def test_daisy_normalization():
img = img_as_float(data.lena()[:64, :64].mean(axis=2))
descs = daisy(img, normalization='l1')
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
assert_almost_equal(np.sum(descs[i, j, :]), 1)
descs_ = daisy(img)
assert_almost_equal(descs, descs_)
descs = daisy(img, normalization='l2')
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
assert_almost_equal(sqrt(np.sum(descs[i, j, :]**2)), 1)
orientations = 8
descs = daisy(img, orientations=orientations, normalization='daisy')
desc_dims = descs.shape[2]
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
for k in range(0, desc_dims, orientations):
assert_almost_equal(
sqrt(np.sum(descs[i, j, k:k + orientations]**2)), 1)
img = np.zeros((50, 50))
descs = daisy(img, normalization='off')
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
assert_almost_equal(np.sum(descs[i, j, :]), 0)
assert_raises(ValueError, daisy, img, normalization='does_not_exist')
def setUpClass(cls):
cls.tmpdir = tempfile.mkdtemp()
cls.db_name = tempfile.mkdtemp(dir=cls.tmpdir)
cls.db = _.DbCreator(cls.db_name, 'lmdb')
_handle, cls.lena_path = tempfile.mkstemp(dir=cls.tmpdir, suffix='.jpg')
with open(cls.lena_path, 'w') as outfile:
PIL.Image.fromarray(data.lena()).save(outfile, format='JPEG', quality=100)
def lena(gray=True, small=True):
im = data.lena()
if small:
im = imresize(im, size=(im.shape[0] / 4, im.shape[1] / 4))
if gray:
im = im.mean(axis=2)
return im
def test_fast_homography():
img = rgb2gray(data.lena()).astype(np.uint8)
img = img[:, :100]
theta = np.deg2rad(30)
scale = 0.5
tx, ty = 50, 50
H = np.eye(3)
S = scale * np.sin(theta)
C = scale * np.cos(theta)
H[:2, :2] = [[C, -S], [S, C]]
H[:2, 2] = [tx, ty]
for mode in ('constant', 'mirror', 'wrap'):
p0 = homography(img, H, mode=mode, order=1)
p1 = fast_homography(img, H, mode=mode)
p1 = np.round(p1)
## import matplotlib.pyplot as plt
## f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)
## ax0.imshow(img)
## ax1.imshow(p0, cmap=plt.cm.gray)
## ax2.imshow(p1, cmap=plt.cm.gray)
## ax3.imshow(np.abs(p0 - p1), cmap=plt.cm.gray)
## plt.show()
d = np.mean(np.abs(p0 - p1))
assert d < 0.2
def test_binary_descriptors_lena_rotation_crosscheck_true():
"""Verify matched keypoints and their corresponding masks results between
lena image and its rotated version with the expected keypoint pairs with
cross_check enabled."""
img = data.lena()
img = rgb2gray(img)
tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
rotated_img = tf.warp(img, tform)
extractor = BRIEF(descriptor_size=512)
keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
extractor.extract(img, keypoints1)
descriptors1 = extractor.descriptors
keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
extractor.extract(rotated_img, keypoints2)
descriptors2 = extractor.descriptors
matches = match_descriptors(descriptors1, descriptors2, cross_check=True)
exp_matches1 = np.array([ 0, 1, 2, 4, 6, 7, 9, 10, 11, 12, 13, 15,
16, 17, 19, 20, 21, 24, 26, 27, 28, 29, 30, 35,
36, 38, 39, 40, 42, 44, 45])
exp_matches2 = np.array([33, 0, 35, 1, 3, 2, 6, 4, 9, 11, 10, 7,
8, 5, 14, 13, 15, 16, 17, 18, 19, 21, 22, 24,
23, 26, 27, 25, 28, 29, 30])
assert_equal(matches[:, 0], exp_matches1)
assert_equal(matches[:, 1], exp_matches2)
def test_theano_overfeat_against_binary():
layer_correspondence = dict(
normal=dict(
large=[(0, 0, None), (1, 2, None), (2, 3, None), (3, 5, None),
(4, 6, None), (5, 9, None), (6, 12, None), (7, 15, None),
(8, 18, None), (9, 19, None), (10, 21, None),
(11, 23, None), (12, 24, None)],
small=[(0, 0, None), (1, 2, None), (2, 3, None), (3, 5, None),
(4, 6, None), (5, 9, None), (6, 12, None), (7, 15, None),
(8, 16, None), (9, 18, None), (10, 20, None),
(11, 21, None)]),
detailed=dict(
large=[(i, i, None) for i in range(25)],
small=[(i, i, None) for i in range(22)]
)
)
rng = np.random.RandomState(42)
image = (rng.rand(320, 320, 3) * 255).astype(np.uint8)
from skimage.data import lena
image = lena()
for detailed, correspondences in layer_correspondence.items():
for net_size, correspondence in correspondences.items():
for theano_layer, binary_layer, cropping in correspondence:
_check_overfeat_layer(image, theano_layer, binary_layer,
net_size == 'large',
detailed == 'detailed',
cropping=cropping)
def test_corner_orientations_lena():
img = rgb2gray(data.lena())
corners = corner_peaks(corner_fast(img, 11, 0.35))
expected = np.array([-1.9195897 , -3.03159624, -1.05991162, -2.89573739,
-2.61607644, 2.98660159])
actual = corner_orientations(img, corners, octagon(3, 2))
assert_almost_equal(actual, expected)
def test_lena():
from skimage import data
from skimage import color
from skimage.transform import resize
in_shape = (200, 200)
n_imgs = 1
lena = resize(color.rgb2gray(data.lena()), in_shape).astype(np.float32)
lena -= lena.min()
lena /= lena.max()
imgs = lena.reshape((n_imgs,) + in_shape)
model = cnnr.models.fg11_ht_l3_1_description
extractor = cnnr.BatchExtractor(in_shape, model)
feat_set, _ = extractor.extract(imgs)
assert feat_set.shape == (n_imgs, 10, 10, 256)
feat_set.shape = n_imgs, -1
test_chunk_computed = feat_set[0, 12798:12802]
test_chunk_expected = np.array([0.03845372, 0.02469639, 0.01009409, 0.02500059], dtype=np.float32)
assert_allclose(test_chunk_computed, test_chunk_expected, rtol=RTOL, atol=ATOL)
def main():
src_rgb_img = data.lena()
#src_rgb_img = io.imread('Child_input.png')
src_Lab_img = color.rgb2lab(src_rgb_img)
src_Lab_img_L = src_Lab_img[:, :, 0]
src_Lab_img_a = src_Lab_img[:, :, 1]
src_Lab_img_b = src_Lab_img[:, :, 2]
#down_sampling with scale = 0.5
scale = 0.25
src = bicubic2d(src_Lab_img_L, scale) * 2.55
#up_sampling with scale = 2.0 to source image by different method
dst_me = bicubic2d(src, 4.0)
dst_other = imresize(
src, 4.0, interp='bicubic') #this method result is int and in [0,255]
#evalue different with PSNR MY result is better
print psnr(dst_me / 2.55, src_Lab_img_L)
print psnr(dst_other / 2.55, src_Lab_img_L)
plt.imshow(dst_me, interpolation="none")
plt.show()
def test_lena():
from skimage import data
from skimage import color
from skimage.transform import resize
in_shape = (200, 200)
n_imgs = 1
lena = resize(color.rgb2gray(data.lena()), in_shape).astype(np.float32)
lena -= lena.min()
lena /= lena.max()
imgs = lena.reshape((n_imgs, ) + in_shape)
model = cnnr.models.fg11_ht_l3_1_description
extractor = cnnr.BatchExtractor(in_shape, model)
feat_set = extractor.extract(imgs)
assert feat_set.shape == (n_imgs, 10, 10, 256)
feat_set.shape = n_imgs, -1
test_chunk_computed = feat_set[0, 12798:12802]
test_chunk_expected = np.array(
[0.03845372, 0.02469639, 0.01009409, 0.02500059], dtype=np.float32)
assert_allclose(test_chunk_computed,
test_chunk_expected,
rtol=RTOL,
atol=ATOL)
def plot_lena_overlay():
plt.figure()
logo = ScipyLogo((300, 300), 180)
logo.plot_snake_curve()
logo.plot_circle()
img = data.lena()
plt.imshow(img)
def test_keypoints_orb_less_than_desired_no_of_keypoints():
img = rgb2gray(lena())
detector_extractor = ORB(n_keypoints=15, fast_n=12,
fast_threshold=0.33, downscale=2, n_scales=2)
detector_extractor.detect(img)
exp_rows = np.array([ 67., 247., 269., 413., 435., 230., 264.,
330., 372.])
exp_cols = np.array([ 157., 146., 111., 70., 180., 136., 336.,
148., 156.])
exp_scales = np.array([ 1., 1., 1., 1., 1., 2., 2., 2., 2.])
exp_orientations = np.array([-105.76503839, -96.28973044, -53.08162354,
-173.4479964 , -175.64733392, -106.07927215,
-163.40016243, 75.80865813, -154.73195911])
exp_response = np.array([ 0.13197835, 0.24931321, 0.44351774,
0.39063076, 0.96770745, 0.04935129,
0.21431068, 0.15826555, 0.42403573])
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
assert_almost_equal(exp_scales, detector_extractor.scales)
assert_almost_equal(exp_response, detector_extractor.responses)
assert_almost_equal(exp_orientations,
np.rad2deg(detector_extractor.orientations), 5)
detector_extractor.detect_and_extract(img)
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
def plot_lena_overlay():
plt.figure()
logo = ScipyLogo((300, 300), 180)
logo.plot_snake_curve()
logo.plot_circle()
img = data.lena()
plt.imshow(img)
def test_histogram_of_oriented_gradients():
img = img_as_float(data.lena()[:256, :].mean(axis=2))
fd = feature.hog(img, orientations=9, pixels_per_cell=(8, 8),
cells_per_block=(1, 1))
assert len(fd) == 9 * (256 // 8) * (512 // 8)
def test_corner_orientations_lena():
img = rgb2gray(data.lena())
corners = corner_peaks(corner_fast(img, 11, 0.35))
expected = np.array([-1.9195897 , -3.03159624, -1.05991162, -2.89573739,
-2.61607644, 2.98660159])
actual = corner_orientations(img, corners, octagon(3, 2))
assert_almost_equal(actual, expected)
def main():
"""Plot example augmentations for Lena and an image loaded from a file."""
# try on a lena image
image = data.lena()
augmenter = ImageAugmenter(image.shape[0], image.shape[1],
hflip=True, vflip=True,
scale_to_percent=1.3, scale_axis_equally=False,
rotation_deg=25, shear_deg=10,
translation_x_px=5, translation_y_px=5)
augmenter.plot_image(image, 100)
# check loading of images from file and augmenting them
image = misc.imread("chameleon.png")
augmenter = ImageAugmenter(image.shape[1], image.shape[0],
hflip=True, vflip=True,
scale_to_percent=1.3, scale_axis_equally=False,
rotation_deg=25, shear_deg=10,
translation_x_px=5, translation_y_px=5)
augmenter.plot_image(image, 50)
# move the channel from index 2 (3rd position) to index 0 (1st position)
# so (y, x, rgb) becomes (rgb, y, x)
# try if it still works
image = np.rollaxis(image, 2, 0)
augmenter = ImageAugmenter(image.shape[2], image.shape[1],
hflip=True, vflip=True,
scale_to_percent=1.3, scale_axis_equally=False,
rotation_deg=25, shear_deg=10,
translation_x_px=5, translation_y_px=5,
channel_is_first_axis=True)
augmenter.plot_image(image, 50)
def test_rotated_lena():
"""
The harris filter should yield the same results with an image and it's
rotation.
"""
im = img_as_float(data.lena().mean(axis=2))
im_rotated = im.T
# Moravec
results = peak_local_max(corner_moravec(im))
results_rotated = peak_local_max(corner_moravec(im_rotated))
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
# Harris
results = peak_local_max(corner_harris(im))
results_rotated = peak_local_max(corner_harris(im_rotated))
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im))
results_rotated = peak_local_max(corner_shi_tomasi(im_rotated))
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
def test_fast_homography():
img = rgb2gray(data.lena()).astype(np.uint8)
img = img[:, :100]
theta = np.deg2rad(30)
scale = 0.5
tx, ty = 50, 50
H = np.eye(3)
S = scale * np.sin(theta)
C = scale * np.cos(theta)
H[:2, :2] = [[C, -S], [S, C]]
H[:2, 2] = [tx, ty]
tform = ProjectiveTransform(H)
coords = warp_coords(tform.inverse, (img.shape[0], img.shape[1]))
for order in range(4):
for mode in ('constant', 'reflect', 'wrap', 'nearest'):
p0 = map_coordinates(img, coords, mode=mode, order=order)
p1 = warp(img, tform, mode=mode, order=order)
# import matplotlib.pyplot as plt
# f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)
# ax0.imshow(img)
# ax1.imshow(p0, cmap=plt.cm.gray)
# ax2.imshow(p1, cmap=plt.cm.gray)
# ax3.imshow(np.abs(p0 - p1), cmap=plt.cm.gray)
# plt.show()
d = np.mean(np.abs(p0 - p1))
assert d < 0.001
def MR_boudnary_extraction(self, img=None):
if img == None:
img = cv2.cvtColor(lena(), cv2.COLOR_RGB2BGR)
lab_img = self._MR_saliency__MR_readimg(img)
mark_color = (1, 0, 0)
labels = self._MR_saliency__MR_superpixel(lab_img)
up_img = lab_img.copy()
up_ids = sp.unique(labels[0, :]).astype(int)
up_mask = sp.zeros(labels.shape).astype(bool)
for i in up_ids:
up_mask = sp.logical_or(up_mask, labels == i)
up_img[up_mask] = mark_color
up_img = mark_boundaries(up_img, labels)
right_img = lab_img.copy()
right_ids = sp.unique(labels[:, labels.shape[1] - 1]).astype(int)
right_mask = sp.zeros(labels.shape).astype(bool)
for i in right_ids:
right_mask = sp.logical_or(right_mask, labels == i)
right_img[right_mask] = mark_color
right_img = mark_boundaries(right_img, labels)
low_img = lab_img.copy()
low_ids = sp.unique(labels[labels.shape[0] - 1, :]).astype(int)
low_mask = sp.zeros(labels.shape).astype(bool)
for i in low_ids:
low_mask = sp.logical_or(low_mask, labels == i)
low_img[low_mask] = mark_color
low_img = mark_boundaries(low_img, labels)
left_img = lab_img.copy()
left_ids = sp.unique(labels[:, 0]).astype(int)
left_mask = sp.zeros(labels.shape).astype(bool)
for i in left_ids:
left_mask = sp.logical_or(left_mask, labels == i)
left_img[left_mask] = mark_color
left_img = mark_boundaries(left_img, labels)
plt.subplot(2, 2, 1)
plt.axis('off')
plt.title('up')
plt.imshow(up_img)
plt.subplot(2, 2, 2)
plt.axis('off')
plt.title('bottom')
plt.imshow(low_img)
plt.subplot(2, 2, 3)
plt.axis('off')
plt.title('left')
plt.imshow(left_img)
plt.subplot(2, 2, 4)
plt.axis('off')
plt.title('right')
plt.imshow(right_img)
plt.show()
def test_fast_homography():
img = rgb2gray(data.lena()).astype(np.uint8)
img = img[:, :100]
theta = np.deg2rad(30)
scale = 0.5
tx, ty = 50, 50
H = np.eye(3)
S = scale * np.sin(theta)
C = scale * np.cos(theta)
H[:2, :2] = [[C, -S], [S, C]]
H[:2, 2] = [tx, ty]
for mode in ('constant', 'mirror', 'wrap'):
p0 = homography(img, H, mode=mode, order=1)
p1 = fast_homography(img, H, mode=mode)
p1 = np.round(p1)
## import matplotlib.pyplot as plt
## f, (ax0, ax1, ax2, ax3) = plt.subplots(1, 4)
## ax0.imshow(img)
## ax1.imshow(p0, cmap=plt.cm.gray)
## ax2.imshow(p1, cmap=plt.cm.gray)
## ax3.imshow(np.abs(p0 - p1), cmap=plt.cm.gray)
## plt.show()
d = np.mean(np.abs(p0 - p1))
assert d < 0.2
def test_tv_denoise_2d(self):
"""
Apply the TV denoising algorithm on the lena image provided
by scipy
"""
# lena image
lena = color.rgb2gray(data.lena())[:256, :256]
# add noise to lena
lena += 0.5 * lena.std() * np.random.randn(*lena.shape)
# clip noise so that it does not exceed allowed range for float images.
lena = np.clip(lena, 0, 1)
# denoise
denoised_lena = filter.tv_denoise(lena, weight=60.0)
# which dtype?
assert denoised_lena.dtype in [np.float, np.float32, np.float64]
from scipy import ndimage
grad = ndimage.morphological_gradient(lena, size=((3, 3)))
grad_denoised = ndimage.morphological_gradient(
denoised_lena, size=((3, 3)))
# test if the total variation has decreased
assert np.sqrt(
(grad_denoised ** 2).sum()) < np.sqrt((grad ** 2).sum()) / 2
denoised_lena_int = filter.tv_denoise(img_as_uint(lena),
weight=60.0, keep_type=True)
assert denoised_lena_int.dtype is np.dtype('uint16')
def test_rotated_lena():
"""
The harris filter should yield the same results with an image and it's
rotation.
"""
im = img_as_float(data.lena().mean(axis=2))
im_rotated = im.T
# Moravec
results = peak_local_max(corner_moravec(im))
results_rotated = peak_local_max(corner_moravec(im_rotated))
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
# Harris
results = peak_local_max(corner_harris(im))
results_rotated = peak_local_max(corner_harris(im_rotated))
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im))
results_rotated = peak_local_max(corner_shi_tomasi(im_rotated))
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
def test_match_keypoints_brief_lena_rotation():
"""Verify matched keypoints result between lena image and its rotated
version with the expected keypoint pairs."""
img = data.lena()
img = rgb2gray(img)
img.shape
tform = tf.SimilarityTransform(scale=1, rotation=0.10, translation=(0, 0))
rotated_img = tf.warp(img, tform)
keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
descriptors1, keypoints1 = brief(img, keypoints1, descriptor_size=512)
keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
descriptors2, keypoints2 = brief(rotated_img,
keypoints2,
descriptor_size=512)
matched_keypoints = match_keypoints_brief(keypoints1,
descriptors1,
keypoints2,
descriptors2,
threshold=0.07)
expected = np.array([[[263, 272], [234, 298]], [[271, 120], [258, 146]],
[[323, 164], [305, 195]], [[414, 70], [405, 111]],
[[435, 181], [415, 223]], [[454, 176], [435, 221]]])
assert_array_equal(matched_keypoints, expected)
def test():
img = skimage.img_as_float(data.lena())
img_size = img.shape[:2]
trans = get_transform(20,15,1.05, 0.02, img_size)
img_transformed = transform.warp(img, trans)
obj_func = lambda x: transform_and_compare(img_transformed, img, x)
x0 = np.array([0,0,1, 0])
results = optimize.fmin_bfgs(obj_func, x0)
transform_estimated = get_simple_transform(results)
transform_optimal = transform.AffineTransform(np.linalg.inv(trans._matrix))
params_optimal = np.concatenate([transform_optimal.translation,
transform_optimal.scale[0:1],
[transform_optimal.rotation]])
img_registered = transform.warp(img_transformed,
transform_estimated)
err_original = mean_sq_diff(img_transformed, img)
err_optimal = transform_and_compare(img_transformed, img, params_optimal)
err_actual = transform_and_compare(img_transformed, img, results)
err_relative = err_optimal/err_original
print "Params optimal:", params_optimal
print "Params estimated:", results
print "Error without registration:", err_original
print "Error of optimal registration:", err_optimal
print "Error of estimated transformation %f (%.2f %% of intial)" % (err_actual,
err_relative*100.)
plt.figure()
plt.subplot(121)
plt.imshow(img_transformed)
plt.subplot(122)
plt.imshow(img_registered)
def test_corner_fast_lena():
img = rgb2gray(data.lena())
expected = np.array([[67, 157], [204, 261], [247, 146], [269, 111],
[318, 158], [386, 73], [413, 70], [435, 180],
[455, 177], [461, 160]])
actual = corner_peaks(corner_fast(img, 12, 0.3))
assert_array_equal(actual, expected)
def test_daisy_normalization():
img = img_as_float(data.lena()[:64, :64].mean(axis=2))
descs = daisy(img, normalization='l1')
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
assert_almost_equal(np.sum(descs[i, j, :]), 1)
descs_ = daisy(img)
assert_almost_equal(descs, descs_)
descs = daisy(img, normalization='l2')
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
assert_almost_equal(sqrt(np.sum(descs[i, j, :] ** 2)), 1)
orientations = 8
descs = daisy(img, orientations=orientations, normalization='daisy')
desc_dims = descs.shape[2]
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
for k in range(0, desc_dims, orientations):
assert_almost_equal(sqrt(np.sum(
descs[i, j, k:k + orientations] ** 2)), 1)
img = np.zeros((50, 50))
descs = daisy(img, normalization='off')
for i in range(descs.shape[0]):
for j in range(descs.shape[1]):
assert_almost_equal(np.sum(descs[i, j, :]), 0)
assert_raises(ValueError, daisy, img, normalization='does_not_exist')
def test_binary_descriptors_lena_rotation_crosscheck_true():
"""Verify matched keypoints and their corresponding masks results between
lena image and its rotated version with the expected keypoint pairs with
cross_check enabled."""
img = data.lena()
img = rgb2gray(img)
tform = tf.SimilarityTransform(scale=1, rotation=0.15, translation=(0, 0))
rotated_img = tf.warp(img, tform, clip=False)
extractor = BRIEF(descriptor_size=512)
keypoints1 = corner_peaks(corner_harris(img), min_distance=5)
extractor.extract(img, keypoints1)
descriptors1 = extractor.descriptors
keypoints2 = corner_peaks(corner_harris(rotated_img), min_distance=5)
extractor.extract(rotated_img, keypoints2)
descriptors2 = extractor.descriptors
matches = match_descriptors(descriptors1, descriptors2, cross_check=True)
exp_matches1 = np.array([
0, 1, 2, 4, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 19, 20, 21, 24, 26,
27, 28, 29, 30, 35, 36, 38, 39, 40, 42, 44, 45
])
exp_matches2 = np.array([
33, 0, 35, 1, 3, 2, 6, 4, 9, 11, 10, 7, 8, 5, 14, 13, 15, 16, 17, 18,
19, 21, 22, 24, 23, 26, 27, 25, 28, 29, 30
])
assert_equal(matches[:, 0], exp_matches1)
assert_equal(matches[:, 1], exp_matches2)
def test_keypoints_orb_less_than_desired_no_of_keypoints():
img = rgb2gray(lena())
detector_extractor = ORB(n_keypoints=15,
fast_n=12,
fast_threshold=0.33,
downscale=2,
n_scales=2)
detector_extractor.detect(img)
exp_rows = np.array([67., 247., 269., 413., 435., 230., 264., 330., 372.])
exp_cols = np.array([157., 146., 111., 70., 180., 136., 336., 148., 156.])
exp_scales = np.array([1., 1., 1., 1., 1., 2., 2., 2., 2.])
exp_orientations = np.array([
-105.76503839, -96.28973044, -53.08162354, -173.4479964, -175.64733392,
-106.07927215, -163.40016243, 75.80865813, -154.73195911
])
exp_response = np.array([
0.13197835, 0.24931321, 0.44351774, 0.39063076, 0.96770745, 0.04935129,
0.21431068, 0.15826555, 0.42403573
])
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
assert_almost_equal(exp_scales, detector_extractor.scales)
assert_almost_equal(exp_response, detector_extractor.responses)
assert_almost_equal(exp_orientations,
np.rad2deg(detector_extractor.orientations), 5)
detector_extractor.detect_and_extract(img)
assert_almost_equal(exp_rows, detector_extractor.keypoints[:, 0])
assert_almost_equal(exp_cols, detector_extractor.keypoints[:, 1])
def test_original_images(self):
images = self.manager.original_images()
n_images = sum(1 for _ in images)
self.assertEqual(n_images, 1)
lena = data.lena()
for image in images:
self.assertEqual(image.shape, lena.shape)
def MR_boundary_saliency(self,img=None):
if img == None:
img = cv2.cvtColor(lena(),cv2.COLOR_RGB2BGR)
lab_img = self._MR_saliency__MR_readimg(img)
labels = self._MR_saliency__MR_superpixel(lab_img)
up,right,low,left = self._MR_saliency__MR_boundary_indictor(labels)
aff = self._MR_saliency__MR_affinity_matrix(lab_img,labels)
up_sal = 1- self._MR_saliency__MR_saliency(aff,up)
up_img = self._MR_saliency__MR_fill_superpixel_with_saliency(labels,up_sal)
up_img = up_img.astype(np.uint8)
right_sal = 1-self._MR_saliency__MR_saliency(aff,right)
right_img = self._MR_saliency__MR_fill_superpixel_with_saliency(labels,right_sal)
right_img = right_img.astype(np.uint8)
low_sal = 1-self._MR_saliency__MR_saliency(aff,low)
low_img = self._MR_saliency__MR_fill_superpixel_with_saliency(labels,low_sal)
low_img = low_img.astype(np.uint8)
left_sal = 1-self._MR_saliency__MR_saliency(aff,left)
left_img = self._MR_saliency__MR_fill_superpixel_with_saliency(labels,left_sal)
left_img = left_img.astype(np.uint8)
plt.subplot(3,2,1)
plt.title('orginal')
plt.axis('off')
plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
plt.subplot(3,2,2)
plt.title('up')
plt.axis('off')
plt.imshow(up_img,'gray')
plt.subplot(3,2,3)
plt.title('right')
plt.axis('off')
plt.imshow(right_img,'gray')
plt.subplot(3,2,4)
plt.title('low')
plt.axis('off')
plt.imshow(low_img,'gray')
plt.subplot(3,2,5)
plt.title('left')
plt.axis('off')
plt.imshow(left_img,'gray')
plt.subplot(3,2,6)
plt.title('integrated')
plt.axis('off')
saliency_map = MR_debuger().saliency(img).astype(np.uint8)
plt.imshow( saliency_map,'gray')
plt.show()
def m1():
#加载字典
mat = scipy.io.loadmat('./Dictionary/dictionary.mat')
Dl_dict = np.asarray(mat['Dl'], dtype='float64')
Dh_dict = np.asarray(mat['Dh'], dtype='float64')
#提取出L通道进行计算其他通道使用Bicubic进行计算
#src_rgb_img = io.imread('./Data/Child_input.png')
src_rgb_img = data.lena()
src = src_rgb_img[:src_rgb_img.shape[0] -
src_rgb_img.shape[0] % 3, :src_rgb_img.shape[1] -
src_rgb_img.shape[0] % 3, :]
src_rgb_img = src
#需要变称散的倍数才能处理
#src_rgb_img = bicubic_2d.bicubic2d(src,1/3.0)
src_rgb_img = bicubic_2d.bicubic2d(src_rgb_img, 1 / 3.0)
src_rgb_img = src_rgb_img[:src_rgb_img.shape[0] -
src_rgb_img.shape[0] % 3, :src_rgb_img.shape[1] -
src_rgb_img.shape[0] % 3, :]
#print src_rgb_img.shape
src_Lab_img = color.rgb2lab(src_rgb_img)
src_Lab_img_L = src_Lab_img[:, :, 0]
src_Lab_img_a = src_Lab_img[:, :, 1]
src_Lab_img_b = src_Lab_img[:, :, 2]
#不同的L通道来源
#dst_Lab_img_L = readSR()
#m = dst_Lab_img_L<0
#dst_Lab_img_L[m] = 0
#print np.max(dst_Lab_img_L),np.min(dst_Lab_img_L)
#dst_Lab_img_L = SR(src_Lab_img_L,Dh_dict,Dl_dict)
dst_Lab_img_L = imresize(src_Lab_img_L, 3.0, 'bicubic')
#dst_Lab_img_L= dst_Lab_img_L/255.0*(np.max(src_Lab_img_L)-np.min(src_Lab_img_L))+np.min(src_Lab_img_L)
dst_Lab_img_a = imresize(src_Lab_img_a, 3.0, 'bicubic')
dst_Lab_img_a = dst_Lab_img_a / 255.0 * (
np.max(src_Lab_img_a) - np.min(src_Lab_img_a)) + np.min(src_Lab_img_a)
dst_Lab_img_b = imresize(src_Lab_img_b, 3.0, 'bicubic')
dst_Lab_img_b = dst_Lab_img_b / 255.0 * (
np.max(src_Lab_img_b) - np.min(src_Lab_img_b)) + np.min(src_Lab_img_b)
img_lab = np.zeros((dst_Lab_img_L.shape[0], dst_Lab_img_L.shape[1], 3))
img_lab[:, :, 0] = dst_Lab_img_L
img_lab[:, :, 1] = dst_Lab_img_a
img_lab[:, :, 2] = dst_Lab_img_b
dst = color.lab2rgb(img_lab)
#src = src[:dst.shape[0],:dst.shape[1]]
#print np.mean(dst[10:-15,10:-15]*255-src[10:-15,10:-15])
#print psnr(dst[10:-15,10:-15]*255,src[10:-15,10:-15])
plt.imshow(dst, interpolation="none")
plt.show()
def MR_showsuperpixel(self,img=None):
if img == None:
img = cv2.cvtColor(lena(),cv2.COLOR_RGB2BGR)
img = self._MR_saliency__MR_readimg(img)
labels = self._MR_saliency__MR_superpixel(img)
plt.axis('off')
plt.imshow(mark_boundaries(img,labels))
plt.show()
def test_is_rgb():
color = data.lena()
gray = data.camera()
assert is_rgb(color)
assert not is_gray(color)
assert is_gray(gray)
assert not is_gray(color)
def test_is_rgb():
color = data.lena()
gray = data.camera()
assert is_rgb(color)
assert not is_gray(color)
assert is_gray(gray)
assert not is_gray(color)
def test_rotated_lena(self):
"""
The harris filter should yield the same results with an image and it's
rotation.
"""
im = img_as_float(data.lena().mean(axis=2))
results = harris(im)
im_rotated = im.T
results_rotated = harris(im_rotated)
assert (results[:, 0] == results_rotated[:, 1]).all()
def test_tv_denoise_float_result_range(self):
# lena image
lena = color.rgb2gray(data.lena())[:256, :256]
int_lena = np.multiply(lena, 255).astype(np.uint8)
assert np.max(int_lena) > 1
denoised_int_lena = filter.tv_denoise(int_lena, weight=60.0)
# test if the value range of output float data is within [0.0:1.0]
assert denoised_int_lena.dtype == np.float
assert np.max(denoised_int_lena) <= 1.0
assert np.min(denoised_int_lena) >= 0.0
def test_tv_denoise_float_result_range(self):
# lena image
lena = color.rgb2gray(data.lena())[:256, :256]
int_lena = np.multiply(lena, 255).astype(np.uint8)
assert np.max(int_lena) > 1
denoised_int_lena = filter.tv_denoise(int_lena, weight=60.0)
# test if the value range of output float data is within [0.0:1.0]
assert denoised_int_lena.dtype == np.float
assert np.max(denoised_int_lena) <= 1.0
assert np.min(denoised_int_lena) >= 0.0
def test_num_peaks():
"""For a bunch of different values of num_peaks, check that
peak_local_max returns exactly the right amount of peaks. Test
is run on Lena in order to produce a sufficient number of corners"""
lena_corners = corner_harris(rgb2gray(data.lena()))
for i in range(20):
n = np.random.random_integers(20)
results = peak_local_max(lena_corners, num_peaks=n)
assert (results.shape[0] == n)
def test_num_peaks():
"""For a bunch of different values of num_peaks, check that
peak_local_max returns exactly the right amount of peaks. Test
is run on Lena in order to produce a sufficient number of corners"""
lena_corners = corner_harris(data.lena())
for i in range(20):
n = np.random.random_integers(20)
results = peak_local_max(lena_corners, num_peaks=n)
assert (results.shape[0] == n)
def test_rotated_lena():
"""
The harris filter should yield the same results with an image and it's
rotation.
"""
im = img_as_float(data.lena().mean(axis=2))
results = harris(im)
im_rotated = im.T
results_rotated = harris(im_rotated)
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
def test_warp_identity():
lena = img_as_float(rgb2gray(data.lena()))
assert len(lena.shape) == 2
assert np.allclose(lena, warp(lena, AffineTransform(rotation=0)))
assert not np.allclose(lena, warp(lena, AffineTransform(rotation=0.1)))
rgb_lena = np.transpose(np.asarray([lena, np.zeros_like(lena), lena]),
(1, 2, 0))
warped_rgb_lena = warp(rgb_lena, AffineTransform(rotation=0.1))
assert np.allclose(rgb_lena, warp(rgb_lena, AffineTransform(rotation=0)))
assert not np.allclose(rgb_lena, warped_rgb_lena)
# assert no cross-talk between bands
assert np.all(0 == warped_rgb_lena[:, :, 1])
def m3():
#加载字典
mat = scipy.io.loadmat('./Dictionary/dictionary.mat')
Dl_dict = np.asarray(mat['Dl'], dtype='float64')
Dh_dict = np.asarray(mat['Dh'], dtype='float64')
#提取出L通道进行计算其他通道使用Bicubic进行计算
src = data.lena()
src_yCrCb = colormanage.rgb2ycbcr(src)
src_Ycbcr_img_Y = bicubic_2d.bicubic2d(src_yCrCb[:, :, 0], 1 / 3.0)
src_Ycbcr_img_Cb = bicubic_2d.bicubic2d(src_yCrCb[:, :, 1], 1 / 3.0)
src_Ycbcr_img_Cr = bicubic_2d.bicubic2d(src_yCrCb[:, :, 2], 1 / 3.0)
src_Ycbcr_img_Y = src_Ycbcr_img_Y[:src_Ycbcr_img_Y.shape[0] -
src_Ycbcr_img_Y.shape[0] %
3, :src_Ycbcr_img_Y.shape[1] -
src_Ycbcr_img_Y.shape[0] % 3]
src_Ycbcr_img_Cb = src_Ycbcr_img_Cb[:src_Ycbcr_img_Cb.shape[0] -
src_Ycbcr_img_Cb.shape[0] %
3, :src_Ycbcr_img_Cb.shape[1] -
src_Ycbcr_img_Cb.shape[0] % 3]
src_Ycbcr_img_Cr = src_Ycbcr_img_Cr[:src_Ycbcr_img_Cr.shape[0] -
src_Ycbcr_img_Cr.shape[0] %
3, :src_Ycbcr_img_Cr.shape[1] -
src_Ycbcr_img_Cr.shape[0] % 3]
#不同的L通道来源
#dst_Lab_img_L = readSR()
#dst_Lab_img_L = imresize(src_Lab_img_L,3.0,'bicubic')/2.55
dst_YCbCr_img_Y = SR(src_Ycbcr_img_Y, Dh_dict, Dl_dict)
dst_YCbCr_img_Cb = bicubic_2d.bicubic2d(src_Ycbcr_img_Cb, 3.0)
dst_YCbCr_img_Cr = bicubic_2d.bicubic2d(src_Ycbcr_img_Cr, 3.0)
img_YCbCr = np.zeros(
(dst_YCbCr_img_Y.shape[0], dst_YCbCr_img_Y.shape[1], 3))
img_YCbCr[:, :, 0] = dst_YCbCr_img_Y
img_YCbCr[:, :, 1] = dst_YCbCr_img_Cb
img_YCbCr[:, :, 2] = dst_YCbCr_img_Cr
dst = colormanage.ycbcr2rgb(img_YCbCr)
src = src[:dst.shape[0], :dst.shape[1]]
print psnr(dst[10:-15, 10:-15, 1], src[10:-15, 10:-15, 1])
print np.mean((dst[10:-15, 10:-15] - src[10:-15, 10:-15])**2)
print np.mean(np.abs(dst[10:-15, 10:-15] * 255 - src[10:-15, 10:-15]))
print psnr(dst[10:-15, 10:-15], src[10:-15, 10:-15])
plt.imshow(dst, interpolation="none")
plt.show()
def test_adapthist_grayscale():
"""Test a grayscale float image
"""
img = skimage.img_as_float(data.lena())
img = rgb2gray(img)
img = np.dstack((img, img, img))
adapted = exposure.equalize_adapthist(img, 10, 9, clip_limit=0.01, nbins=128)
assert_almost_equal = np.testing.assert_almost_equal
assert img.shape == adapted.shape
assert_almost_equal(peak_snr(img, adapted), 97.531, 3)
assert_almost_equal(norm_brightness_err(img, adapted), 0.0313, 3)
return data, adapted
def main():
"""Plot example augmentations for Lena and an image loaded from a file."""
# try on a lena image
print('Plotting float lena, preserve range')
image = data.lena().astype(float)
augmenter = ImageAugmenter(image.shape[0],
image.shape[1],
hflip=True,
vflip=True,
scale_to_percent=1.3,
scale_axis_equally=False,
rotation_deg=25,
shear_deg=10,
translation_x_px=5,
translation_y_px=5,
preserve_range=True)
augmenter.plot_image(image, 10)
print('Plotting chameleon')
# check loading of images from file and augmenting them
image = misc.imread("chameleon.png")
augmenter = ImageAugmenter(image.shape[1],
image.shape[0],
hflip=True,
vflip=True,
scale_to_percent=1.3,
scale_axis_equally=False,
rotation_deg=25,
shear_deg=10,
translation_x_px=5,
translation_y_px=5)
augmenter.plot_image(image / 255.0, 50)
print('Plotting chameleon with channel_is_first_axis=True')
# move the channel from index 2 (3rd position) to index 0 (1st position)
# so (y, x, rgb) becomes (rgb, y, x)
# try if it still works
image = np.rollaxis(image, 2, 0)
augmenter = ImageAugmenter(image.shape[2],
image.shape[1],
hflip=True,
vflip=True,
scale_to_percent=1.3,
scale_axis_equally=False,
rotation_deg=25,
shear_deg=10,
translation_x_px=5,
translation_y_px=5,
channel_is_first_axis=True)
augmenter.plot_image(image, 50)
def test_adapthist_grayscale():
'''Test a grayscale float image
'''
img = skimage.img_as_float(data.lena())
img = rgb2gray(img)
img = np.dstack((img, img, img))
adapted = exposure.equalize_adapthist(img, 10, 9, clip_limit=0.01,
nbins=128)
assert_almost_equal = np.testing.assert_almost_equal
assert img.shape == adapted.shape
assert peak_snr(img, adapted) > 95.0
assert norm_brightness_err(img, adapted) < 0.05
return data, adapted
def test_collection_viewer():
img = data.lena()
img_collection = tuple(pyramid_gaussian(img))
view = CollectionViewer(img_collection)
make_key_event(48)
view.update_index('', 2),
assert_equal(view.image, img_collection[2])
view.keyPressEvent(make_key_event(53))
assert_equal(view.image, img_collection[5])
view._format_coord(10, 10)
def test_adapthist_color():
'''Test an RGB color uint16 image
'''
img = skimage.img_as_uint(data.lena())
adapted = exposure.equalize_adapthist(img, clip_limit=0.01)
assert_almost_equal = np.testing.assert_almost_equal
assert adapted.min() == 0
assert adapted.max() == 1.0
assert img.shape == adapted.shape
full_scale = skimage.exposure.rescale_intensity(img)
assert_almost_equal(peak_snr(full_scale, adapted), 102.940, 3)
assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.0110, 3)
return data, adapted
def test_adapthist_color():
'''Test an RGB color uint16 image
'''
img = skimage.img_as_uint(data.lena())
adapted = exposure.equalize_adapthist(img, clip_limit=0.01)
assert_almost_equal = np.testing.assert_almost_equal
assert adapted.min() == 0
assert adapted.max() == 1.0
assert img.shape == adapted.shape
full_scale = skimage.exposure.rescale_intensity(img)
assert peak_snr(img, adapted) > 95.0
assert norm_brightness_err(img, adapted) < 0.05
return data, adapted
def test_main():
src_rgb_img = data.lena()
src_YCrCb_img = rgb2ycbcr(src_rgb_img)
p = ycbcr2rgb(src_YCrCb_img)
c =np.asarray(p,dtype='uint8')
plt.imshow(c)
print src_rgb_img.dtype,src_rgb_img.shape,np.max(src_rgb_img),np.min(src_rgb_img)
print c.dtype,c.shape,np.mean((c-src_rgb_img)**2),np.max(c),np.min(c)
plt.show()
def test_collection_viewer():
img = data.lena()
img_collection = tuple(pyramid_gaussian(img))
view = CollectionViewer(img_collection)
make_key_event(48)
view.update_index('', 2),
assert_equal(view.image, img_collection[2])
view.keyPressEvent(make_key_event(53))
assert_equal(view.image, img_collection[5])
view._format_coord(10, 10)
def test_adapthist_alpha():
"""Test an RGBA color image
"""
img = skimage.img_as_float(data.lena())
alpha = np.ones((img.shape[0], img.shape[1]), dtype=float)
img = np.dstack((img, alpha))
adapted = exposure.equalize_adapthist(img)
assert adapted.shape != img.shape
img = img[:, :, :3]
full_scale = skimage.exposure.rescale_intensity(img)
assert img.shape == adapted.shape
assert_almost_equal = np.testing.assert_almost_equal
assert_almost_equal(peak_snr(full_scale, adapted), 106.86, 2)
assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.0509, 3)
def test_descs_shape():
img = img_as_float(data.lena()[:256, :256].mean(axis=2))
radius = 20
step = 8
descs = daisy(img, radius=radius, step=step)
assert (descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step)))
assert (descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step)))
img = img[:-1, :-2]
radius = 5
step = 3
descs = daisy(img, radius=radius, step=step)
assert (descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step)))
assert (descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step)))
def test_adapthist_alpha():
"""Test an RGBA color image
"""
img = skimage.img_as_float(data.lena())
alpha = np.ones((img.shape[0], img.shape[1]), dtype=float)
img = np.dstack((img, alpha))
adapted = exposure.equalize_adapthist(img)
assert adapted.shape != img.shape
img = img[:, :, :3]
full_scale = skimage.exposure.rescale_intensity(img)
assert img.shape == adapted.shape
assert_almost_equal = np.testing.assert_almost_equal
assert_almost_equal(peak_snr(full_scale, adapted), 106.86, 2)
assert_almost_equal(norm_brightness_err(full_scale, adapted), 0.0509, 3)
def test_descs_shape():
img = img_as_float(data.lena()[:256, :256].mean(axis=2))
radius = 20
step = 8
descs = daisy(img, radius=radius, step=step)
assert(descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step)))
assert(descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step)))
img = img[:-1, :-2]
radius = 5
step = 3
descs = daisy(img, radius=radius, step=step)
assert(descs.shape[0] == ceil((img.shape[0] - radius * 2) / float(step)))
assert(descs.shape[1] == ceil((img.shape[1] - radius * 2) / float(step)))
def test_corner_fast_lena():
img = rgb2gray(data.lena())
expected = np.array([[ 67, 157],
[204, 261],
[247, 146],
[269, 111],
[318, 158],
[386, 73],
[413, 70],
[435, 180],
[455, 177],
[461, 160]])
actual = corner_peaks(corner_fast(img, 12, 0.3))
assert_array_equal(actual, expected)
def test_daisy_desc_dims():
img = img_as_float(data.lena()[:128, :128].mean(axis=2))
rings = 2
histograms = 4
orientations = 3
descs = daisy(img, rings=rings, histograms=histograms,
orientations=orientations)
assert(descs.shape[2] == (rings * histograms + 1) * orientations)
rings = 4
histograms = 5
orientations = 13
descs = daisy(img, rings=rings, histograms=histograms,
orientations=orientations)
assert(descs.shape[2] == (rings * histograms + 1) * orientations)