import numpy as np import pandas as pd from PIL import Image import os import torch import utilities from skimage.transform import resize import albumentations as A from albumentations.pytorch import ToTensorV2 import cv2 ## -----------------------------------------------------------------------------------------------------------------## ## CHEST DATASET ## ## -----------------------------------------------------------------------------------------------------------------## """ LINK: https://www.kaggle.com/datasets/nikhilpandey360/chest-xray-masks-and-labels X-ray images in this data set have been acquired from the tuberculosis control program of the Department of Health and Human Services of Montgomery County, MD, USA. This set contains 138 posterior-anterior x-rays, of which 80 x-rays are normal and 58 x-rays are abnormal with manifestations of tuberculosis. All images are de-identified and available in DICOM format. The set covers a wide range of abnormalities, including effusions and miliary patterns. """ class Chest(torch.utils.data.Dataset): def __init__(self, prefix, phase, size=(512, 512), num_channels=1, fuse_heatmap=False, sigma=8): self.phase = phase self.new_size = size self.dataset_name = 'Chest' self.transforms = self.get_transforms() self.num_channels = num_channels self.fuse_heatmap = fuse_heatmap self.sigma = sigma self.num_landmarks = 6 self.pth_Image = os.path.join(prefix, 'pngs') self.pth_Label = os.path.join(prefix, 'labels') # file index files = [i[:-4] for i in sorted(os.listdir(self.pth_Image))] exclude_list = ['CHNCXR_0059_0', 'CHNCXR_0178_0', 'CHNCXR_0228_0', 'CHNCXR_0267_0', 'CHNCXR_0295_0', 'CHNCXR_0310_0', 'CHNCXR_0285_0', 'CHNCXR_0276_0', 'CHNCXR_0303_0'] if exclude_list is not None: st = set(exclude_list) files = [f for f in files if f not in st] n = len(files) train_num = 195 val_num = 34 test_num = n - train_num - val_num if self.phase == 'train': self.indexes = files[:train_num] elif self.phase == 'validate': self.indexes = files[train_num:-test_num] elif self.phase == 'test': self.indexes = files[-test_num:] elif self.phase == 'all': self.indexes = files else: raise Exception("Unknown phase: {phase}".format(phase=phase)) def __getitem__(self, index): name = self.indexes[index] ret = {'name': name} img, img_size= self.readImage(os.path.join(self.pth_Image, name + '.png')) points = self.readLandmark(name) heatmaps = utilities.points_to_heatmap(points, sigma=self.sigma, img_size=self.new_size, fuse=self.fuse_heatmap) transformed = self.transforms(image=img, masks=heatmaps) # img shape: CxHxW | heatmaps is a list of CxHxW: example: [CxHxW, CxHxW, CxHxW, CxHxW, CxHxW, CxHxW] img, heatmaps = transformed['image'], transformed['masks'] # Image is a torch tensor [C, H, W] ret['image'] = img ret['landmarks'] = torch.FloatTensor(points) # Convert heatmaps to torch tensor [C, H, W]. Stack to give new dimension and float32 type to avoid error in loss function ret['heatmaps'] = torch.stack([hm.float() for hm in heatmaps]) ret['original_size'] = torch.FloatTensor(img_size) ret['resized_size'] = torch.FloatTensor(self.new_size) return ret def __len__(self): return len(self.indexes) def readLandmark(self, name): path = os.path.join(self.pth_Label, name + '.txt') points = [] with open(path, 'r') as f: n = int(f.readline()) for i in range(n): ratios = [float(i) for i in f.readline().split()] points.append(ratios) return np.array(points) def readImage(self, path): if self.num_channels == 3: img = Image.open(path).convert('RGB') arr = np.array(img).astype(np.float32) elif self.num_channels == 1: img = Image.open(path).convert('L') arr = np.array(img).astype(np.float32) arr = np.expand_dims(arr, 2) else: raise ValueError('Channels must be either 1 or 3') # Original size in (width, height) origin_size = img.size resized_image = resize(arr, (self.new_size[0], self.new_size[1], self.num_channels)) return resized_image, origin_size def get_transforms(self): if self.phase == 'train': return A.Compose([ A.ShiftScaleRotate(shift_limit=0.02, scale_limit=0, rotate_limit=2, border_mode=cv2.BORDER_REPLICATE, p=0.5), #A.Perspective(scale=(0, 0.02), pad_mode=cv2.BORDER_REPLICATE, p=0.5), #A.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(-0.2, 0.2), p=0.5), #A.Resize(self.new_size[0], self.new_size[1]), A.Normalize(normalization='min_max'), A.pytorch.ToTensorV2() ]) elif self.phase == 'validate': return A.Compose([ #A.Resize(self.new_size[0], self.new_size[1]), A.Normalize(normalization='min_max'), A.pytorch.ToTensorV2() ]) elif self.phase == 'test': return A.Compose([ #A.Resize(self.new_size[0], self.new_size[1]), A.Normalize(normalization='min_max'), A.pytorch.transforms.ToTensorV2() ]) else: raise ValueError('phase must be either "train" or "validate" or "test"') ## -----------------------------------------------------------------------------------------------------------------## ## HAND DATASET ## ## -----------------------------------------------------------------------------------------------------------------## """ LINK: https://ipilab.usc.edu/research/baaweb/ ASI: Asian; BLK: African American; CAU: Caucasian; HIS: Hispanic. """ class Hand(torch.utils.data.Dataset): def __init__(self, prefix, phase, size=(512, 368), num_channels=1, fuse_heatmap=False, sigma=5): self.phase = phase self.new_size = size self.dataset_name = 'Hand' self.transforms = self.get_transforms() self.num_channels = num_channels self.fuse_heatmap = fuse_heatmap self.sigma = sigma self.num_landmarks = 37 self.pth_Image = os.path.join(prefix, 'jpg') self.labels = pd.read_csv(os.path.join( prefix, 'labels/all.csv'), header=None, index_col=0) # file index index_set = set(self.labels.index) # Set of all the labels files = [i[:-4] for i in sorted(os.listdir(self.pth_Image))] # -4 to cut ".jpg" # List of all the images files = [i for i in files if int(i) in index_set] # List of filters that has a label n = len(files) train_num = 550 val_num = 59 test_num = n - train_num - val_num if phase == 'train': self.indexes = files[:train_num] elif phase == 'validate': self.indexes = files[train_num:-test_num] elif phase == 'test': self.indexes = files[-test_num:] elif phase == 'all': self.indexes = files else: raise Exception("Unknown phase: {phase}".format(phase=phase)) def __getitem__(self, index): name = self.indexes[index] ret = {'name': name} img, img_size = self.readImage( os.path.join(self.pth_Image, name + '.jpg')) points = self.readLandmark(name, img_size) heatmaps = utilities.points_to_heatmap(points, sigma=self.sigma, img_size=self.new_size, fuse=self.fuse_heatmap) transformed = self.transforms(image=img, masks=heatmaps) img, heatmaps = transformed['image'], transformed['masks'] ret['image'] = img ret['landmarks'] = torch.FloatTensor(points) ret['heatmaps'] = torch.stack([hm.float() for hm in heatmaps]) ret['original_size'] = torch.FloatTensor(img_size) ret['resized_size'] = torch.FloatTensor(self.new_size) return ret def __len__(self): return len(self.indexes) def readLandmark(self, name, origin_size): li = list(self.labels.loc[int(name), :]) points = [] for i in range(0, len(li), 2): ratios = (li[i] / origin_size[0], li[i + 1] / origin_size[1]) points.append(ratios) return np.array(points) def readImage(self, path): if self.num_channels == 3: img = Image.open(path).convert('RGB') arr = np.array(img).astype(np.float32) elif self.num_channels == 1: img = Image.open(path).convert('L') arr = np.array(img).astype(np.float32) arr = np.expand_dims(arr, 2) else: raise ValueError('Channels must be either 1 or 3') # Original size in (width, height) origin_size = img.size resized_image = resize(arr, (self.new_size[0], self.new_size[1], self.num_channels)) return resized_image, origin_size def get_transforms(self): if self.phase == 'train': return A.Compose([ A.ShiftScaleRotate(shift_limit=0.02, scale_limit=(-0.02, 0.02), rotate_limit=2, border_mode=cv2.BORDER_REPLICATE, p=0.5), #A.Perspective(scale=(0, 0.02), pad_mode=cv2.BORDER_REPLICATE, p=0.5), #A.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(-0.2, 0.2), p=0.5), #A.Resize(self.new_size[0], self.new_size[1]), A.Normalize(normalization='min_max'), A.pytorch.ToTensorV2() ]) elif self.phase == 'validate': return A.Compose([ #A.Resize(self.new_size[0], self.new_size[1]), A.Normalize(normalization='min_max'), A.pytorch.ToTensorV2() ]) elif self.phase == 'test': return A.Compose([ #A.Resize(self.new_size[0], self.new_size[1]), A.Normalize(normalization='min_max'), A.pytorch.transforms.ToTensorV2() ]) else: raise ValueError('phase must be either "train" or "validate" or "test"') ## -----------------------------------------------------------------------------------------------------------------## ## CEPHALOMETRIC DATASET ## ## -----------------------------------------------------------------------------------------------------------------## """ LINK: https://www.kaggle.com/datasets/c34a0ef0cd3cfd5c5afbdb30f8541e887171f19f196b1ad63790ca5b28c0ec93 https://figshare.com/s/37ec464af8e81ae6ebbf?file=5466581 """ class Cephalo(torch.utils.data.Dataset): def __init__(self, prefix, phase, size=(512, 416), num_channels=1, fuse_heatmap=False, sigma=5): self.phase = phase self.new_size = size self.dataset_name = 'Cephalo' self.transforms = self.get_transforms() self.num_channels = num_channels self.fuse_heatmap = fuse_heatmap self.sigma = sigma self.num_landmarks = 19 self.pth_Image = os.path.join(prefix, 'jpg') self.pth_label_junior = os.path.join(prefix, '400_junior') self.pth_label_senior = os.path.join(prefix, '400_senior') # file index files = [i[:-4] for i in sorted(os.listdir(self.pth_Image))] n = len(files) if phase == 'train': self.indexes = files[:130] elif phase == 'validate': self.indexes = files[130:150] elif phase == 'test': self.indexes = files[150:400] elif phase == 'all': self.indexes = files else: raise Exception("Unknown phase: {phase}".format(phase=phase)) def __getitem__(self, index): name = self.indexes[index] ret = {'name': name} img, img_size = self.readImage(os.path.join(self.pth_Image, name+'.jpg')) points = self.readLandmark(name, img_size) heatmaps = utilities.points_to_heatmap(points, sigma=self.sigma, img_size=self.new_size, fuse=self.fuse_heatmap) transformed = self.transforms(image=img, masks=heatmaps) img, heatmaps = transformed['image'], transformed['masks'] ret['image'] = img ret['landmarks'] = torch.FloatTensor(points) ret['heatmaps'] = torch.stack([hm.float() for hm in heatmaps]) ret['original_size'] = torch.FloatTensor(img_size) ret['resized_size'] = torch.FloatTensor(self.new_size) return ret def __len__(self): return len(self.indexes) def readLandmark(self, name, origin_size): points = [] with open(os.path.join(self.pth_label_junior, name + '.txt')) as f1: with open(os.path.join(self.pth_label_senior, name + '.txt')) as f2: for i in range(self.num_landmarks): landmark1 = f1.readline().rstrip('\n').split(',') landmark2 = f2.readline().rstrip('\n').split(',') # Average of junior and senior landmarks landmark = [(float(i) + float(j)) / 2 for i, j in zip(landmark1, landmark2)] #landmark = [float(i) for i in landmark1] ratios = (landmark[0] / origin_size[0], landmark[1] / origin_size[1]) points.append(ratios) return np.array(points) def readImage(self, path): if self.num_channels == 3: img = Image.open(path).convert('RGB') arr = np.array(img).astype(np.float32) elif self.num_channels == 1: img = Image.open(path).convert('L') arr = np.array(img).astype(np.float32) arr = np.expand_dims(arr, 2) else: raise ValueError('Channels must be either 1 or 3') # Original size in (width, height) origin_size = img.size resized_image = resize(arr, (self.new_size[0], self.new_size[1], self.num_channels)) return resized_image, origin_size def get_transforms(self): if self.phase == 'train': return A.Compose([ A.ShiftScaleRotate(shift_limit=0.02, scale_limit=(-0.02, 0.02), rotate_limit=2, border_mode=cv2.BORDER_REPLICATE, p=0.5), #A.Perspective(scale=(0, 0.02), pad_mode=cv2.BORDER_REPLICATE, p=0.5), #A.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1), contrast_limit=(-0.2, 0.2), p=0.5), #A.Resize(self.new_size[0], self.new_size[1]), A.Normalize(normalization='min_max'), A.pytorch.ToTensorV2() ]) elif self.phase == 'validate': return A.Compose([ #A.Resize(self.new_size[0], self.new_size[1]), A.Normalize(normalization='min_max'), A.pytorch.ToTensorV2() ]) elif self.phase == 'test': return A.Compose([ #A.Resize(self.new_size[0], self.new_size[1]), A.Normalize(normalization='min_max'), A.pytorch.transforms.ToTensorV2() ]) else: raise ValueError('phase must be either "train" or "validate" or "test"')