def region_print(target, code, y0, x0, y1, x1):
img_target = draw_multiblock_lbp(target,
y0,
x0,
y1,
x1,
lbp_code=code,
alpha=0.5)
plt.ion()
plt.imshow(img_target)
#plt.draw()
#plt.show()
plt.pause(0.05)
from __future__ import print_function from skimage.feature import multiblock_lbp, draw_multiblock_lbp import numpy as np from numpy.testing import assert_equal from skimage.transform import integral_image from matplotlib import pyplot as plt # Create test matrix where first and fifth rectangles starting # from top left clockwise have greater value than the central one. test_img = np.zeros((9, 9), dtype='uint8') test_img[3:6, 3:6] = 1 test_img[:3, :3] = 50 test_img[6:, 6:] = 50 print(test_img) # First and fifth bits should be filled. This correct value will # be compared to the computed one. correct_answer = 0b10001000 int_img = integral_image(test_img) print(int_img) lbp_code = multiblock_lbp(int_img, 0, 0, 3, 3) print(lbp_code) img = draw_multiblock_lbp(test_img, 0, 0, 90, 90, lbp_code=lbp_code, alpha=0.5) plt.imshow(img, interpolation='nearest') plt.show()
assert_equal(correct_answer, lbp_code)
######################################################################
# Now let's apply the operator to a real image and see how the visualization
# works.
from skimage import data
from matplotlib import pyplot as plt
from skimage.feature import draw_multiblock_lbp
test_img = data.coins()
int_img = integral_image(test_img)
lbp_code = multiblock_lbp(int_img, 0, 0, 90, 90)
img = draw_multiblock_lbp(test_img, 0, 0, 90, 90,
lbp_code=lbp_code, alpha=0.5)
plt.imshow(img, interpolation='nearest')
plt.show()
######################################################################
# On the above plot we see the result of computing a MB-LBP and visualization
# of the computed feature. The rectangles that have less intensities' sum
# than the central rectangle are marked in cyan. The ones that have higher
# intensity values are marked in white. The central rectangle is left
# untouched.