c++ - 将 numpy 的数组 reshape 转换为等效的 OpenCV

Question

我在将 numpy 的 ndarray 函数转换为它们的等效函数时遇到问题 OpenCV C++ 调用以将 n 维 cv::Mat reshape /拆分为适当的切片。 特别是我正在尝试转换 OpenCV python2 示例“texture_flow.py” (>= OpenCV 2.4.3) 到 C++。我在下面的代码片段中标记了有问题的行。

# [......]
img = cv2.imread(fn)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# simple width and height tuple
h, w = img.shape[:2]

eigen = cv2.cornerEigenValsAndVecs(gray, 15, 3)
print eigen.shape # prints: (height, widht, 6), i.e. 6 channels

# Problem 1:
# OpenCV's reshape function is not sufficient to do this.
# probably must be split into several steps...
eigen = eigen.reshape(h, w, 3, 2)  # [[e1, e2], v1, v2]
print eigen.shape # prints: (height, width, 3, 2)

# Problem 2:
# I assume this is meant to get the the v1 and v2 matrices 
# from the previous reshape
flow = eigen[:,:,2]
print flow.shape # prints: (height, width, 2), i.e. 2 channels

vis = img.copy()
# C++: vis.data[i] = (uchar)((192 + (int)vis.data[i]) / 2);
vis[:] = (192 + np.uint32(vis)) / 2

d = 12

# Problem 3:
# Can probably be split into 2 nested for-loops 
points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)

# [......]

谁能帮我把有问题的行翻译成 C++？

Answer 1

仔细想想，一切都比想象中容易。只是有趣的 numpy 数组语法让我感到困惑。 reshape numpy 的数组只是访问生成的 cv::Mat“特征”的单个 channel 的 python 方式。 以下代码是 OpenCV 的“texture_flow.py”的 C++ 版本(取自 OpenCV 2.4.3)。生成的流图像与 python 版本不是 100% 相同，但已经足够接近了。

#include <opencv2/opencv.hpp>
#include <iostream>

int main (int argc, char** argv)
{
    cv::TickMeter tm;
    tm.start();
    cv::Mat img = cv::imread(argv[1]);
    cv::Mat gray = cv::Mat();
    cv::cvtColor(img, gray, CV_BGR2GRAY);
    // to preserve the original image
    cv::Mat flow = gray.clone();
    int width = img.cols;
    int height = img.rows;
    int graySize = width * height;
    // "brighten" the flow image 
    // C++ version of:
    // vis[:] = (192 + np.uint32(vis)) / 2
    for (unsigned int i=0; i<graySize; ++i)
    {
         flow.data[i] = (uchar)((192 + (int)flow.data[i]) / 2);
    }
    cv::Mat eigen = cv::Mat(height, width, CV_32FC(6));
    cv::cornerEigenValsAndVecs(gray, eigen, 15, 3);
    // this is the equivalent to all the numpy's reshaping etc. to 
    // generate the flow arrays
    // simply use channel 4 and 5 as the actual flow array in C++
    std::vector<cv::Mat> channels;
    cv::split(eigen, channels);

    int d = 12;
    cv::Scalar col(0, 0, 0);
    // C++ version of:
    // points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)
    // including the actual line drawing part
    for (unsigned int y=(d/2); y<flow.rows; y+=d)
    {
         for (unsigned int x=(d/2); x<flow.cols; x+=d)
         {
             if (x < flow.cols && y < flow.rows)
             {
                 cv::Point p(x, y);
                 float dx = channels[4].at<float>(p) * (d/2);
                 float dy = channels[5].at<float>(p) * (d/2);
                 cv::Point p0(p.x - dx, p.y - dy);
                 cv::Point p1(p.x + dx, p.y + dy);
                 cv::line(flow, p0, p1, col, 1);
              }
         }
    }
    tm.stop();
    std::cout<<"Flow image generated in "<<tm.getTimeMilli()<<" ms."<<std::endl;
    cv::imshow("FLOW", flow);
    cv::waitKey();
    return 0;
}