文章目录
Multi-Camera Color Correction via Hybrid Histogram Matching
这是一篇直方图匹配的论文，通过直方图匹配达到颜色转换的目的。
主要分为2个步骤：
1个是全局的直方图匹配
2是局部颜色的调整（把累计直方图的直角展开）
1. 计算直方图， 累计直方图， 直方图均衡化
直方图
累计直方图
直方图均衡化后的直方图
import cv2
import numpy as np
from matplotlib import pyplot as plt
def cal_hist(img):
'''
:param img: single channel image
:return: hist
'''
hist, hist_edge = np.histogram(img, bins=256, range=[0, 256])
# for i in range(256):
# hist[i] = np.count_nonzero(img==i)
# print(hist_edge)
return hist
def cal_cum_hist(hist):
cum_hist = np.zeros_like(hist)
for i in np.arange(1, 256):
cum_hist[i] = cum_hist[i-1] + hist[i]
return cum_hist
def cal_hist_equalize(img):
'''
:param img: single channel image
:return:
'''
h, w = img.shape
hist = cal_hist(img)
cum_hist = cal_cum_hist(hist)
lut = 255 * cum_hist / (h * w)
lut = (lut + 0.5).astype(np.uint8)
out = lut[img]
return out, lut
2. 直方图规定化，直方图映射。
即将图像一 的直方图 映射为 图像二的直方图，使图像一的色彩接近图像二
def cal_hist_map(img, tgt_img):
out1, lut1 = cal_hist_equalize(img)
out2, lut2 = cal_hist_equalize(tgt_img)
lut_invert = np.zeros_like(lut2)
# 反转
lut_invert = np.ones_like(lut2) * -1
for i in range(0, 256):
i = 255 - i # lut2[i]可能对应很多重复的i, 赋值为最小的一个。
lut_invert[lut2[i]] = i
# lut_invert 可能会有一些空缺点, 通过插值填充。
if lut_invert[0] == -1:
lut_invert[0] = lut_invert.min()
if lut_invert[255] == -1:
lut_invert[255] = lut_invert.max()
xx = []
yy = []
xxx = []
for i in range(0, 256):
if lut_invert[i] != -1:
xx.append(i)
yy.append( lut_invert[i])
else:
xxx.append(i)
lut_invert[lut_invert==-1] = np.interp(xxx, xx, yy)
# 通过插值填充 或这 直接赋值
# for i in range(1, 256):
# if lut_invert[i] == -1:
# lut_invert[i] = lut_invert[i-1]
lut = np.zeros_like(lut1)
for i in range(256):
lut[i] = lut_invert[lut1[i]]
out = lut[img]
return out, lut
3. 实验环节
3.1 输入图像
来源：原论文仓库
3.2 均衡化效果
直方图均衡化本文代码和opencv效果对比，基本一致：
3.3 映射效果
直方图映射效果：看起来还行
实际应用到大图的时候出现伪影：
4. 针对3实验环节的伪影 做处理和优化，就是HMM这篇论文的目的
复现的效果：
左中右分别为， 图1，图2，映射后的图1（色调接近图2）
过滤噪声（数目比较少的值，累计直方图可能出现横线的地方）后，插值前的查找表：
插值后：
更多实验图像可以运行下面的代码：
import cv2
import numpy as np
from matplotlib import pyplot as plt
from scipy.signal import savgol_filter
from python.hist_equalize import cal_hist_equalize, cal_hist, cal_cum_hist
def cal_noisy_index(hist1, h1, w1):
'''找出 噪声的 index,这些值需要重新插值'''
ts = [] # remove noise index
sort_index = np.argsort(hist1)
#print('sort value:', hist1[sort_index])
s = 0
for i in range(len(sort_index)):
t = sort_index[i]
s += hist1[t]
if s < 0.05 * (h1 * w1):
ts.append(t)
#print('ts num:', len(ts))
return ts
def cal_hist_map2(img, tgt_img):
h1, w1 = img.shape
h2, w2 = tgt_img.shape
hist1 = cal_hist(img)
cum_hist1 = cal_cum_hist(hist1)
hist2 = cal_hist(tgt_img)
cum_hist2 = cal_cum_hist(hist2)
lut1 = 255 * cum_hist1 / (h1 * w1)
lut1 = (lut1 + 0.5).astype(np.uint8)
lut2 = 255 * cum_hist2 / (h2 * w2)
lut2 = (lut2 + 0.5).astype(np.uint8)
print('lut2:', lut2)
lut_invert = np.ones_like(lut2) * -1
for i in range(0, 256):
i = 255 - i # lut2[i]可能对应很多重复的i, 赋值为第一个。
lut_invert[lut2[i]] = i
# lut_invert 可能会有一些空缺点, 通过插值填充。
if lut_invert[0] == -1:
lut_invert[0] = lut_invert.min()
if lut_invert[255] == -1:
lut_invert[255] = lut_invert.max()
xx = []
yy = []
xxx = []
for i in range(0, 256):
if lut_invert[i] != -1:
xx.append(i)
yy.append(lut_invert[i])
else:
xxx.append(i)
lut_invert[lut_invert == -1] = np.interp(xxx, xx, yy)
# 通过插值填充 或这 直接赋值
# for i in range(1, 256):
# if lut_invert[i] == -1:
# lut_invert[i] = lut_invert[i-1]
lut = np.zeros_like(lut1)
for i in range(256):
lut[i] = lut_invert[lut1[i]]
# min_val = tgt_img.min()
# max_val = tgt_img.max()
# lut = np.clip(lut, min_val, max_val)
'''找出 噪声的 index,这些值需要重新插值'''
ts = cal_noisy_index(hist1, h1, w1)
ts += cal_noisy_index(hist2, h2, w2)
ts = np.unique(ts)
xx = []
yy = []
xx.append(0)
yy.append(lut[0]) # xx, yy的选择也很重要, 可以设为0
lut_ret = lut.copy()
for i in range(1, 255):
if i not in ts:
xx.append(i)
yy.append(lut[i])
xx.append(255)
yy.append(lut[-1]) # xx, yy的选择也很重要, 也可以设为255看下效果
lut_ret[ts] = np.interp(ts, xx, yy )
lut_ret2 = (np.array(lut_ret).astype(np.int16) + np.array(lut)) / 2 + 0.5
lut_ret = lut_ret.astype(np.uint8)
for i in range(len(lut)):
print(lut[i], lut_ret[i])
plt.figure()
x = range(256)
plt.title('origin lut and interp lut')
plt.plot(x, lut, 'r.', x, lut_ret, 'b-', x, ((lut_ret.astype(np.int16) + lut) / 2), 'y')
plt.show()
# smooth with savgol_filter
lut_ret = savgol_filter(lut_ret, 15, 1)
out = lut_ret[img]
return out, lut_ret, xx, yy
def process(file1, file2, file3, file4):
# file1 = r'D:\code_color\HHM-master\6to7\6to7.png'
# file2 = r'D:\code_color\HHM-master\6to7\7to6.png'
img1 = cv2.imread(file1, 1)
img = img1.copy()
img2 = cv2.imread(file2, 1)
out, lut0, xx0, yy0 = cal_hist_map2(img1[..., 0], img2[..., 0])
out, lut1, xx1, yy1 = cal_hist_map2(img1[..., 1], img2[..., 1])
out, lut2, xx2, yy2 = cal_hist_map2(img1[..., 2], img2[..., 2])
plt.figure()
x = np.arange(256)
plt.plot(x, lut0, 'b+')
plt.plot(x, lut1, 'g+')
plt.plot(x, lut2, 'r+')
plt.show()
plt.figure()
plt.plot(xx0, yy0, 'b+')
plt.plot(xx1, yy1, 'g+')
plt.plot(xx2, yy2, 'r+')
plt.show()
# file3 = r'D:\code_color\HHM-master\6to7\6.png'
# file4 = r'D:\code_color\HHM-master\6to7\7.png'
img1 = cv2.imread(file3, 1)
img = img1.copy()
img2 = cv2.imread(file4, 1)
img1[..., 0] = lut0[img1[..., 0]]
img1[..., 1] = lut1[img1[..., 1]]
img1[..., 2] = lut2[img1[..., 2]]
cv2.imwrite(file3[:-4] + '_my.png', img1)
plt.figure()
plt.imshow(np.hstack((img, img2, img1))[..., ::-1])
plt.show()
if __name__ == "__main__":
file2 = r'D:\code_color\HHM-master\data1\4to5.png'
file1 = r'D:\code_color\HHM-master\data1\5to4.png'
file4 = r'D:\code_color\HHM-master\data1\4.png'
file3 = r'D:\code_color\HHM-master\data1\5.png'
# file2 = r'D:\code\3dlut\lut1.png'
# file1 = r'D:\code\3dlut\lut2.png'
# file4 = r'D:\code\3dlut\lut1.png'
# file3 = r'D:\code\3dlut\lut2.png'
file2 = r'D:\code_color\HHM-master\6to7\6to7.png'
file1 = r'D:\code_color\HHM-master\6to7\7to6.png'
file4 = r'D:\code_color\HHM-master\6to7\6.png'
file3 = r'D:\code_color\HHM-master\6to7\7.png'
process(file1, file2, file3, file4)