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Uniform-Circular-LBP.py
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Uniform-Circular-LBP.py
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import numpy as np
import cv2
from matplotlib import pyplot as plt
import math
def bilinear_interpolation(x, y, img):
x1, y1 = int(r), int(c)
x2, y2 = math.ceil(r), math.ceil(c)
r1 = (x2 - x) / (x2 - x1) * get_pixel_else_0(img, x1, y1) + (x - x1) / (x2 - x1) * get_pixel_else_0(img, x2, y1)
r2 = (x2 - x) / (x2 - x1) * get_pixel_else_0(img, x1, y2) + (x - x1) / (x2 - x1) * get_pixel_else_0(img, x2, y2)
return (y2 - y) / (y2 - y1) * r1 + (y - y1) / (y2 - y1) * r2
def thresholded(center, pixels):
out = []
for a in pixels:
if a >= center:
out.append(1)
else:
out.append(0)
return out
def get_pixel_else_0(image, idx, idy):
if idx < int(len(image)) - 1 and idy < len(image[0]):
return image[idx,idy]
else:
return 0
def find_variations(pixel_values):
prev = pixel_values[-1]
t = 0
for p in range(0, len(pixel_values)):
cur = pixel_values[p]
if cur != prev:
t += 1
prev = cur
return t
img = cv2.imread('aneesh.jpeg', 0)
transformed_img = cv2.imread('aneesh.jpeg', 0)
unassigned = []
pixel_values = set()
P = 8 # number of pixels
R = 1 # radius
variating_blocks = 0
for x in range(0, len(img)):
for y in range(0, len(img[0])):
center = img[x,y]
pixels = []
for point in range(1, P + 1):
r = x + R * math.cos(2 * math.pi * point / P)
c = y - R * math.sin(2 * math.pi * point / P)
if r < 0 or c < 0:
pixels.append(0)
continue
if int(r) == r:
if int(c) != c:
c1 = int(c)
c2 = math.ceil(c)
w1 = (c2 - c) / (c2 - c1)
w2 = (c - c1) / (c2 - c1)
pixels.append(int((w1 * get_pixel_else_0(img, int(r), int(c)) + \
w2 * get_pixel_else_0(img, int(r), math.ceil(c))) / (w1 + w2)))
else:
pixels.append(get_pixel_else_0(img, int(r), int(c)))
elif int(c) == c:
r1 = int(r)
r2 = math.ceil(r)
w1 = (r2 - r) / (r2 - r1)
w2 = (r - r1) / (r2 - r1)
pixels.append((w1 * get_pixel_else_0(img, int(r), int(c)) + \
w2 * get_pixel_else_0(img, math.ceil(r), int(c))) / (w1 + w2))
else:
pixels.append(bilinear_interpolation(r, c, img))
values = thresholded(center, pixels)
variations = find_variations(values)
if variations <= 2:
res = 0
variating_blocks += 1
for a in range(0, len(values)):
res += values[a] * 2 ** a
transformed_img.itemset((x,y), res)
pixel_values.add(res)
else:
unassigned.append((x,y))
print x
unassigned_value = len(pixel_values)
pixel_values = sorted(pixel_values)
no_of_pixel_values = len(pixel_values)
trans_p1_u2 = {}
for p in range(0, len(pixel_values)):
trans_p1_u2[pixel_values[p]] = p
for r in range(0, len(transformed_img)):
for c in range(0, len(transformed_img[0])):
if (r,c) in unassigned:
transformed_img.itemset((r,c), unassigned_value)
else:
p1 = transformed_img[r,c]
transformed_img.itemset((r,c), trans_p1_u2[p1])
cv2.imshow('image', img)
cv2.imshow('thresholded image', transformed_img)
hist,bins = np.histogram(transformed_img.flatten(),no_of_pixel_values + 1,[0,no_of_pixel_values])
cdf = hist.cumsum()
cdf_normalized = cdf * hist.max()/ cdf.max()
plt.plot(cdf_normalized, color = 'b')
plt.show()
plt.hist(transformed_img.flatten(),no_of_pixel_values,[0,no_of_pixel_values], color = 'b')
plt.xlim([0,no_of_pixel_values])
plt.legend(('cdf','histogram'), loc = 'upper left')
plt.show()
cv2.waitKey(0)
cv2.destroyAllWindows()