def test_slic_consistency_across_image_magnitude():
# verify that that images of various scales across integer and float dtypes
# give the same segmentation result
img_uint8 = data.cat()[:256, :128]
img_uint16 = 256 * img_uint8.astype(np.uint16)
img_float32 = img_as_float(img_uint8)
img_float32_norm = img_float32 / img_float32.max()
seg1 = slic(img_uint8)
seg2 = slic(img_uint16)
seg3 = slic(img_float32)
seg4 = slic(img_float32_norm)
np.testing.assert_array_equal(seg1, seg2)
np.testing.assert_array_equal(seg1, seg3)
np.testing.assert_array_equal(seg1, seg4)
def test_slic_consistency_across_image_magnitude():
# verify that that images of various scales across integer and float dtypes
# give the same segmentation result
img_uint8 = data.cat()[:256, :128]
img_uint16 = 256 * img_uint8.astype(np.uint16)
img_float32 = img_as_float(img_uint8)
img_float32_norm = img_float32 / img_float32.max()
seg1 = slic(img_uint8)
seg2 = slic(img_uint16)
seg3 = slic(img_float32)
seg4 = slic(img_float32_norm)
np.testing.assert_array_equal(seg1, seg2)
np.testing.assert_array_equal(seg1, seg3)
# Floating point cases can have mismatch due to floating point error
# exact match was observed on x86_64, but mismatches seen no i686.
# For now just verify that a similar number of superpixels are present in
# each case.
n_seg1 = seg1.max()
n_seg4 = seg4.max()
assert abs(n_seg1 - n_seg4) / n_seg1 < 0.5
from skimage import data
from skimage.transform import rotate, rescale
from utils import show_image
image = data.cat()
show_image(image)
# Rotate the image 90 degrees clockwise
rotated_img = rotate(image, -90)
show_image(rotated_img, 'rotated_img')
# Rescale with anti aliasing
rescaled_with_aa = rescale(rotated_img,
1 / 4,
anti_aliasing=True,
multichannel=True)
# Rescale without anti aliasing
rescaled_without_aa = rescale(rotated_img, 1 / 4)
# Show the resulting images
show_image(rescaled_with_aa, "Transformed with anti aliasing")
show_image(rescaled_without_aa, "Transformed without anti aliasing")
image = caller()
plt.figure()
plt.title(name)
if image.ndim == 2:
plt.imshow(image, cmap=plt.cm.gray)
else:
plt.imshow(image)
plt.show()
############################################################################
# Thumbnail image for the gallery
# sphinx_gallery_thumbnail_number = -1
fig, axs = plt.subplots(nrows=3, ncols=3)
for ax in axs.flat:
ax.axis("off")
axs[0, 0].imshow(data.astronaut())
axs[0, 1].imshow(data.binary_blobs(), cmap=plt.cm.gray)
axs[0, 2].imshow(data.brick(), cmap=plt.cm.gray)
axs[1, 0].imshow(data.colorwheel())
axs[1, 1].imshow(data.camera(), cmap=plt.cm.gray)
axs[1, 2].imshow(data.cat())
axs[2, 0].imshow(data.checkerboard(), cmap=plt.cm.gray)
axs[2, 1].imshow(data.clock(), cmap=plt.cm.gray)
further_img = np.full((300, 300), 255)
for xpos in [100, 150, 200]:
further_img[150 - 10:150 + 10, xpos - 10:xpos + 10] = 0
axs[2, 2].imshow(further_img, cmap=plt.cm.gray)
plt.subplots_adjust(wspace=0.1, hspace=0.1)