kwcoco.util.delayed_ops package

Submodules

• kwcoco.util.delayed_ops.delayed_base module
• kwcoco.util.delayed_ops.delayed_leafs module
• kwcoco.util.delayed_ops.delayed_nodes module
• kwcoco.util.delayed_ops.helpers module

Module contents

Functionality has been ported to delayed_image

class kwcoco.util.delayed_ops.DelayedArray(subdata=None)

Bases: DelayedUnaryOperation

A generic NDArray.

property shape

Returns: None | Tuple[int | None, …]

class kwcoco.util.delayed_ops.DelayedAsXarray(subdata=None, dsize=None, channels=None)

Bases: DelayedImage

Casts the data to an xarray object in the finalize step

Example;

>>> # xdoctest: +REQUIRES(module:xarray)
>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image import DelayedLoad
>>> # without channels
>>> base = DelayedLoad.demo(dsize=(16, 16)).prepare()
>>> self = base.as_xarray()
>>> final = self._validate().finalize()
>>> assert len(final.coords) == 0
>>> assert final.dims == ('y', 'x', 'c')
>>> # with channels
>>> base = DelayedLoad.demo(dsize=(16, 16), channels='r|g|b').prepare()
>>> self = base.as_xarray()
>>> final = self._validate().finalize()
>>> assert final.coords.indexes['c'].tolist() == ['r', 'g', 'b']
>>> assert final.dims == ('y', 'x', 'c')

optimize()

Returns

DelayedImage

class kwcoco.util.delayed_ops.DelayedChannelConcat(parts, dsize=None)

Bases: ImageOpsMixin, DelayedConcat

Stacks multiple arrays together.

Example

>>> from delayed_image import *  # NOQA
>>> from delayed_image.delayed_leafs import DelayedLoad
>>> dsize = (307, 311)
>>> c1 = DelayedNans(dsize=dsize, channels='foo')
>>> c2 = DelayedLoad.demo('astro', dsize=dsize, channels='R|G|B').prepare()
>>> cat = DelayedChannelConcat([c1, c2])
>>> warped_cat = cat.warp({'scale': 1.07}, dsize=(328, 332))
>>> warped_cat._validate()
>>> warped_cat.finalize()

Example

>>> # Test case that failed in initial implementation
>>> # Due to incorrectly pushing channel selection under the concat
>>> from delayed_image import *  # NOQA
>>> import kwimage
>>> fpath = kwimage.grab_test_image_fpath()
>>> base1 = DelayedLoad(fpath, channels='r|g|b').prepare()
>>> base2 = DelayedLoad(fpath, channels='x|y|z').prepare().scale(2)
>>> base3 = DelayedLoad(fpath, channels='i|j|k').prepare().scale(2)
>>> bands = [base2, base1[:, :, 0].scale(2).evaluate(),
>>>          base1[:, :, 1].evaluate().scale(2),
>>>          base1[:, :, 2].evaluate().scale(2), base3]
>>> delayed = DelayedChannelConcat(bands)
>>> delayed = delayed.warp({'scale': 2})
>>> delayed = delayed[0:100, 0:55, [0, 2, 4]]
>>> delayed.write_network_text()
>>> delayed.optimize()

property channels

Returns: None | FusedChannelSpec

property shape

Returns: Tuple[int | None, int | None, int | None]

optimize()

Returns

DelayedImage

take_channels(channels)

This method returns a subset of the vision data with only the specified bands / channels.

Parameters

channels (List[int] | slice | channel_spec.FusedChannelSpec) – List of integers indexes, a slice, or a channel spec, which is typically a pipe (|) delimited list of channel codes. See ChannelSpec for more detials.

Returns

a delayed vision operation that only operates on the following channels.

Return type

DelayedArray

Example

>>> # xdoctest: +REQUIRES(module:kwcoco)
>>> from delayed_image.delayed_nodes import *  # NOQA
>>> import kwcoco
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> self = delayed = dset.coco_image(1).delay(mode=1)
>>> channels = 'B11|B8|B1|B10'
>>> new = self.take_channels(channels)

Example

>>> # xdoctest: +REQUIRES(module:kwcoco)
>>> # Complex case
>>> import kwcoco
>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image.delayed_leafs import DelayedLoad
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral')
>>> delayed = dset.coco_image(1).delay(mode=1)
>>> astro = DelayedLoad.demo('astro', channels='r|g|b').prepare()
>>> aligned = astro.warp(kwimage.Affine.scale(600 / 512), dsize='auto')
>>> self = combo = DelayedChannelConcat(delayed.parts + [aligned])
>>> channels = 'B1|r|B8|g'
>>> new = self.take_channels(channels)
>>> new_cropped = new.crop((slice(10, 200), slice(12, 350)))
>>> new_opt = new_cropped.optimize()
>>> datas = new_opt.finalize()
>>> if 1:
>>>     new_cropped.write_network_text(with_labels='name')
>>>     new_opt.write_network_text(with_labels='name')
>>> vizable = kwimage.normalize_intensity(datas, axis=2)
>>> self._validate()
>>> new._validate()
>>> new_cropped._validate()
>>> new_opt._validate()
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> stacked = kwimage.stack_images(vizable.transpose(2, 0, 1))
>>> kwplot.imshow(stacked)

Example

>>> # xdoctest: +REQUIRES(module:kwcoco)
>>> # Test case where requested channel does not exist
>>> import kwcoco
>>> from delayed_image.delayed_nodes import *  # NOQA
>>> dset = kwcoco.CocoDataset.demo('vidshapes8-multispectral', use_cache=1, verbose=100)
>>> self = delayed = dset.coco_image(1).delay(mode=1)
>>> channels = 'B1|foobar|bazbiz|B8'
>>> new = self.take_channels(channels)
>>> new_cropped = new.crop((slice(10, 200), slice(12, 350)))
>>> fused = new_cropped.finalize()
>>> assert fused.shape == (190, 338, 4)
>>> assert np.all(np.isnan(fused[..., 1:3]))
>>> assert not np.any(np.isnan(fused[..., 0]))
>>> assert not np.any(np.isnan(fused[..., 3]))

property num_overviews

Returns: int

as_xarray()

Returns

DelayedAsXarray

undo_warps(remove=None, retain=None, squash_nans=False, return_warps=False)

Attempts to “undo” warping for each concatenated channel and returns a list of delayed operations that are cropped to the right regions.

Typically you will retrain offset, theta, and shear to remove scale. This ensures the data is spatially aligned up to a scale factor.

Parameters

• remove (List[str]) – if specified, list components of the warping to remove. Can include: “offset”, “scale”, “shearx”, “theta”. Typically set this to [“scale”].

• retain (List[str]) – if specified, list components of the warping to retain. Can include: “offset”, “scale”, “shearx”, “theta”. Mutually exclusive with “remove”. If neither remove or retain is specified, retain is set to [].

• squash_nans (bool) – if True, pure nan channels are squashed into a 1x1 array as they do not correspond to a real source.

• return_warps (bool) – if True, return the transforms we applied. I.e. the transform from the self to the returned parts. This is useful when you need to warp objects in the original space into the jagged space.

Returns

The List[DelayedImage] are the parts i.e. the new images with the warping undone. The List[Affine]: is the transforms from self to each item in parts

Return type

List[DelayedImage] | Tuple[List[DelayedImage] | List[Affine]]

Example

>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image.delayed_leafs import DelayedLoad
>>> from delayed_image.delayed_leafs import DelayedNans
>>> import ubelt as ub
>>> import kwimage
>>> import kwarray
>>> import numpy as np
>>> # Demo case where we have different channels at different resolutions
>>> base = DelayedLoad.demo(channels='r|g|b').prepare().dequantize({'quant_max': 255})
>>> bandR = base[:, :, 0].scale(100 / 512)[:, :-50].evaluate()
>>> bandG = base[:, :, 1].scale(300 / 512).warp({'theta': np.pi / 8, 'about': (150, 150)}).evaluate()
>>> bandB = base[:, :, 2].scale(600 / 512)[:150, :].evaluate()
>>> bandN = DelayedNans((600, 600), channels='N')
>>> # Make a concatenation of images of different underlying native resolutions
>>> delayed_vidspace = DelayedChannelConcat([
>>>     bandR.scale(6, dsize=(600, 600)).optimize(),
>>>     bandG.warp({'theta': -np.pi / 8, 'about': (150, 150)}).scale(2, dsize=(600, 600)).optimize(),
>>>     bandB.scale(1, dsize=(600, 600)).optimize(),
>>>     bandN,
>>> ]).warp({'scale': 0.7}).optimize()
>>> vidspace_box = kwimage.Boxes([[100, 10, 270, 160]], 'ltrb')
>>> vidspace_poly = vidspace_box.to_polygons()[0]
>>> vidspace_slice = vidspace_box.to_slices()[0]
>>> self = delayed_vidspace[vidspace_slice].optimize()
>>> print('--- Aligned --- ')
>>> self.write_network_text()
>>> squash_nans = True
>>> undone_all_parts, tfs1 = self.undo_warps(squash_nans=squash_nans, return_warps=True)
>>> undone_scale_parts, tfs2 = self.undo_warps(remove=['scale'], squash_nans=squash_nans, return_warps=True)
>>> stackable_aligned = self.finalize().transpose(2, 0, 1)
>>> stackable_undone_all = []
>>> stackable_undone_scale = []
>>> print('--- Undone All --- ')
>>> for undone in undone_all_parts:
...     undone.write_network_text()
...     stackable_undone_all.append(undone.finalize())
>>> print('--- Undone Scale --- ')
>>> for undone in undone_scale_parts:
...     undone.write_network_text()
...     stackable_undone_scale.append(undone.finalize())
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> canvas0 = kwimage.stack_images(stackable_aligned, axis=1)
>>> canvas1 = kwimage.stack_images(stackable_undone_all, axis=1)
>>> canvas2 = kwimage.stack_images(stackable_undone_scale, axis=1)
>>> canvas0 = kwimage.draw_header_text(canvas0, 'Rescaled Aligned Channels')
>>> canvas1 = kwimage.draw_header_text(canvas1, 'Unwarped Channels')
>>> canvas2 = kwimage.draw_header_text(canvas2, 'Unscaled Channels')
>>> canvas = kwimage.stack_images([canvas0, canvas1, canvas2], axis=0)
>>> canvas = kwimage.fill_nans_with_checkers(canvas)
>>> kwplot.imshow(canvas)

class kwcoco.util.delayed_ops.DelayedConcat(parts, axis)

Bases: DelayedNaryOperation

Stacks multiple arrays together.

property shape

Returns: None | Tuple[int | None, …]

class kwcoco.util.delayed_ops.DelayedCrop(subdata, space_slice=None, chan_idxs=None)

Bases: DelayedImage

Crops an image along integer pixel coordinates.

Example

>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image import DelayedLoad
>>> base = DelayedLoad.demo(dsize=(16, 16)).prepare()
>>> # Test Fuse Crops Space Only
>>> crop1 = base[4:12, 0:16]
>>> self = crop1[2:6, 0:8]
>>> opt = self._opt_fuse_crops()
>>> self.write_network_text()
>>> opt.write_network_text()
>>> #
>>> # Test Channel Select Via Index
>>> self = base[:, :, [0]]
>>> self.write_network_text()
>>> final = self._finalize()
>>> assert final.shape == (16, 16, 1)
>>> assert base[:, :, [0, 1]].finalize().shape == (16, 16, 2)
>>> assert base[:, :, [2, 0, 1]].finalize().shape == (16, 16, 3)

Example

>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image import DelayedLoad
>>> base = DelayedLoad.demo(dsize=(16, 16)).prepare()
>>> # Test Discontiguous Channel Select Via Index
>>> self = base[:, :, [0, 2]]
>>> self.write_network_text()
>>> final = self._finalize()
>>> assert final.shape == (16, 16, 2)

optimize()

Returns

DelayedImage

Example

>>> # Test optimize nans
>>> from delayed_image import DelayedNans
>>> import kwimage
>>> base = DelayedNans(dsize=(100, 100), channels='a|b|c')
>>> self = base[0:10, 0:5]
>>> # Should simply return a new nan generator
>>> new = self.optimize()
>>> self.write_network_text()
>>> new.write_network_text()
>>> assert len(new.as_graph().nodes) == 1

class kwcoco.util.delayed_ops.DelayedDequantize(subdata, quantization)

Bases: DelayedImage

Rescales image intensities from int to floats.

The output is usually between 0 and 1. This also handles transforming nodata into nan values.

optimize()

Returns

DelayedImage

Example

>>> # Test a case that caused an error in development
>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image import DelayedLoad
>>> fpath = kwimage.grab_test_image_fpath()
>>> base = DelayedLoad(fpath, channels='r|g|b').prepare()
>>> quantization = {'quant_max': 255, 'nodata': 0}
>>> self = base.get_overview(1).dequantize(quantization)
>>> self.write_network_text()
>>> opt = self.optimize()

class kwcoco.util.delayed_ops.DelayedFrameStack(parts)

Bases: DelayedStack

Stacks multiple arrays together.

class kwcoco.util.delayed_ops.DelayedIdentity(data, channels=None, dsize=None)

Bases: DelayedImageLeaf

Returns an ndarray as-is

Example

self = DelayedNans((10, 10), channel_spec.FusedChannelSpec.coerce(‘rgb’)) region_slices = (slice(5, 10), slice(1, 12)) delayed = self.crop(region_slices)

Example

>>> from delayed_image import *  # NOQA
>>> arr = kwimage.checkerboard()
>>> self = DelayedIdentity(arr, channels='gray')
>>> warp = self.warp({'scale': 1.07})
>>> warp.optimize().finalize()

class kwcoco.util.delayed_ops.DelayedImage(subdata=None, dsize=None, channels=None)

Bases: ImageOpsMixin, DelayedArray

For the case where an array represents a 2D image with multiple channels

property shape

Returns: None | Tuple[int | None, int | None, int | None]

property num_channels

Returns: None | int

property dsize

Returns: None | Tuple[int | None, int | None]

property channels

Returns: None | FusedChannelSpec

property num_overviews

Returns: int

take_channels(channels)

This method returns a subset of the vision data with only the specified bands / channels.

Parameters

channels (List[int] | slice | channel_spec.FusedChannelSpec) – List of integers indexes, a slice, or a channel spec, which is typically a pipe (|) delimited list of channel codes. See ChannelSpec for more detials.

Returns

a new delayed load with a fused take channel operation

Return type

DelayedCrop

Note

The channel subset must exist here or it will raise an error. A better implementation (via pymbolic) might be able to do better

Example

>>> #
>>> # Test Channel Select Via Code
>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image import DelayedLoad
>>> self = DelayedLoad.demo(dsize=(16, 16), channels='r|g|b').prepare()
>>> channels = 'r|b'
>>> new = self.take_channels(channels)._validate()
>>> new2 = new[:, :, [1, 0]]._validate()
>>> new3 = new2[:, :, [1]]._validate()

Example

>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image import DelayedLoad
>>> self = DelayedLoad.demo('astro').prepare()
>>> channels = [2, 0]
>>> new = self.take_channels(channels)
>>> new3 = new.take_channels([1, 0])
>>> new._validate()
>>> new3._validate()

>>> final1 = self.finalize()
>>> final2 = new.finalize()
>>> final3 = new3.finalize()
>>> assert np.all(final1[..., 2] == final2[..., 0])
>>> assert np.all(final1[..., 0] == final2[..., 1])
>>> assert final2.shape[2] == 2

>>> assert np.all(final1[..., 2] == final3[..., 1])
>>> assert np.all(final1[..., 0] == final3[..., 0])
>>> assert final3.shape[2] == 2

Example

>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image import DelayedLoad
>>> self = DelayedLoad.demo(dsize=(16, 16), channels='r|g|b').prepare()
>>> # Case where a channel doesn't exist
>>> channels = 'r|b|magic'
>>> new = self.take_channels(channels)
>>> assert len(new.parts) == 2
>>> new._validate()

get_transform_from_leaf()

Returns the transformation that would align data with the leaf

evaluate()

Evaluate this node and return the data as an identity.

Returns

DelayedIdentity

undo_warp(remove=None, retain=None, squash_nans=False, return_warp=False)

Attempts to “undo” warping for each concatenated channel and returns a list of delayed operations that are cropped to the right regions.

Typically you will retrain offset, theta, and shear to remove scale. This ensures the data is spatially aligned up to a scale factor.

Parameters

• remove (List[str]) – if specified, list components of the warping to remove. Can include: “offset”, “scale”, “shearx”, “theta”. Typically set this to [“scale”].

• retain (List[str]) – if specified, list components of the warping to retain. Can include: “offset”, “scale”, “shearx”, “theta”. Mutually exclusive with “remove”. If neither remove or retain is specified, retain is set to [].

• squash_nans (bool) – if True, pure nan channels are squashed into a 1x1 array as they do not correspond to a real source.

• return_warp (bool) – if True, return the transform we applied. This is useful when you need to warp objects in the original space into the jagged space.

SeeAlso:

DelayedChannelConcat.undo_warps

Example

>>> # Test similar to undo_warps, but on each channel separately
>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image.delayed_leafs import DelayedLoad
>>> from delayed_image.delayed_leafs import DelayedNans
>>> import ubelt as ub
>>> import kwimage
>>> import kwarray
>>> import numpy as np
>>> # Demo case where we have different channels at different resolutions
>>> base = DelayedLoad.demo(channels='r|g|b').prepare().dequantize({'quant_max': 255})
>>> bandR = base[:, :, 0].scale(100 / 512)[:, :-50].evaluate()
>>> bandG = base[:, :, 1].scale(300 / 512).warp({'theta': np.pi / 8, 'about': (150, 150)}).evaluate()
>>> bandB = base[:, :, 2].scale(600 / 512)[:150, :].evaluate()
>>> bandN = DelayedNans((600, 600), channels='N')
>>> B0 = bandR.scale(6, dsize=(600, 600)).optimize()
>>> B1 = bandG.warp({'theta': -np.pi / 8, 'about': (150, 150)}).scale(2, dsize=(600, 600)).optimize()
>>> B2 = bandB.scale(1, dsize=(600, 600)).optimize()
>>> vidspace_box = kwimage.Boxes([[-10, -10, 270, 160]], 'ltrb').scale(1 / .7).quantize()
>>> vidspace_poly = vidspace_box.to_polygons()[0]
>>> vidspace_slice = vidspace_box.to_slices()[0]
>>> # Test with the padded crop
>>> self0 = B0.crop(vidspace_slice, wrap=0, clip=0, pad=10).optimize()
>>> self1 = B1.crop(vidspace_slice, wrap=0, clip=0, pad=10).optimize()
>>> self2 = B2.crop(vidspace_slice, wrap=0, clip=0, pad=10).optimize()
>>> parts = [self0, self1, self2]
>>> # Run the undo on each channel
>>> undone_scale_parts = [d.undo_warp(remove=['scale']) for d in parts]
>>> print('--- Aligned --- ')
>>> stackable_aligned = []
>>> for d in parts:
>>>     d.write_network_text()
>>>     stackable_aligned.append(d.finalize())
>>> print('--- Undone Scale --- ')
>>> stackable_undone_scale = []
>>> for undone in undone_scale_parts:
...     undone.write_network_text()
...     stackable_undone_scale.append(undone.finalize())
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> canvas0 = kwimage.stack_images(stackable_aligned, axis=1, pad=5, bg_value='kw_darkgray')
>>> canvas2 = kwimage.stack_images(stackable_undone_scale, axis=1, pad=5, bg_value='kw_darkgray')
>>> canvas0 = kwimage.draw_header_text(canvas0, 'Rescaled Channels')
>>> canvas2 = kwimage.draw_header_text(canvas2, 'Native Scale Channels')
>>> canvas = kwimage.stack_images([canvas0, canvas2], axis=0, bg_value='kw_darkgray')
>>> canvas = kwimage.fill_nans_with_checkers(canvas)
>>> kwplot.imshow(canvas)

class kwcoco.util.delayed_ops.DelayedImageLeaf(subdata=None, dsize=None, channels=None)

Bases: DelayedImage

get_transform_from_leaf()

Returns the transformation that would align data with the leaf

Returns

kwimage.Affine

optimize()

class kwcoco.util.delayed_ops.DelayedLoad(fpath, channels=None, dsize=None, nodata_method=None)

Bases: DelayedImageLeaf

Reads an image from disk.

If a gdal backend is available, and the underlying image is in the appropriate formate (e.g. COG) this will return a lazy reference that enables fast overviews and crops.

Example

>>> from delayed_image import *  # NOQA
>>> self = DelayedLoad.demo(dsize=(16, 16)).prepare()
>>> data1 = self.finalize()

Example

>>> # xdoctest: +REQUIRES(module:osgeo)
>>> # Demo code to develop support for overviews
>>> from delayed_image import *  # NOQA
>>> import kwimage
>>> import ubelt as ub
>>> fpath = kwimage.grab_test_image_fpath(overviews=3)
>>> self = DelayedLoad(fpath, channels='r|g|b').prepare()
>>> print(f'self={self}')
>>> print('self.meta = {}'.format(ub.repr2(self.meta, nl=1)))
>>> quantization = {
>>>     'quant_max': 255,
>>>     'nodata': 0,
>>> }
>>> node0 = self
>>> node1 = node0.get_overview(2)
>>> node2 = node1[13:900, 11:700]
>>> node3 = node2.dequantize(quantization)
>>> node4 = node3.warp({'scale': 0.05})
>>> #
>>> data0 = node0._validate().finalize()
>>> data1 = node1._validate().finalize()
>>> data2 = node2._validate().finalize()
>>> data3 = node3._validate().finalize()
>>> data4 = node4._validate().finalize()
>>> node4.write_network_text()

Example

>>> # xdoctest: +REQUIRES(module:osgeo)
>>> # Test delayed ops with int16 and nodata values
>>> from delayed_image import *  # NOQA
>>> import kwimage
>>> from delayed_image.helpers import quantize_float01
>>> import ubelt as ub
>>> dpath = ub.Path.appdir('delayed_image/tests/test_delay_nodata').ensuredir()
>>> fpath = dpath / 'data.tif'
>>> data = kwimage.ensure_float01(kwimage.grab_test_image())
>>> poly = kwimage.Polygon.random(rng=321032).scale(data.shape[0])
>>> poly.fill(data, np.nan)
>>> data_uint16, quantization = quantize_float01(data)
>>> nodata = quantization['nodata']
>>> kwimage.imwrite(fpath, data_uint16, nodata=nodata, backend='gdal', overviews=3)
>>> # Test loading the data
>>> self = DelayedLoad(fpath, channels='r|g|b', nodata_method='float').prepare()
>>> node0 = self
>>> node1 = node0.dequantize(quantization)
>>> node2 = node1.warp({'scale': 0.51}, interpolation='lanczos')
>>> node3 = node2[13:900, 11:700]
>>> node4 = node3.warp({'scale': 0.9}, interpolation='lanczos')
>>> node4.write_network_text()
>>> node5 = node4.optimize()
>>> node5.write_network_text()
>>> node6 = node5.warp({'scale': 8}, interpolation='lanczos').optimize()
>>> node6.write_network_text()
>>> #
>>> data0 = node0._validate().finalize()
>>> data1 = node1._validate().finalize()
>>> data2 = node2._validate().finalize()
>>> data3 = node3._validate().finalize()
>>> data4 = node4._validate().finalize()
>>> data5 = node5._validate().finalize()
>>> data6 = node6._validate().finalize()
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> stack1 = kwimage.stack_images([data1, data2, data3, data4, data5])
>>> stack2 = kwimage.stack_images([stack1, data6], axis=1)
>>> kwplot.imshow(stack2)

property fpath

classmethod demo(key='astro', dsize=None, channels=None)

prepare()

If metadata is missing, perform minimal IO operations in order to prepopulate metadata that could help us better optimize the operation tree.

Returns

DelayedLoad

class kwcoco.util.delayed_ops.DelayedNans(dsize=None, channels=None)

Bases: DelayedImageLeaf

Constructs nan channels as needed

Example

self = DelayedNans((10, 10), channel_spec.FusedChannelSpec.coerce(‘rgb’)) region_slices = (slice(5, 10), slice(1, 12)) delayed = self.crop(region_slices)

Example

>>> from delayed_image import *  # NOQA
>>> dsize = (307, 311)
>>> c1 = DelayedNans(dsize=dsize, channels='foo')
>>> c2 = DelayedLoad.demo('astro', dsize=dsize, channels='R|G|B').prepare()
>>> cat = DelayedChannelConcat([c1, c2])
>>> warped_cat = cat.warp({'scale': 1.07}, dsize=(328, 332))._validate()
>>> warped_cat._validate().optimize().finalize()

class kwcoco.util.delayed_ops.DelayedNaryOperation(parts)

Bases: DelayedOperation

For operations that have multiple input arrays

children()

Yields

Any

class kwcoco.util.delayed_ops.DelayedOperation

Bases: NiceRepr

nesting()

Returns

Dict[str, dict]

as_graph()

Builds the underlying graph structure as a networkx graph with human readable labels.

Returns

networkx.DiGraph

write_network_text(with_labels=True, rich=0)

property shape

Returns: None | Tuple[int | None, …]

children()

Yields

Any

prepare()

If metadata is missing, perform minimal IO operations in order to prepopulate metadata that could help us better optimize the operation tree.

Returns

DelayedOperation2

finalize(prepare=True, optimize=True, **kwargs)

Evaluate the operation tree in full.

Parameters

• prepare (bool) – ensure prepare is called to ensure metadata exists if possible before optimizing. Defaults to True.

• optimize (bool) – ensure the graph is optimized before loading. Default to True.

• **kwargs – for backwards compatibility, these will allow for in-place modification of select nested parameters.

Returns

ArrayLike

Notes

Do not overload this method. Overload DelayedOperation2._finalize() instead.

optimize()

Returns

DelayedOperation2

class kwcoco.util.delayed_ops.DelayedOverview(subdata, overview)

Bases: DelayedImage

Downsamples an image by a factor of two.

If the underlying image being loaded has precomputed overviews it simply loads these instead of downsampling the original image, which is more efficient.

Example

>>> # xdoctest: +REQUIRES(module:osgeo)
>>> # Make a complex chain of operations and optimize it
>>> from delayed_image import *  # NOQA
>>> import kwimage
>>> fpath = kwimage.grab_test_image_fpath(overviews=3)
>>> dimg = DelayedLoad(fpath, channels='r|g|b').prepare()
>>> dimg = dimg.get_overview(1)
>>> dimg = dimg.get_overview(1)
>>> dimg = dimg.get_overview(1)
>>> dopt = dimg.optimize()
>>> if 1:
>>>     import networkx as nx
>>>     dimg.write_network_text()
>>>     dopt.write_network_text()
>>> print(ub.repr2(dopt.nesting(), nl=-1, sort=0))
>>> final0 = dimg._finalize()[:]
>>> final1 = dopt._finalize()[:]
>>> assert final0.shape == final1.shape
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> kwplot.imshow(final0, pnum=(1, 2, 1), fnum=1, title='raw')
>>> kwplot.imshow(final1, pnum=(1, 2, 2), fnum=1, title='optimized')

property num_overviews

Returns: int

optimize()

Returns

DelayedImage

class kwcoco.util.delayed_ops.DelayedStack(parts, axis)

Bases: DelayedNaryOperation

Stacks multiple arrays together.

property shape

Returns: None | Tuple[int | None, …]

class kwcoco.util.delayed_ops.DelayedUnaryOperation(subdata)

Bases: DelayedOperation

For operations that have a single input array

children()

Yields

Any

class kwcoco.util.delayed_ops.DelayedWarp(subdata, transform, dsize='auto', antialias=True, interpolation='linear', border_value='auto', noop_eps=0)

Bases: DelayedImage

Applies an affine transform to an image.

Example

>>> from delayed_image.delayed_nodes import *  # NOQA
>>> from delayed_image import DelayedLoad
>>> self = DelayedLoad.demo(dsize=(16, 16)).prepare()
>>> warp1 = self.warp({'scale': 3})
>>> warp2 = warp1.warp({'theta': 0.1})
>>> warp3 = warp2._opt_fuse_warps()
>>> warp3._validate()
>>> print(ub.repr2(warp2.nesting(), nl=-1, sort=0))
>>> print(ub.repr2(warp3.nesting(), nl=-1, sort=0))

property transform

Returns: kwimage.Affine

optimize()

Returns

DelayedImage

Example

>>> # Demo optimization that removes a noop warp
>>> from delayed_image import DelayedLoad
>>> import kwimage
>>> base = DelayedLoad.demo(channels='r|g|b').prepare()
>>> self = base.warp(kwimage.Affine.eye())
>>> new = self.optimize()
>>> assert len(self.as_graph().nodes) == 2
>>> assert len(new.as_graph().nodes) == 1

Example

>>> # Test optimize nans
>>> from delayed_image import DelayedNans
>>> import kwimage
>>> base = DelayedNans(dsize=(100, 100), channels='a|b|c')
>>> self = base.warp(kwimage.Affine.scale(0.1))
>>> # Should simply return a new nan generator
>>> new = self.optimize()
>>> assert len(new.as_graph().nodes) == 1

Example

>>> # Test optimize nans
>>> from delayed_image import DelayedLoad
>>> import kwimage
>>> base = DelayedLoad.demo(channels='r|g|b').prepare()
>>> transform = kwimage.Affine.scale(1.0 + 1e-7)
>>> self = base.warp(transform, dsize=base.dsize)
>>> # An optimize will not remove a warp if there is any
>>> # doubt if it is the identity.
>>> new = self.optimize()
>>> assert len(self.as_graph().nodes) == 2
>>> assert len(new.as_graph().nodes) == 2
>>> # But we can specify a threshold where it will
>>> self._set_nested_params(noop_eps=1e-6)
>>> new = self.optimize()
>>> assert len(self.as_graph().nodes) == 2
>>> assert len(new.as_graph().nodes) == 1

class kwcoco.util.delayed_ops.ImageOpsMixin

Bases: object

crop(space_slice=None, chan_idxs=None, clip=True, wrap=True, pad=0)

Crops an image along integer pixel coordinates.

Parameters

• space_slice (Tuple[slice, slice]) – y-slice and x-slice.

• chan_idxs (List[int]) – indexes of bands to take

• clip (bool) – if True, the slice is interpreted normally, where it won’t go past the image extent, otherwise slicing into negative regions or past the image bounds will result in padding. Defaults to True.

• wrap (bool) – if True, negative indexes “wrap around”, otherwise they are treated as is. Defaults to True.

• pad (int | List[Tuple[int, int]]) – if specified, applies extra padding

Returns

DelayedImage

Example

>>> from delayed_image import DelayedLoad
>>> import kwimage
>>> self = DelayedLoad.demo().prepare()
>>> self = self.dequantize({'quant_max': 255})
>>> self = self.warp({'scale': 1 / 2})
>>> pad = 0
>>> h, w = space_dims = self.dsize[::-1]
>>> grid = list(ub.named_product({
>>>     'left': [0, -64], 'right': [0, 64],
>>>     'top': [0, -64], 'bot': [0, 64],}))
>>> grid += [
>>>     {'left': 64, 'right': -64, 'top': 0, 'bot': 0},
>>>     {'left': 64, 'right': 64, 'top': 0, 'bot': 0},
>>>     {'left': 0, 'right': 0, 'top': 64, 'bot': -64},
>>>     {'left': 64, 'right': -64, 'top': 64, 'bot': -64},
>>> ]
>>> crops = []
>>> for pads in grid:
>>>     space_slice = (slice(pads['top'], h + pads['bot']),
>>>                    slice(pads['left'], w + pads['right']))
>>>     delayed = self.crop(space_slice)
>>>     crop = delayed.finalize()
>>>     yyxx = kwimage.Boxes.from_slice(space_slice, wrap=False, clip=0).toformat('_yyxx').data[0]
>>>     title = '[{}:{}, {}:{}]'.format(*yyxx)
>>>     crop_canvas = kwimage.draw_header_text(crop, title, fit=True, bg_color='kw_darkgray')
>>>     crops.append(crop_canvas)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> canvas = kwimage.stack_images_grid(crops, pad=16, bg_value='kw_darkgreen')
>>> canvas = kwimage.fill_nans_with_checkers(canvas)
>>> kwplot.imshow(canvas, title='Normal Slicing: Cropped Images With Wrap+Clipped Slices', doclf=1, fnum=1)
>>> kwplot.show_if_requested()

Example

>>> # Demo the case with pads / no-clips / no-wraps
>>> from delayed_image import DelayedLoad
>>> import kwimage
>>> self = DelayedLoad.demo().prepare()
>>> self = self.dequantize({'quant_max': 255})
>>> self = self.warp({'scale': 1 / 2})
>>> pad = [(64, 128), (32, 96)]
>>> pad = [(0, 20), (0, 0)]
>>> pad = 0
>>> pad = 8
>>> h, w = space_dims = self.dsize[::-1]
>>> grid = list(ub.named_product({
>>>     'left': [0, -64], 'right': [0, 64],
>>>     'top': [0, -64], 'bot': [0, 64],}))
>>> grid += [
>>>     {'left': 64, 'right': -64, 'top': 0, 'bot': 0},
>>>     {'left': 64, 'right': 64, 'top': 0, 'bot': 0},
>>>     {'left': 0, 'right': 0, 'top': 64, 'bot': -64},
>>>     {'left': 64, 'right': -64, 'top': 64, 'bot': -64},
>>> ]
>>> crops = []
>>> for pads in grid:
>>>     space_slice = (slice(pads['top'], h + pads['bot']),
>>>                    slice(pads['left'], w + pads['right']))
>>>     delayed = self._padded_crop(space_slice, pad=pad)
>>>     crop = delayed.finalize(optimize=1)
>>>     yyxx = kwimage.Boxes.from_slice(space_slice, wrap=False, clip=0).toformat('_yyxx').data[0]
>>>     title = '[{}:{}, {}:{}]'.format(*yyxx)
>>>     if pad:
>>>         title += f'{chr(10)}pad={pad}'
>>>     crop_canvas = kwimage.draw_header_text(crop, title, fit=True, bg_color='kw_darkgray')
>>>     crops.append(crop_canvas)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> canvas = kwimage.stack_images_grid(crops, pad=16, bg_value='kw_darkgreen', resize='smaller')
>>> canvas = kwimage.fill_nans_with_checkers(canvas)
>>> kwplot.imshow(canvas, title='Negative Slicing: Cropped Images With clip=False wrap=False', doclf=1, fnum=2)
>>> kwplot.show_if_requested()

warp(transform, dsize='auto', **warp_kwargs)

Applys an affine transformation to the image. See DelayedWarp.

Parameters

• transform (ndarray | dict | kwimage.Affine) – a coercable affine matrix. See kwimage.Affine for details on what can be coerced.

• dsize (Tuple[int, int] | str) – The width / height of the output canvas. If ‘auto’, dsize is computed such that the positive coordinates of the warped image will fit in the new canvas. In this case, any pixel that maps to a negative coordinate will be clipped. This has the property that the input transformation is not modified.

• antialias (bool) – if True determines if the transform is downsampling and applies antialiasing via gaussian a blur. Defaults to False

• interpolation (str) – interpolation code or cv2 integer. Interpolation codes are linear, nearest, cubic, lancsoz, and area. Defaults to “linear”.

• border_value (int | float | str) – if auto will be nan for float and 0 for int.

• noop_eps (float) – This is the tolerance for optimizing a warp away. If the transform has all of its decomposed parameters (i.e. scale, rotation, translation, shear) less than this value, the warp node can be optimized away. Defaults to 0.

Returns

DelayedImage

scale(scale, dsize='auto', **warp_kwargs)

An alias for self.warp({“scale”: scale}, …)

dequantize(quantization)

Rescales image intensities from int to floats.

Parameters

quantization (Dict[str, Any]) – see delayed_image.helpers.dequantize()

Returns

DelayedDequantize

get_overview(overview)

Downsamples an image by a factor of two.

Parameters

overview (int) – the overview to use (assuming it exists)

Returns

DelayedOverview

as_xarray()

Returns

DelayedAsXarray

get_transform_from(src)

Find a transform from a given node (src) to this node (self / dst).

Given two delayed images src and dst that share a common leaf, find the transform from src to dst.

Parameters

src (DelayedOperation) – the other view to get a transform to. This must share a leaf with self (which is the dst).

Returns

The transform that warps the space of src to the space of self.

Return type

kwimage.Affine

Example

>>> from delayed_image import *  # NOQA
>>> from delayed_image.delayed_leafs import DelayedLoad
>>> base = DelayedLoad.demo().prepare()
>>> src = base.scale(2)
>>> dst = src.warp({'scale': 4, 'offset': (3, 5)})
>>> transform = dst.get_transform_from(src)
>>> tf = transform.decompose()
>>> assert tf['scale'] == (4, 4)
>>> assert tf['offset'] == (3, 5)

Example

>>> from delayed_image import demo
>>> self = demo.non_aligned_leafs()
>>> leaf = list(self._leaf_paths())[0][0]
>>> tf1 = self.get_transform_from(leaf)
>>> tf2 = leaf.get_transform_from(self)
>>> np.allclose(np.linalg.inv(tf2), tf1)