def test_hough_peaks_dist():
img = np.zeros((100, 100), dtype=np.bool_)
img[:, 30] = True
img[:, 40] = True
hspace, angles, dists = tf.hough(img)
assert len(tf.hough_peaks(hspace, angles, dists, min_distance=5)[0]) == 2
assert len(tf.hough_peaks(hspace, angles, dists, min_distance=15)[0]) == 1
def test_hough_peaks_dist():
img = np.zeros((100, 100), dtype=np.bool_)
img[:, 30] = True
img[:, 40] = True
hspace, angles, dists = tf.hough_line(img)
assert len(tf.hough_peaks(hspace, angles, dists, min_distance=5)[0]) == 2
assert len(tf.hough_peaks(hspace, angles, dists, min_distance=15)[0]) == 1
def test_hough_peaks_num():
img = np.zeros((100, 100), dtype=np.bool_)
img[:, 30] = True
img[:, 40] = True
hspace, angles, dists = tf.hough_line(img)
assert len(tf.hough_peaks(hspace, angles, dists, min_distance=0,
min_angle=0, num_peaks=1)[0]) == 1
def test_hough_peaks_angle():
img = np.zeros((100, 100), dtype=np.bool_)
img[:, 0] = True
img[0, :] = True
hspace, angles, dists = tf.hough(img)
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
theta = np.linspace(0, np.pi, 100)
hspace, angles, dists = tf.hough(img, theta)
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
theta = np.linspace(np.pi / 3, 4. / 3 * np.pi, 100)
hspace, angles, dists = tf.hough(img, theta)
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
def test_hough_peaks_angle():
img = np.zeros((100, 100), dtype=np.bool_)
img[:, 0] = True
img[0, :] = True
hspace, angles, dists = tf.hough_line(img)
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
theta = np.linspace(0, np.pi, 100)
hspace, angles, dists = tf.hough_line(img, theta)
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
theta = np.linspace(np.pi / 3, 4. / 3 * np.pi, 100)
hspace, angles, dists = tf.hough_line(img, theta)
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=45)[0]) == 2
assert len(tf.hough_peaks(hspace, angles, dists, min_angle=90)[0]) == 1
plt.imshow(image, cmap=plt.cm.gray)
plt.title('Input image')
plt.subplot(132)
plt.imshow(np.log(1 + h),
extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]),
d[-1], d[0]],
cmap=plt.cm.gray, aspect=1/1.5)
plt.title('Hough transform')
plt.xlabel('Angles (degrees)')
plt.ylabel('Distance (pixels)')
plt.subplot(133)
plt.imshow(image, cmap=plt.cm.gray)
rows, cols = image.shape
for _, angle, dist in zip(*hough_peaks(h, theta, d)):
y0 = (dist - 0 * np.cos(angle)) / np.sin(angle)
y1 = (dist - cols * np.cos(angle)) / np.sin(angle)
plt.plot((0, cols), (y0, y1), '-r')
plt.axis((0, cols, rows, 0))
plt.title('Detected lines')
# Line finding, using the Probabilistic Hough Transform
image = data.camera()
edges = canny(image, 2, 1, 25)
lines = probabilistic_hough(edges, threshold=10, line_length=5, line_gap=3)
plt.figure(figsize=(8, 3))
plt.subplot(131)
plt.title('Input image')
plt.subplot(132)
plt.imshow(np.log(1 + h),
extent=[np.rad2deg(theta[-1]),
np.rad2deg(theta[0]), d[-1], d[0]],
cmap=plt.cm.gray,
aspect=1 / 1.5)
plt.title('Hough transform')
plt.xlabel('Angles (degrees)')
plt.ylabel('Distance (pixels)')
plt.subplot(133)
plt.imshow(image, cmap=plt.cm.gray)
rows, cols = image.shape
for _, angle, dist in zip(*hough_peaks(h, theta, d)):
y0 = (dist - 0 * np.cos(angle)) / np.sin(angle)
y1 = (dist - cols * np.cos(angle)) / np.sin(angle)
plt.plot((0, cols), (y0, y1), '-r')
plt.axis((0, cols, rows, 0))
plt.title('Detected lines')
# Line finding, using the Probabilistic Hough Transform
image = data.camera()
edges = canny(image, 2, 1, 25)
lines = probabilistic_hough(edges, threshold=10, line_length=5, line_gap=3)
plt.figure(figsize=(8, 3))
plt.subplot(131)
