[英]Translate numpy's array reshape to OpenCV equivalent
我有一個問題是將numpy的ndarray函數轉換為等效的OpenCV C ++調用,以將n維cv :: Mat重新整形/拆分成適當的切片。 特別是我試圖將OpenCV python2示例“texture_flow.py”(> = OpenCV 2.4.3)轉換為C ++。 我在下面的代碼段中標記了有問題的行。
# [......]
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)
# [......]
有人可以幫我翻譯有問題的行到C ++嗎?
在徹底考慮之后,這比預期的要容易得多。 這只是有趣的numpy數組語法令我感到困惑。 重新編寫numpy的數組只是訪問生成的cv :: Mat“eigen”的單個通道的python方式。 以下代碼是OpenCV的“texture_flow.py”的C ++版本(取自OpenCV 2.4.3)。 生成的流圖像與python版本不是100%相同,但它足夠接近。
#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;
}
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.