漫画气球检测：如何计算 OpenCV 中矢量 <RotatedRect> 椭圆内的白色像素？

Question

我到处寻找答案，但找不到。

我正在制作一个漫画气球检测程序，我需要找到一个在轮廓内具有特定百分比白色的椭圆（百分比稍后确定），因此我需要计算内部的白色像素轮廓，我不知道如何。

我已经尝试过 countNonZero()，但是因为它的参数是一个数组，所以它不接受我声明为 vector<RotatedRect> 的 minEllipse[i] 或 contours[i]。

代码如下：

// Modified version of thresold_callback function 
// from http://docs.opencv.org/doc/tutorials/imgproc/shapedescriptors/bounding_rotated_ellipses/bounding_rotated_ellipses.html
            Mat fittingEllipse(int, void*, Mat inputImage)
            {
                Mat threshold_output;
                vector<vector<Point> > contours;
                vector<Vec4i> hierarchy;
                int numberOfCaptions = 0;

                // Detect edges using Threshold
                threshold(inputImage, threshold_output, 224, 250, THRESH_BINARY);

                findContours(inputImage, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

                vector<RotatedRect> minEllipse(contours.size());
                Mat drawing = Mat::zeros(inputImage.size(), CV_8UC3);

                for (int i = 0; i < contours.size(); i++)
                {
                    if (contours[i].size() > 5)
                        minEllipse[i] = fitEllipse(Mat(contours[i]));
                }

                int totalContourSize = 0, whitepixels, blackpixels;

                //Draw ellipse/caption
                for (int i = 0; i < contours.size(); i++)
                {
                    Scalar color = Scalar(255, 0, 0);

                    if (minEllipse[i].size.height >= inputImage.rows / 8 && //IJIP-290-libre.pdf
                        minEllipse[i].size.width >= inputImage.cols / 10 && //IJIP-290-libre.pdf
                        minEllipse[i].size.height < inputImage.rows / 3  &&
                        minEllipse[i].size.width < inputImage.cols / 3 &&
                        (
                        (minEllipse[i].angle >= 0 && minEllipse[i].angle <= 10) ||
                        (minEllipse[i].angle >= 80 && minEllipse[i].angle <= 100) ||
                        (minEllipse[i].angle >= 170 && minEllipse[i].angle <= 190) ||
                        (minEllipse[i].angle >= 260 && minEllipse[i].angle <= 280) ||
                        (minEllipse[i].angle >= 350 && minEllipse[i].angle <= 360)
                        )) {

                        ellipse(drawing, minEllipse[i], color, -1, 8);
                    }
                }

                drawing = binarizeImage(drawing);
                return drawing;
            } // end of fittingEllipse


            Mat CaptionDetection(Mat inputImage){
                Mat outputImage, binaryImage, captionDetectImage;

                binaryImage = captionDetectImage = binarizeImage(inputImage);
                threshold(captionDetectImage, captionDetectImage, 224, 250, 0); //IJIP-290-libre.pdf

                GaussianBlur(captionDetectImage, captionDetectImage, Size(9, 9), 0, 0);
                captionDetectImage = fittingEllipse(0, 0, captionDetectImage);

                //binaryImage = invertImage(binaryImage);

                outputImage = inputImage;

                for (int i = 0; i < inputImage.rows; i++) {
                    for (int j = 0; j < inputImage.cols; j++) {
                        if (captionDetectImage.at<uchar>(i, j) == 0) {
                            outputImage.at<Vec3b>(i, j)[0] = outputImage.at<Vec3b>(i, j)[1] = outputImage.at<Vec3b>(i, j)[2] = 0;
                        }
                    }
                }

                return outputImage;
            } // end of CaptionDetection

非常庞大的 if 语句让我获得漫画气球检测的准确率仅为 53%（更不用说所有错误检测），这就是为什么我需要获得轮廓中白色像素的百分比获得更高的百分比。

编辑：

我想要的输出是整个漫画页面除了漫画气球外都是黑色的，然后计算那里的白色和黑色像素的数量

ONLY 在 CaptionDetection 函数上，我应该计算每个字幕的像素数吗

最终答案

我编辑了用户 Kornel 提供的代码

            Mat fittingEllipse(int, void*, Mat inputImage)
            {
                Mat outputImage;
                vector<Vec4i> hierarchy;
                int numberOfCaptions = 0;

                // Detect edges using Threshold
                threshold(inputImage, inputImage, 224, 250, THRESH_BINARY);

                findContours(inputImage, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

                vector<RotatedRect> minEllipse(contours.size());

                for (int i = 0; i < contours.size(); i++)
                {
                    if (contours[i].size() > 5)
                        minEllipse[i] = fitEllipse(Mat(contours[i]));
                }

                //Draw ellipse/caption
                outputImage = Mat::zeros(inputImage.size(), CV_8UC3);
                for (int i = 0; i < contours.size(); i++)
                {
                    Scalar color = Scalar(255, 255, 255);
                    Mat drawing = Mat::zeros(inputImage.size(), CV_8UC3);

                    ellipse(drawing, minEllipse[i], color, -1, 8);

                    drawing = binarizeImage(drawing);
                    int area = countNonZero(drawing);

                    if ((area >= 10000 && area <= 40000) &&
                        (
                        (minEllipse[i].angle >= 0 && minEllipse[i].angle <= 10) ||
                        (minEllipse[i].angle >= 80 && minEllipse[i].angle <= 100) ||
                        (minEllipse[i].angle >= 170 && minEllipse[i].angle <= 190) ||
                        (minEllipse[i].angle >= 260 && minEllipse[i].angle <= 280) ||
                        (minEllipse[i].angle >= 350 && minEllipse[i].angle <= 360)
                        )){
                        ellipse(outputImage, minEllipse[i], color, -1, 8);
                        captionMask[captionCount] = drawing;
                        captionCount++;
                    }
                }

                imwrite((string)SAVE_FILE_DEST + "out.jpg", outputImage);

                return outputImage;
            } // end of fittingEllipse
            Mat replaceROIWithOrigImage(Mat inputImg, Mat mask, int k){
                Mat outputImage = inputImg;
                Mat maskImg = mask;
                imwrite((string)SAVE_FILE_DEST + "inputbefore[" + to_string(k) + "].jpg", inputImg);
                for (int i = 0; i < inputImg.rows; i++) {
                    for (int j = 0; j < inputImg.cols; j++) {

                        if (maskImg.at<uchar>(i, j) == 0) {
                            inputImg.at<Vec3b>(i, j)[0] = inputImg.at<Vec3b>(i, j)[1] = inputImg.at<Vec3b>(i, j)[2] = 0;
                        }

                    }
                }
                imwrite((string)SAVE_FILE_DEST + "maskafter[" + to_string(k) + "].jpg", inputImg);
                return inputImg;
            }

            Mat CaptionDetection(Mat inputImage){
                Mat outputImage, binaryImage, captionDetectImage;

                binaryImage = captionDetectImage = binarizeImage(inputImage);
                threshold(captionDetectImage, captionDetectImage, 224, 250, 0); //IJIP-290-libre.pdf

                GaussianBlur(captionDetectImage, captionDetectImage, Size(9, 9), 0, 0);
                captionDetectImage = fittingEllipse(0, 0, captionDetectImage);

                for (int i = 0; i < captionCount; i++){

                    Mat replacedImg = replaceROIWithOrigImage(inputImage.clone(), captionMask[i], i);

                    int area = countNonZero(binarizeImage(replacedImg));

                    cout << area << endl;
                }

                return outputImage;
            } // end of CaptionDetection

fittingEllipse() 中的 if 条件稍后将进行编辑以提高准确性。

感谢用户 a-Jays 和 Kornel 的帮助和时间！

Answer 1

假设您有一个旋转的矩形 rRect，它在您的代码中定义了一个椭圆，就像 minEllipse[i] 一样。

首先，它的面积可以通过闭合公式area = a * b * PI来估算，其中a和b是半长轴和半短轴（1分别为椭圆的长轴和短轴的 ⁄2)，所以：

cv::RotatedRect rRect(cv::Point2f(100.0f, 100.0f), cv::Size2f(100.0f, 50.0f), 30.0f);
float area = (rRect.size.width / 2.0f) * (rRect.size.height / 2.0f) * M_PI;

或者更短一点：

float area = (rRect.size.area() / 4.0f) * M_PI;

或者，您可以通过 cv::ellipse() 将其简单地绘制在遮罩上，即：

cv::Mat mask = cv::Mat::zeros(200, 200, CV_8UC1);
cv::ellipse(mask, rRect, cv::Scalar::all(255), -1);

并且您照常计算非零元素：

int area = cv::countNonZero(mask);

漫画气球检测：如何计算 OpenCV 中矢量 <RotatedRect> 椭圆内的白色像素？

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c++

opencv

image-processing

feature-extraction

feature-detection