使用 cython 包装错误级别分析算法的 opencv 实现

Question

我已经使用 c++（opencv 版本 2.4）实现了错误级别分析算法，我想使用 cython 为它构建一个 python 包装器。我已经阅读了 cython for c++ 文档的一部分，但它对我没有帮助，而且我没有找到任何额外的在线实现包装器的信息。如果有人能指导我并帮助我解决这个问题，那就太好了。

这是我要为其构建 pyhton 包装器的代码：

#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <vector> 

// Control
int scale = 15,
quality = 75;

// Image containers
cv::Mat input_image,
compressed_image;

void processImage(int, void*)
{

// Setting up parameters and JPEG compression
std::vector<int> parameters;
parameters.push_back(CV_IMWRITE_JPEG_QUALITY);
parameters.push_back(quality);
cv::imwrite("lena.jpeg", input_image, parameters);

// Reading temp image from the disk
compressed_image = cv::imread("lena.jpeg");

if (compressed_image.empty())
{
  std::cout << "> Error loading temp image" << std::endl;
  exit(EXIT_FAILURE);
}

cv::Mat output_image = cv::Mat::zeros(input_image.size(), CV_8UC3);

// Compare values through matrices
for (int row = 0; row < input_image.rows; ++row)
{
 const uchar* ptr_input = input_image.ptr<uchar>(row);
 const uchar* ptr_compressed = compressed_image.ptr<uchar>(row);
 uchar* ptr_out = output_image.ptr<uchar>(row);

    for (int column = 0; column < input_image.cols; column++)
    {
        // Calc abs diff for each color channel multiplying by a scale factor
        ptr_out[0] = abs(ptr_input[0] - ptr_compressed[0]) * scale;
        ptr_out[1] = abs(ptr_input[1] - ptr_compressed[1]) * scale;
        ptr_out[2] = abs(ptr_input[2] - ptr_compressed[2]) * scale;

        ptr_input += 3;
        ptr_compressed += 3;
        ptr_out += 3;
    }
}

// Shows processed image
cv::imshow("Error Level Analysis", output_image);
} 

int main (int argc, char* argv[])
{
// Verifica se o número de parâmetros necessário foi informado
if (argc < 2)
{
 std::cout << "> You need to provide an image as parameter" << std::endl;
 return EXIT_FAILURE;
}

// Read the image
input_image = cv::imread(argv[1]);

// Check image load
if (input_image.empty())
{
  std::cout << "> Error loading input image" << std::endl;
  return EXIT_FAILURE;
}

// Set up window and trackbar
cv::namedWindow("Error Level Analysis", CV_WINDOW_AUTOSIZE);
cv::imshow("Error Level Analysis", input_image);
cv::createTrackbar("Scale", "Error Level Analysis", &scale, 100,   processImage);
cv::createTrackbar("Quality", "Error Level Analysis", &quality, 100, processImage);

// Press 'q' to quit
while (char(cv::waitKey(0)) != 'q') {};

return EXIT_SUCCESS;
}

https://github.com/shreyneil/image_test/blob/master/ela.cpp

欢迎投稿。谢谢。

Answer 1

您并不清楚您希望通过此实现什么，但是使这些函数可从 Cython 调用非常容易。首先对 main 进行一些小的更改 - 它需要重命名，以便它不再充当程序的主要功能，并且由于您只使用第二个命令行参数作为文件名，因此您应该更改它至：

void some_function(char* filename) {
    // Read the image
    input_image = cv::imread(filename);
    // everything else the same
}

然后创建您的 Cython 包装器 cy_wrap.pyx。这有两个部分。首先，您需要告诉 Cython 您的两个 C++ 函数 (cdef extern from)。其次，您需要编写一个小的包装函数，可以从 Python:

中调用它们

cdef extern from "ela.hpp":
    # you'll need to create ela.hpp with declarations for your two functions
    void processImage(int, void*)
    void some_function(char* filename)

# and Python wrappers
def processImagePy():
   # since the parameters are ignored in C++ we can pass anything
   processImage(0,NULL)

def some_functionPy(filename):
   # automatic conversion from string to char*
   some_function(filename)

使用此模块，您将能够调用 processImagePy 和 some_functionPy。

要将其编译为 Python 模块，您需要编写一个 setup.py 文件。我建议你关注 the template given in the Cython documentation (which you have read, right?). Your source files will be cy_wrap.pyx and ela.cpp. You'll probably want to link to the OpenCV library. You'll need to specify language="c++"

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