o ep@sdZddlmZmZmZddlmZmZddlZddl Z ddl Z ddl Z ddlmZddlmZd d Ze eGd d d ejZGd ddeZGdddeZGdddeZGdddeZdS)z Augmenters that apply pooling operations to images. List of augmenters: * :class:`AveragePooling` * :class:`MaxPooling` * :class:`MinPooling` * :class:`MedianPooling` )print_functiondivisionabsolute_import)ABCMetaabstractmethodN)meta) parameterscCstdd|Dr |S|dd\}}||dkr!||||7}||dkr/||||7}t||||gt|ddS)NcSsg|]}|dkqS)r).0Zaxisr r ID:\Projects\ConvertPro\env\Lib\site-packages\imgaug/augmenters/pooling.py sz0_compute_shape_after_pooling..rr )anytuplelist)Z image_shapeksize_hksize_wheightwidthr r r _compute_shape_after_poolings  rcseZdZ   dfdd ZeddZdd Zd d Zd d ZddZ ddZ ddZ ddZ ddZ ddZddZddZZS)_AbstractPoolingBaseTN deprecatedcs<tt|j||||dtj|dddd|_||_d|_dS)N)seedname random_state deterministic kernel_size)rNF)Z value_rangeZ allow_floatsT)superr__init__iapZ!handle_discrete_kernel_size_paramr keep_size_resize_hm_and_sm_arraysselfrr!rrrr __class__r r r.s  z_AbstractPoolingBase.__init__cCsdS)z@Apply pooling method with given kernel height/width to an image.Nr r$imageZ kernel_size_hZ kernel_size_wr r r _pool_image=sz _AbstractPoolingBase._pool_imagecCs~|d}|jddurdnd}|jdj|f|dd}|dkr$|}n |jdj|f|dd}t|ddt|ddfS)Nr rsingletwor)r)Z duplicaterZ draw_samplesnpZclip)r$nb_rowsrZrssmodekernel_sizes_hkernel_sizes_wr r r _draw_samplesAs      z"_AbstractPoolingBase._draw_samplescCsZ|jdur |jr |S||j|}|jD]}t|d|jf|j|}t||j |q|S)Nz_augment_%s_by_samples) imagesr!r1r-columnsgetattrrvaluesetattrZ attr_name)r$batchrparentshookssamplescolumnZ value_augr r r _augment_batch_Rs  z$_AbstractPoolingBase._augment_batch_c Cs|jst|}|\}}tt|||}|D](\}\}}} |dks$| dkr=|||| } |jr9t| |jdd} | ||<q|SNr r)r!r enumeratezipr)iaZimresize_single_imageshape) r$r2r:r/r0genir(rrZ image_pooledr r r _augment_images_by_samples_s z/_AbstractPoolingBase._augment_images_by_samplescC|||dS)NZarr_0to1#_augment_hms_and_segmaps_by_samples)r$Zheatmapsr:r r r _augment_heatmaps_by_samplesrz1_AbstractPoolingBase._augment_heatmaps_by_samplescCrE)NZarrrF)r$Zsegmapsr:r r r %_augment_segmentation_maps_by_sampleswrIz:_AbstractPoolingBase._augment_segmentation_maps_by_samplesc Cs|jr|S|\}}tt|||}|D]3\}\}} } | dks"| dkrF|jr8tt||j| | } || dd}t|j| | } | |_|||<q|Sr=)r!r>r?r"rr4rAresize) r$Z augmentablesr:Z arr_attr_namer/r0rBrCZ augmentablerrZ new_shape_arr new_shaper r r rG|s& z8_AbstractPoolingBase._augment_hms_and_segmaps_by_samplesc Csf|jr|S|\}}tt|||}|D]\}\}}} |dks"| dkr0t|j|| } || ||<q|S)Nr )r!r>r?rrAZon_) r$Zkeypoints_on_imagesr:r/r0rBrCZkpsoirrrLr r r _augment_keypoints_by_samplessz2_AbstractPoolingBase._augment_keypoints_by_samplescCs tj|j|d}|j||ddS)Nr:)Z recoverer) functoolspartialrMZ_apply_to_polygons_as_keypoints)r$Zpolygons_on_imagesr:funcr r r _augment_polygons_by_sampless z1_AbstractPoolingBase._augment_polygons_by_samplescCtj|j|d}|||SNrNrOrPrMZ_apply_to_cbaois_as_keypoints)r$Zline_strings_on_imagesr:rQr r r _augment_line_strings_by_sampless z5_AbstractPoolingBase._augment_line_strings_by_samplescCrSrTrU)r$Zbounding_boxes_on_imagesr:rQr r r "_augment_bounding_boxes_by_sampless z7_AbstractPoolingBase._augment_bounding_boxes_by_samplescCs |j|jgS)z=See :func:`~imgaug.augmenters.meta.Augmenter.get_parameters`.)rr!)r$r r r get_parameterss z#_AbstractPoolingBase.get_parameters)TNNrr)__name__ __module__ __qualname__rrr)r1r<rDrHrJrGrMrRrVrWrX __classcell__r r r%r r*s$  rc0eZdZdZ   d fdd Zdd ZZS) AveragePoolinga 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). 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. .. note:: During heatmap or segmentation map augmentation, the respective arrays are not changed, only the shapes of the underlying images are updated. This is because imgaug can handle maps/maks that are larger/smaller than their corresponding image. **Supported dtypes**: See :func:`~imgaug.imgaug.avg_pool`. Attributes ---------- kernel_size : int or tuple of int or list of int or imgaug.parameters.StochasticParameter or tuple of tuple of int or tuple of list of int or tuple of imgaug.parameters.StochasticParameter, optional The kernel size of the pooling operation. * If an int, then that value will be used for all images for both kernel height and width. * If a tuple ``(a, b)``, then a value from the discrete range ``[a..b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image and used for both kernel height and width. * If a StochasticParameter, then a value will be sampled per image from that parameter per image and used for both kernel height and width. * If a tuple of tuple of int given as ``((a, b), (c, d))``, then two values will be sampled independently from the discrete ranges ``[a..b]`` and ``[c..d]`` per image and used as the kernel height and width. * If a tuple of lists of int, then two values will be sampled independently per image, one from the first list and one from the second, and used as the kernel height and width. * If a tuple of StochasticParameter, then two values will be sampled indepdently per image, one from the first parameter and one from the second, and used as the kernel height and width. keep_size : bool, optional After pooling, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the pooled image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.AveragePooling(2) Create an augmenter that always pools with a kernel size of ``2 x 2``. >>> aug = iaa.AveragePooling(2, keep_size=False) 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, 8]) Create an augmenter that always pools either with a kernel size of ``2 x 2`` or ``8 x 8``. >>> aug = iaa.AveragePooling((1, 7)) 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), (1, 7))) 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``. rTNrc tt|j||||||ddSN)rr!rrrr)rr^rr#r%r r r5  zAveragePooling.__init__cCt|||fSN)r@Zavg_poolr'r r r r)=szAveragePooling._pool_imager_TNNrrrYrZr[__doc__rr)r\r r r%r r^smr^cr]) MaxPoolinga 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.. .. note:: During heatmap or segmentation map augmentation, the respective arrays are not changed, only the shapes of the underlying images are updated. This is because imgaug can handle maps/maks that are larger/smaller than their corresponding image. **Supported dtypes**: See :func:`~imgaug.imgaug.max_pool`. Attributes ---------- kernel_size : int or tuple of int or list of int or imgaug.parameters.StochasticParameter or tuple of tuple of int or tuple of list of int or tuple of imgaug.parameters.StochasticParameter, optional The kernel size of the pooling operation. * If an int, then that value will be used for all images for both kernel height and width. * If a tuple ``(a, b)``, then a value from the discrete range ``[a..b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image and used for both kernel height and width. * If a StochasticParameter, then a value will be sampled per image from that parameter per image and used for both kernel height and width. * If a tuple of tuple of int given as ``((a, b), (c, d))``, then two values will be sampled independently from the discrete ranges ``[a..b]`` and ``[c..d]`` per image and used as the kernel height and width. * If a tuple of lists of int, then two values will be sampled independently per image, one from the first list and one from the second, and used as the kernel height and width. * If a tuple of StochasticParameter, then two values will be sampled indepdently per image, one from the first parameter and one from the second, and used as the kernel height and width. keep_size : bool, optional After pooling, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the pooled image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.MaxPooling(2) Create an augmenter that always pools with a kernel size of ``2 x 2``. >>> aug = iaa.MaxPooling(2, keep_size=False) 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, 8]) Create an augmenter that always pools either with a kernel size of ``2 x 2`` or ``8 x 8``. >>> aug = iaa.MaxPooling((1, 7)) 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), (1, 7))) 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``. r_TNrcrarb)rrirr#r%r r rrczMaxPooling.__init__cCrdre)r@Zmax_poolr'r r r r)zMaxPooling._pool_imagerfrgr r r%r riDiricr]) MinPoolinga 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. .. note:: During heatmap or segmentation map augmentation, the respective arrays are not changed, only the shapes of the underlying images are updated. This is because imgaug can handle maps/maks that are larger/smaller than their corresponding image. **Supported dtypes**: See :func:`~imgaug.imgaug.min_pool`. Attributes ---------- kernel_size : int or tuple of int or list of int or imgaug.parameters.StochasticParameter or tuple of tuple of int or tuple of list of int or tuple of imgaug.parameters.StochasticParameter, optional The kernel size of the pooling operation. * If an int, then that value will be used for all images for both kernel height and width. * If a tuple ``(a, b)``, then a value from the discrete range ``[a..b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image and used for both kernel height and width. * If a StochasticParameter, then a value will be sampled per image from that parameter per image and used for both kernel height and width. * If a tuple of tuple of int given as ``((a, b), (c, d))``, then two values will be sampled independently from the discrete ranges ``[a..b]`` and ``[c..d]`` per image and used as the kernel height and width. * If a tuple of lists of int, then two values will be sampled independently per image, one from the first list and one from the second, and used as the kernel height and width. * If a tuple of StochasticParameter, then two values will be sampled indepdently per image, one from the first parameter and one from the second, and used as the kernel height and width. keep_size : bool, optional After pooling, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the pooled image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.MinPooling(2) Create an augmenter that always pools with a kernel size of ``2 x 2``. >>> aug = iaa.MinPooling(2, keep_size=False) 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, 8]) Create an augmenter that always pools either with a kernel size of ``2 x 2`` or ``8 x 8``. >>> aug = iaa.MinPooling((1, 7)) 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), (1, 7))) 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``. r_TNrcrarb)rrlrr#r%r r r)rczMinPooling.__init__cCrdre)r@Zmin_poolr'r r r r)1rjzMinPooling._pool_imagerfrgr r r%r rlrkrlcr]) MedianPoolinga 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. .. note:: During heatmap or segmentation map augmentation, the respective arrays are not changed, only the shapes of the underlying images are updated. This is because imgaug can handle maps/maks that are larger/smaller than their corresponding image. **Supported dtypes**: See :func:`~imgaug.imgaug.median_pool`. Attributes ---------- kernel_size : int or tuple of int or list of int or imgaug.parameters.StochasticParameter or tuple of tuple of int or tuple of list of int or tuple of imgaug.parameters.StochasticParameter, optional The kernel size of the pooling operation. * If an int, then that value will be used for all images for both kernel height and width. * If a tuple ``(a, b)``, then a value from the discrete range ``[a..b]`` will be sampled per image. * If a list, then a random value will be sampled from that list per image and used for both kernel height and width. * If a StochasticParameter, then a value will be sampled per image from that parameter per image and used for both kernel height and width. * If a tuple of tuple of int given as ``((a, b), (c, d))``, then two values will be sampled independently from the discrete ranges ``[a..b]`` and ``[c..d]`` per image and used as the kernel height and width. * If a tuple of lists of int, then two values will be sampled independently per image, one from the first list and one from the second, and used as the kernel height and width. * If a tuple of StochasticParameter, then two values will be sampled indepdently per image, one from the first parameter and one from the second, and used as the kernel height and width. keep_size : bool, optional After pooling, the result image will usually have a different height/width compared to the original input image. If this parameter is set to True, then the pooled image will be resized to the input image's size, i.e. the augmenter's output shape is always identical to the input shape. seed : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. name : None or str, optional See :func:`~imgaug.augmenters.meta.Augmenter.__init__`. random_state : None or int or imgaug.random.RNG or numpy.random.Generator or numpy.random.BitGenerator or numpy.random.SeedSequence or numpy.random.RandomState, optional Old name for parameter `seed`. Its usage will not yet cause a deprecation warning, but it is still recommended to use `seed` now. Outdated since 0.4.0. deterministic : bool, optional Deprecated since 0.4.0. See method ``to_deterministic()`` for an alternative and for details about what the "deterministic mode" actually does. Examples -------- >>> import imgaug.augmenters as iaa >>> aug = iaa.MedianPooling(2) Create an augmenter that always pools with a kernel size of ``2 x 2``. >>> aug = iaa.MedianPooling(2, keep_size=False) 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, 8]) Create an augmenter that always pools either with a kernel size of ``2 x 2`` or ``8 x 8``. >>> aug = iaa.MedianPooling((1, 7)) 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), (1, 7))) 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``. r_TNrcrarb)rrmrr#r%r r rrczMedianPooling.__init__cCrdre)r@Z median_poolr'r r r r)rjzMedianPooling._pool_imagerfrgr r r%r rm:rkrm)rh __future__rrrabcrrrOsixnumpyr,Zimgaugr@rr r r add_metaclassZ Augmenterrr^rirlrmr r r r s"   }{{