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| import os | |
| import numpy as np | |
| import cv2 | |
| import albumentations | |
| from PIL import Image | |
| from torch.utils.data import Dataset | |
| class SegmentationBase(Dataset): | |
| def __init__(self, | |
| data_csv, data_root, segmentation_root, | |
| size=None, random_crop=False, interpolation="bicubic", | |
| n_labels=182, shift_segmentation=False, | |
| ): | |
| self.n_labels = n_labels | |
| self.shift_segmentation = shift_segmentation | |
| self.data_csv = data_csv | |
| self.data_root = data_root | |
| self.segmentation_root = segmentation_root | |
| with open(self.data_csv, "r") as f: | |
| self.image_paths = f.read().splitlines() | |
| self._length = len(self.image_paths) | |
| self.labels = { | |
| "relative_file_path_": [l for l in self.image_paths], | |
| "file_path_": [os.path.join(self.data_root, l) | |
| for l in self.image_paths], | |
| "segmentation_path_": [os.path.join(self.segmentation_root, l.replace(".jpg", ".png")) | |
| for l in self.image_paths] | |
| } | |
| size = None if size is not None and size<=0 else size | |
| self.size = size | |
| if self.size is not None: | |
| self.interpolation = interpolation | |
| self.interpolation = { | |
| "nearest": cv2.INTER_NEAREST, | |
| "bilinear": cv2.INTER_LINEAR, | |
| "bicubic": cv2.INTER_CUBIC, | |
| "area": cv2.INTER_AREA, | |
| "lanczos": cv2.INTER_LANCZOS4}[self.interpolation] | |
| self.image_rescaler = albumentations.SmallestMaxSize(max_size=self.size, | |
| interpolation=self.interpolation) | |
| self.segmentation_rescaler = albumentations.SmallestMaxSize(max_size=self.size, | |
| interpolation=cv2.INTER_NEAREST) | |
| self.center_crop = not random_crop | |
| if self.center_crop: | |
| self.cropper = albumentations.CenterCrop(height=self.size, width=self.size) | |
| else: | |
| self.cropper = albumentations.RandomCrop(height=self.size, width=self.size) | |
| self.preprocessor = self.cropper | |
| def __len__(self): | |
| return self._length | |
| def __getitem__(self, i): | |
| example = dict((k, self.labels[k][i]) for k in self.labels) | |
| image = Image.open(example["file_path_"]) | |
| if not image.mode == "RGB": | |
| image = image.convert("RGB") | |
| image = np.array(image).astype(np.uint8) | |
| if self.size is not None: | |
| image = self.image_rescaler(image=image)["image"] | |
| segmentation = Image.open(example["segmentation_path_"]) | |
| assert segmentation.mode == "L", segmentation.mode | |
| segmentation = np.array(segmentation).astype(np.uint8) | |
| if self.shift_segmentation: | |
| # used to support segmentations containing unlabeled==255 label | |
| segmentation = segmentation+1 | |
| if self.size is not None: | |
| segmentation = self.segmentation_rescaler(image=segmentation)["image"] | |
| if self.size is not None: | |
| processed = self.preprocessor(image=image, | |
| mask=segmentation | |
| ) | |
| else: | |
| processed = {"image": image, | |
| "mask": segmentation | |
| } | |
| example["image"] = (processed["image"]/127.5 - 1.0).astype(np.float32) | |
| segmentation = processed["mask"] | |
| onehot = np.eye(self.n_labels)[segmentation] | |
| example["segmentation"] = onehot | |
| return example | |
| class Examples(SegmentationBase): | |
| def __init__(self, size=None, random_crop=False, interpolation="bicubic"): | |
| super().__init__(data_csv="data/sflckr_examples.txt", | |
| data_root="data/sflckr_images", | |
| segmentation_root="data/sflckr_segmentations", | |
| size=size, random_crop=random_crop, interpolation=interpolation) | |