Delete utils/data_transforms.py

utils/data_transforms.py

@@ -1,267 +0,0 @@
-import monai 
-import cv2
-from monai.transforms import MapTransform
-import math 
-import numpy as np
-import torch
-import morphsnakes as ms
-import monai
-import nrrd
-import torchvision.transforms as transforms
-from monai.transforms import (
-    Activations, AsDiscreteD, AsDiscrete, Compose, CastToTypeD, RandSpatialCropd,
-    ToTensorD, CropForegroundD, Resized, GaussianSmoothD, 
-    LoadImageD, TransposeD, OrientationD, ScaleIntensityRangeD,
-    RandAffineD, ResizeWithPadOrCropd, ToTensor,
-    FillHoles, KeepLargestConnectedComponent, HistogramNormalizeD, NormalizeIntensityD
-)
-
-
-
-def define_transforms_loadonly():
-    transformations = Compose([
-        LoadImageD(keys=["mask"], reader="NrrdReader", ensure_channel_first=True),
-        ConvertMaskValues(keys=["mask"], keep_classes=["liver", "tumor"]),
-        ToTensor()
-    ])
-    return transformations
-
-
-def define_post_processing(config):
-      # Post-processing transforms
-      post_processing = [
-          # Apply softmax activation to convert logits to probabilities
-          Activations(sigmoid=True),
-          # Convert predicted probabilities to discrete values (0 or 1)
-          AsDiscrete(argmax=True, to_onehot=None if len(config['KEEP_CLASSES']) <= 2 else len(config['KEEP_CLASSES'])),
-          # Remove small connected components for 1=liver and 2=tumor
-          KeepLargestConnectedComponent(applied_labels=[1]),
-          # Fill holes in the binary mask for 1=liver and 2=tumor
-          FillHoles(applied_labels=[1]),
-          ToTensor()
-      ]
-
-      return Compose(post_processing)
-
-def define_transforms(config):
-
-      transformations_test = [
-              LoadImageD(keys=["image", "mask"], reader="NrrdReader", ensure_channel_first=True),
-              # Orient up and down
-              OrientationD(keys=["image", "mask"], axcodes="PLI"),
-              ToTensorD(keys=["image", "mask"])
-              # histogram equilization or normalization
-              # HistogramNormalizeD(keys=["image"], num_bins=256, min=0, max=1),
-              # Intensity normalization 
-              # NormalizeIntensityD(keys=["image"]),
-              #CastToTypeD(keys=["image"], dtype=torch.float32),
-              #CastToTypeD(keys=["mask"], dtype=torch.int32),
-          ]
-    
-      if config['MASKNONLIVER']:
-          transformations_test.extend(
-              [
-                MaskOutNonliver(mask_key="mask"),
-                CropForegroundD(keys=["image", "mask"], source_key="image", allow_smaller=True), 
-              ]
-          )
-          
-      transformations_test.append(
-          # Windowing based on liver parameters
-          ScaleIntensityRangeD(keys=["image"],
-            a_min=config['HU_RANGE'][0],
-            a_max=config['HU_RANGE'][1],
-            b_min=0.0, b_max=1.0, clip=True
-          )
-      )
-      
-      if config['PREPROCESSING'] == "clihe":
-          transformations_test.append(CLIHE(keys=["image"]))
-      
-      elif config['PREPROCESSING'] == "gaussian":
-          transformations_test.append(GaussianSmoothD(keys=["image"], sigma=0.5))
-
-      # convert labels to 0,1,2 instead of 0,1,2,3,4
-      transformations_test.append(ConvertMaskValues(keys=["mask"], keep_classes=config['KEEP_CLASSES']))
-
-      if len(config['KEEP_CLASSES']) > 2: # NEEDED FOR MULTICLASS  https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb
-          transformations_test.append(AsDiscreteD(keys=["mask"], to_onehot=len(config['KEEP_CLASSES']))) # (N, C, H, W) 2d; (1, C, H, W, Z)
-      
-      if "3D" not in config['MODEL_NAME']:
-          transformations_test.append(TransposeD(keys=["image", "mask"], indices=(3,0,1,2)))
-
-      # training transforms include data augmentation
-      transformations_train = transformations_test.copy()
-      if config['MASKNONLIVER']: transformations_test = transformations_test[:4] + transformations_test[5:] # do not crop to liver foregroudn 
-
-      if config['DATA_AUGMENTATION']:
-          if "3D" in config["MODEL_NAME"]:
-              transformations_train.append(
-                  RandAffineD(keys=["image", "mask"], prob=0.2, padding_mode="border",
-                              mode="bilinear", spatial_size=config['ROI_SIZE'],
-                              rotate_range=(0.15,0.15,0.15), #translate_range=(30,30,30), 
-                              scale_range=(0.1,0.1,0.1)))
-          else:
-              transformations_train.append(
-                  RandAffineD(keys=["image", "mask"], prob=0.2, padding_mode="border",
-                              mode="bilinear", #spatial_size=(512, 512),
-                              rotate_range=(0.15,0.15), #translate_range=(30,30), 
-                              scale_range=(0.1,0.1)))
-
-      transformations_train.extend(
-          [
-            RandSpatialCropd(keys=["image", "mask"], roi_size=config['ROI_SIZE'], random_size=False),
-            ResizeWithPadOrCropd(keys=["image", "mask"], spatial_size=config['ROI_SIZE'], method="end", mode='constant', value=0)
-          ]
-      )
-
-      postprocessing_transforms = define_post_processing(config)
-      preprocessing_transforms_test = Compose(transformations_test)
-      preprocessing_transforms_train = Compose(transformations_train)
-      preprocessing_transforms_train.set_random_state(seed=1)
-      preprocessing_transforms_test.set_random_state(seed=1)
-
-      return preprocessing_transforms_train, preprocessing_transforms_test, postprocessing_transforms
-      
-      
-
-class CLIHE(MapTransform):
-    def __init__(self, keys, allow_missing_keys=False):
-        super().__init__(allow_missing_keys)
-        self.keys = keys
-
-    def __call__(self, data):
-        for key in self.keys:
-            if len(data['image'].shape) > 3: # 3D image
-                data[key] = self.apply_clahe_3d(data[key]) # [B, 1, H, W, Z]
-            else:
-                data[key] = self.apply_clahe_2d(data[key]) # [B, 1, H, W, Z]
-        return data
-
-    def apply_clahe_3d(self, image):
-        image = np.asarray(image)
-        clahe_slices = []
-        for slice_idx in range(image.shape[-1]):
-            # Extract the current slice
-            slice_2d = image[0, :, :, slice_idx]
-
-            # Apply CLAHE to the current slice
-            # slice_2d = cv2.medianBlur(slice_2d, 5)
-            # slice_2d = cv2.anisotropicDiffusion(slice_2d, alpha=0.1, K=1, iterations=50)
-            # slice_2d = anisotropic_diffusion(slice_2d)
-            # slice_2d = cv2.Sobel(slice_2d, cv2.CV_64F, dx=1, dy=1, ksize=5)
-            clahe = cv2.createCLAHE(clipLimit=1, tileGridSize=(16,16))
-            slice_2d = clahe.apply(slice_2d.astype(np.uint8))
-            #cv2.threshold(clahe_slice, 155, 255, cv2.THRESH_BINARY)
-            kernel = np.ones((2,2), np.float32)/4
-            slice_2d = cv2.filter2D(slice_2d, -1, kernel)
-            #t = anisodiff2D(delta_t=0.2,kappa=50)
-            #slice_2d = t.fit(slice_2d)
-
-            # Append the CLAHE enhanced slice to the list
-            clahe_slices.append(slice_2d)
-
-            # Stack the CLAHE enhanced slices along the slice axis to form the 3D image
-            clahe_image = np.stack(clahe_slices, axis=-1)
-
-        return torch.from_numpy(clahe_image[None,:])
-
-    def apply_clahe_2d(self, image):
-        image = np.asarray(image)
-
-        clahe = cv2.createCLAHE(clipLimit=5)
-        clahe_slice = clahe.apply(image[0].astype(np.uint8))
-
-        return torch.from_numpy(clahe_slice)
-
-
-
-class GaussianFilter(MapTransform):
-    def __init__(self, keys, allow_missing_keys=False):
-        super().__init__(allow_missing_keys)
-        self.keys = keys
-
-    def __call__(self, data):
-        for key in self.keys:
-            if len(data['image'].shape) > 3: # 3D image
-                data[key] = self.apply_clahe_3d(data[key]) # [B, 1, H, W, Z]
-            else:
-                data[key] = self.apply_clahe_2d(data[key]) # [B, 1, H, W, Z]
-        return data
-
-    def apply_clahe_3d(self, image):
-        image = np.asarray(image)
-        clahe_slices = []
-        for slice_idx in range(image.shape[-1]):
-            # Extract the current slice
-            slice_2d = image[0, :, :, slice_idx]
-
-            # Apply CLAHE to the current slice
-            kernel = np.ones((3,3), np.float32)/9
-            slice_2d = cv2.filter2D(slice_2d, -1, kernel)
-            
-            # Append the CLAHE enhanced slice to the list
-            clahe_slices.append(slice_2d)
-
-            # Stack the CLAHE enhanced slices along the slice axis to form the 3D image
-            clahe_image = np.stack(clahe_slices, axis=-1)
-
-        return torch.from_numpy(clahe_image[None,:])
-
-    def apply_clahe_2d(self, image):
-        image = np.asarray(image)
-
-        kernel = np.ones((3,3), np.float32)/9
-        slice_2d = cv2.filter2D(image, -1, kernel)
-
-        return torch.from_numpy(slice_2d)
-
-
-class Morphsnakes(MapTransform):
-    # https://github.com/pmneila/morphsnakes/blob/master/morphsnakes.py
-    def __init__(self, allow_missing_keys=False):
-        super().__init__(allow_missing_keys)
-
-    def __call__(self, data):
-        if np.sum(data['mask'][-1]) > 0:
-            res = ms.morphological_chan_vese(data['image'][0], iterations=2, init_level_set=data['mask'][-1])
-            data['mask'] = res
-        return data
-        
-        
-class MaskOutNonliver(MapTransform):
-    def __init__(self, allow_missing_keys=False, mask_key="mask"):
-        super().__init__(allow_missing_keys)
-        self.mask_key = mask_key
-
-    def __call__(self, data):
-        # mask out non-liver regions of an image
-        # non-liver regions are liver, tumor, or portal vein
-        if data[self.mask_key].shape != data['image'].shape:
-            return data
-        data['image'][data[self.mask_key] >= 4] = -1000
-        data['image'][data[self.mask_key] <= 0] = -1000
-        return data
-        
-        
-class ConvertMaskValues(MapTransform):
-    def __init__(self, keys, allow_missing_keys=False, keep_classes=["normal", "liver", "tumor"]):
-        super().__init__(keys, allow_missing_keys)
-        self.keep_classes = keep_classes
-
-    def __call__(self, data):
-        # original labels: 0 for normal region, 1 for liver, 2 for tumor mass, 3 for portal vein, and 4 for abdominal aorta.
-        # converted labels: 0 for normal region and abdominal aorta, 1 for liver and portal vein, 2 for tumor mass
-
-        for key in self.keys:
-          data[key][data[key] > 4] = 4 # one patient had class label = 5, converted to 4
-          if key in data:
-            if "liver" not in self.keep_classes:
-                data[key][data[key] == 1] = 0
-            if "tumor" not in self.keep_classes:
-                data[key][data[key] == 2] = 1
-            if "portal vein" not in self.keep_classes:
-                data[key][data[key] == 3] = 1
-            if "abdominal aorta" not in self.keep_classes:
-                data[key][data[key] >= 4] = 0
-        return data