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cthon的cython文档的一些部分，但它没有帮助我，而且我没有找到任何有关在线实现包装器的额外信息。如果有人可以指导我并帮助我解决这个问题，那真的很棒。

这是我想要构建python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

欢迎捐款。谢谢。

Answer 1

你希望通过这个实现目标并不是很清楚，但是从Cython中调用这些函数非常容易。首先对main进行一些小的更改 - 它将需要重命名，以便它不再作为程序的主函数，并且由于您只使用第二个命令行参数作为文件名，您应该将其更改为：

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

然后创建你的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，您需要编写一个可以从Python调用这些函数的小包装器函数：

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setup.py文件。我建议你关注the template given in the Cython documentation（你读过，对吗？）。您的源文件将是cy_wrap.pyx和ela.cpp。您可能想要链接到OpenCV库。你需要specify language="c++"