为什么使用Python和C++将图像归一化为0到1时图像数据不同.我使用opencv

问：

这是我的 C++ 代码：

void writeImageDataToFile(const cv::Mat& image, const std::string& filePath) {
    std::ofstream outputFile(filePath);
    if (!outputFile.is_open()) {
        std::cout << "Failed to open file for writing: " << filePath << std::endl;
        return;
    }

    for (int channel = 0; channel < image.channels(); ++channel) {
        outputFile << "Channel " << channel << ":" << std::endl;
        for (int row = 0; row < image.rows; ++row) {
            for (int col = 0; col < image.cols; ++col) {
                outputFile << std::fixed << std::setprecision(6) << image.at<cv::Vec3f>(row, col)[channel] << " ";
            }
            outputFile << std::endl;
        }
        outputFile << std::endl;
    }

    outputFile.close();
}

int cntt = -1;
for (const auto& directory : directories) {
    std::vector<std::string> imagePaths = GetImagePaths(directory);

    
    for (const auto& imagePath : imagePaths) {
        cv::Mat image = cv::imread(imagePath);
        if (image.empty()) {
            std::cout << "Failed to read image: " << imagePath << std::endl;
            continue;
        }

        cntt++;
        cv::cvtColor(image, image, cv::COLOR_BGR2RGB); 
    

        cv::Mat resizedImage;
        cv::resize(image, resizedImage, cv::Size(32, 32));
        
        cv::Mat normalizedImage;
        resizedImage.convertTo(normalizedImage, CV_32F);

        cv::Scalar new_mean(0.9966, 0.9973, 0.9974);
        cv::Scalar new_std(0.0102, 0.0078, 0.0074);

    
        normalizedImage /= 255.0;
        for (int i = 0; i < 3; i++) {
            normalizedImage.row(i) = (normalizedImage.row(i) - new_mean[i]) / new_std[i];
        }
        
    
        std::string outputFilePath = "normalized_image_data" + std::to_string(cntt) + ".txt";
        writeImageDataToFile(normalizedImage, outputFilePath);
        
        
    }
}

这是我的python代码：

import os
import cv2
import numpy as np

def write_image_data_to_file(image, file_path):
    with open(file_path, 'w') as output_file:
        for channel in range(image.shape[0]):
            output_file.write(f"Channel {channel}:\n")
            for row in range(image.shape[1]):
                for col in range(image.shape[2]):
                    output_file.write(f"{image[channel, row, col]:.6f} ")
                output_file.write("\n")
            output_file.write("\n")


input_folder = 'input0'  # Change to your input folder name
output_folder = 'output_folder3'  # Change to your desired output folder name
new_mean = [0.9966, 0.9973, 0.9974]
new_std = [0.0102, 0.0078, 0.0074]


if not os.path.exists(output_folder):
    os.makedirs(output_folder)


for filename in os.listdir(input_folder):
    if filename.endswith(".jpg"):
        image_path = os.path.join(input_folder, filename)

        
        image = cv2.imread(image_path)

        
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
        image = cv2.resize(image, (32, 32))
        image = np.transpose(image, (2, 0, 1))

        
        image = (image / 255.0).astype(np.float32)
        for i in range(3):
            image[i] = (image[i] - new_mean[i]) / new_std[i]

        
        output_file_path = os.path.join(output_folder, filename.replace(".jpg", ".txt"))

        
        write_image_data_to_file(image, output_file_path)

但我可以看到，使用 C++ 代码打印图像数据的 Txt 文件： normalized_image_data0.txt：（“......“代表省略相同） 图片 ： 3x32x32

Channel 0:
0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 ...(32 count)
0.346153 0.346153  ...
0.351349 0.351349 ...
1.000000 1.000000 ...
1.000000 1.000000 ...
1.000000 1.000000 ...
1.000000 1.000000 ...
1.000000 1.000000 ...
...

Channel 1:
0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 0.333336 ...
0.346153 0.346153 ...
0.351349 0.351349 ...
1.000000 1.000000 ...
1.000000 1.000000 ...
1.000000 1.000000 ...
...
1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.980392 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.956863 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.992157 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
1.000000 1.000000 ...
1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.992157 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.996078 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
...

Channel 2:
0.333336 0.333336 ...
0.346153 0.346153 ...
0.351349 0.351349...
1.000000 1.000000 ...
1.000000 1.000000 ...
...
1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.992157 0.988235 0.980392 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 0.792157 0.988235 1.000000 1.000000 1.000000 0.984314 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
...

使用 Python 代码打印图像数据的 txt 文件：

Channel 0:
0.333336 0.333336 ...
0.333336 0.333336 ...
0.333336 0.333336 ...
...

Channel 1:
0.346150 0.346150 ...
0.346150 0.346150 ...
...
Channel 2:
0.351353 0.351353 ...
0.351353 0.351353 ...
...

图像数据完全不同。为什么？ 我现在需要确保 C++ opencv 中预处理的图像与 Python opencv 中的图像相同。这是我的最终目标。这是对应于 0.txt 的原始图像 0.jgp