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我正在尝试使用 floodFill 将我非常缓慢的天真分割重写为更快的东西。一年前我排除了meanShiftFiltering，因为很难标记颜色然后找到它们的轮廓。

当前版本的 opencv 似乎有一个快速的新函数，它使用均值偏移标记段：gpu::meanShiftSegmentation()。它产生如下图像：

meanShiftSegmentation() 输出
（来源：ekran.org）

所以这在我看来非常接近能够生成轮廓。如何运行 findContours 来生成细分？

在我看来，这可以通过从图像中提取标记颜色来完成，然后测试图像中的哪些像素值与每个标签颜色匹配以制作适合 findContours 的布尔图像。这就是我在下面所做的（但它有点慢，让我觉得应该有更好的方法）：

Mat image = imread("test.png");

...
// gpu operations on image resulting in gpuOpen
...

// Mean shift
TermCriteria iterations = TermCriteria(CV_TERMCRIT_ITER, 2, 0);
gpu::meanShiftSegmentation(gpuOpen, segments, 10, 20, 300, iterations);

// convert to greyscale (HSV image)
vector<Mat> channels;
split(segments, channels);

// get labels from histogram of image.
int size = 256;
labels = Mat(256, 1, CV_32SC1);
calcHist(&channels.at(2), 1, 0, Mat(), labels, 1, &size, 0);

// Loop through hist bins
for (int i=0; i<256; i++) {
    float count = labels.at<float>(i);

    // Does this bin represent a label in the image?
    if (count > 0) {
        // find areas of the image that match this label and findContours on the result.
        Mat label = Mat(channels.at(2).rows, channels.at(2).cols, CV_8UC1, Scalar::all(i)); // image filled with label colour.
        Mat boolImage = (channels.at(2) == label); // which pixels in labeled image are identical to this label?
        vector<vector<Point>> labelContours;
        findContours(boolImage, labelContours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
        // Loop through contours.
        for (int idx = 0; idx < labelContours.size(); idx++) {
            // get bounds for this contour.
            bounds = boundingRect(labelContours[idx]);

            // create ROI for bounds to extract this region
            Mat patchROI = image(bounds);
            Mat maskROI = boolImage(bounds);
        }
    }
}

这是最好的方法还是有更好的方法来获取标签颜色？meanShiftSegmentation 提供这些信息似乎是合乎逻辑的？（颜色值向量，或每个标签的掩码向量等）

谢谢你。

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1 回答 1

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以下是另一种在不丢弃 meanShiftSegmentation 结果中的颜色信息的情况下执行此操作的方法。我没有比较两者的性能。

// Loop through whole image, pixel and pixel and then use the colour to index an array of bools indicating presence.

vector<Scalar> colours;
vector<Scalar>::iterator colourIter;
vector< vector< vector<bool> > > colourSpace;
vector< vector< vector<bool> > >::iterator colourSpaceBIter; 
vector< vector<bool> >::iterator colourSpaceGIter;
vector<bool>::iterator colourSpaceRIter;

// Initialize 3D Vector
colourSpace.resize(256);
for (int i = 0; i < 256; i++) {
    colourSpace[i].resize(256);
    for (int j = 0; j < 256; j++) {
        colourSpace[i][j].resize(256);
    }
}

// Loop through pixels in the image (should be fastish, look into LUT for faster)
uchar r, g, b;
for (int i = 0; i < segments.rows; i++)
{
    Vec3b* pixel = segments.ptr<Vec3b>(i); // point to first pixel in row
    for (int j = 0; j < segments.cols; j++)
    {
        b = pixel[j][0];
        g = pixel[j][1];
        r = pixel[j][2];

        colourSpace[b][g][r] = true; // this colour is in the image.
        //cout << "BGR: " << int(b) << " " << int(g) << " " << int(r) << endl;
    }
}

// Get all the unique colours from colourSpace
// loop through colourSpace
int bi=0;
for (colourSpaceBIter = colourSpace.begin(); colourSpaceBIter != colourSpace.end(); colourSpaceBIter++) {
    int gi=0;
    for (colourSpaceGIter = colourSpaceBIter->begin(); colourSpaceGIter != colourSpaceBIter->end(); colourSpaceGIter++) {
        int ri=0;
        for (colourSpaceRIter = colourSpaceGIter->begin(); colourSpaceRIter != colourSpaceGIter->end(); colourSpaceRIter++) {
            if (*colourSpaceRIter)
                colours.push_back( Scalar(bi,gi,ri) );
            ri++;
        }
        gi++;
    }
    bi++;
}

// For each colour
int segmentCount = 0;
for (colourIter = colours.begin(); colourIter != colours.end(); colourIter++) {

    Mat label = Mat(segments.rows, segments.cols, CV_8UC3, *colourIter); // image filled with label colour.
    Mat boolImage = Mat(segments.rows, segments.cols, CV_8UC3);
    inRange(segments, *colourIter, *colourIter, boolImage); // which pixels in labeled image are identical to this label?

    vector<vector<Point> > labelContours;
    findContours(boolImage, labelContours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);

    // Loop through contours.
    for (int idx = 0; idx < labelContours.size(); idx++) {
        // get bounds for this contour.
        Rect bounds = boundingRect(labelContours[idx]);
        float area = contourArea(labelContours[idx]);

        // Draw this contour on a new blank image
        Mat maskImage = Mat::zeros(boolImage.rows, boolImage.cols, boolImage.type());
        drawContours(maskImage, labelContours, idx, Scalar(255,255,255), CV_FILLED);

        Mat patchROI = frame(bounds);
        Mat maskROI = maskImage(bounds);
    }
    segmentCount++;
}
于 2015-06-11T00:50:49.287 回答