如何设置伽玛校正的最佳值

Question

我正在尝试对图像使用伽玛校正。但是我只手动更改伽玛校正的值。有什么方法可以自动计算最佳伽玛校正值？例如。带有亮度直方图。

代码：

# import the necessary packages
from __future__ import print_function
import numpy as np
import argparse
import cv2
def adjust_gamma(image, gamma=1.0):
    # build a lookup table mapping the pixel values [0, 255] to
    # their adjusted gamma values
    invGamma = 1.0 / gamma
    table = np.array([((i / 255.0) ** invGamma) * 255
        for i in np.arange(0, 256)]).astype("uint8")
    # apply gamma correction using the lookup table
    return cv2.LUT(image, table)


# load the original image
original = cv2.imread('image.jpg')

# loop over various values of gamma
for gamma in np.arange(0.0, 3.5, 0.5):
    # ignore when gamma is 1 (there will be no change to the image)
    if gamma == 1:
        continue
    # apply gamma correction and show the images
    gamma = gamma if gamma > 0 else 0.1
    adjusted = adjust_gamma(original, gamma=gamma)
    cv2.putText(adjusted, "g={}".format(gamma), (10, 30),
        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 3)
    cv2.imshow("Images", np.hstack([original, adjusted]))
    cv2.waitKey(0)

Answer 1

在Python / OpenCV中有两种方法可以做到这一点。两者均基于对数（中间灰色）/对数（平均值）的比率。结果通常是合理的，尤其是对于深色图像，但并非在所有情况下都有效。对于亮图像，请反转灰度图像或值图像，对暗图像进行处理，然后再次反转并重新组合（如果使用值图像）。

• 阅读输入内容
• 转换为灰度或HSV值
• 计算灰度或值通道上的对数比率log（中灰）/ log（平均值）
• 将输入或值提高到比率的幂
• 如果使用值通道，请将新的值通道与色相和饱和度通道合并，然后转换回RGB

输入：

import cv2
import numpy as np
import math

# read image
img = cv2.imread('lioncuddle1.jpg')

# METHOD 1: RGB

# convert img to gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# compute gamma = log(mid*255)/log(mean)
mid = 0.5
mean = np.mean(gray)
gamma = math.log(mid*255)/math.log(mean)
print(gamma)

# do gamma correction
img_gamma1 = np.power(img, gamma).clip(0,255).astype(np.uint8)



# METHOD 2: HSV (or other color spaces)

# convert img to HSV
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
hue, sat, val = cv2.split(hsv)

# compute gamma = log(mid*255)/log(mean)
mid = 0.5
mean = np.mean(val)
gamma = math.log(mid*255)/math.log(mean)
print(gamma)

# do gamma correction on value channel
val_gamma = np.power(val, gamma).clip(0,255).astype(np.uint8)

# combine new value channel with original hue and sat channels
hsv_gamma = cv2.merge([hue, sat, val_gamma])
img_gamma2 = cv2.cvtColor(hsv_gamma, cv2.COLOR_HSV2BGR)

# show results
cv2.imshow('input', img)
cv2.imshow('result1', img_gamma1)
cv2.imshow('result2', img_gamma2)
cv2.waitKey(0)
cv2.destroyAllWindows()

# save results
cv2.imwrite('lioncuddle1_gamma1.jpg', img_gamma1)
cv2.imwrite('lioncuddle1_gamma2.jpg', img_gamma2)

方法1的结果：

方法2的结果