python - 如何有效地将函数应用于图像中每个像素的每个通道（用于颜色转换）？

Question

我正在尝试实施 Reinhard 的方法，以使用目标图像的颜色分布对研究项目传入的图像进行颜色归一化。我已经让代码正常工作并且输出正确，但速度很慢。遍历 300 张图像大约需要 20 分钟。我很确定瓶颈是我如何处理将函数应用于每个图像。我目前正在遍历图像的每个像素并将下面的函数应用于每个通道。

def reinhard(target, img):

    #converts image and target from BGR colorspace to l alpha beta
    lAB_img = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
    lAB_tar = cv2.cvtColor(target, cv2.COLOR_BGR2Lab)

    #finds mean and standard deviation for each color channel across the entire image
    (mean, std) = cv2.meanStdDev(lAB_img)
    (mean_tar, std_tar) = cv2.meanStdDev(lAB_tar)

    #iterates over image implementing formula to map color normalized pixels to target image
    for y in range(512):
        for x in range(512):
            lAB_tar[x, y, 0] = (lAB_img[x, y, 0] - mean[0]) / std[0] * std_tar[0] + mean_tar[0]
            lAB_tar[x, y, 1] = (lAB_img[x, y, 1] - mean[1]) / std[1] * std_tar[1] + mean_tar[1]
            lAB_tar[x, y, 2] = (lAB_img[x, y, 2] - mean[2]) / std[2] * std_tar[2] + mean_tar[2]
    mapped = cv2.cvtColor(lAB_tar, cv2.COLOR_Lab2BGR)
    return mapped

我的主管告诉我，我可以尝试使用矩阵一次性应用该函数以改善运行时间，但我不确定如何去做。

标签： pythonalgorithmnumpyopencvimage-processing

Answer 1

原文和目标：</p>

使用 Reinhard 方法的颜色转移结果5 ms：

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我更喜欢numpy vectorized operations在python loops.

# implementing the formula
#(Io - mo)/so*st + mt  = Io * (st/so) + mt - mo*(st/so)
ratio = (std_tar/std_ori).reshape(-1)
offset = (mean_tar - mean_ori*std_tar/std_ori).reshape(-1)
lab_tar = cv2.convertScaleAbs(lab_ori*ratio + offset)

这是代码：

# 2019/02/19 by knight-金
# https://stackoverflow.com/a/54757659/3547485

import numpy as np
import cv2

def reinhard(target, original):
    # cvtColor: COLOR_BGR2Lab
    lab_tar = cv2.cvtColor(target, cv2.COLOR_BGR2Lab)
    lab_ori = cv2.cvtColor(original, cv2.COLOR_BGR2Lab)

    # meanStdDev: calculate mean and stadard deviation
    mean_tar, std_tar = cv2.meanStdDev(lab_tar)
    mean_ori, std_ori = cv2.meanStdDev(lab_ori)

    # implementing the formula
    #(Io - mo)/so*st + mt  = Io * (st/so) + mt - mo*(st/so)
    ratio = (std_tar/std_ori).reshape(-1)
    offset = (mean_tar - mean_ori*std_tar/std_ori).reshape(-1)
    lab_tar = cv2.convertScaleAbs(lab_ori*ratio + offset)

    # convert back
    mapped = cv2.cvtColor(lab_tar, cv2.COLOR_Lab2BGR)
    return mapped

if __name__ == "__main__":
    ori = cv2.imread("ori.png")
    tar = cv2.imread("tar.png")

    mapped = reinhard(tar, ori)
    cv2.imwrite("mapped.png", mapped)

    mapped_inv = reinhard(ori, tar)
    cv2.imwrite("mapped_inv.png", mapped)