漫画气球检测：如何计算向量中的白色像素 <RotatedRect> OpenCV中的椭圆形？

Question

我一直在到处寻找答案，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的白色的椭圆（百分比待定），因此为什么我需要计算轮廓内的白色像素而我却不不知道如何

我已经尝试过countNonZero()但是由于该参数是一个数组，因此它不接受声明为vector<RotatedRect> minEllipse[i]或contours[i] 。

下面是代码：

// 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

非常笨重的if语句仅使我获得漫画气球检测的准确率达到53％（更不用说所有错误检测了），这就是为什么我需要获得轮廓中白色像素所占百分比的原因，发现白色像素所占的百分比更高。

编辑：

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

我只应在CaptionDetection函数上计算每个字幕的像素数

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

稍后将编辑fittingEllipse()的if条件，以提高准确性。

感谢您对a-Jays和Kornel的帮助和时间用户！

Answer 1

假设您有一个旋转矩形rRect ，它定义了一个椭圆，就像代码中的minEllipse[i]一样。

首先，可以通过闭合公式area = a * b * PI来估计area = a * b * PI ，其中a和b是半长轴和半短轴（椭圆的长轴和短轴的1⁄2），因此，

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;

或更短：

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

---

或者，您可以通过cv::ellipse()将其简单地绘制在蒙版上，即：

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

您通常会计算非零元素：

int area = cv::countNonZero(mask);