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All Samples(6)  |  Call(3)  |  Derive(0)  |  Import(3)

src/s/c/scikits-image-0.7.1/doc/gh-pages/0.3/_downloads/plot_hough_transform.py   scikits-image(Download)
from scikits.image.transform import hough, probabilistic_hough
from scikits.image.filter import canny
from scikits.image import data
image[idx, idx] = 255
h, theta, d = hough(image)
plt.figure(figsize=(12, 5))

src/s/c/scikits-image-0.7.1/doc/gh-pages/0.3/plots/hough_tf.py   scikits-image(Download)
import numpy as np
import matplotlib.pyplot as plt
from scikits.image.transform import hough
img += np.random.random(img.shape) > 0.95
out, angles, d = hough(img)
plt.subplot(1, 2, 1)

src/e/i/eitwave-HEAD/test_hough_data.py   eitwave(Download)
from sim import wave2d
from visualize import visualize
from scikits.image.transform import hough
from scikits.image.morphology import greyscale_dilate
import numpy as np
    # 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