Is it possible to turn this c++ code into python code?

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int main() {
cv::Mat color = cv::imread("../houghCircles.png");
cv::namedWindow("input"); cv::imshow("input", color);

cv::Mat canny;

cv::Mat gray;
/// Convert it to gray
cv::cvtColor( color, gray, CV_BGR2GRAY );

// compute canny (don't blur with that image quality!!)
cv::Canny(gray, canny, 200,20);
cv::namedWindow("canny2"); cv::imshow("canny2", canny>0);

std::vector<cv::Vec3f> circles;

/// Apply the Hough Transform to find the circles
cv::HoughCircles( gray, circles, CV_HOUGH_GRADIENT, 1, 60, 200, 20, 0, 0 );

/// Draw the circles detected
for( size_t i = 0; i < circles.size(); i++ ) 
{
    Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
    int radius = cvRound(circles[i][2]);
    cv::circle( color, center, 3, Scalar(0,255,255), -1);
    cv::circle( color, center, radius, Scalar(0,0,255), 1 );
}

//compute distance transform:
cv::Mat dt;
cv::distanceTransform(255-(canny>0), dt, CV_DIST_L2 ,3);
cv::namedWindow("distance transform"); cv::imshow("distance transform", dt/255.0f);

// test for semi-circles:
float minInlierDist = 2.0f;
for( size_t i = 0; i < circles.size(); i++ ) 
{
    // test inlier percentage:
    // sample the circle and check for distance to the next edge
    unsigned int counter = 0;
    unsigned int inlier = 0;

    cv::Point2f center((circles[i][0]), (circles[i][2]));
    float radius = (circles[i][2]);

    // maximal distance of inlier might depend on the size of the circle
    float maxInlierDist = radius/25.0f;
    if(maxInlierDist<minInlierDist) maxInlierDist = minInlierDist;

    //TODO: maybe paramter incrementation might depend on circle size!
    for(float t =0; t<2*3.14159265359f; t+= 0.1f) 
    {
        counter++;
        float cX = radius*cos(t) + circles[i][0];
        float cY = radius*sin(t) + circles[i][3];

        if(dt.at<float>(cY,cX) < maxInlierDist) 
        {
            inlier++;
            cv::circle(color, cv::Point2i(cX,cY),3, cv::Scalar(0,255,0));
        } 
       else
            cv::circle(color, cv::Point2i(cX,cY),3, cv::Scalar(255,0,0));
    }
    std::cout << 100.0f*(float)inlier/(float)counter << " % of a circle with radius " << radius << " detected" << std::endl;
}

cv::namedWindow("output"); cv::imshow("output", color);
cv::imwrite("houghLinesComputed.png", color);

cv::waitKey(-1);
return 0;

}