def test_fill_from_augmented_normalized_batch(self):
batch = ia.UnnormalizedBatch(
images=np.zeros((1, 2, 2, 3), dtype=np.uint8),
heatmaps=[np.zeros((2, 2, 1), dtype=np.float32)],
segmentation_maps=[np.zeros((2, 2, 1), dtype=np.int32)],
keypoints=[[(0, 0)]],
bounding_boxes=[[ia.BoundingBox(0, 0, 1, 1)]],
polygons=[[ia.Polygon([(0, 0), (1, 0), (1, 1)])]],
line_strings=[[ia.LineString([(0, 0), (1, 0)])]])
batch_norm = ia.Batch(
images=np.zeros((1, 2, 2, 3), dtype=np.uint8),
heatmaps=[
ia.HeatmapsOnImage(np.zeros((2, 2, 1), dtype=np.float32),
shape=(2, 2, 3))
],
segmentation_maps=[
ia.SegmentationMapsOnImage(np.zeros((2, 2, 1), dtype=np.int32),
shape=(2, 2, 3))
],
keypoints=[
ia.KeypointsOnImage([ia.Keypoint(0, 0)], shape=(2, 2, 3))
],
bounding_boxes=[
ia.BoundingBoxesOnImage([ia.BoundingBox(0, 0, 1, 1)],
shape=(2, 2, 3))
],
polygons=[
ia.PolygonsOnImage([ia.Polygon([(0, 0), (1, 0), (1, 1)])],
shape=(2, 2, 3))
],
line_strings=[
ia.LineStringsOnImage([ia.LineString([(0, 0), (1, 0)])],
shape=(2, 2, 3))
])
batch_norm.images_aug = batch_norm.images_unaug
batch_norm.heatmaps_aug = batch_norm.heatmaps_unaug
batch_norm.segmentation_maps_aug = batch_norm.segmentation_maps_unaug
batch_norm.keypoints_aug = batch_norm.keypoints_unaug
batch_norm.bounding_boxes_aug = batch_norm.bounding_boxes_unaug
batch_norm.polygons_aug = batch_norm.polygons_unaug
batch_norm.line_strings_aug = batch_norm.line_strings_unaug
batch = batch.fill_from_augmented_normalized_batch(batch_norm)
assert batch.images_aug.shape == (1, 2, 2, 3)
assert ia.is_np_array(batch.heatmaps_aug[0])
assert ia.is_np_array(batch.segmentation_maps_aug[0])
assert batch.keypoints_aug[0][0] == (0, 0)
assert batch.bounding_boxes_aug[0][0].x1 == 0
assert batch.polygons_aug[0][0].exterior[0][0] == 0
assert batch.line_strings_aug[0][0].coords[0][0] == 0
def test_to_normalized_batch__all_columns(self):
batch = ia.UnnormalizedBatch(
images=np.zeros((1, 2, 2, 3), dtype=np.uint8),
heatmaps=[np.zeros((2, 2, 1), dtype=np.float32)],
segmentation_maps=[np.zeros((2, 2, 1), dtype=np.int32)],
keypoints=[[(0, 0)]],
bounding_boxes=[[ia.BoundingBox(0, 0, 1, 1)]],
polygons=[[ia.Polygon([(0, 0), (1, 0), (1, 1)])]],
line_strings=[[ia.LineString([(0, 0), (1, 0)])]])
batch_norm = batch.to_normalized_batch()
assert isinstance(batch_norm, ia.Batch)
assert ia.is_np_array(batch_norm.images_unaug)
assert batch_norm.images_unaug.shape == (1, 2, 2, 3)
assert isinstance(batch_norm.heatmaps_unaug[0], ia.HeatmapsOnImage)
assert isinstance(batch_norm.segmentation_maps_unaug[0],
ia.SegmentationMapsOnImage)
assert isinstance(batch_norm.keypoints_unaug[0], ia.KeypointsOnImage)
assert isinstance(batch_norm.bounding_boxes_unaug[0],
ia.BoundingBoxesOnImage)
assert isinstance(batch_norm.polygons_unaug[0], ia.PolygonsOnImage)
assert isinstance(batch_norm.line_strings_unaug[0],
ia.LineStringsOnImage)
assert batch_norm.get_column_names() == [
"images", "heatmaps", "segmentation_maps", "keypoints",
"bounding_boxes", "polygons", "line_strings"
]
def test_get_column_names__all_columns(self):
batch = ia.Batch(
images=np.zeros((1, 2, 2, 3), dtype=np.uint8),
heatmaps=[np.zeros((2, 2, 1), dtype=np.float32)],
segmentation_maps=[np.zeros((2, 2, 1), dtype=np.int32)],
keypoints=[
ia.KeypointsOnImage([ia.Keypoint(x=0, y=0)], shape=(2, 2, 3))
],
bounding_boxes=[
ia.BoundingBoxesOnImage([ia.BoundingBox(0, 0, 1, 1)],
shape=(2, 2, 3))
],
polygons=[
ia.PolygonsOnImage([ia.Polygon([(0, 0), (1, 0), (1, 1)])],
shape=(2, 2, 3))
],
line_strings=[
ia.LineStringsOnImage([ia.LineString([(0, 0), (1, 0)])],
shape=(2, 2, 3))
])
names = batch.get_column_names()
assert names == [
"images", "heatmaps", "segmentation_maps", "keypoints",
"bounding_boxes", "polygons", "line_strings"
]
def example_augment_images_and_linestrings():
print("Example: Augment Images and LineStrings")
import numpy as np
import imgaug as ia
import imgaug.augmenters as iaa
images = np.zeros((2, 128, 128, 3), dtype=np.uint8) # two example images
images[:, 64, 64, :] = 255
ls = [[ia.LineString([(10.5, 10.5), (50.5, 10.5), (50.5, 50.5)])],
[
ia.LineString([(0.0, 64.5), (64.5, 0.0), (128.0, 128.0),
(64.5, 128.0), (128.0, 0.0)])
]]
seq = iaa.Sequential([
iaa.AdditiveGaussianNoise(scale=0.05 * 255),
iaa.Affine(translate_px={"x": (1, 5)})
])
images_aug, ls_aug = seq(images=images, line_strings=ls)
def test_to_batch_in_augmentation__all_columns(self):
batch = ia.Batch(
images=np.zeros((1, 2, 2, 3), dtype=np.uint8),
heatmaps=[
ia.HeatmapsOnImage(np.zeros((2, 2, 1), dtype=np.float32),
shape=(2, 2, 3))
],
segmentation_maps=[
ia.SegmentationMapsOnImage(np.zeros((2, 2, 1), dtype=np.int32),
shape=(2, 2, 3))
],
keypoints=[
ia.KeypointsOnImage([ia.Keypoint(x=0, y=0)], shape=(2, 2, 3))
],
bounding_boxes=[
ia.BoundingBoxesOnImage([ia.BoundingBox(0, 0, 1, 1)],
shape=(2, 2, 3))
],
polygons=[
ia.PolygonsOnImage([ia.Polygon([(0, 0), (1, 0), (1, 1)])],
shape=(2, 2, 3))
],
line_strings=[
ia.LineStringsOnImage([ia.LineString([(0, 0), (1, 0)])],
shape=(2, 2, 3))
])
batch_inaug = batch.to_batch_in_augmentation()
assert isinstance(batch_inaug, ia.BatchInAugmentation)
assert ia.is_np_array(batch_inaug.images)
assert batch_inaug.images.shape == (1, 2, 2, 3)
assert isinstance(batch_inaug.heatmaps[0], ia.HeatmapsOnImage)
assert isinstance(batch_inaug.segmentation_maps[0],
ia.SegmentationMapsOnImage)
assert isinstance(batch_inaug.keypoints[0], ia.KeypointsOnImage)
assert isinstance(batch_inaug.bounding_boxes[0],
ia.BoundingBoxesOnImage)
assert isinstance(batch_inaug.polygons[0], ia.PolygonsOnImage)
assert isinstance(batch_inaug.line_strings[0], ia.LineStringsOnImage)
assert batch_inaug.get_column_names() == [
"images", "heatmaps", "segmentation_maps", "keypoints",
"bounding_boxes", "polygons", "line_strings"
]
def test_two_images_and_line_strings(self):
rng = iarandom.RNG(0)
images = rng.integers(0, 256, size=(2, 32, 32, 3), dtype=np.uint8)
ls = []
for x in np.linspace(0, 256, 4):
for y in np.linspace(0, 256, 4):
ls.append(
ia.LineString([(x, y), (x + 20, y), (x + 20, y + 20),
(x, y + 20)]))
lsoi1 = ia.LineStringsOnImage(ls, shape=images[0].shape)
lsoi2 = lsoi1.deepcopy()
image1_w_overlay = lsoi1.draw_on_image(images[0])
image2_w_overlay = lsoi2.draw_on_image(images[1])
debug_image = iaa.draw_debug_image(images, line_strings=[lsoi1, lsoi2])
assert self._image_contains(images[0, ...], debug_image)
assert self._image_contains(images[1, ...], debug_image)
assert self._image_contains(image1_w_overlay, debug_image)
assert self._image_contains(image2_w_overlay, debug_image)
def test_get_rowwise_shapes__nonimages(self):
heatmaps = [
ia.HeatmapsOnImage(np.zeros((1, 2, 1), dtype=np.float32),
shape=(1, 2, 3))
]
segmaps = [
ia.SegmentationMapsOnImage(np.zeros((1, 2, 1), dtype=np.int32),
shape=(1, 2, 3))
]
keypoints = [ia.KeypointsOnImage([ia.Keypoint(0, 0)], shape=(1, 2, 3))]
bounding_boxes = [
ia.BoundingBoxesOnImage([ia.BoundingBox(0, 1, 2, 3)],
shape=(1, 2, 3))
]
polygons = [
ia.PolygonsOnImage([ia.Polygon([(0, 0), (1, 0), (1, 1)])],
shape=(1, 2, 3))
]
line_strings = [
ia.LineStringsOnImage([ia.LineString([(0, 0), (1, 0)])],
shape=(1, 2, 3))
]
kwargs = [{
"heatmaps": heatmaps
}, {
"segmentation_maps": segmaps
}, {
"keypoints": keypoints
}, {
"bounding_boxes": bounding_boxes
}, {
"polygons": polygons
}, {
"line_strings": line_strings
}]
for kwargs_i in kwargs:
batch = ia.BatchInAugmentation(**kwargs_i)
shapes = batch.get_rowwise_shapes()
assert shapes == [(1, 2, 3)]
def test_deepcopy(self):
batch = ia.Batch()
observed = batch.deepcopy()
keys = list(observed.__dict__.keys())
assert len(keys) >= 14
for attr_name in keys:
assert getattr(observed, attr_name) is None
batch = ia.Batch(images=np.zeros((1, 1, 3), dtype=np.uint8))
observed = batch.deepcopy()
for attr_name in observed.__dict__.keys():
if attr_name != "images_unaug":
assert getattr(observed, attr_name) is None
assert ia.is_np_array(observed.images_unaug)
batch = ia.Batch(
images=np.zeros((1, 1, 3), dtype=np.uint8),
heatmaps=[
ia.HeatmapsOnImage(np.zeros((1, 1, 1), dtype=np.float32),
shape=(4, 4, 3))
],
segmentation_maps=[
ia.SegmentationMapOnImage(np.zeros((1, 1), dtype=np.int32),
shape=(5, 5, 3),
nb_classes=20)
],
keypoints=[
ia.KeypointsOnImage([ia.Keypoint(x=1, y=2)], shape=(6, 6, 3))
],
bounding_boxes=[
ia.BoundingBoxesOnImage(
[ia.BoundingBox(x1=1, y1=2, x2=3, y2=4)], shape=(7, 7, 3))
],
polygons=[
ia.PolygonsOnImage([ia.Polygon([(0, 0), (10, 0), (10, 10)])],
shape=(100, 100, 3))
],
line_strings=[
ia.LineStringsOnImage(
[ia.LineString([(1, 1), (11, 1), (11, 11)])],
shape=(101, 101, 3))
],
data={
"test": 123,
"foo": "bar",
"test2": [1, 2, 3]
})
observed = batch.deepcopy()
for attr_name in observed.__dict__.keys():
if "_unaug" not in attr_name and attr_name != "data":
assert getattr(observed, attr_name) is None
assert ia.is_np_array(observed.images_unaug)
assert observed.images_unaug.shape == (1, 1, 3)
assert isinstance(observed.heatmaps_unaug[0], ia.HeatmapsOnImage)
assert isinstance(observed.segmentation_maps_unaug[0],
ia.SegmentationMapOnImage)
assert isinstance(observed.keypoints_unaug[0], ia.KeypointsOnImage)
assert isinstance(observed.bounding_boxes_unaug[0],
ia.BoundingBoxesOnImage)
assert isinstance(observed.polygons_unaug[0], ia.PolygonsOnImage)
assert isinstance(observed.line_strings_unaug[0],
ia.LineStringsOnImage)
assert isinstance(observed.data, dict)
assert observed.heatmaps_unaug[0].shape == (4, 4, 3)
assert observed.segmentation_maps_unaug[0].shape == (5, 5, 3)
assert observed.keypoints_unaug[0].shape == (6, 6, 3)
assert observed.bounding_boxes_unaug[0].shape == (7, 7, 3)
assert observed.polygons_unaug[0].shape == (100, 100, 3)
assert observed.line_strings_unaug[0].shape == (101, 101, 3)
assert observed.heatmaps_unaug[0].arr_0to1.shape == (1, 1, 1)
assert observed.segmentation_maps_unaug[0].arr.shape == (1, 1, 20)
assert observed.keypoints_unaug[0].keypoints[0].x == 1
assert observed.keypoints_unaug[0].keypoints[0].y == 2
assert observed.bounding_boxes_unaug[0].bounding_boxes[0].x1 == 1
assert observed.bounding_boxes_unaug[0].bounding_boxes[0].y1 == 2
assert observed.bounding_boxes_unaug[0].bounding_boxes[0].x2 == 3
assert observed.bounding_boxes_unaug[0].bounding_boxes[0].y2 == 4
assert observed.polygons_unaug[0].polygons[0].exterior[0, 0] == 0
assert observed.polygons_unaug[0].polygons[0].exterior[0, 1] == 0
assert observed.polygons_unaug[0].polygons[0].exterior[1, 0] == 10
assert observed.polygons_unaug[0].polygons[0].exterior[1, 1] == 0
assert observed.polygons_unaug[0].polygons[0].exterior[2, 0] == 10
assert observed.polygons_unaug[0].polygons[0].exterior[2, 1] == 10
assert observed.line_strings_unaug[0].line_strings[0].coords[0, 0] == 1
assert observed.line_strings_unaug[0].line_strings[0].coords[0, 1] == 1
assert observed.line_strings_unaug[0].line_strings[0].coords[1,
0] == 11
assert observed.line_strings_unaug[0].line_strings[0].coords[1, 1] == 1
assert observed.line_strings_unaug[0].line_strings[0].coords[2,
0] == 11
assert observed.line_strings_unaug[0].line_strings[0].coords[2,
1] == 11
assert observed.data["test"] == 123
assert observed.data["foo"] == "bar"
assert observed.data["test2"] == [1, 2, 3]
def lsoi_flipped(self):
ls = [ia.LineString([(0, 3 - 0), (2, 3 - 0), (2, 3 - 2)])]
return [ia.LineStringsOnImage(ls, shape=self.image.shape)]
def lsoi(self):
ls = [ia.LineString([(0, 0), (2, 0), (2, 2)])]
return [ia.LineStringsOnImage(ls, shape=self.image.shape)]
def test_deepcopy_every_argument_provided(self):
images = np.zeros((1, 1, 1, 3), dtype=np.uint8)
heatmaps = [
ia.HeatmapsOnImage(np.zeros((1, 1, 1), dtype=np.float32),
shape=(4, 4, 3))
]
segmentation_maps = [
ia.SegmentationMapsOnImage(np.zeros((1, 1), dtype=np.int32),
shape=(5, 5, 3))
]
keypoints = [
ia.KeypointsOnImage([ia.Keypoint(x=1, y=2)], shape=(6, 6, 3))
]
bounding_boxes = [
ia.BoundingBoxesOnImage([ia.BoundingBox(x1=1, y1=2, x2=3, y2=4)],
shape=(7, 7, 3))
]
polygons = [
ia.PolygonsOnImage([ia.Polygon([(0, 0), (10, 0), (10, 10)])],
shape=(100, 100, 3))
]
line_strings = [
ia.LineStringsOnImage([ia.LineString([(1, 1), (11, 1), (11, 11)])],
shape=(101, 101, 3))
]
data = {"test": 123, "foo": "bar", "test2": [1, 2, 3]}
batch = ia.Batch(images=images,
heatmaps=heatmaps,
segmentation_maps=segmentation_maps,
keypoints=keypoints,
bounding_boxes=bounding_boxes,
polygons=polygons,
line_strings=line_strings,
data=data)
observed = batch.deepcopy()
for attr_name in observed.__dict__.keys():
if "_unaug" not in attr_name and attr_name != "data":
assert getattr(observed, attr_name) is None
# must not be identical
assert observed.images_unaug is not images
assert observed.heatmaps_unaug is not heatmaps
assert observed.segmentation_maps_unaug is not segmentation_maps
assert observed.keypoints_unaug is not keypoints
assert observed.bounding_boxes_unaug is not bounding_boxes
assert observed.polygons_unaug is not polygons
assert observed.line_strings_unaug is not line_strings
assert observed.data is not data
# verify that lists were not shallow-copied
assert observed.heatmaps_unaug[0] is not heatmaps[0]
assert observed.segmentation_maps_unaug[0] is not segmentation_maps[0]
assert observed.keypoints_unaug[0] is not keypoints[0]
assert observed.bounding_boxes_unaug[0] is not bounding_boxes[0]
assert observed.polygons_unaug[0] is not polygons[0]
assert observed.line_strings_unaug[0] is not line_strings[0]
assert observed.data["test2"] is not data["test2"]
# but must be equal
assert ia.is_np_array(observed.images_unaug)
assert observed.images_unaug.shape == (1, 1, 1, 3)
assert isinstance(observed.heatmaps_unaug[0], ia.HeatmapsOnImage)
assert isinstance(observed.segmentation_maps_unaug[0],
ia.SegmentationMapsOnImage)
assert isinstance(observed.keypoints_unaug[0], ia.KeypointsOnImage)
assert isinstance(observed.bounding_boxes_unaug[0],
ia.BoundingBoxesOnImage)
assert isinstance(observed.polygons_unaug[0], ia.PolygonsOnImage)
assert isinstance(observed.line_strings_unaug[0],
ia.LineStringsOnImage)
assert isinstance(observed.data, dict)
assert observed.heatmaps_unaug[0].shape == (4, 4, 3)
assert observed.segmentation_maps_unaug[0].shape == (5, 5, 3)
assert observed.keypoints_unaug[0].shape == (6, 6, 3)
assert observed.bounding_boxes_unaug[0].shape == (7, 7, 3)
assert observed.polygons_unaug[0].shape == (100, 100, 3)
assert observed.line_strings_unaug[0].shape == (101, 101, 3)
assert observed.heatmaps_unaug[0].arr_0to1.shape == (1, 1, 1)
assert observed.segmentation_maps_unaug[0].arr.shape == (1, 1, 1)
assert observed.keypoints_unaug[0].keypoints[0].x == 1
assert observed.keypoints_unaug[0].keypoints[0].y == 2
assert observed.bounding_boxes_unaug[0].bounding_boxes[0].x1 == 1
assert observed.bounding_boxes_unaug[0].bounding_boxes[0].y1 == 2
assert observed.bounding_boxes_unaug[0].bounding_boxes[0].x2 == 3
assert observed.bounding_boxes_unaug[0].bounding_boxes[0].y2 == 4
assert observed.polygons_unaug[0].polygons[0].exterior[0, 0] == 0
assert observed.polygons_unaug[0].polygons[0].exterior[0, 1] == 0
assert observed.polygons_unaug[0].polygons[0].exterior[1, 0] == 10
assert observed.polygons_unaug[0].polygons[0].exterior[1, 1] == 0
assert observed.polygons_unaug[0].polygons[0].exterior[2, 0] == 10
assert observed.polygons_unaug[0].polygons[0].exterior[2, 1] == 10
assert observed.line_strings_unaug[0].line_strings[0].coords[0, 0] == 1
assert observed.line_strings_unaug[0].line_strings[0].coords[0, 1] == 1
assert observed.line_strings_unaug[0].line_strings[0].coords[1,
0] == 11
assert observed.line_strings_unaug[0].line_strings[0].coords[1, 1] == 1
assert observed.line_strings_unaug[0].line_strings[0].coords[2,
0] == 11
assert observed.line_strings_unaug[0].line_strings[0].coords[2,
1] == 11
assert observed.data["test"] == 123
assert observed.data["foo"] == "bar"
assert observed.data["test2"] == [1, 2, 3]
