opencv人脸识别预测错误

Question

I have stumbled upon an exception in my application that I am unable to get rid of...

I am trying to write a simple image face recognition program with all three face recognition algorithms(Eigen, Fisher and LBPH).

The unchandled exception is caused by line :

Fisher_prediction = Fisher_model->predict(crop);

and theerror message says: Unhandled exception at at 0x000007FEFDB3A49D in FaceRecognition.exe: Microsoft C++ exception: cv::Exception at memory location 0x00000000002782B0.

and is caused by: msvcr110d.dll!_CxxThrowException(void * pExceptionObject, const _s__ThrowInfo * pThrowInfo) Line 152 C++

Any sugestions where am I going wrong??

here is the rest of the code:

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);

All the necessary #include and variables along with the classifier are initialised at the start of the program.

Answer 1

我做错的是我正在将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是200x200px的图片），因此该算法无法基于比一个更大的分辨率图像进行人脸识别在数据库中。