augmenters.pooling

AveragePooling

Apply average pooling to images.

This augmenter pools images with kernel sizes H x W by averaging the pixel values within these windows. For e.g. 2 x 2 this halves the image size. Optionally, the augmenter will automatically re-upscale the image to the input size (by default this is activated).

This augmenter does not affect heatmaps, segmentation maps or coordinates-based augmentables (e.g. keypoints, bounding boxes, …).

Note that this augmenter is very similar to AverageBlur. AverageBlur applies averaging within windows of given kernel size without striding, while AveragePooling applies striding corresponding to the kernel size, with optional upscaling afterwards. The upscaling is configured to create “pixelated”/”blocky” images by default.

API link: AveragePooling

Example. Create an augmenter that always pools with a kernel size of 2 x 2:

import imgaug.augmenters as iaa
aug = iaa.AveragePooling(2)

Example. Create an augmenter that always pools with a kernel size of 2 x 2 and does not resize back to the input image size, i.e. the resulting images have half the resolution:

aug = iaa.AveragePooling(2, keep_size=False)

Example. Create an augmenter that always pools either with a kernel size of 2 x 2 or 8 x 8:

aug = iaa.AveragePooling([2, 8])

Example. Create an augmenter that always pools with a kernel size of 1 x 1 (does nothing) to 7 x 7. The kernel sizes are always symmetric.

aug = iaa.AveragePooling((1, 7))

Example. Create an augmenter that always pools with a kernel size of H x W where H and W are both sampled independently from the range [1..7]. E.g. resulting kernel sizes could be 3 x 7 or 5 x 1.

aug = iaa.AveragePooling(((1, 7), (1, 7)))

MaxPooling

Apply max pooling to images.

This augmenter pools images with kernel sizes H x W by taking the maximum pixel value over windows. For e.g. 2 x 2 this halves the image size. Optionally, the augmenter will automatically re-upscale the image to the input size (by default this is activated).

The maximum within each pixel window is always taken channelwise.

This augmenter does not affect heatmaps, segmentation maps or coordinates-based augmentables (e.g. keypoints, bounding boxes, …).

API link: MaxPooling

Example. Create an augmenter that always pools with a kernel size of 2 x 2:

import imgaug.augmenters as iaa
aug = iaa.MaxPooling(2)

Example. Create an augmenter that always pools with a kernel size of 2 x 2 and does not resize back to the input image size, i.e. the resulting images have half the resolution:

aug = iaa.MaxPooling(2, keep_size=False)

Example. Create an augmenter that always pools either with a kernel size of 2 x 2 or 8 x 8:

aug = iaa.MaxPooling([2, 8])

Example. Create an augmenter that always pools with a kernel size of 1 x 1 (does nothing) to 7 x 7. The kernel sizes are always symmetric.

aug = iaa.MaxPooling((1, 7))

Example. Create an augmenter that always pools with a kernel size of H x W where H and W are both sampled independently from the range [1..7]. E.g. resulting kernel sizes could be 3 x 7 or 5 x 1.

aug = iaa.MaxPooling(((1, 7), (1, 7)))

MinPooling

Apply minimum pooling to images.

This augmenter pools images with kernel sizes H x W by taking the minimum pixel value over windows. For e.g. 2 x 2 this halves the image size. Optionally, the augmenter will automatically re-upscale the image to the input size (by default this is activated).

The minimum within each pixel window is always taken channelwise.

This augmenter does not affect heatmaps, segmentation maps or coordinates-based augmentables (e.g. keypoints, bounding boxes, …).

API link: MinPooling

Example. Create an augmenter that always pools with a kernel size of 2 x 2:

import imgaug.augmenters as iaa
aug = iaa.MinPooling(2)

Example. Create an augmenter that always pools with a kernel size of 2 x 2 and does not resize back to the input image size, i.e. the resulting images have half the resolution.

aug = iaa.MinPooling(2, keep_size=False)

Example. Create an augmenter that always pools either with a kernel size of 2 x 2 or 8 x 8:

aug = iaa.MinPooling([2, 8])

Example. Create an augmenter that always pools with a kernel size of 1 x 1 (does nothing) to 7 x 7. The kernel sizes are always symmetric.

aug = iaa.MinPooling((1, 7))

Example. Create an augmenter that always pools with a kernel size of H x W where H and W are both sampled independently from the range [1..7]. E.g. resulting kernel sizes could be 3 x 7 or 5 x 1.

aug = iaa.MinPooling(((1, 7), (1, 7)))

MedianPooling

Apply median pooling to images.

This augmenter pools images with kernel sizes H x W by taking the median pixel value over windows. For e.g. 2 x 2 this halves the image size. Optionally, the augmenter will automatically re-upscale the image to the input size (by default this is activated).

The median within each pixel window is always taken channelwise.

This augmenter does not affect heatmaps, segmentation maps or coordinates-based augmentables (e.g. keypoints, bounding boxes, …).

API link: MedianPooling

Example. Create an augmenter that always pools with a kernel size of 2 x 2:

import imgaug.augmenters as iaa
aug = iaa.MedianPooling(2)

Example. Create an augmenter that always pools with a kernel size of 2 x 2 and does not resize back to the input image size, i.e. the resulting images have half the resolution:

aug = iaa.MedianPooling(2, keep_size=False)

Example. Create an augmenter that always pools either with a kernel size of 2 x 2 or 8 x 8:

aug = iaa.MedianPooling([2, 8])

Example. Create an augmenter that always pools with a kernel size of 1 x 1 (does nothing) to 7 x 7. The kernel sizes are always symmetric.

aug = iaa.MedianPooling((1, 7))

Example. Create an augmenter that always pools with a kernel size of H x W where H and W are both sampled independently from the range [1..7]. E.g. resulting kernel sizes could be 3 x 7 or 5 x 1.

aug = iaa.MedianPooling(((1, 7), (1, 7)))