Update app.py

app.py

@@ -3,69 +3,72 @@ import gradio as gr
 from fastai.vision.all import *
 import PIL
 import torchvision.transforms as transforms
-from albumentations import (
-    Compose,
-    OneOf,
-    ElasticTransform,
-    GridDistortion, 
-    OpticalDistortion,
-    HorizontalFlip,
-    Rotate,
-    Transpose,
-    CLAHE,
-    ShiftScaleRotate
-)
+# from albumentations import (
+#     Compose,
+#     OneOf,
+#     ElasticTransform,
+#     GridDistortion, 
+#     OpticalDistortion,
+#     HorizontalFlip,
+#     Rotate,
+#     Transpose,
+#     CLAHE,
+#     ShiftScaleRotate
+# )
 
-def get_y_fn (x):
-    return Path(str(x).replace("Images","Labels").replace("color","gt").replace(".jpg",".png"))
+# def get_y_fn (x):
+#     return Path(str(x).replace("Images","Labels").replace("color","gt").replace(".jpg",".png"))
 
-def ParentSplitter(x):
-    return Path(x).parent.name==test_name
+# def ParentSplitter(x):
+#     return Path(x).parent.name==test_name
 
-class SegmentationAlbumentationsTransform(ItemTransform):
-    split_idx = 0
+# class SegmentationAlbumentationsTransform(ItemTransform):
+#     split_idx = 0
     
-    def __init__(self, aug): 
-        self.aug = aug
+#     def __init__(self, aug): 
+#         self.aug = aug
         
-    def encodes(self, x):
-        img,mask = x
-        aug = self.aug(image=np.array(img), mask=np.array(mask))
-        return PILImage.create(aug["image"]), PILMask.create(aug["mask"])
+#     def encodes(self, x):
+#         img,mask = x
+#         aug = self.aug(image=np.array(img), mask=np.array(mask))
+#         return PILImage.create(aug["image"]), PILMask.create(aug["mask"])
 
-transforms=Compose([HorizontalFlip(p=0.5),
-                    Rotate(p=0.40,limit=10),GridDistortion()
-                    ],p=1)
-transformPipeline=SegmentationAlbumentationsTransform(transforms)
+# transforms=Compose([HorizontalFlip(p=0.5),
+#                     Rotate(p=0.40,limit=10),GridDistortion()
+#                     ],p=1)
+# transformPipeline=SegmentationAlbumentationsTransform(transforms)
 
-class TargetMaskConvertTransform(ItemTransform):
-    def __init__(self): 
-        pass
-    def encodes(self, x):
-        img,mask = x
+# class TargetMaskConvertTransform(ItemTransform):
+#     def __init__(self): 
+#         pass
+#     def encodes(self, x):
+#         img,mask = x
         
-        #Convert to array
-        mask = np.array(mask)
+#         #Convert to array
+#         mask = np.array(mask)
         
-        # Aquí definimos cada clase en la máscara
-        # uva:
-        mask[mask==255]=1
-        # hojas:
-        mask[mask==150]=2
-        # conductores:
-        mask[mask==76]=3
-        mask[mask==74]=3
-        # madera:
-        mask[mask==29]=4
-        mask[mask==25]=4
+#         # Aquí definimos cada clase en la máscara
+#         # uva:
+#         mask[mask==255]=1
+#         # hojas:
+#         mask[mask==150]=2
+#         # conductores:
+#         mask[mask==76]=3
+#         mask[mask==74]=3
+#         # madera:
+#         mask[mask==29]=4
+#         mask[mask==25]=4
         
-        # Back to PILMask
-        mask = PILMask.create(mask)
-        return img, mask
+#         # Back to PILMask
+#         mask = PILMask.create(mask)
+#         return img, mask
 
-repo_id = "paascorb/practica3_Segmentation"
+# repo_id = "paascorb/practica3_Segmentation"
 
-learner = from_pretrained_fastai(repo_id)
+# learner = from_pretrained_fastai(repo_id)
+
+learn = unet_learner(trainDLS,resnet18,cbs=callbacks,metrics=[DiceMulti()]).to_fp16()
+learn.load('model')
 
 def transform_image(image):
     my_transforms = transforms.Compose([transforms.ToTensor(),