图像光照校正处理（白平衡）及其速度优化 -opencv+python
https://github.com/18150167970/image_process_tool/blob/master/lighting_enhancement.py
先上效果图，从上到下分别为
img : 原图
img1：均值白平衡法
img2: 完美反射
img3: 灰度世界假设
img4: 基于图像分析的偏色检测及颜色校正方法
img5: 动态阈值算法
img6: gamma校正
img7: HDR校正
7种变换原理：
灰度世界、完美反射、动态阈值原理及其c++实现： https://www.cnblogs.com/Imageshop/archive/2013/04/20/3032062.html
基于图像分析的偏色检测及颜色校正方法：http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ckjs200805003
gamma校正：https://blog.csdn.net/tkokof1/article/details/87878442
HDR校正：contrast image correction
7种变换代码：
参考了：https://blog.csdn.net/qq_36187544/article/details/97657927 中几种白平衡的实现，对其进行速度优化，并增加了新的白平衡算法（伽马校正与HDR校正）。
github地址为：https://github.com/18150167970/image_process_tool/blob/master/lighting_enhancement.py 欢迎提意见和star
# Copyright 2020 chenli Authors. All Rights Reserved.
import cv2
import numpy as np
import math
def mean_white_balance(img):
"""
第一种简单的求均值白平衡法
:param img: cv2.imread读取的图片数据
:return: 返回的白平衡结果图片数据
"""
# 读取图像
b, g, r = cv2.split(img)
r_avg = cv2.mean(r)[0]
g_avg = cv2.mean(g)[0]
b_avg = cv2.mean(b)[0]
# 求各个通道所占增益
k = (r_avg + g_avg + b_avg) / 3
kr = k / r_avg
kg = k / g_avg
kb = k / b_avg
r = cv2.addWeighted(src1=r, alpha=kr, src2=0, beta=0, gamma=0)
g = cv2.addWeighted(src1=g, alpha=kg, src2=0, beta=0, gamma=0)
b = cv2.addWeighted(src1=b, alpha=kb, src2=0, beta=0, gamma=0)
balance_img = cv2.merge([b, g, r])
return balance_img
def perfect_reflective_white_balance(img_input):
"""
完美反射白平衡
STEP 1：计算每个像素的R\G\B之和
STEP 2：按R+G+B值的大小计算出其前Ratio%的值作为参考点的的阈值T
STEP 3：对图像中的每个点，计算其中R+G+B值大于T的所有点的R\G\B分量的累积和的平均值
STEP 4：对每个点将像素量化到[0,255]之间
依赖ratio值选取而且对亮度最大区域不是白色的图像效果不佳。
:param img: cv2.imread读取的图片数据
:return: 返回的白平衡结果图片数据
"""
img = img_input.copy()
b, g, r = cv2.split(img)
m, n, t = img.shape
sum_ = np.zeros(b.shape)
# for i in range(m):
# for j in range(n):
# sum_[i][j] = int(b[i][j]) + int(g[i][j]) + int(r[i][j])
sum_ = b.astype(np.int32) + g.astype(np.int32) + r.astype(np.int32)
hists, bins = np.histogram(sum_.flatten(), 766, [0, 766])
Y = 765
num, key = 0, 0
ratio = 0.01
while Y >= 0:
num += hists[Y]
if num > m * n * ratio / 100:
key = Y
break
Y = Y - 1
# sum_b, sum_g, sum_r = 0, 0, 0
# for i in range(m):
# for j in range(n):
# if sum_[i][j] >= key:
# sum_b += b[i][j]
# sum_g += g[i][j]
# sum_r += r[i][j]
# time = time + 1
sum_b = b[sum_ >= key].sum()
sum_g = g[sum_ >= key].sum()
sum_r = r[sum_ >= key].sum()
time = (sum_ >= key).sum()
avg_b = sum_b / time
avg_g = sum_g / time
avg_r = sum_r / time
maxvalue = float(np.max(img))
# maxvalue = 255
# for i in range(m):
# for j in range(n):
# b = int(img[i][j][0]) * maxvalue / int(avg_b)
# g = int(img[i][j][1]) * maxvalue / int(avg_g)
# r = int(img[i][j][2]) * maxvalue / int(avg_r)
# if b > 255:
# b = 255
# if b < 0:
# b = 0
# if g > 255:
# g = 255
# if g < 0:
# g = 0
# if r > 255:
# r = 255
# if r < 0:
# r = 0
# img[i][j][0] = b
# img[i][j][1] = g
# img[i][j][2] = r
b = img[:, :, 0].astype(np.int32) * maxvalue / int(avg_b)
g = img[:, :, 1].astype(np.int32) * maxvalue / int(avg_g)
r = img[:, :, 2].astype(np.int32) * maxvalue / int(avg_r)
b[b > 255] = 255
b[b < 0] = 0
g[g > 255] = 255
g[g < 0] = 0
r[r > 255] = 255
r[r < 0] = 0
img[:, :, 0] = b
img[:, :, 1] = g
img[:, :, 2] = r
return img
def gray_world_assumes_white_balance(img):
"""
灰度世界假设
:param img: cv2.imread读取的图片数据
:return: 返回的白平衡结果图片数据
"""
B, G, R = np.double(img[:, :, 0]), np.double(img[:, :, 1]), np.double(img[:, :, 2])
B_ave, G_ave, R_ave = np.mean(B), np.mean(G), np.mean(R)
K = (B_ave + G_ave + R_ave) / 3
Kb, Kg, Kr = K / B_ave, K / G_ave, K / R_ave
Ba = (B * Kb)
Ga = (G * Kg)
Ra = (R * Kr)
# for i in range(len(Ba)):
# for j in range(len(Ba[0])):
# Ba[i][j] = 255 if Ba[i][j] > 255 else Ba[i][j]
# Ga[i][j] = 255 if Ga[i][j] > 255 else Ga[i][j]
# Ra[i][j] = 255 if Ra[i][j] > 255 else Ra[i][j]
Ba[Ba > 255] = 255
Ga[Ga > 255] = 255
Ra[Ra > 255] = 255
# print(np.mean(Ba), np.mean(Ga), np.mean(Ra))
dst_img = np.uint8(np.zeros_like(img))
dst_img[:, :, 0] = Ba
dst_img[:, :, 1] = Ga
dst_img[:, :, 2] = Ra
return dst_img
def color_correction_of_image_analysis(img):
"""
基于图像分析的偏色检测及颜色校正方法
:param img: cv2.imread读取的图片数据
:return: 返回的白平衡结果图片数据
"""
def detection(img):
"""计算偏色值"""
img_lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(img_lab)
d_a, d_b, M_a, M_b = 0, 0, 0, 0
for i in range(m):
for j in range(n):
d_a = d_a + a[i][j]
d_b = d_b + b[i][j]
d_a, d_b = (d_a / (m * n)) - 128, (d_b / (n * m)) - 128
D = np.sqrt((np.square(d_a) + np.square(d_b)))
for i in range(m):
for j in range(n):
M_a = np.abs(a[i][j] - d_a - 128) + M_a
M_b = np.abs(b[i][j] - d_b - 128) + M_b
M_a, M_b = M_a / (m * n), M_b / (m * n)
M = np.sqrt((np.square(M_a) + np.square(M_b)))
k = D / M
# print('偏色值:%f' % k)
return
b, g, r = cv2.split(img)
# print(img.shape)
m, n = b.shape
# detection(img)
I_r_2 = np.zeros(r.shape)
I_b_2 = np.zeros(b.shape)
# sum_I_r_2, sum_I_r, sum_I_b_2, sum_I_b, sum_I_g = 0, 0, 0, 0, 0
# max_I_r_2, max_I_r, max_I_b_2, max_I_b, max_I_g = int(r[0][0] ** 2), int(r[0][0]), int(b[0][0] ** 2), int(
# b[0][0]), int(g[0][0])
#
# for i in range(m):
# for j in range(n):
# I_r_2[i][j] = int(r[i][j] ** 2)
# I_b_2[i][j] = int(b[i][j] ** 2)
# sum_I_r_2 = I_r_2[i][j] + sum_I_r_2
# sum_I_b_2 = I_b_2[i][j] + sum_I_b_2
# sum_I_g = g[i][j] + sum_I_g
# sum_I_r = r[i][j] + sum_I_r
# sum_I_b = b[i][j] + sum_I_b
# if max_I_r < r[i][j]:
# max_I_r = r[i][j]
# if max_I_r_2 < I_r_2[i][j]:
# max_I_r_2 = I_r_2[i][j]
# if max_I_g < g[i][j]:
# max_I_g = g[i][j]
# if max_I_b_2 < I_b_2[i][j]:
# max_I_b_2 = I_b_2[i][j]
# if max_I_b < b[i][j]:
# max_I_b = b[i][j]
I_r_2 = (r.astype(np.float32) ** 2).astype(np.float32)
I_b_2 = (b.astype(np.float32) ** 2).astype(np.float32)
sum_I_r_2 = I_r_2.sum()
sum_I_b_2 = I_b_2.sum()
sum_I_g = g.sum()
sum_I_r = r.sum()
sum_I_b = b.sum()
max_I_r = r.max()
max_I_g = g.max()
max_I_b = b.max()
max_I_r_2 = I_r_2.max()
max_I_b_2 = I_b_2.max()
[u_b, v_b] = np.matmul(np.linalg.inv([[sum_I_b_2, sum_I_b], [max_I_b_2, max_I_b]]), [sum_I_g, max_I_g])
[u_r, v_r] = np.matmul(np.linalg.inv([[sum_I_r_2, sum_I_r], [max_I_r_2, max_I_r]]), [sum_I_g, max_I_g])
# print(u_b, v_b, u_r, v_r)
# b0, g0, r0 = np.zeros(b.shape, np.uint8), np.zeros(g.shape, np.uint8), np.zeros(r.shape, np.uint8)
# for i in range(m):
# for j in range(n):
# b_point = u_b * (b[i][j] ** 2) + v_b * b[i][j]
# g0[i][j] = g[i][j]
# # r0[i][j] = r[i][j]
# r_point = u_r * (r[i][j] ** 2) + v_r * r[i][j]
# if r_point > 255:
# r0[i][j] = 255
# else:
# if r_point < 0:
# r0[i][j] = 0
# else:
# r0[i][j] = r_point
# if b_point > 255:
# b0[i][j] = 255
# else:
# if b_point < 0:
# b0[i][j] = 0
# else:
# b0[i][j] = b_point
b_point = u_b * (b.astype(np.float32) ** 2) + v_b * b.astype(np.float32)
r_point = u_r * (r.astype(np.float32) ** 2) + v_r * r.astype(np.float32)
b_point[b_point > 255] = 255
b_point[b_point < 0] = 0
b = b_point.astype(np.uint8)
r_point[r_point > 255] = 255
r_point[r_point < 0] = 0
r = r_point.astype(np.uint8)
return cv2.merge([b, g, r])
def dynamic_threshold_white_balance(img):
"""
动态阈值算法
算法分为两个步骤：白点检测和白点调整。
只是白点检测不是与完美反射算法相同的认为最亮的点为白点，而是通过另外的规则确定
:param img: cv2.imread读取的图片数据
:return: 返回的白平衡结果图片数据
"""
b, g, r = cv2.split(img)
"""
YUV空间
"""
def con_num(x):
if x > 0:
return 1
if x < 0:
return -1
if x == 0:
return 0
yuv_img = cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
(y, u, v) = cv2.split(yuv_img)
# y, u, v = cv2.split(img)
m, n = y.shape
sum_u, sum_v = 0, 0
max_y = np.max(y.flatten())
# print(max_y)
# for i in range(m):
# for j in range(n):
# sum_u = sum_u + u[i][j]
# sum_v = sum_v + v[i][j]
sum_u = u.sum()
sum_v = v.sum()
avl_u = sum_u / (m * n)
avl_v = sum_v / (m * n)
du, dv = 0, 0
# print(avl_u, avl_v)
# for i in range(m):
# for j in range(n):
# du = du + np.abs(u[i][j] - avl_u)
# dv = dv + np.abs(v[i][j] - avl_v)
du = (np.abs(u - avl_u)).sum()
dv = (np.abs(v - avl_v)).sum()
avl_du = du / (m * n)
avl_dv = dv / (m * n)
num_y, yhistogram, ysum = np.zeros(y.shape), np.zeros(256), 0
radio = 0.5 # 如果该值过大过小，色温向两极端发展
# for i in range(m):
# for j in range(n):
# value = 0
# if np.abs(u[i][j] - (avl_u + avl_du * con_num(avl_u))) < radio * avl_du or np.abs(
# v[i][j] - (avl_v + avl_dv * con_num(avl_v))) < radio * avl_dv:
# value = 1
# else:
# value = 0
#
# if value <= 0:
# continue
# num_y[i][j] = y[i][j]
# yhistogram[int(num_y[i][j])] = 1 + yhistogram[int(num_y[i][j])]
# ysum += 1
u_temp = np.abs(u.astype(np.float32) - (avl_u.astype(np.float32) + avl_du * con_num(avl_u))) < radio * avl_du
v_temp = np.abs(v.astype(np.float32) - (avl_v.astype(np.float32) + avl_dv * con_num(avl_v))) < radio * avl_dv
temp = u_temp | v_temp
num_y[temp] = y[temp]
# yhistogram = cv2.calcHist(num_y, 0, u_temp | v_temp, [256], [0, 256])
# yhistogram[num_y[u_temp | v_temp].flatten().astype(np.int32)] += 1
for i in range(m):
for j in range(n):
if temp[i][j] > 0:
yhistogram[int(num_y[i][j])] = 1 + yhistogram[int(num_y[i][j])]
ysum = (temp).sum()
sum_yhistogram = 0
# hists2, bins = np.histogram(yhistogram, 256, [0, 256])
# print(hists2)
Y = 255
num, key = 0, 0
while Y >= 0:
num += yhistogram[Y]
if num > 0.1 * ysum: # 取前10%的亮点为计算值，如果该值过大易过曝光，该值过小调整幅度小
key = Y
break
Y = Y - 1
# print(key)
sum_r, sum_g, sum_b, num_rgb = 0, 0, 0, 0
# for i in range(m):
# for j in range(n):
# if num_y[i][j] > key:
# sum_r = sum_r + r[i][j]
# sum_g = sum_g + g[i][j]
# sum_b = sum_b + b[i][j]
# num_rgb += 1
num_rgb = (num_y > key).sum()
sum_r = r[num_y > key].sum()
sum_g = g[num_y > key].sum()
sum_b = b[num_y > key].sum()
if num_rgb == 0:
return img
# print("num_rgb", num_rgb)
avl_r = sum_r / num_rgb
avl_g = sum_g / num_rgb
avl_b = sum_b / num_rgb
# for i in range(m):
# for j in range(n):
# b_point = int(b[i][j]) * int(max_y) / avl_b
# g_point = int(g[i][j]) * int(max_y) / avl_g
# r_point = int(r[i][j]) * int(max_y) / avl_r
# if b_point > 255:
# b[i][j] = 255
# else:
# if b_point < 0:
# b[i][j] = 0
# else:
# b[i][j] = b_point
# if g_point > 255:
# g[i][j] = 255
# else:
# if g_point < 0:
# g[i][j] = 0
# else:
# g[i][j] = g_point
# if r_point > 255:
# r[i][j] = 255
# else:
# if r_point < 0:
# r[i][j] = 0
# else:
# r[i][j] = r_point
b_point = b.astype(np.float32) * int(max_y) / avl_b
g_point = g.astype(np.float32) * int(max_y) / avl_g
r_point = r.astype(np.float32) * int(max_y) / avl_r
b_point[b_point > 255] = 255
b_point[b_point < 0] = 0
b = b_point.astype(np.uint8)
g_point[g_point > 255] = 255
g_point[g_point < 0] = 0
g = g_point.astype(np.uint8)
r_point[r_point > 255] = 255
r_point[r_point < 0] = 0
r = r_point.astype(np.uint8)
return cv2.merge([b, g, r])
def gamma_trans(img):
"""
gamma 校正
使用自适应gamma校正
:param img: cv2.imread读取的图片数据
:return: 返回的gamma校正后的图片数据
"""
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
mean = np.mean(img_gray)
gamma = math.log10(0.5) / math.log10(mean / 255) # 公式计算gamma
gamma_table = [np.power(x / 255.0, gamma) * 255.0 for x in range(256)] # 建立映射表
gamma_table = np.round(np.array(gamma_table)).astype(np.uint8) # 颜色值为整数
return cv2.LUT(img, gamma_table) # 图片颜色查表。另外可以根据光强（颜色）均匀化原则设计自适应算法。
def contrast_image_correction(img):
"""
contrast image correction论文的python复现，HDR技术
:param img: cv2.imread读取的图片数据
:return: 返回的HDR校正后的图片数据
"""
img_yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
mv = cv2.split(img_yuv)
img_y = mv[0].copy();
# temp = img_y
temp = cv2.bilateralFilter(mv[0], 9, 50, 50)
# for i in range(len(img)):
# for j in range(len(img[0])):
# exp = np.power(2, (128 - (255 - temp[i][j])) / 128.0)
# temp[i][j] = int(255 * np.power(img_y[i][j] / 255.0, exp))
# # print(exp.dtype)
# print(temp.dtype)
exp = np.power(2, (128.0 - (255 - temp).astype(np.float32)) / 128.0)
temp = (255 * np.power(img_y.flatten() / 255.0, exp.flatten())).astype(np.uint8)
temp = temp.reshape((img_y.shape))
dst = img.copy()
img_y[img_y == 0] = 1
for k in range(3):
val = temp / img_y
val1 = img[:, :, k].astype(np.int32) + img_y.astype(np.int32)
val2 = (val * val1 + img[:, :, k] - img_y) / 2
dst[:, :, k] = val2.astype(np.int32)
# for i in range(len(img)):
# for j in range(len(img[0])):
# if (img_y[i][j] == 0):
# dst[i, j, :] = 0
# else:
# for k in range(3):
# val = temp[i, j]/img_y[i, j]
# val1 = int(img[i, j, k]) + int(img_y[i, j])
# val2 = (val * val1+ img[i, j, k] - img_y[i, j]) / 2
# dst[i, j, k] = int(val2)
# """
# BUG:直接用下面计算方法会导致值溢出，导致计算结果不正确
# """
# dst[i, j, 0] = (temp[i, j] * (img[i, j, 0] + img_y[i, j]) / img_y[i, j] + img[i, j, 0] - img_y[
# i, j]) / 2
# dst[i, j, 1] = (temp[i, j] * (img[i, j, 1] + img_y[i, j]) / img_y[i, j] + img[i, j, 1] - img_y[
# i, j]) / 2
# dst[i, j, 2] = (temp[i, j] * (img[i, j, 2] + img_y[i, j]) / img_y[i, j] + img[i, j, 2] - img_y[
# i, j]) / 2
return dst
if __name__ == '__main__':
"""
img : 原图
img1：均值白平衡法
img2: 完美反射
img3: 灰度世界假设
img4: 基于图像分析的偏色检测及颜色校正方法
img5: 动态阈值算法
img6: gamma校正
img7: HDR校正
"""
import time
img = cv2.imread("4.jpg")
start_time = time.time()
# img = cv2.resize(img, (256, 512), cv2.INTER_LINEAR)
# img1 = mean_white_balance(img)
# img2 = perfect_reflective_white_balance(img)
# img3 = gray_world_assumes_white_balance(img)
# img4 = color_correction_of_image_analysis(img)
# img5 = dynamic_threshold_white_balance(img)
# img6 = gamma_trans(img) # gamma变换
# img7 = contrast_image_correction(img)
img8 = flip_img(img)
end_time = time.time()
print("use time=", end_time - start_time)
cv2.imshow("image1", img)
cv2.imshow("image2", img8)
# img_stack = np.vstack([img, img1, img2, img3])
# img_stack2 = np.vstack([img4, img5, img6, img7])
# cv2.imshow("image1",img_stack)
# cv2.imshow("image2",img_stack2)
cv2.waitKey(0)
参考文献：
灰度世界、完美反射、动态阈值原理及其c++实现： https://www.cnblogs.com/Imageshop/archive/2013/04/20/3032062.html
基于图像分析的偏色检测及颜色校正方法：http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ckjs200805003
gamma校正：https://blog.csdn.net/tkokof1/article/details/87878442
HDR校正：contrast image correction
代码实现：https://blog.csdn.net/qq_36187544/article/details/97657927
have fun（笑）