如何在ubuntu的opencv和dlib上裁剪图像 我想用Ubuntu上的C++在OpenCV和DLIB上做这些。p> 使用dlib检测人脸。（我已经这样做了。） 仅裁剪嘴部周围的图像

这是我的密码。它基于dlib示例代码

#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/image_processing/render_face_detections.h>
#include <dlib/image_processing.h>
#include <dlib/gui_widgets.h>
#include <dlib/image_io.h>
#include <iostream>
#include <opencv2/opencv.hpp>
#include <highgui.h>


using namespace dlib;
using namespace std;

// ----------------------------------------------------------------------------------------

int main(int argc, char** argv)
{  
    try
    {
        // This example takes in a shape model file and then a list of images to
        // process.  We will take these filenames in as command line arguments.
        // Dlib comes with example images in the examples/faces folder so give
        // those as arguments to this program.
        if (argc == 1)
        {
            cout << "Call this program like this:" << endl;
            cout << "./face_landmark_detection_ex shape_predictor_68_face_landmarks.dat faces/*.jpg" << endl;
            cout << "\nYou can get the shape_predictor_68_face_landmarks.dat file from:\n";
            cout << "http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2" << endl;
            return 0;
        }

        // We need a face detector.  We will use this to get bounding boxes for
        // each face in an image.
        frontal_face_detector detector = get_frontal_face_detector();
        // And we also need a shape_predictor.  This is the tool that will predict face
        // landmark positions given an image and face bounding box.  Here we are just
        // loading the model from the shape_predictor_68_face_landmarks.dat file you gave
        // as a command line argument.
        shape_predictor sp;
        deserialize(argv[1]) >> sp;
    cv::Mat cimg = cv::imread(argv[1]);


        image_window win, win_faces;
        // Loop over all the images provided on the command line.
        for (int i = 2; i < argc; ++i)
        {
            cout << "processing image " << argv[i] << endl;
            array2d<rgb_pixel> img;
            load_image(img, argv[i]);
/*
            // Make the image larger so we can detect small faces.
            pyramid_up(img);
*/
            // Now tell the face detector to give us a list of bounding boxes
            // around all the faces in the image.
            std::vector<rectangle> dets = detector(img);
            cout << "Number of faces detected: " << dets.size() << endl;

            // Now we will go ask the shape_predictor to tell us the pose of
            // each face we detected.
            std::vector<full_object_detection> shapes;
            for (unsigned long j = 0; j < dets.size(); ++j)
            {
                full_object_detection shape = sp(img, dets[j]);
                cout << "number of parts: "<< shape.num_parts() << endl;
                cout << "pixel position of first part:  " << shape.part(0) << endl;
                cout << "pixel position of second part: " << shape.part(1) << endl;
                // You get the idea, you can get all the face part locations if
                // you want them.  Here we just store them in shapes so we can
                // put them on the screen.
                shapes.push_back(shape);
            }

        // Crop the original image to the defined ROI */
        cv::Rect roi;
        roi.x = 0;
        roi.y = 0;
        roi.width = 200;
        roi.height = 200;

        cv::Mat crop = cimg(roi);
        cv::imshow("crop", crop);

            // Now let's view our face poses on the screen.
/*
            win.clear_overlay();
            win.set_image(img);
            win.add_overlay(render_face_detections(shapes));

            // We can also extract copies of each face that are cropped, rotated upright,
            // and scaled to a standard size as shown here:
            //dlib::array<array2d<rgb_pixel> > face_chips;
            //extract_image_chips(img, get_face_chip_details(shapes), face_chips);
            //win_faces.set_image(tile_images(face_chips));
*/

            cout << "Hit enter to process the next image..." << endl;
            cin.get();
        }
    }
    catch (exception& e)
    {
        cout << "\nexception thrown!" << endl;
        cout << e.what() << endl;
    }
}

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// ----------------------------------------------------------------------------------------
int main（int argc，字符**argv）
{  
尝试
{
//此示例接收一个形状模型文件，然后接收一个要创建的图像列表
//我们将把这些文件名作为命令行参数。
//Dlib在examples/faces文件夹中附带示例图像，因此请给出
//这些都是这个程序的参数。
如果（argc==1）
{
您的代码中可能有许多错误：

cv:：Mat cimg=cv:：imread（argv[1]）；-argv[1]是检测器文件，而不是图像-您将得到空图像，这就是程序崩溃的原因
您没有遍历图像。请尝试以下操作：

for (int i = 2; i < argc; ++i)
{
    cout << "processing image " << argv[i] << endl;
    cv::Mat cvimg = cv::imgread(argv[i]);
    dlib::cv_image<rgb_pixel> img(cvimg);
...

dlib::rectangle r = dets[j];
cv::Rect roi(r.left(), r.top(), r.width(), r.height());
cv::Mat face = cvimg(roi);

但它将是全脸图像。如果您只想裁剪嘴巴，则应使用shape predictor的输出（未测试-请检查编译是否良好）：

这将产生未对齐的口腔图像
很明显，您定义的roi（0,0200200）超出了图像的范围。检查cimg的行和列是否大于200。您能给我一条完整的线吗？我做不好。
std::vector<rectangle> dets = detector(img);

dlib::rectangle r = dets[j];
cv::Rect roi(r.left(), r.top(), r.width(), r.height());
cv::Mat face = cvimg(roi);

full_object_detection shape = sp(img, dets[j]);
auto mouth_left = shape.part(45);
auto mouth_right = shape.part(54);
unsigned long mouth_width = (mouth_right - mouth_left).length();
double padding = 0.2;
cv::Rect roi(mouth_left.x() - mouth_width * padding, mouth_left.y() - mouth_width*0.5, mouth_width * (1 + padding * 2), mouth_width);
cv::Mat mouth = cvimg(roi);