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@ -30,39 +30,21 @@ from PIL import Image |
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from joblib import load |
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import paddlers |
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from .functions import normalize, horizontal_flip, permute, vertical_flip, center_crop, is_poly, \ |
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horizontal_flip_poly, horizontal_flip_rle, vertical_flip_poly, vertical_flip_rle, crop_poly, \ |
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crop_rle, expand_poly, expand_rle, resize_poly, resize_rle, dehaze, select_bands, \ |
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to_intensity, to_uint8, img_flip, img_simple_rotate |
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from .functions import ( |
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normalize, horizontal_flip, permute, vertical_flip, center_crop, is_poly, |
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horizontal_flip_poly, horizontal_flip_rle, vertical_flip_poly, |
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vertical_flip_rle, crop_poly, crop_rle, expand_poly, expand_rle, |
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resize_poly, resize_rle, dehaze, select_bands, to_intensity, to_uint8, |
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img_flip, img_simple_rotate, decode_seg_mask) |
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__all__ = [ |
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"Compose", |
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"DecodeImg", |
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"Resize", |
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"RandomResize", |
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"ResizeByShort", |
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"RandomResizeByShort", |
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"ResizeByLong", |
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"RandomHorizontalFlip", |
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"RandomVerticalFlip", |
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"Normalize", |
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"CenterCrop", |
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"RandomCrop", |
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"RandomScaleAspect", |
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"RandomExpand", |
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"Pad", |
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"MixupImage", |
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"RandomDistort", |
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"RandomBlur", |
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"RandomSwap", |
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"Dehaze", |
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"ReduceDim", |
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"SelectBand", |
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"ArrangeSegmenter", |
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"ArrangeChangeDetector", |
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"ArrangeClassifier", |
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"ArrangeDetector", |
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"RandomFlipOrRotate", |
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"Compose", "DecodeImg", "Resize", "RandomResize", "ResizeByShort", |
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"RandomResizeByShort", "ResizeByLong", "RandomHorizontalFlip", |
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"RandomVerticalFlip", "Normalize", "CenterCrop", "RandomCrop", |
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"RandomScaleAspect", "RandomExpand", "Pad", "MixupImage", "RandomDistort", |
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"RandomBlur", "RandomSwap", "Dehaze", "ReduceDim", "SelectBand", |
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"ArrangeSegmenter", "ArrangeChangeDetector", "ArrangeClassifier", |
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"ArrangeDetector", "RandomFlipOrRotate", "ReloadMask" |
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] |
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interp_dict = { |
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@ -80,16 +62,15 @@ class Compose(object): |
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All input images should be in Height-Width-Channel ([H, W, C]) format. |
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Args: |
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transforms (list[paddlers.transforms.Transform]): List of data preprocess or augmentation operators. |
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arrange (list[paddlers.transforms.Arrange]|None, optional): If not None, the Arrange operator will be used to |
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arrange the outputs of `transforms`. Defaults to None. |
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transforms (list[paddlers.transforms.Transform]): List of data preprocess or |
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augmentation operators. |
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Raises: |
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TypeError: Invalid type of transforms. |
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ValueError: Invalid length of transforms. |
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""" |
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def __init__(self, transforms, arrange=None): |
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def __init__(self, transforms): |
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super(Compose, self).__init__() |
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if not isinstance(transforms, list): |
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raise TypeError( |
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@ -99,12 +80,14 @@ class Compose(object): |
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raise ValueError( |
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"Length of transforms must not be less than 1, but received is {}." |
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.format(len(transforms))) |
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transforms = copy.deepcopy(transforms) |
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self.arrange = self._pick_arrange(transforms) |
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self.transforms = transforms |
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self.arrange = arrange |
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def __call__(self, sample): |
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""" |
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This is equivalent to sequentially calling compose_obj.apply_transforms() and compose_obj.arrange_outputs(). |
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This is equivalent to sequentially calling compose_obj.apply_transforms() |
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and compose_obj.arrange_outputs(). |
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""" |
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sample = self.apply_transforms(sample) |
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@ -126,6 +109,17 @@ class Compose(object): |
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sample = self.arrange(sample) |
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return sample |
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def _pick_arrange(self, transforms): |
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arrange = None |
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for idx, op in enumerate(transforms): |
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if isinstance(op, Arrange): |
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if idx != len(transforms) - 1: |
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raise ValueError( |
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"Arrange operator must be placed at the end of the list." |
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) |
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arrange = transforms.pop(idx) |
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return arrange |
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class Transform(object): |
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""" |
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@ -270,7 +264,9 @@ class DecodeImg(Transform): |
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Returns: |
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dict: Decoded sample. |
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""" |
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if 'image' in sample: |
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sample['image_ori'] = copy.deepcopy(sample['image']) |
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sample['image'] = self.apply_im(sample['image']) |
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if 'image2' in sample: |
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sample['image2'] = self.apply_im(sample['image2']) |
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@ -280,6 +276,7 @@ class DecodeImg(Transform): |
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sample['image'] = self.apply_im(sample['image_t1']) |
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sample['image2'] = self.apply_im(sample['image_t2']) |
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if 'mask' in sample: |
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sample['mask_ori'] = copy.deepcopy(sample['mask']) |
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sample['mask'] = self.apply_mask(sample['mask']) |
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im_height, im_width, _ = sample['image'].shape |
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se_height, se_width = sample['mask'].shape |
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@ -287,6 +284,7 @@ class DecodeImg(Transform): |
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raise ValueError( |
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"The height or width of the image is not same as the mask.") |
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if 'aux_masks' in sample: |
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sample['aux_masks_ori'] = copy.deepcopy(sample['aux_masks_ori']) |
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sample['aux_masks'] = list( |
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map(self.apply_mask, sample['aux_masks'])) |
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# TODO: check the shape of auxiliary masks |
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@ -1759,6 +1757,15 @@ class RandomSwap(Transform): |
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return sample |
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class ReloadMask(Transform): |
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def apply(self, sample): |
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sample['mask'] = decode_seg_mask(sample['mask_ori']) |
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if 'aux_masks' in sample: |
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sample['aux_masks'] = list( |
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map(decode_seg_mask, sample['aux_masks_ori'])) |
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return sample |
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class Arrange(Transform): |
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def __init__(self, mode): |
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super().__init__() |
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Bobholamovic
3 years ago