OpenCV 错误：图像步长错误（矩阵不连续）

Question

通过命令行启动我的程序时，遇到这样的问题： OpenCV 错误：图像步骤错误（矩阵不连续，因此无法更改其行数） un cv::Mat::reshape，文件 C:\builds\2_4_PackSlave-win64-vc12-shared\opencv\modules \core\src\matrix.cpp，第 802 行。

程序代码：

#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/objdetect/objdetect.hpp"

#include <iostream>
#include <fstream>
#include <sstream>

using namespace cv;
using namespace std;

static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
    std::ifstream file(filename.c_str(), ifstream::in);
    if (!file) {
        string error_message = "No valid input file was given, please check the given filename.";
        CV_Error(CV_StsBadArg, error_message);
    }
    string line, path, classlabel;
    while (getline(file, line)) {
        stringstream liness(line);
        getline(liness, path, separator);
        getline(liness, classlabel);
        if (!path.empty() && !classlabel.empty()) {
            images.push_back(imread(path, 0));
            labels.push_back(atoi(classlabel.c_str()));
        }
    }
}

int main(int argc, const char *argv[]) {
    // Check for valid command line arguments, print usage
    // if no arguments were given.
    if (argc != 4) {
        cout << "usage: " << argv[0] << " </path/to/haar_cascade> </path/to/csv.ext> </path/to/device id>" << endl;
        cout << "\t </path/to/haar_cascade> -- Path to the Haar Cascade for face detection." << endl;
        cout << "\t </path/to/csv.ext> -- Path to the CSV file with the face database." << endl;
        cout << "\t <device id> -- The webcam device id to grab frames from." << endl;
        exit(1);
    }
    // Get the path to your CSV:
    string fn_haar = string(argv[1]);
    string fn_csv = string(argv[2]);
    int deviceId = atoi(argv[3]);
    // These vectors hold the images and corresponding labels:
    vector<Mat> images;
    vector<int> labels;
    // Read in the data (fails if no valid input filename is given, but you'll get an error message):
    try {
        read_csv(fn_csv, images, labels);
    }
    catch (cv::Exception& e) {
        cerr << "Error opening file \"" << fn_csv << "\". Reason: " << e.msg << endl;
        // nothing more we can do
        exit(1);
    }
    // Get the height from the first image. We'll need this
    // later in code to reshape the images to their original
    // size AND we need to reshape incoming faces to this size:
    int im_width = images[0].cols;
    int im_height = images[0].rows;
    // Create a FaceRecognizer and train it on the given images:
    Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
    model->train(images, labels);
    // That's it for learning the Face Recognition model. You now
    // need to create the classifier for the task of Face Detection.
    // We are going to use the haar cascade you have specified in the
    // command line arguments:
    //
    CascadeClassifier haar_cascade;
    haar_cascade.load(fn_haar);
    // Get a handle to the Video device:
    VideoCapture cap(deviceId);
    // Check if we can use this device at all:
    if (!cap.isOpened()) {
        cerr << "Capture Device ID " << deviceId << "cannot be opened." << endl;
        return -1;
    }
    // Holds the current frame from the Video device:
    Mat frame;
    for (;;) {
        cap >> frame;
        // Clone the current frame:
        Mat original = frame.clone();
        // Convert the current frame to grayscale:
        Mat gray;
        cvtColor(original, gray, CV_BGR2GRAY);
        // Find the faces in the frame:
        vector< Rect_<int> > faces;
        haar_cascade.detectMultiScale(gray, faces);
        // At this point you have the position of the faces in
        // faces. Now we'll get the faces, make a prediction and
        // annotate it in the video. Cool or what?
        for (int i = 0; i < faces.size(); i++) {
            // Process face by face:
            Rect face_i = faces[i];
            // Crop the face from the image. So simple with OpenCV C++:
            Mat face = gray(face_i);
            // Resizing the face is necessary for Eigenfaces and Fisherfaces. You can easily
            // verify this, by reading through the face recognition tutorial coming with OpenCV.
            // Resizing IS NOT NEEDED for Local Binary Patterns Histograms, so preparing the
            // input data really depends on the algorithm used.
            //
            // I strongly encourage you to play around with the algorithms. See which work best
            // in your scenario, LBPH should always be a contender for robust face recognition.
            //
            // Since I am showing the Fisherfaces algorithm here, I also show how to resize the
            // face you have just found:
            Mat face_resized;
            cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
            // Now perform the prediction, see how easy that is:
            int prediction = model->predict(face_resized);
            // And finally write all we've found out to the original image!
            // First of all draw a green rectangle around the detected face:
            rectangle(original, face_i, CV_RGB(0, 255, 0), 1);
            // Create the text we will annotate the box with:
            string box_text = format("Prediction = %d", prediction);
            // Calculate the position for annotated text (make sure we don't
            // put illegal values in there):
            int pos_x = std::max(face_i.tl().x - 10, 0);
            int pos_y = std::max(face_i.tl().y - 10, 0);
            // And now put it into the image:
            putText(original, box_text, Point(pos_x, pos_y), FONT_HERSHEY_PLAIN, 1.0, CV_RGB(0, 255, 0), 2.0);
        }
        // Show the result:
        imshow("face_recognizer", original);
        // And display it:
        char key = (char)waitKey(20);
        // Exit this loop on escape:
        if (key == 27)
            break;
    }
    return 0;
}

我必须做什么？

Answer 1

FisherFaceRecognizer（也是 Eigen）尝试将图像“扁平化”为单行 (reshape()) 以进行训练和测试。

如果 Mat 是不连续的（因为它要么是填充的，要么只是一个 submat/roi），这不起作用。

（再一次，'fileNotFound' 也算作'非连续';]）

如果您的图片是.bmp ，很有可能某些图像编辑器会填充您的图像，因此行大小是 4 的因子。

也许您可以在外部将您的 imgs 批量转换为 .png 或 .pgm

在加载火车图像后调整它们的大小会有所帮助（任何复制它的东西）

或者，在加载代码中更改这一行：

images.push_back(imread(path, 0));

到：

Mat m = imread(path, 1);
Mat m2;
cvtColor(m,m2,CV_BGR_GRAY);
images.push_back(m2);

Answer 2

这是您的 csv 文件中的斜线/反斜线问题。 比如我的是这样的：

sujets\s1/1.pgm;0
sujets\s1/10.pgm;0
...
sujets\s1/9.pgm;0
sujets\s2/1.pgm;1
sujets\s2/10.pgm;1
...
sujets\s2/9.pgm;1
sujets\s3/1.pgm;2
sujets\s3/10.pgm;2
...
sujets\s3/9.pgm;2
sujets\s4/1.pgm;3
sujets\s4/10.pgm;3
...
sujets\s4/9.pgm;3

为此改变：

sujets/s1/1.pgm;0
sujets/s1/10.pgm;0
...
sujets/s1/9.pgm;0
sujets/s2/1.pgm;1
sujets/s2/10.pgm;1
...
sujets/s2/9.pgm;1
sujets/s3/1.pgm;2
sujets/s3/10.pgm;2
...
sujets/s3/9.pgm;2
sujets/s4/1.pgm;3
sujets/s4/10.pgm;3
...
sujets/s4/9.pgm;3

做了什么

Answer 3

我认为问题可能出在Mat face = gray(face_i)。
请阅读 cmets 进行说明。

 Rect face_i = faces[i];
 // This operation makes a new header for the specified sub-array of
 // *this, thus it is a 0(1) operation, that is, no matrix data is 
 // copied. So matrix elements are no longer stored continuously without
 // gaps at the end of each row.
 Mat face = gray(face_i);
 ...
 Mat face_resized;

 // Here new memory for face_resized should be allocated, but I'm not sure. 
 // If not then it is the reason of the error, because in this case 
 // face_resized will contain not continuous data (=face).
 cv::resize(face, face_resized, Size(im_width, im_height), 1.0, 1.0, INTER_CUBIC);
 ...
 // here reshape(face_resized) will be called and will throw the error if 
 // matrix is not continuous
 int prediction = model->predict(face_resized);