Created
November 29, 2020 11:42
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Function for partitioning image into sub images
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| import numpy as np | |
| from itertools import accumulate, repeat, product | |
| def partitions_with_overlap(image, partition_sizes, partitions_per_dim): | |
| """ | |
| Partition an image with overlap to list of images. | |
| :param image: The image to partition. | |
| :param partition_sizes: The sizes of the partition in each dimension. | |
| :param partitions_per_dim: The number of partition per dimension. | |
| :return: A list of images. | |
| """ | |
| shape = image.shape | |
| assert len(shape) == len(partition_sizes) == len(partitions_per_dim) | |
| dim_parts = [] | |
| for s, p, n in zip(shape, partition_sizes, partitions_per_dim): | |
| strides = [(0, p)] | |
| if n > 1: | |
| overlap_diff = p - (p * n - s) / (n - 1) | |
| strides.extend([(a, a + p) for a in accumulate(repeat(overlap_diff, n - 1))]) | |
| dim_parts.append(strides) | |
| return [image[[np.s_[round(d0):round(d1)] for d0, d1 in dim_splits]] for dim_splits in product(*dim_parts)] |
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