通过命令行启动我的程序时，有这样的问题： OpenCV错误：图像步骤错误（矩阵不连续，因此其行数无法更改）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？
答案 0 :(得分：5)
这不起作用，如果Mat是非连续的（因为它只是填充或只是submat / roi）。
（然后再说'fileNotFound'也算'非连续';）
如果您的图片是例如.bmp，很有可能是某些图像编辑器填充了你的图像，因此行大小是4倍。
也许你可以将你的imgs批量转换为外部的.png或.pgm
否则在加载火车图像后调整它们的大小会有所帮助（任何东西，它都会复制它）
或者，在加载代码中更改此行：
images.push_back(imread(path, 0));
到：
Mat m = imread(path, 1);
Mat m2;
cvtColor(m,m2,CV_BGR_GRAY);
images.push_back(m2);
答案 1 :(得分：0)
它是csv文件中的斜杠/反斜杠问题。 例如我的是这样的：
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
改变这个：
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
做了招数
答案 2 :(得分：0)
我认为问题可能出在Mat face = gray(face_i)
阅读评论以获得澄清。
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);