def test_hough_angles():
img = np.zeros((10, 10))
img[0, 0] = 1
out, angles, d = tf.hough(img, np.linspace(0, 360, 10))
assert_equal(len(angles), 10)
def ridgeDetection(image):
from scikits.image import transform
out, angles, d = transform.hough(errors)
pylab.figure()
pylab.imshow(out, cmap=pylab.cm.bone)
pylab.xlabel('Angle (degree)')
pylab.ylabel('Distance %d (pixel)' % d[0])
def test_hough():
# Generate a test image
img = np.zeros((100, 100), dtype=int)
for i in range(25, 75):
img[100 - i, i] = 1
out, angles, d = tf.hough(img)
y, x = np.where(out == out.max())
dist = d[y[0]]
theta = angles[x[0]]
assert_equal(dist > 70, dist < 72)
assert_equal(theta > 0.78, theta < 0.79)
import numpy as np
import matplotlib.pyplot as plt
# Construct test image
image = np.zeros((100, 100))
# Classic straight-line Hough transform
idx = np.arange(25, 75)
image[idx[::-1], idx] = 255
image[idx, idx] = 255
h, theta, d = hough(image)
plt.figure(figsize=(12, 5))
plt.subplot(121)
plt.imshow(image, cmap=plt.cm.gray)
plt.title('Input image')
plt.subplot(122)
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)')
diffs = []
diffmap = 255 * (abs(input_maps[1] - input_maps[0]) > diffthresh)
for i in range(0, ndiff):
# difference map
diffmap = 255 * (abs(input_maps[i + 1] - input_maps[i]) > diffthresh)
# keep
diffs.append(diffmap)
# extract the image
img = diffmap
# Perform the hough transform on each of the difference maps
transform, theta, d = hough(img)
# Filter the hough transform results and find the best lines
# in the data
indices = (transform > votethresh).nonzero()
distances = d[indices[0]]
theta = theta[indices[1]]
n = len(indices[1])
print("Found " + str(n) + " lines.")
# Perform the inverse transform to get a series of rectangular
# images that show where the wavefront is.
invTransform = sunpy.map.BaseMap(input_maps[i + 1])
invTransform.data = np.zeros(imgShape)
for i in range(0, n):
nextLine = htLine(distances[i], theta[i], np.zeros(shape=imgShape))
diffs = []
temp = 255*(abs(input_maps[14] - input_maps[13]) > diffthresh)
for i in range(0,ndiff):
# difference map
diffmap = 255*(abs(input_maps[i+1] - input_maps[i]) > diffthresh)
# keep
diffs.append(diffmap)
# extract the image
img = diffmap
# Perform the hough transform on each of the difference maps
transform,theta,d = hough(img)
# Filter the hough transform results and find the best lines
# in the data
indices = (transform >votethresh).nonzero()
distances = d[indices[0]]
theta = theta[indices[1]]
n =len(indices[1])
print("Found " + str(n) + " lines.")
# Perform the inverse transform to get a series of rectangular
# images that show where the wavefront is.
invTransform = sunpy.map.BaseMap(input_maps[i+1])
invTransform.data = np.zeros(imgShape)
for i in range(0,n):
nextLine = htLine( distances[i],theta[i], np.zeros(shape=imgShape) )
import numpy as np
import matplotlib.pyplot as plt
from scikits.image.transform import hough
img = np.zeros((100, 150), dtype=bool)
img[30, :] = 1
img[:, 65] = 1
img[35:45, 35:50] = 1
for i in range(90):
img[i, i] = 1
img += np.random.random(img.shape) > 0.95
out, angles, d = hough(img)
plt.subplot(1, 2, 1)
plt.imshow(img, cmap=plt.cm.gray)
plt.title('Input image')
plt.subplot(1, 2, 2)
plt.imshow(out, cmap=plt.cm.bone,
extent=(d[0], d[-1],
np.rad2deg(angles[0]), np.rad2deg(angles[-1])))
plt.title('Hough transform')
plt.xlabel('Angle (degree)')
plt.ylabel('Distance (pixel)')
plt.subplots_adjust(wspace=0.4)
plt.show()
