• API：normalize

图像归一化的四种方式

2. 图像运算时转为float32以保持精度，显示时转为uint8类型。

在 NORM_MINMAX 模式下，alpha表示归一化后的最小值，beta表示归一化后的最大值。
在NORM_L1、NORM_L2、NORM_INF 模式下，alpha表示执行相应归一化后矩阵的范数值，beta不使用。

C++

#include <opencv2/opencv.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main(int argc, const char *argv[])
{
    Mat src = imread("D:/vcprojects/images/test.png");
    if (src.empty()) {
        printf("could not load image...\n");
        return -1;
    }
    namedWindow("input", WINDOW_AUTOSIZE);
    imshow("input", src);

    Mat gray, gray_f;
    cvtColor(src, gray, COLOR_BGR2GRAY);

    // 转换为浮点数类型数组
    gray.convertTo(gray, CV_32F); // 从CV_8UC1转为CV_32F

    // scale and shift by NORM_MINMAX
    Mat dst = Mat::zeros(gray.size(), CV_32FC1);
    normalize(gray, dst, 1.0, 0, NORM_MINMAX);
    Mat result = dst * 255;
    result.convertTo(dst, CV_8UC1);
    imshow("NORM_MINMAX", dst);

    // scale and shift by NORM_INF
    normalize(gray, dst, 1.0, 0, NORM_INF);
    result = dst * 255;
    result.convertTo(dst, CV_8UC1);
    imshow("NORM_INF", dst);

    // scale and shift by NORM_L1
    normalize(gray, dst, 1.0, 0, NORM_L1);
    result = dst * 10000000;
    result.convertTo(dst, CV_8UC1);
    imshow("NORM_L1", dst);

    // scale and shift by NORM_L2
    normalize(gray, dst, 1.0, 0, NORM_L2);
    result = dst * 10000;
    result.convertTo(dst, CV_8UC1);
    imshow("NORM_L2", dst);

    waitKey(0);
    return 0;
}

Python

import cv2 as cv
import numpy as np

src = cv.imread("D:/vcprojects/images/test.png")
cv.namedWindow("input", cv.WINDOW_AUTOSIZE)
cv.imshow("input", src)
gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)

# 转换为浮点数类型数组
gray = np.float32(gray) # 若不转，
print(gray)

# scale and shift by NORM_MINMAX
dst = np.zeros(gray.shape, dtype=np.float32) # 数据类型要采用float32
cv.normalize(gray, dst=dst, alpha=0, beta=1.0, norm_type=cv.NORM_MINMAX)
print(dst)
cv.imshow("NORM_MINMAX", np.uint8(dst*255)) # 转为uint8格式

# scale and shift by NORM_INF
dst = np.zeros(gray.shape, dtype=np.float32)
cv.normalize(gray, dst=dst, alpha=1.0, beta=0, norm_type=cv.NORM_INF)
print(dst)
cv.imshow("NORM_INF", np.uint8(dst*255))

# scale and shift by NORM_L1
dst = np.zeros(gray.shape, dtype=np.float32)
cv.normalize(gray, dst=dst, alpha=1.0, beta=0, norm_type=cv.NORM_L1)
print(dst)
cv.imshow("NORM_L1", np.uint8(dst*10000000))

# scale and shift by NORM_L2
dst = np.zeros(gray.shape, dtype=np.float32)
cv.normalize(gray, dst=dst, alpha=1.0, beta=0, norm_type=cv.NORM_L2)
print(dst)
cv.imshow("NORM_L2", np.uint8(dst*10000))

cv.waitKey(0)
cv.destroyAllWindows()