Question

我一直在寻找能找到答案但我无法找到的答案。

我正在制作一个漫画气球检测程序，我需要找到一个椭圆，在轮廓内部具有特定百分比的白色（百分比将在稍后决定），因此我需要计算轮廓内的白色像素我不知道怎么做。

我尝试了countNonZero()，但由于该参数是数组，因此不接受声明为minEllipse[i]的{{1}}或contours[i]。

以下是代码：

vector<RotatedRect>

非常庞大的if语句让我获得漫画气球检测的准确率只有53％（更不用说所有的错误检测），这就是为什么我需要在轮廓中获得白色像素的百分比更高的百分比。

修改

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

// 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

函数上的

ONLY 应该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

CaptionDetection

要编辑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中的if条件，以便以后获得更好的准确性。

感谢您的帮助和时间用户a-Jays和Kornel！

Answer 1

假设您有一个旋转的矩形rRect，它在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（1/2）椭圆的长轴和短轴），分别为：

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;

或者更短一些：

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

或者，您可以通过cv::ellipse()简单地在掩码上绘制它，即：

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

你像往常一样计算非零元素：

int area = cv::countNonZero(mask);

漫画气球检测：如何计算OpenCV中矢量<rotatingrect> Ellipse内的白色像素？</rotaterect>

1 个答案: