def test_augment_segmaps__kernel_size_is_two__no_keep_size(self):
from imgaug.augmentables.segmaps import SegmentationMapsOnImage
arr = np.int32([[0, 1, 2], [1, 2, 3]])
segmaps = SegmentationMapsOnImage(arr, shape=(6, 6, 3))
aug = self.augmenter(2, keep_size=False)
segmaps_aug = aug.augment_segmentation_maps(segmaps)
expected = segmaps.resize((1, 2))
assert segmaps_aug.shape == (3, 3, 3)
assert segmaps_aug.arr.shape == (1, 2, 1)
assert np.allclose(segmaps_aug.arr, expected.arr)
def quokka_segmentation_map(size=None, extract=None):
"""Return a segmentation map for the standard example quokka image.
Added in 0.5.0. (Moved from ``imgaug.imgaug``.)
Parameters
----------
size : None or float or tuple of int, optional
See :func:`~imgaug.imgaug.quokka`.
extract : None or 'square' or tuple of number or imgaug.augmentables.bbs.BoundingBox or imgaug.augmentables.bbs.BoundingBoxesOnImage
See :func:`~imgaug.imgaug.quokka`.
Returns
-------
imgaug.augmentables.segmaps.SegmentationMapsOnImage
Segmentation map object.
"""
# pylint: disable=invalid-name
import skimage.draw
# TODO get rid of this deferred import
from imgaug.augmentables.segmaps import SegmentationMapsOnImage
with open(_QUOKKA_ANNOTATIONS_FP, "r") as f:
json_dict = json.load(f)
xx = []
yy = []
for kp_dict in json_dict["polygons"][0]["keypoints"]:
x = kp_dict["x"]
y = kp_dict["y"]
xx.append(x)
yy.append(y)
img_seg = np.zeros((643, 960, 1), dtype=np.int32)
rr, cc = skimage.draw.polygon(np.array(yy),
np.array(xx),
shape=img_seg.shape)
img_seg[rr, cc, 0] = 1
if extract is not None:
bb = _quokka_normalize_extract(extract)
img_seg = bb.extract_from_image(img_seg)
segmap = SegmentationMapsOnImage(img_seg, shape=img_seg.shape[0:2] + (3, ))
if size is not None:
shape_resized = _compute_resized_shape(img_seg.shape, size)
segmap = segmap.resize(shape_resized[0:2])
segmap.shape = tuple(shape_resized[0:2]) + (3, )
return segmap
def process_image_segmentation(image, segmap, input_w, input_h, output_w,
output_h, augment):
# resize the image to standard size
if (input_w and input_h) or augment:
segmap = SegmentationMapsOnImage(segmap, shape=image.shape)
if (input_w and input_h):
# Rescale image and segmaps
image = ia.imresize_single_image(image, (input_w, input_h))
segmap = segmap.resize((output_w, output_h),
interpolation="nearest")
if augment:
aug_pipe = _create_augment_pipeline()
image, segmap = aug_pipe(image=image, segmentation_maps=segmap)
return image, segmap.get_arr()
