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_rotated_img():
"""
The harris filter should yield the same results with an image and it's
rotation.
"""
im = img_as_float(data.astronaut().mean(axis=2))
im_rotated = im.T
# Moravec
results = peak_local_max(corner_moravec(im),
min_distance=10, threshold_rel=0)
results_rotated = peak_local_max(corner_moravec(im_rotated),
min_distance=10, threshold_rel=0)
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),
min_distance=10, threshold_rel=0)
results_rotated = peak_local_max(corner_harris(im_rotated),
min_distance=10, threshold_rel=0)
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),
min_distance=10, threshold_rel=0)
results_rotated = peak_local_max(corner_shi_tomasi(im_rotated),
min_distance=10, threshold_rel=0)
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
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_rotated_img():
"""
The harris filter should yield the same results with an image and it's
rotation.
"""
im = img_as_float(data.astronaut().mean(axis=2))
im_rotated = im.T
# Moravec
results = peak_local_max(corner_moravec(im),
min_distance=10, threshold_rel=0)
results_rotated = peak_local_max(corner_moravec(im_rotated),
min_distance=10, threshold_rel=0)
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),
min_distance=10, threshold_rel=0)
results_rotated = peak_local_max(corner_harris(im_rotated),
min_distance=10, threshold_rel=0)
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),
min_distance=10, threshold_rel=0)
results_rotated = peak_local_max(corner_shi_tomasi(im_rotated),
min_distance=10, threshold_rel=0)
assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
def test_rotated_img():
"""
The harris filter should yield the same results with an image and it's
rotation.
"""
im = img_as_float(data.astronaut().mean(axis=2))
im_rotated = im.T
# Moravec
results = np.nonzero(corner_moravec(im))
results_rotated = np.nonzero(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 = np.nonzero(corner_harris(im))
results_rotated = np.nonzero(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 = np.nonzero(corner_shi_tomasi(im))
results_rotated = np.nonzero(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_noisy_square_image():
im = np.zeros((50, 50)).astype(float)
im[:25, :25] = 1.
np.random.seed(seed=1234)
im = im + np.random.uniform(size=im.shape) * .2
# Moravec
results = peak_local_max(corner_moravec(im),
min_distance=10,
threshold_rel=0)
# undefined number of interest points
assert results.any()
# Harris
results = peak_local_max(corner_harris(im, method='k'),
min_distance=10,
threshold_rel=0)
assert len(results) == 1
results = peak_local_max(corner_harris(im, method='eps'),
min_distance=10,
threshold_rel=0)
assert len(results) == 1
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im, sigma=1.5),
min_distance=10,
threshold_rel=0)
assert len(results) == 1
def test_square_image():
im = np.zeros((50, 50)).astype(float)
im[:25, :25] = 1.
# Moravec
results = peak_local_max(corner_moravec(im),
min_distance=10,
threshold_rel=0)
# interest points along edge
assert len(results) == 57
# Harris
results = peak_local_max(corner_harris(im, method='k'),
min_distance=10,
threshold_rel=0)
# interest at corner
assert len(results) == 1
results = peak_local_max(corner_harris(im, method='eps'),
min_distance=10,
threshold_rel=0)
# interest at corner
assert len(results) == 1
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im),
min_distance=10,
threshold_rel=0)
# interest at corner
assert len(results) == 1
def test_square_image():
im = np.zeros((50, 50)).astype(float)
im[:25, :25] = 1.
# Moravec
results = peak_local_max(corner_moravec(im),
min_distance=10, threshold_rel=0)
# interest points along edge
assert len(results) == 57
# Harris
results = peak_local_max(corner_harris(im, method='k'),
min_distance=10, threshold_rel=0)
# interest at corner
assert len(results) == 1
results = peak_local_max(corner_harris(im, method='eps'),
min_distance=10, threshold_rel=0)
# interest at corner
assert len(results) == 1
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im),
min_distance=10, threshold_rel=0)
# interest at corner
assert len(results) == 1
def read_image(self, image_name, size=None):
options = self.get_options()
if size:
im = np.array(Image.open(image_name).convert("L").resize(size))
else:
im = np.array(Image.open(image_name).convert("L"))
options["image"] = im
feature = corner_moravec(**options)
# plt.imshow(feature)
# plt.show()
return feature.reshape((1, feature.shape[0] * feature.shape[1]))[0]
def test_noisy_square_image():
im = np.zeros((50, 50)).astype(float)
im[:25, :25] = 1.
im = im + np.random.uniform(size=im.shape) * .2
# Moravec
results = peak_local_max(corner_moravec(im))
# undefined number of interest points
assert results.any()
# Harris
results = peak_local_max(corner_harris(im, sigma=1.5))
assert len(results) == 1
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im, sigma=1.5))
assert len(results) == 1
def test_square_image():
im = np.zeros((50, 50)).astype(float)
im[:25, :25] = 1.
# Moravec
results = peak_local_max(corner_moravec(im))
# interest points along edge
assert len(results) == 57
# Harris
results = peak_local_max(corner_harris(im))
# interest at corner
assert len(results) == 1
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im))
# interest at corner
assert len(results) == 1
def test_square_image():
im = np.zeros((50, 50)).astype(float)
im[:25, :25] = 1.
# Moravec
results = peak_local_max(corner_moravec(im))
# interest points along edge
assert len(results) == 57
# Harris
results = peak_local_max(corner_harris(im))
# interest at corner
assert len(results) == 1
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im))
# interest at corner
assert len(results) == 1
def test_noisy_square_image():
im = np.zeros((50, 50)).astype(float)
im[:25, :25] = 1.
np.random.seed(seed=1234)
im = im + np.random.uniform(size=im.shape) * .2
# Moravec
results = peak_local_max(corner_moravec(im),
min_distance=10, threshold_rel=0)
# undefined number of interest points
assert results.any()
# Harris
results = peak_local_max(corner_harris(im, method='k'),
min_distance=10, threshold_rel=0)
assert len(results) == 1
results = peak_local_max(corner_harris(im, method='eps'),
min_distance=10, threshold_rel=0)
assert len(results) == 1
# Shi-Tomasi
results = peak_local_max(corner_shi_tomasi(im, sigma=1.5),
min_distance=10, threshold_rel=0)
assert len(results) == 1
def run(self, ips, snap, img, para = None): cimg = feature.corner_moravec(img, window_size=para['size']) pts = feature.corner_peaks(cimg, min_distance=1) ips.roi = ROI([Points(pts[:,::-1])])
def run(self, ips, snap, img, para = None): cimg = feature.corner_moravec(img, window_size=para['size']) pts = feature.corner_peaks(cimg, min_distance=1) self.ips.roi = PointRoi([tuple(i[::-1]) for i in pts])