Opencv与Python：pointPolygonTest给出明显错误的结果 [英] Opencv with Python: pointPolygonTest gives obviously wrong result

问题描述

我有一个轮廓下面。因为我想提取这个轮廓内的所有像素和黑色的一切去除噪声，我使用cv2.pointPolygonTest为目的。

I have a contour below. As I wanted to extract all the pixels inside this contour and black out everything else to remove noise, I used cv2.pointPolygonTest for the purpose.

下面是我使用的代码尝试创建掩码。

Below are the code I used to attempt to create the mask.

inside_or_not = np.zeros(img.shape[:2],dtype = np.int32)
for i in range(0,img.shape[0]):
    for j in range(0,img.shape[1]):
        inside_or_not[i,j] = cv2.pointPolygonTest(body_line,(i,j),False)

里面的点。除此之外，我期望在轮廓上的点的数量，因此从cv2.pointPolygonTest返回0应当匹配定义轮廓的像素的数量。然而，当我运行sum（sum（inside_or_not == 0）），它不匹配轮廓上的像素数。
我也用鼠标点击点击一个点显然在轮廓内，并把这一点进入测试;但是返回-1表示测试失败。

Only 2 points were found to be inside the point. On top of that, I expect the number of points lying on the contour, hence returning 0 from the cv2.pointPolygonTest should match the number of pixels defining the contour. However when I run sum(sum(inside_or_not == 0)), it does not match the No. of pixels on the contour. I also used mouse click to click a point obviously inside the contour and put that point into the test; but -1 is returned indicating the test failed.

我也使用了一个approximPolyDP函数来尝试用更少的顶点逼近轮廓。这一次有点多返回。但我不知道为什么！

I also used a approxPolyDP function to attempt to approximate the contour with less vertices. This time a bit more points were returned. However I have no idea why!

任何帮助，感谢，谢谢。

Any help is appreciated, thanks.

推荐答案

由于你有一个定义良好的轮廓，你可以使用 findContour c>使用 CV_RETR_EXTERNAL 检测外部轮廓，并用 drawContour（...，CV_FILLED） / code>。一旦你有了面具，你可以使用 copyTo 根据面具仅复制原始图像的部分。

Since you have a well defined contour, you can simply use findContour with CV_RETR_EXTERNAL to detect the external contour, and draw the region inside it with drawContour(..., CV_FILLED). Once you have the mask, you can use copyTo to copy only the parts of your original image according to the mask.

这里是一个例子，它是C ++，但是移植到python很简单。

Here an example, it's C++, but the porting to python is straightforward.

#include <opencv2\opencv.hpp>
#include <vector>

using namespace std;
using namespace cv;

int main(int argc, char** argv)
{
    // Load image
    Mat3b img = imread("path_to_image_shape");

    // Just to have a background image the same size of your image.
    Mat3b bkg = imread("path_to_image_background");
    resize(bkg, bkg, img.size());

    // Convert to gray
    Mat1b gray;
    cvtColor(img, gray, COLOR_BGR2GRAY);

    // Get external contour
    vector<vector<Point>> contours;
    findContours(gray.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);

    // contours has size 1 here, you need to check this in a real application

    // Create a mask with the inner part of the contour
    Mat1b mask(img.size(), uchar(0));
    drawContours(mask, contours, 0, Scalar(255), CV_FILLED);

    // Create a black RGB image
    Mat3b res(img.size(), Vec3b(0,0,0));

    // Copy the image according to the mask
    bkg.copyTo(res, mask);

    imshow("result", res);
    waitKey();

    return 0;
}

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