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wls_filter.py
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wls_filter.py
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import numpy as np
from scipy import sparse
from scipy.sparse.linalg import spsolve
import scipy
import cv2
# img format: lab for example al bl
# guide img format: lab
# height x width x channel,
def wls_filter_test(img, guide, alpha=1.2, Lambda=0.032):
epsilon = 0.0001
height = guide.shape[0]
width = guide.shape[1]
n = height * width
grayImgF_ = 1.0*cv2.cvtColor(guide, cv2.COLOR_RGB2GRAY)/255.0
grayImgF = grayImgF_
gradWeightX = np.zeros_like(grayImgF, dtype='float')
gradWeightY = np.zeros_like(grayImgF, dtype='float')
for y in range(height-1):
for x in range(width-1):
if x + 1 < width:
gx = grayImgF[y, x + 1] - grayImgF[y, x]
gradWeightX[y, x] = Lambda / (np.power(np.abs(gx), alpha) + epsilon)
if y + 1 < height:
gy = grayImgF[y + 1, x] - grayImgF[y, x]
gradWeightY[y, x] = Lambda / (np.power(np.abs(gy), alpha) + epsilon)
cv2.imwrite('grad_x.png', (gradWeightX*255.0).astype('uint8'))
A = sparse.lil_matrix((n, n))
bs0 = np.zeros(n, dtype='float')
bs1 = np.zeros(n, dtype='float')
bs2 = np.zeros(n, dtype='float')
xs0 = np.zeros(n, dtype='float')
xs1 = np.zeros(n, dtype='float')
xs2 = np.zeros(n, dtype='float')
for y in range(height):
for x in range(width):
a = np.zeros(5, dtype='float')
ii = y * width + x
if y - 1 >= 0:
gyw = gradWeightY[y-1, x]
a[2] += 1.0 * gyw
a[0] -= 1.0 * gyw
A[ii, ii- width] = a[0]
if x - 1 >= 0:
gxw = gradWeightX[y, x-1]
a[2] += 1.0 * gxw
a[1] -= 1.0 * gxw
A[ii, ii - 1] = a[1]
if x + 1 < width:
gxw = gradWeightX[y, x]
a[2] += 1.0 * gxw
a[3] -= 1.0 * gxw
A[ii, ii + 1] = a[3]
if y + 1 < height:
gyw = gradWeightY[y, x]
a[2] += 1.0 * gyw
a[4] -= 1.0 * gyw
A[ii, ii + width] = a[4]
a[2] += 1.0
A[ii, ii] = a[2]
r, g, b = img[y, x, :]
xs0[ii] = 0.0
xs1[ii] = 0.0
xs2[ii] = 0.0
bs0[ii] = float(r)/255
bs1[ii] = float(g)/255
bs2[ii] = float(b)/255
xs0 = np.clip(spsolve(A, bs0), 0, 255)
xs1 = np.clip(spsolve(A, bs1), 0, 255)
xs2 = np.clip(spsolve(A, bs2), 0, 255)
c0 = np.reshape(xs0, (height, width))
c1 = np.reshape(xs1, (height, width))
c2 = np.reshape(xs2, (height, width))
print(np.max(cv2.merge((c0, c1, c2))))
result = (cv2.merge((c0, c1, c2))*255).astype('uint8')
return result
#cv2.imwrite('result.png', result)
#return cv2.merge((c0, c1, c2))/255.0
def each_channel(img_channel, guide_channel, alpha=1.2, Lambda=1.0):
epsilon = 0.0001
grayImgF = guide_channel
height = grayImgF.shape[0]
width = grayImgF.shape[1]
n = height * width
gradWeightX = np.zeros_like(grayImgF, dtype='float')
gradWeightY = np.zeros_like(grayImgF, dtype='float')
for y in range(height - 1):
for x in range(width - 1):
if x + 1 < width:
gx = grayImgF[y, x + 1] - grayImgF[y, x]
gradWeightX[y, x] = Lambda / (np.power(np.abs(gx), alpha) + epsilon)
if y + 1 < height:
gy = grayImgF[y + 1, x] - grayImgF[y, x]
gradWeightY[y, x] = Lambda / (np.power(np.abs(gy), alpha) + epsilon)
cv2.imwrite('grad_x.png', (gradWeightX * 255.0).astype('uint8'))
A = sparse.lil_matrix((n, n))
bs0 = np.zeros(n, dtype='float')
bs1 = np.zeros(n, dtype='float')
bs2 = np.zeros(n, dtype='float')
xs0 = np.zeros(n, dtype='float')
xs1 = np.zeros(n, dtype='float')
xs2 = np.zeros(n, dtype='float')
for y in range(height):
for x in range(width):
a = np.zeros(5, dtype='float')
ii = y * width + x
if y - 1 >= 0:
gyw = gradWeightY[y - 1, x]
a[2] += 1.0 * gyw
a[0] -= 1.0 * gyw
A[ii, ii - width] = a[0]
if x - 1 >= 0:
gxw = gradWeightX[y, x - 1]
a[2] += 1.0 * gxw
a[1] -= 1.0 * gxw
A[ii, ii - 1] = a[1]
if x + 1 < width:
gxw = gradWeightX[y, x]
a[2] += 1.0 * gxw
a[3] -= 1.0 * gxw
A[ii, ii + 1] = a[3]
if y + 1 < height:
gyw = gradWeightY[y, x]
a[2] += 1.0 * gyw
a[4] -= 1.0 * gyw
A[ii, ii + width] = a[4]
a[2] += 1.0
A[ii, ii] = a[2]
r = img_channel[y, x]
xs0[ii] = 0.0
bs0[ii] = float(r)
xs0 = spsolve(A, bs0)
c0 = np.reshape(xs0, (height, width))
return c0
# guide lab 0~1
def wls_filter(img, guide, alpha=1.2, Lambda=0.032):
grayImgF_ = guide#1.0*cv2.cvtColor(guide, cv2.COLOR_RGB2LAB)/255.0
l, a, b = cv2.split(grayImgF_)
c0 = each_channel(img[:, :, 0], l)
c1 = each_channel(img[:, :, 1], l)
c2 = each_channel(img[:, :, 2], l)
print(np.max(cv2.merge((c0, c1, c2))))
#result = (cv2.merge((c0, c1, c2))).astype('uint8')
#cv2.imwrite('result.png', result)
return cv2.merge((c0, c1, c2))
if __name__ == '__main__':
img1 = 1.0 * cv2.imread('final_result7001400.png')/255.0
img2 = 1.0 * cv2.imread('v2_2272.jpeg')/255.0
result = wls_filter(img1, img2, 1.2, 1) + img2 - wls_filter(img2, img2, 1.2, 1)
result = np.clip(result, 0, 1)
diff = img1 - result
cv2.imwrite('result2.png', (result*255.0).astype('uint8'))
cv2.imwrite('diff.png', (diff).astype('uint8'))