Color Transfer 实验
April 07, 2016
Color Transfer 是基于一阶统计特性的颜色迁移，可以将参考图像的颜色传输到目标图像。
只利用opencv的图片读取和写入操作
首先将图片从rgb转换到Lαβ色彩空间
def rgb2lab(img):
def f(t):
return t ** (1.0/3) if t > 0.008856 else 7.787 * t + 16.0 / 116.0
ref_X = 95.047
ref_Y = 100.000
ref_Z = 108.883
print 'start rgb2lab'
res = np.zeros(img.shape)
for i in range(img.shape[0]):
for j in range(img.shape[1]):
(B, G, R) = img[i, j]
var_R = R / 255.0
var_G = G / 255.0
var_B = B / 255.0
if var_R > 0.04045:
var_R = ( ( var_R + 0.055 ) / 1.055 ) ** 2.4
else:
var_R = var_R / 12.92
if var_G > 0.04045:
var_G = ( ( var_G + 0.055 ) / 1.055 ) ** 2.4
else:
var_G = var_G / 12.92
if var_B > 0.04045:
var_B = ( ( var_B + 0.055 ) / 1.055 ) ** 2.4
else:
var_B = var_B / 12.92
var_R = var_R * 100
var_G = var_G * 100
var_B = var_B * 100
X = var_R * 0.4124 + var_G * 0.3576 + var_B * 0.1805
Y = var_R * 0.2126 + var_G * 0.7152 + var_B * 0.0722
Z = var_R * 0.0193 + var_G * 0.1192 + var_B * 0.9505
var_X = X / ref_X
var_Y = Y / ref_Y
var_Z = Z / ref_Z
if var_X > 0.008856:
var_X = var_X ** ( 1/3.0 )
else:
var_X = ( 7.787 * var_X ) + ( 16 / 116.0 )
if var_Y > 0.008856:
var_Y = var_Y ** ( 1/3.0 )
else:
var_Y = ( 7.787 * var_Y ) + ( 16 / 116.0 )
if var_Z > 0.008856:
var_Z = var_Z ** ( 1/3.0 )
else:
var_Z = ( 7.787 * var_Z ) + ( 16 / 116.0 )
L = ( 116 * var_Y ) - 16
a = 500 * ( var_X - var_Y )
b = 200 * ( var_Y - var_Z )
L = 255 * L / 100.0
a = 128 + a
b = 128 + b
res[i, j] = (L, a, b)
return res
def lab2rgb(img):
res = np.zeros(img.shape, dtype=np.uint8)
img = img.astype('uint8')
print 'lab2rgb start'
for i in range(img.shape[0]):
for j in range(img.shape[1]):
(L, a, b) = img[i, j]
L = 100.0 * L / 255.0
a = a - 128
b = b - 128
var_Y = ( L + 16 ) / 116.0
var_X = a / 500.0 + var_Y
var_Z = var_Y - b / 200.0
if var_Y ** 3 > 0.008856:
var_Y = var_Y ** 3
else:
var_Y = ( var_Y - 16.0 / 116.0 ) / 7.787
if var_X ** 3 > 0.008856:
var_X = var_X ** 3
else:
var_X = ( var_X - 16.0 / 116.0 ) / 7.787
if var_Z ** 3 > 0.008856:
var_Z = var_Z ** 3
else:
var_Z = ( var_Z - 16.0 / 116.0 ) / 7.787
var_R = var_X * 3.2406 + var_Y * (-1.5372) + var_Z * (-0.4986)
var_G = var_X * (-0.9689) + var_Y * 1.8758 + var_Z * 0.0415
var_B = var_X * 0.0557 + var_Y * (-0.2040) + var_Z * 1.0570
if var_R > 0.0031308:
var_R = 1.055 * ( var_R ** ( 1 / 2.4 ) ) - 0.055
else:
var_R = 12.92 * var_R
if var_G > 0.0031308:
var_G = 1.055 * ( var_G ** ( 1 / 2.4 ) ) - 0.055
else:
var_G = 12.92 * var_G
if var_B > 0.0031308:
var_B = 1.055 * ( var_B ** ( 1 / 2.4 ) ) - 0.055
else:
var_B = 12.92 * var_B
R = np.uint8(max(var_R * 255, 0))
G = np.uint8(max(var_G * 255, 0))
B = np.uint8(max(var_B * 255, 0))
print 'end'
return res对输入图像做以下变换
def lab_stats(img_lab):
(l, a, b) = split(img_lab)
return {
'l': {
'mean': np.mean(l),
'sigma': np.std(l),
},
'a': {
'mean': np.mean(a),
'sigma': np.std(a),
},
'b': {
'mean': np.mean(b),
'sigma': np.std(b),
}
}
src_lab = rgb2lab(src)
tag_lab = rgb2lab(tag)
src_stats = lab_stats(src_lab)
tag_stats = lab_stats(tag_lab)
(l, a, b) = split(tag_lab)
l = (l - tag_stats['l']['mean']) \
* (tag_stats['l']['sigma'] / src_stats['l']['sigma']) \
+ src_stats['l']['mean']
a = (a - tag_stats['a']['mean']) \
* (tag_stats['a']['sigma'] / src_stats['a']['sigma']) \
+ src_stats['a']['mean']
b = (b - tag_stats['b']['mean']) \
* (tag_stats['b']['sigma'] / src_stats['b']['sigma']) \
+ src_stats['b']['mean']
l = np.clip(l, 0, 255)
a = np.clip(a, 0, 255)
b = np.clip(b, 0, 255)
res_lab = np.zeros(tag.shape)
for i in range(tag.shape[0]):
for j in range(tag.shape[1]):
for k in range(3):
res_lab[i, j][k] = (l, a, b)[k][i, j]
res = lab2rgb(res_lab)结果示例，输入1.jpg
和2.jpg
输入结果图片为
