trw.transforms.crop¶
Module Contents¶
Functions¶
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Crop an image |
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Calculate the offsets of array to randomly crop it with shape crop_shape |
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Randomly crop a numpy array of samples given a target size. This works for an arbitrary number of dimensions |
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Randomly crop a list of arrays. Apply the same cropping for each array element |
- trw.transforms.crop._crop_5d(image, min, max)¶
- trw.transforms.crop._crop_4d(image, min, max)¶
- trw.transforms.crop._crop_3d(image, min, max)¶
- trw.transforms.crop._crop_2d(image, min, max)¶
- trw.transforms.crop._crop_1d(image, min, max)¶
- trw.transforms.crop.batch_crop(images: trw.basic_typing.TensorNCX, min_index: Sequence[int], max_index_exclusive: Sequence[int]) trw.basic_typing.TensorNCX¶
Crop an image :param images: images with shape [N * …] :param min_index: a sequence of size len(array.shape)-1 indicating cropping start :param max_index_exclusive: a sequence of size len(array.shape)-1 indicating cropping end (excluded)
- Returns
a cropped images
- trw.transforms.crop.transform_batch_random_crop_offset(array: trw.basic_typing.TensorNCX, crop_shape: Sequence[Union[None, int]]) numpy.ndarray¶
Calculate the offsets of array to randomly crop it with shape crop_shape
Examples
Crop a 3D arrays stored as NCX. Use None to keep dim=1: >>> arrays = torch.zeros([10, 1, 64, 64, 64]) >>> cropped_arrays = transform_batch_random_crop_offset(array, crop_shape=[None, 16, 16, 16]) >>> cropped_arrays.shape [10, 1, 16, 16, 16]
- Parameters
array – a numpy array. Samples are stored in the first dimension
crop_shape – a sequence of size len(array.shape)-1 indicating the shape of the crop. If a dimension is None, the whole axis is kept for this dimension.
- Returns
a offsets to crop the array
- trw.transforms.crop.transform_batch_random_crop(array: trw.basic_typing.TensorNCX, crop_shape: Sequence[Union[int, None]], offsets: Sequence[Sequence[int]] = None, return_offsets: bool = False) Union[trw.basic_typing.TensorNCX, Tuple[trw.basic_typing.TensorNCX, Sequence[Sequence[int]]]]¶
Randomly crop a numpy array of samples given a target size. This works for an arbitrary number of dimensions
- Parameters
array – a numpy or Torch array. Samples are stored in the first dimension
crop_shape – a sequence of size len(array.shape)-1 indicating the shape of the crop. If None in one of the element of the shape, take the whole dimension
offsets – if None, offsets will be randomly created to crop with crop_shape, else an array indicating the crop position for each sample
return_offsets – if True, returns a tuple (cropped array, offsets)
- Returns
a cropped array and optionally the crop positions
- trw.transforms.crop.transform_batch_random_crop_joint(arrays: Sequence[trw.basic_typing.TensorNCX], crop_shape: Sequence[Union[None, int]], return_offsets: bool = False) Union[List[trw.basic_typing.TensorNCX], Tuple[List[trw.basic_typing.TensorNCX], trw.basic_typing.TensorNCX]]¶
Randomly crop a list of arrays. Apply the same cropping for each array element
- Parameters
arrays – a list of numpy or Torch arrays. Samples are stored in the first dimension
crop_shape – a sequence of size len(array.shape)-1 indicating the size of the cropped tensor
return_offsets – if true, returns where the cropping started
- Returns
a cropped array, [cropping offset]