mirrors/opencv_contrib
1
0
Fork 0
Code Pull Requests Packages Releases Activity

vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

Browse Source
This commit is contained in:
Gitea Mirror Bot
2026-08-22 00:11:13 +08:00
commit 12022378a3
3872 changed files with 2513409 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/face/samples/facemark_demo_lbf.cpp
+182
View File
@@ -0,0 +1,182 @@
/*
This file was part of GSoC Project: Facemark API for OpenCV
Final report: https://gist.github.com/kurnianggoro/74de9121e122ad0bd825176751d47ecc
Student: Laksono Kurnianggoro
Mentor: Delia Passalacqua
*/
/*----------------------------------------------
* Usage:
* facemark_demo_lbf <face_cascade_model> <saved_model_filename> <training_images> <annotation_files> [test_files]
*
* Example:
* facemark_demo_lbf ../face_cascade.xml ../LBF.model ../images_train.txt ../points_train.txt ../test.txt
*
* Notes:
* the user should provides the list of training images_train
* accompanied by their corresponding landmarks location in separated files.
* example of contents for images_train.txt:
* ../trainset/image_0001.png
* ../trainset/image_0002.png
* example of contents for points_train.txt:
* ../trainset/image_0001.pts
* ../trainset/image_0002.pts
* where the image_xxxx.pts contains the position of each face landmark.
* example of the contents:
* version: 1
* n_points: 68
* {
* 115.167660 220.807529
* 116.164839 245.721357
* 120.208690 270.389841
* ...
* }
* example of the dataset is available at https://ibug.doc.ic.ac.uk/download/annotations/ibug.zip
*--------------------------------------------------*/
#include <stdio.h>
#include <fstream>
#include <sstream>
#include <iostream>
#include "opencv2/core.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/face.hpp"
using namespace std;
using namespace cv;
using namespace cv::face;
static bool myDetector( InputArray image, OutputArray roi, CascadeClassifier *face_detector);
static bool parseArguments(int argc, char** argv, String & cascade,
String & model, String & images, String & annotations, String & testImages
);
int main(int argc, char** argv)
{
String cascade_path,model_path,images_path, annotations_path, test_images_path;
if(!parseArguments(argc, argv, cascade_path,model_path,images_path, annotations_path, test_images_path))
return -1;
/*create the facemark instance*/
FacemarkLBF::Params params;
params.model_filename = model_path;
params.cascade_face = cascade_path;
Ptr<FacemarkLBF> facemark = FacemarkLBF::create(params);
CascadeClassifier face_cascade;
face_cascade.load(params.cascade_face.c_str());
facemark->setFaceDetector((FN_FaceDetector)myDetector, &face_cascade);
/*Loads the dataset*/
std::vector<String> images_train;
std::vector<String> landmarks_train;
loadDatasetList(images_path,annotations_path,images_train,landmarks_train);
Mat image;
std::vector<Point2f> facial_points;
for(size_t i=0;i<images_train.size();i++){
printf("%i/%i :: %s\n", (int)(i+1), (int)images_train.size(),images_train[i].c_str());
image = imread(images_train[i].c_str());
loadFacePoints(landmarks_train[i],facial_points);
facemark->addTrainingSample(image, facial_points);
}
/*train the Algorithm*/
facemark->training();
/*test using some images*/
String testFiles(images_path), testPts(annotations_path);
if(!test_images_path.empty()){
testFiles = test_images_path;
testPts = test_images_path; //unused
}
std::vector<String> images;
std::vector<String> facePoints;
loadDatasetList(testFiles, testPts, images, facePoints);
std::vector<Rect> rects;
CascadeClassifier cc(params.cascade_face.c_str());
for(size_t i=0;i<images.size();i++){
std::vector<std::vector<Point2f> > landmarks;
cout<<images[i];
Mat img = imread(images[i]);
facemark->getFaces(img, rects);
facemark->fit(img, rects, landmarks);
for(size_t j=0;j<rects.size();j++){
drawFacemarks(img, landmarks[j], Scalar(0,0,255));
rectangle(img, rects[j], Scalar(255,0,255));
}
if(rects.size()>0){
cout<<endl;
imshow("result", img);
waitKey(0);
}else{
cout<<"face not found"<<endl;
}
}
}
bool myDetector(InputArray image, OutputArray faces, CascadeClassifier *face_cascade)
{
Mat gray;
if (image.channels() > 1)
cvtColor(image, gray, COLOR_BGR2GRAY);
else
gray = image.getMat().clone();
equalizeHist(gray, gray);
std::vector<Rect> faces_;
face_cascade->detectMultiScale(gray, faces_, 1.4, 2, CASCADE_SCALE_IMAGE, Size(30, 30));
Mat(faces_).copyTo(faces);
return true;
}
bool parseArguments(int argc, char** argv,
String & cascade,
String & model,
String & images,
String & annotations,
String & test_images
){
const String keys =
"{ @c cascade | | (required) path to the face cascade xml file fo the face detector }"
"{ @i images | | (required) path of a text file contains the list of paths to all training images}"
"{ @a annotations | | (required) Path of a text file contains the list of paths to all annotations files}"
"{ @m model | | (required) path to save the trained model }"
"{ t test-images | | Path of a text file contains the list of paths to the test images}"
"{ help h usage ? | | facemark_demo_lbf -cascade -images -annotations -model [-t] \n"
" example: facemark_demo_lbf ../face_cascade.xml ../images_train.txt ../points_train.txt ../lbf.model}"
;
CommandLineParser parser(argc, argv,keys);
parser.about("hello");
if (parser.has("help")){
parser.printMessage();
return false;
}
cascade = String(parser.get<String>("cascade"));
model = String(parser.get<string>("model"));
images = String(parser.get<string>("images"));
annotations = String(parser.get<string>("annotations"));
test_images = String(parser.get<string>("t"));
cout<<"cascade : "<<cascade.c_str()<<endl;
cout<<"model : "<<model.c_str()<<endl;
cout<<"images : "<<images.c_str()<<endl;
cout<<"annotations : "<<annotations.c_str()<<endl;
if(cascade.empty() || model.empty() || images.empty() || annotations.empty()){
std::cerr << "one or more required arguments are not found" << '\n';
parser.printMessage();
return false;
}
return true;
}