Update app.py
Browse files
app.py
CHANGED
@@ -3,65 +3,65 @@ 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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repo_id = "paascorb/practica3_Segmentation"
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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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from albumentations import (
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Compose,
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OneOf,
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ElasticTransform,
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GridDistortion,
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OpticalDistortion,
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HorizontalFlip,
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Rotate,
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Transpose,
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CLAHE,
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ShiftScaleRotate
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)
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def get_y_fn (x):
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return Path(str(x).replace("Images","Labels").replace("color","gt").replace(".jpg",".png"))
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def ParentSplitter(x):
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return Path(x).parent.name==test_name
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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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transforms=Compose([HorizontalFlip(p=0.5),
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Rotate(p=0.40,limit=10),GridDistortion()
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],p=1)
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transformPipeline=SegmentationAlbumentationsTransform(transforms)
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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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#Convert to array
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mask = np.array(mask)
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# Aquí definimos cada clase en la máscara
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# uva:
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mask[mask==255]=1
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# hojas:
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mask[mask==150]=2
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# conductores:
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mask[mask==76]=3
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mask[mask==74]=3
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# madera:
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mask[mask==29]=4
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mask[mask==25]=4
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# Back to PILMask
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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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