def test_probabilistic_hough():
# Generate a test image
img = np.zeros((100, 100), dtype=int)
for i in range(25, 75):
img[100 - i, i] = 100
img[i, i] = 100
# decrease default theta sampling because similar orientations may confuse
# as mentioned in article of Galambos et al
theta = np.linspace(0, np.pi, 45)
lines = transform.probabilistic_hough_line(img,
threshold=10,
line_length=10,
line_gap=1,
theta=theta)
# sort the lines according to the x-axis
sorted_lines = []
for line in lines:
line = list(line)
line.sort(key=lambda x: x[0])
sorted_lines.append(line)
assert ([(25, 75), (74, 26)] in sorted_lines)
assert ([(25, 25), (74, 74)] in sorted_lines)
# Execute with default theta
transform.probabilistic_hough_line(img, line_length=10, line_gap=3)
def test_probabilistic_hough():
# Generate a test image
img = np.zeros((100, 100), dtype=int)
for i in range(25, 75):
img[100 - i, i] = 100
img[i, i] = 100
# decrease default theta sampling because similar orientations may confuse
# as mentioned in article of Galambos et al
theta = np.linspace(0, np.pi, 45)
lines = tf.probabilistic_hough_line(img, threshold=10, line_length=10,
line_gap=1, theta=theta)
# sort the lines according to the x-axis
sorted_lines = []
for line in lines:
line = list(line)
line.sort(key=lambda x: x[0])
sorted_lines.append(line)
assert([(25, 75), (74, 26)] in sorted_lines)
assert([(25, 25), (74, 74)] in sorted_lines)
