def test_instance_cropper_keeping_full_image(min_labels):
labels_reader = providers.LabelsReader(min_labels)
instance_centroid_finder = instance_centroids.InstanceCentroidFinder(
center_on_anchor_part=True,
anchor_part_names="A",
skeletons=labels_reader.labels.skeletons)
instance_cropper = instance_cropping.InstanceCropper(crop_width=160,
crop_height=160,
keep_full_image=True)
ds = instance_centroid_finder.transform_dataset(
labels_reader.make_dataset())
ds = instance_cropper.transform_dataset(ds)
example = next(iter(ds))
assert example["instance_image"].shape == (160, 160, 1)
assert example["instance_image"].dtype == tf.uint8
assert example["full_image_height"] == 384
assert example["full_image_height"].dtype == tf.int32
assert example["full_image_width"] == 384
assert example["full_image_width"].dtype == tf.int32
assert example["image"].shape == (384, 384, 1)
assert example["image"].dtype == tf.uint8
def test_instance_confidence_map_generator(min_labels):
labels_reader = providers.LabelsReader(min_labels)
instance_centroid_finder = instance_centroids.InstanceCentroidFinder(
center_on_anchor_part=True,
anchor_part_names="A",
skeletons=labels_reader.labels.skeletons,
)
instance_cropper = instance_cropping.InstanceCropper(
crop_width=160, crop_height=160
)
instance_confmap_generator = InstanceConfidenceMapGenerator(
sigma=5, output_stride=2, all_instances=False
)
ds = labels_reader.make_dataset()
ds = instance_centroid_finder.transform_dataset(ds)
ds = instance_cropper.transform_dataset(ds)
ds = instance_confmap_generator.transform_dataset(ds)
example = next(iter(ds))
assert example["instance_confidence_maps"].shape == (80, 80, 2)
assert example["instance_confidence_maps"].dtype == tf.float32
assert "all_instance_confidence_maps" not in example
points = (
example["center_instance"].numpy() / instance_confmap_generator.output_stride
)
cms = example["instance_confidence_maps"].numpy()
np.testing.assert_allclose(
cms[(points[:, 1]).astype(int), (points[:, 0]).astype(int), :],
[[0.9139312, 0.0], [0.0, 0.94459903]],
)
def test_multi_confidence_map_generator_centroids(min_labels):
labels_reader = providers.LabelsReader(min_labels)
instance_centroid_finder = instance_centroids.InstanceCentroidFinder(
center_on_anchor_part=True,
anchor_part_names="A",
skeletons=labels_reader.labels.skeletons,
)
multi_confmap_generator = MultiConfidenceMapGenerator(
sigma=5, output_stride=2, centroids=True
)
ds = labels_reader.make_dataset()
ds = instance_centroid_finder.transform_dataset(ds)
ds = multi_confmap_generator.transform_dataset(ds)
example = next(iter(ds))
assert example["centroid_confidence_maps"].shape == (192, 192, 1)
assert example["centroid_confidence_maps"].dtype == tf.float32
centroids = example["centroids"].numpy() / multi_confmap_generator.output_stride
centroid_cms = example["centroid_confidence_maps"].numpy()
np.testing.assert_allclose(
centroid_cms[int(centroids[0, 1]), int(centroids[0, 0]), :], [0.9811318]
)
np.testing.assert_allclose(
centroid_cms[int(centroids[1, 1]), int(centroids[1, 0]), :], [0.8642299]
)
def test_instance_confidence_map_generator_with_all_instances(min_labels):
labels_reader = providers.LabelsReader(min_labels)
instance_centroid_finder = instance_centroids.InstanceCentroidFinder(
center_on_anchor_part=True,
anchor_part_names="A",
skeletons=labels_reader.labels.skeletons,
)
instance_cropper = instance_cropping.InstanceCropper(crop_width=160,
crop_height=160)
instance_confmap_generator = InstanceConfidenceMapGenerator(
sigma=5 / 2, output_stride=2, all_instances=True)
ds = labels_reader.make_dataset()
ds = instance_centroid_finder.transform_dataset(ds)
ds = instance_cropper.transform_dataset(ds)
ds = instance_confmap_generator.transform_dataset(ds)
example = next(iter(ds))
assert "instance_confidence_maps" in example
assert example["all_instance_confidence_maps"].shape == (80, 80, 2)
assert example["all_instance_confidence_maps"].dtype == tf.float32
instances = (example["all_instances"].numpy() /
instance_confmap_generator.output_stride)
all_cms = example["all_instance_confidence_maps"].numpy()
x = (instances[:, :, 0]).astype(int)
y = (instances[:, :, 1]).astype(int)
x[(x < 0) | (x >= all_cms.shape[1])] = 0
y[(y < 0) | (y >= all_cms.shape[0])] = 0
np.testing.assert_allclose(
all_cms[y, x, :],
[[[0.91393119, 0.], [0., 0.94459903]], [[0., 0.], [0., 0.]]],
atol=1e-6)
def test_instance_cropper(min_labels):
labels_reader = providers.LabelsReader(min_labels)
instance_centroid_finder = instance_centroids.InstanceCentroidFinder(
center_on_anchor_part=True,
anchor_part_names="A",
skeletons=labels_reader.labels.skeletons)
instance_cropper = instance_cropping.InstanceCropper(crop_width=160,
crop_height=160,
keep_full_image=False)
ds = instance_centroid_finder.transform_dataset(
labels_reader.make_dataset())
ds = instance_cropper.transform_dataset(ds)
example = next(iter(ds))
assert example["instance_image"].shape == (160, 160, 1)
assert example["instance_image"].dtype == tf.uint8
assert example["bbox"].shape == (4, )
assert example["bbox"].dtype == tf.float32
assert example["center_instance"].shape == (2, 2)
assert example["center_instance"].dtype == tf.float32
assert example["center_instance_ind"] == 0
assert example["center_instance_ind"].dtype == tf.int32
assert example["all_instances"].shape == (2, 2, 2)
assert example["all_instances"].dtype == tf.float32
assert example["centroid"].shape == (2, )
assert example["centroid"].dtype == tf.float32
assert example["full_image_height"] == 384
assert example["full_image_height"].dtype == tf.int32
assert example["full_image_width"] == 384
assert example["full_image_width"].dtype == tf.int32
assert example["video_ind"] == 0
assert example["video_ind"].dtype == tf.int32
assert example["frame_ind"] == 0
assert example["frame_ind"].dtype == tf.int64
np.testing.assert_array_equal(example["scale"], (1.0, 1.0))
assert example["scale"].dtype == tf.float32
assert example["skeleton_inds"].shape == (2, )
assert example["skeleton_inds"].dtype == tf.int32
assert "image" not in example
def test_instance_centroid_finder(min_labels):
labels_reader = providers.LabelsReader(min_labels)
labels_ds = labels_reader.make_dataset()
instance_centroid_finder = instance_centroids.InstanceCentroidFinder(
center_on_anchor_part=False)
ds = instance_centroid_finder.transform_dataset(labels_ds)
example = next(iter(ds))
assert example["centroids"].dtype == tf.float32
np.testing.assert_allclose(
example["centroids"], [[122.49705, 180.57481], [242.28264, 195.62775]])
def test_instance_centroid_finder_anchored(min_labels):
labels_reader = providers.LabelsReader(min_labels)
labels_ds = labels_reader.make_dataset()
instance_centroid_finder = instance_centroids.InstanceCentroidFinder(
center_on_anchor_part=True,
anchor_part_names="A",
skeletons=labels_reader.labels.skeletons)
ds = instance_centroid_finder.transform_dataset(labels_ds)
example = next(iter(ds))
assert example["centroids"].dtype == tf.float32
np.testing.assert_allclose(example["centroids"],
[[92.65221, 202.72598], [205.93005, 187.88963]])