape(mask,(480,640)) |
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return Image.fromarray(mask.astype('uint8')) |
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gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=[gr.outputs.Image(type="pil", label="Predicci贸n")], examples=['color_155.jpg','color_154.jpg']).launch(share=False) |