geowatch.tasks.fusion.datamodules.data_utils module

I dont like the name of this file. I want to rename it, but it exists to keep the size of the datamodule down for now.

Todo

• [ ] Break BalancedSampleTree and BalancedSampleForest into their own balanced sampling module.

• [ ] Make a good augmentation module

• [ ] Determine where MultiscaleMask should live.

geowatch.tasks.fusion.datamodules.data_utils.resolve_scale_request(request=None, data_gsd=None)

Helper for handling user and machine specified spatial scale requests

Parameters:

• request (None | float | str) – Indicate a relative or absolute requested scale. If given as a float, this is interpreted as a scale factor relative to the underlying data. If given as a string, it will accept the format “{:f} *GSD” and resolve to an absolute GSD. Defaults to 1.0.

• data_gsd (None | float) – if specified, this indicates the GSD of the underlying data. (Only valid for geospatial data). TODO: is there a better generalization?

Returns:

resolvedcontaining keys

scale (float): the scale factor to obtain the requested gsd (float | None): if data_gsd is given, this is the absolute

GSD of the request.

Return type:

Dict[str, Any]

Note

The returned scale is relative to the DATA. If you are resizing a sampled image, then use it directly, but if you are adjusting a sample WINDOW, then it needs to be used inversely.

Example

>>> from geowatch.tasks.fusion.datamodules.data_utils import *  # NOQA
>>> resolve_scale_request(1.0)
>>> resolve_scale_request('native')
>>> resolve_scale_request('10 GSD', data_gsd=10)
>>> resolve_scale_request('20 GSD', data_gsd=10)

Example

>>> from geowatch.tasks.fusion.datamodules.data_utils import *  # NOQA
>>> import ubelt as ub
>>> grid = list(ub.named_product({
>>>     'request': ['10GSD', '30GSD'],
>>>     'data_gsd': [10, 30],
>>> }))
>>> grid += list(ub.named_product({
>>>     'request': [None, 1.0, 2.0, 0.25, 'native'],
>>>     'data_gsd': [None, 10, 30],
>>> }))
>>> for kwargs in grid:
>>>     print('kwargs = {}'.format(ub.urepr(kwargs, nl=0)))
>>>     resolved = resolve_scale_request(**kwargs)
>>>     print('resolved = {}'.format(ub.urepr(resolved, nl=0)))
>>>     print('---')

geowatch.tasks.fusion.datamodules.data_utils.abslog_scaling(arr)

geowatch.tasks.fusion.datamodules.data_utils.fliprot(img, rot_k=0, flip_axis=None, axes=(0, 1))

Parameters:

• img (ndarray) – H, W, C

• rot_k (int) – number of ccw rotations

• flip_axis (Tuple[int, …]) – either [], [0], [1], or [0, 1]. 0 is the y axis and 1 is the x axis.

• axes (Typle[int, int]) – the location of the y and x axes

Example

>>> img = np.arange(16).reshape(4, 4)
>>> unique_fliprots = [
>>>     {'rot_k': 0, 'flip_axis': None},
>>>     {'rot_k': 0, 'flip_axis': (0,)},
>>>     {'rot_k': 1, 'flip_axis': None},
>>>     {'rot_k': 1, 'flip_axis': (0,)},
>>>     {'rot_k': 2, 'flip_axis': None},
>>>     {'rot_k': 2, 'flip_axis': (0,)},
>>>     {'rot_k': 3, 'flip_axis': None},
>>>     {'rot_k': 3, 'flip_axis': (0,)},
>>> ]
>>> for params in unique_fliprots:
>>>     img_fw = fliprot(img, **params)
>>>     img_inv = inv_fliprot(img_fw, **params)
>>>     assert np.all(img == img_inv)

geowatch.tasks.fusion.datamodules.data_utils.fliprot_annot(annot, rot_k, flip_axis=None, axes=(0, 1), canvas_dsize=None)

geowatch.tasks.fusion.datamodules.data_utils.inv_fliprot_annot(annot, rot_k, flip_axis=None, axes=(0, 1), canvas_dsize=None)

geowatch.tasks.fusion.datamodules.data_utils.inv_fliprot(img, rot_k=0, flip_axis=None, axes=(0, 1))

Undo a fliprot

Parameters:

img (ndarray) – H, W, C

geowatch.tasks.fusion.datamodules.data_utils.samecolor_nodata_mask(stream, hwc, relevant_bands, use_regions=0, samecolor_values=None)

Find a 2D mask that indicates what values should be set to nan. This is typically done by finding clusters of zeros in specific bands.

Example

>>> from geowatch.tasks.fusion.datamodules.data_utils import *  # NOQA
>>> import kwcoco
>>> import kwarray
>>> stream = kwcoco.FusedChannelSpec.coerce('foo|red|green|bar')
>>> stream_oset = ub.oset(stream)
>>> relevant_bands = ['red', 'green']
>>> relevant_band_idxs = [stream_oset.index(b) for b in relevant_bands]
>>> rng = kwarray.ensure_rng(0)
>>> hwc = (rng.rand(32, 32, stream.numel()) * 3).astype(int)
>>> use_regions = 0
>>> samecolor_values = {0}
>>> samecolor_mask = samecolor_nodata_mask(
>>>     stream, hwc, relevant_bands, use_regions=use_regions,
>>>     samecolor_values=samecolor_values)
>>> assert samecolor_mask.sum() == (hwc[..., relevant_band_idxs] == 0).any(axis=2).sum()

class geowatch.tasks.fusion.datamodules.data_utils.MultiscaleMask

Bases: object

A helper class to build up a mask indicating what pixels are unobservable based on data from different resolution.

In othe words, if you have multiple masks, and each mask has a different resolution, then this will iteravely upscale the masks to the largest resolution so far and perform a logical or. This helps keep the memory footprint small.

Todo

Does this live in kwimage?

CommandLine

xdoctest -m geowatch.tasks.fusion.datamodules.data_utils MultiscaleMask --show

Example

>>> from geowatch.tasks.fusion.datamodules.data_utils import *  # NOQA
>>> image = kwimage.grab_test_image()
>>> image = kwimage.ensure_float01(image)
>>> rng = kwarray.ensure_rng(1)
>>> mask1 = kwimage.Mask.random(shape=(12, 12), rng=rng).data
>>> mask2 = kwimage.Mask.random(shape=(32, 32), rng=rng).data
>>> mask3 = kwimage.Mask.random(shape=(16, 16), rng=rng).data
>>> omask = MultiscaleMask()
>>> omask.update(mask1)
>>> omask.update(mask2)
>>> omask.update(mask3)
>>> masked_image = omask.apply(image, np.nan)
>>> # Now we can use our upscaled masks on an image.
>>> masked_image = kwimage.fill_nans_with_checkers(masked_image, on_value=0.3)
>>> # xdoctest: +REQUIRES(--show)
>>> import kwplot
>>> kwplot.autompl()
>>> inputs = kwimage.stack_images(
>>>     [kwimage.atleast_3channels(m * 255) for m in [mask1, mask2, mask3]],
>>>     pad=2, bg_value='kw_green', axis=1)
>>> kwplot.imshow(inputs, pnum=(1, 3, 1), title='input masks')
>>> kwplot.imshow(omask.mask, pnum=(1, 3, 2), title='final mask')
>>> kwplot.imshow(masked_image, pnum=(1, 3, 3), title='masked image')
>>> kwplot.show_if_requested()

update(mask)

Expand the observable mask to the larger data and take the logical or of the resized masks.

apply(image, value)

Set the locations in image that correspond to this mask to value.

property masked_fraction