cv::Mat 与 float 互换，实现 argmax 得到像素点分类

Functions：

preprocess 预处理图片，resize, [0, 1], normalize, pass to float array
cvtArray2Mat 将 float array 存放的数据再存为 cv::Mat
channelArgMax 取每个 channel 数组中最大值下标作为预测种类，use argmax

#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

using namespace std;
using namespace cv;

// define an array
static const float norm_means[] = {0.406, 0.456, 0.485}; // src
static const float norm_stds[] = {0.225, 0.224, 0.229};

static const int INPUT_H = 256;
static const int INPUT_W = 320;
static const int INPUT_C = 3;
static const int STEP = INPUT_H * INPUT_W;

// test sample test_pt
const Point2i test_pt(0, 0); // x,y

// 数组长度
template<class T>
int getArrayLen(T &array) {
    return (sizeof(array) / sizeof(array[0]));
}

// 数组最大值下标
template<class ForwardIterator>
inline int argmax(ForwardIterator first, ForwardIterator last) {
    return std::distance(first, std::max_element(first, last));
}

void preprocess(cv::Mat src, float *data) {
    // 1.resize
    cv::resize(src, src, cv::Size(INPUT_W, INPUT_H), cv::INTER_NEAREST);

    // 2.uchar->CV_32F, scale to [0,1]
    src.convertTo(src, CV_32F);
    src /= 255.0;

    // 3.split R,G,B and normal each channel using norm_means,norm_stds
    vector<cv::Mat> channels;
    cv::split(src, channels);
    cv::Scalar means, stds;
    for (int i = 0; i < 3; ++i) {
        cv::Mat a = channels[i]; // b
        cv::meanStdDev(a, means, stds);
        a = a / stds.val[0] * norm_stds[i]; // change std, mean also change
        means = cv::mean(a); // recompute mean!
        a = a - means.val[0] + norm_means[i];
        channels[i] = a;
    }

    // R,G,B. split channel test
    printf("%f, %f, %f\n", channels[2].at<float>(test_pt),
           channels[1].at<float>(test_pt), channels[0].at<float>(test_pt));

    // 4.pass to data, ravel()
    int index = 0;
    for (int c = 2; c >= 0; --c) { // R,G,B
        for (int h = 0; h < INPUT_H; ++h) {
            for (int w = 0; w < INPUT_W; ++w) {
                data[index] = channels[c].at<float>(h, w); // R->G->B
                index++;
            }
        }
    }

    // R,G,B. float array test
    int idx = INPUT_W * test_pt.y + test_pt.x;
    printf("%f, %f, %f\n", data[idx], data[idx + STEP], data[idx + STEP * 2]);

}

cv::Mat cvtArray2Mat(const float *data) {
    // reshape
    cv::Mat out = cv::Mat::zeros(INPUT_H, INPUT_W, CV_32FC3);
    int index = 0;
    for (int h = 0; h < INPUT_H; ++h) {
        for (int w = 0; w < INPUT_W; ++w) {
            out.at<Vec3f>(h, w) = {data[index], data[index + STEP], data[index + STEP * 2]}; // R,G,B
            index++; // update STEP times
        }
    }
    // R,G,B. recover Mat test
    cout << out.at<Vec3f>(test_pt) << endl;
    return out;
}


cv::Mat channelArgMax(cv::Mat src) {
    cv::Mat out = cv::Mat::zeros(INPUT_H, INPUT_W, CV_8U);
    for (int h = 0; h < INPUT_H; ++h) {
        for (int w = 0; w < INPUT_W; ++w) {
            uchar *p = src.ptr(h, w); // prob of a point
            out.at<uchar>(h, w) = (uchar) argmax(p, p + 3);
        }
    }
    return out;
}


int main() {

    // preprocess input and pass to data
    cv::Mat src = cv::imread("/Users/shuai/CLionProjects/CV/CVTest/luffy.jpg");

    float input[INPUT_C * INPUT_H * INPUT_W]; // C,H,W;
    preprocess(src, input); // pass src -> input

    // float array -> float Mat
    cv::Mat out = cvtArray2Mat(input);

    // channel argmax
    out = channelArgMax(out);
    
    // resize to print predicted result
    cv::resize(out, out, cv::Size(100, 40), cv::INTER_NEAREST);
    for (int h = 0; h < 40; ++h) {
        for (int w = 0; w < 100; ++w) {
            cout << (int) out.at<uchar>(h, w);
        }
        cout << endl;
    }

    return 0;
}

0.699532, 0.703471, 0.686600
0.699532, 0.703471, 0.686600
[0.699532, 0.703471, 0.6866]
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读入的原图：

luffy.jpg