opencv人脸识别预测错误

Question

我在应用程序中偶然发现了一个无法摆脱的异常。。。

我正在尝试用所有三种人脸识别算法（Eigen、Fisher和LBPH）编写一个简单的图像人脸识别程序。

未处理的异常由以下行引起：

Fisher_prediction = Fisher_model->predict(crop);

错误信息显示：Unhandled exception at at 0x000007FEFDB3A49D in FaceRecognition.exe: Microsoft C++ exception: cv::Exception at memory location 0x00000000002782B0.

由msvcr110d.dll!_CxxThrowException(void * pExceptionObject, const _s__ThrowInfo * pThrowInfo) Line 152 C++ 引起

有什么建议我哪里错了吗？？

这是代码的其余部分：

Mat frame = imread("1.jpg");
    // Apply the classifier to the frame
    if (!frame.empty()) {
        cvtColor(frame, frame_gray, COLOR_BGR2GRAY);
        equalizeHist(frame_gray, frame_gray);
        // Detect faces
        face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30));
        // Set Region of Interest
        cv::Rect roi_b;
        cv::Rect roi_c;
        size_t ic = 0; // ic is index of current element
        int ac = 0; // ac is area of current element
        size_t ib = 0; // ib is index of biggest element
        int ab = 0; // ab is area of biggest element
        // Iterate through all current elements (detected faces)
        for (ic = 0; ic < faces.size(); ic++) {
            roi_c.x = faces[ic].x;
            roi_c.y = faces[ic].y;
            roi_c.width = (faces[ic].width);
            roi_c.height = (faces[ic].height);
            ac = roi_c.width * roi_c.height; // Get the area of current element (detected face)
            roi_b.x = faces[ib].x;
            roi_b.y = faces[ib].y;
            roi_b.width = (faces[ib].width);
            roi_b.height = (faces[ib].height);
            ab = roi_b.width * roi_b.height; // Get the area of biggest element, at beginning it is same as "current" element
            if (ac > ab) {
                ib = ic;
                roi_b.x = faces[ib].x;
                roi_b.y = faces[ib].y;
                roi_b.width = (faces[ib].width);
                roi_b.height = (faces[ib].height);
            }
            crop = frame(roi_b);
            cv::resize(crop, res, Size(img_width, img_height), 0, 0, INTER_LINEAR); // This will be needed later while saving images
            cvtColor(crop, gray, CV_BGR2GRAY); // Convert cropped image to Grayscale
            Point pt1(faces[ic].x, faces[ic].y); // Display detected faces on main window - live stream from camera
            Point pt2((faces[ic].x + faces[ic].height), (faces[ic].y + faces[ic].width));
            //rectangle(frame, pt1, pt2, Scalar(0, 255, 0), 2, 8, 0);
            /* Calculate the position for annotated text */
            int pos_x = std::max(roi_b.tl().x - 10, 0);
            int pos_y = std::max(roi_b.tl().y - 10, 0);
        if(createdFisher) {
            Fisher_prediction = Fisher_model->predict(crop);
            QString Fisher_qs = QString::number(Fisher_prediction);
            /* Create the text we will annotate the box with */
            string Fisher_text = format("Prediction Fisherfaces = %d", Fisher_prediction);
            putText(frame, Fisher_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
            /* Draw a green rectangle around the detected face */
            rectangle(frame, pt1, pt2, MATCH_COLOR, 1);
            ui.txtConsole->appendPlainText(QString("Fisherfaces - " + Fisher_qs));
        }
        if(createdEigen) {
            Eigen_prediction = Eigen_model->predict(crop);
            QString Eigen_qs = QString::number(Eigen_prediction);
            /* Create the text we will annotate the box with */
            string Eigen_text = format("Prediction Eigenfaces = %d", Eigen_prediction);
            putText(frame, Eigen_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
            /* Draw a green rectangle around the detected face */
            rectangle(frame, pt1, pt2, MATCH_COLOR, 1);
            ui.txtConsole->appendPlainText(QString("Eigenfaces - " + Eigen_qs));
        }
        if(createdLBPH) {
            LBPH_prediction = LBPH_model->predict(crop);
            QString LBPH_qs = QString::number(LBPH_prediction);
            /* Create the text we will annotate the box with */
            string LBPH_text = format("Prediction LBPH = %d", LBPH_prediction);
            putText(frame, LBPH_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0,255,0), 2.0);
            /* Draw a green rectangle around the detected face */
            rectangle(frame, pt1, pt2, MATCH_COLOR, 1);
            ui.txtConsole->appendPlainText(QString("Linear Binary Patern Histogram - " + LBPH_qs));
        }
        }
        putText(frame, text, cvPoint(30, 30), FONT_HERSHEY_COMPLEX_SMALL, 0.8, cvScalar(0, 0, 255), 1, CV_AA);
        imshow("original", frame);
        if (!crop.empty()) {
            imshow("detected", crop);
        }
        else
            destroyWindow("detected");
    }
    int c = waitKey(0);

所有必要的#include和变量以及分类器都在程序开始时初始化。

Answer 1

我做错的是，我将一张未调整大小的图片传递给人脸识别器（我在数据库中使用的图片为200x200px），因此算法无法基于比数据库中更大分辨率的图像进行人脸识别。