def test_keypoints_to_heatmaps(self):
from supermariopy.imageutils import keypoints_to_heatmaps, RangeError
keypoints = np.stack([np.linspace(-1, 1, 10),
np.linspace(-1, 1, 10)],
axis=1)
heatmaps = keypoints_to_heatmaps((512, 512), keypoints)
assert heatmaps.shape == (512, 512, 10)
keypoints = np.stack([np.arange(10), np.arange(10)], axis=1)
with pytest.raises(RangeError):
heatmaps = keypoints_to_heatmaps((512, 512), keypoints)
def batched_keypoints_to_segments(
img: np.ndarray,
keypoints: np.ndarray,
segmentation_algorithm: SegmentationAlgorithm,
) -> Union[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
n_keypoints = keypoints.shape[0]
MAP = segmentation_algorithm(img, keypoints)
MAP_colorized = imageutils.make_colors(n_keypoints + 1,
with_background=True,
background_id=0)[MAP]
heatmaps = imageutils.keypoints_to_heatmaps(img.shape[:2], keypoints,
segmentation_algorithm.var)
heatmaps *= heatmaps > 0.8
heatmaps_rgb = imageutils.colorize_heatmaps(
heatmaps[np.newaxis, ...], imageutils.make_colors(n_keypoints))
img_resized = cv2.resize(img, (256, 256), cv2.INTER_LINEAR)
img_resized = imageutils.convert_range(img_resized, [0, 255], [0, 1])
im_with_keypoints = imageutils.draw_keypoint_markers(
img_resized,
keypoints,
marker_list=[str(i)
for i in range(10)] + ["x", "o", "v", "<", ">", "*"],
font_scale=1,
thickness=4,
)
im_with_keypoints = cv2.resize(im_with_keypoints,
(img.shape[1], img.shape[1]),
cv2.INTER_LINEAR)
return MAP, MAP_colorized, heatmaps_rgb, im_with_keypoints
def test_segmentationFromKeypoints(self):
from supermariopy.crf import SegmentationFromKeypoints
from supermariopy import imageutils
from skimage import data
n_keypoints = 10
var = 0.05
keypoints = np.stack(
[np.linspace(-1, 1, n_keypoints),
np.linspace(-1, 1, n_keypoints)],
axis=1)
img = data.astronaut()
segmentation_algorithm = SegmentationFromKeypoints(var)
labels = segmentation_algorithm(img, keypoints)
labels_rgb = imageutils.make_colors(n_keypoints + 1)[labels]
heatmaps = imageutils.keypoints_to_heatmaps(img.shape[:2], keypoints,
var)
heatmaps_rgb = imageutils.colorize_heatmaps(
heatmaps[np.newaxis, ...], imageutils.make_colors(n_keypoints))
fig, axes = plt.subplots(1, 2, figsize=(8, 4))
axes[0].imshow(labels_rgb)
axes[0].set_axis_off()
axes[1].imshow(np.squeeze(heatmaps_rgb))
axes[1].set_axis_off()
return fig
def __call__(self, image: np.ndarray, keypoints: np.ndarray) -> np.ndarray:
if not imageutils.is_in_range(image, [0, 255]):
raise imageutils.RangeError(image, "image", [0, 255])
dynamic_range = image.max() / image.min()
is_low_range = dynamic_range <= 2.0
if is_low_range:
import warnings
warnings.warn(
Warning(
"image has low dynamic range. Maybe convert to [0, 255]?"))
h, w, c = image.shape
keypoint_probs = imageutils.keypoints_to_heatmaps((h, w),
keypoints,
var=self.var)
keypoint_probs = np.rollaxis(keypoint_probs, 2, 0)
bg_prob = 1.0 - np.amax(keypoint_probs, axis=0, keepdims=True)
probs = np.concatenate([bg_prob, keypoint_probs], axis=0)
n_labels = probs.shape[0]
probs_flat = np.reshape(probs, (n_labels, -1))
d = dcrf.DenseCRF(h * w, n_labels)
# flatten everything for dense crf
# Set unary according to keypoints
U = -np.log(probs_flat + 1.0e-6).astype(np.float32)
d.setUnaryEnergy(U.copy(order="C"))
# This creates the color-independent features and then add them to the CRF
feats = create_pairwise_gaussian(sdims=(3, 3), shape=image.shape[:2])
d.addPairwiseEnergy(
feats,
compat=3,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC,
)
# This creates the color-dependent features and then add them to the CRF
feats = create_pairwise_bilateral(sdims=(80, 80),
schan=(13, 13, 13),
img=image,
chdim=2)
d.addPairwiseEnergy(
feats,
compat=10,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC,
)
# Run five inference steps.
Q = d.inference(self.n_steps)
# Find out the most probable class for each pixel.
MAP = np.argmax(Q, axis=0)
MAP = MAP.reshape((h, w))
return MAP
def test_colorize_heatmaps(self):
from supermariopy.imageutils import (
keypoints_to_heatmaps,
make_colors,
colorize_heatmaps,
)
from matplotlib import pyplot as plt
keypoints = np.stack([np.linspace(-1, 1, 10),
np.linspace(-1, 1, 10)],
axis=1)
heatmaps = keypoints_to_heatmaps((512, 512), keypoints)
colors = make_colors(keypoints.shape[0])
img_marked = colorize_heatmaps(heatmaps[np.newaxis, ...], colors)
fig, ax = plt.subplots(1, 1, figsize=(6, 6))
ax.imshow(np.squeeze(img_marked))
return fig
def batched_keypoints_to_segments(img, keypoints, segmentation_algorithm):
n_keypoints = keypoints.shape[0]
MAP = segmentation_algorithm(img, keypoints)
MAP_colorized = imageutils.make_colors(n_keypoints + 1,
with_background=True,
background_id=0)[MAP]
heatmaps = imageutils.keypoints_to_heatmaps(img.shape[:2], keypoints,
segmentation_algorithm.var)
heatmaps *= heatmaps > 0.8
heatmaps_rgb = imageutils.colorize_heatmaps(
heatmaps[np.newaxis, ...], imageutils.make_colors(n_keypoints))
im_with_keypoints = imageutils.draw_keypoint_markers(
imageutils.convert_range(img, [0, 255], [0, 1]),
keypoints,
marker_list=[str(i)
for i in range(10)] + ["x", "o", "v", "<", ">", "*"],
font_scale=0.5,
)
return MAP, MAP_colorized, heatmaps_rgb, im_with_keypoints