如何有效地將函數應用於圖像中每個像素的每個通道（用於顏色轉換）？

Question

我正在嘗試使用Reinhard的方法來使用目標圖像的顏色分布來為研究項目的傳入圖像進行顏色標准化。 我已經讓代碼工作，它輸出正確，但它很慢。 迭代300張圖像大約需要20分鍾。 我很確定瓶頸是我如何處理將函數應用於每個圖像。 我正在迭代圖像的每個像素，並將下面的函數應用於每個通道。

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

我的主管告訴我，我可以嘗試使用矩陣一次性應用該功能來改善運行時間，但我不確定如何去做。

Answer 1

原始和目標：

使用Reinhard'method在5 ms顏色轉移：

---

我更喜歡在除python loops之外的numpy vectorized operations實現formulat。

# 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)

這是代碼：

# 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)

Answer 2

在查看了numpy文檔后，我設法弄明白了。 我只需要用正確的數組訪問替換我的嵌套for循環。 用這種方法迭代所有300張圖像花了不到一分鍾。

lAB_tar[:,:,0] = (lAB_img[:,:,0] - mean[0])/std[0] * std_tar[0] + mean_tar[0]
lAB_tar[:,:,1] = (lAB_img[:,:,1] - mean[1])/std[1] * std_tar[1] + mean_tar[1]
lAB_tar[:,:,2] = (lAB_img[:,:,2] - mean[2])/std[2] * std_tar[2] + mean_tar[2]