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bccbec79fd
Face alignment (#1199) * This commit will add a new functionality of one millisecond face_alignment to OpenCV. Face alignment is a computer vision technology for identifying the geometric structure of human faces in digital images. Given the location and size of a face, it automatically determines the shape of the face components such as eyes and nose. Added following functions : 1) Application to train a face landmark detector. 2) Application to detect face landmarks using a trained model. 3) Application to swap faces using face landmark detection 4) Application to detect landmarks in a video. Merged the code with a global facemark API. Added Doxygen Documentation for the Class created. Added tutorials for the samples added. Added visualisations depicting error rate and training time. Made desired changes fix fix fix fix fix fix fix fix fix * face: drop duplicated file -face_alignmentImpl.hpp +face_alignmentimpl.hpp * face: minor refactoring - replace license headers - fix usage of "precomp.hpp"
64 lines
3.1 KiB
C++
64 lines
3.1 KiB
C++
#include "opencv2/core.hpp"
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#include <iostream>
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#include <string>
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using namespace cv;
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using namespace std;
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int main(int argc,const char ** argv){
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CommandLineParser parser(argc, argv,
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"{ help h usage ? | | give the following arguments in following format }"
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"{ filename f |. | (required) path to file which you want to create as config file [example - /data/config.xml] }"
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"{ cascade_depth cd | 10 | (required) This stores the depth of cascade of regressors used for training.}"
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"{ tree_depth td | 4 | (required) This stores the depth of trees created as weak learners during gradient boosting.}"
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"{ num_trees_per_cascade_level| 500 | (required) This stores number of trees required per cascade level.}"
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"{ learning_rate | 0.1 | (required) This stores the learning rate for gradient boosting.}"
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"{ oversampling_amount | 20 | (required) This stores the oversampling amount for the samples.}"
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"{ num_test_coordinates | 400 | (required) This stores number of test coordinates required for making the split.}"
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"{ lambda | 0.1 | (required) This stores the value used for calculating the probabilty.}"
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"{ num_test_splits | 20 | (required) This stores the number of test splits to be generated before making the best split.}"
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);
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// Read in the input arguments
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if (parser.has("help")){
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parser.printMessage();
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cerr << "TIP: Use absolute paths to avoid any problems with the software!" << endl;
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return 0;
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}
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//These variables have been initialised as defined in the research paper "One millisecond face alignment" CVPR 2014
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int cascade_depth = 15;
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int tree_depth = 4;
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int num_trees_per_cascade_level = 500;
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float learning_rate = float(0.1);
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int oversampling_amount = 20;
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int num_test_coordinates = 400;
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float lambda = float(0.1);
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int num_test_splits = 20;
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cascade_depth = parser.get<int>("cascade_depth");
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tree_depth = parser.get<int>("tree_depth");
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num_trees_per_cascade_level = parser.get<int>("num_trees_per_cascade_level");
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learning_rate = parser.get<float>("learning_rate");
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oversampling_amount = parser.get<int>("oversampling_amount");
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num_test_coordinates = parser.get<int>("num_test_coordinates");
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lambda = parser.get<float>("lambda");
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num_test_splits = parser.get<int>("num_test_splits");
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string filename(parser.get<string>("filename"));
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FileStorage fs(filename, FileStorage::WRITE);
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if (!fs.isOpened())
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{
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cerr << "Failed to open " << filename << endl;
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parser.printMessage();
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return -1;
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}
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fs << "cascade_depth" << cascade_depth;
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fs << "tree_depth"<< tree_depth;
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fs << "num_trees_per_cascade_level" << num_trees_per_cascade_level;
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fs << "learning_rate" << learning_rate;
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fs << "oversampling_amount" << oversampling_amount;
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fs << "num_test_coordinates" << num_test_coordinates;
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fs << "lambda" << lambda ;
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fs << "num_test_splits"<< num_test_splits;
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fs.release();
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cout << "Write Done." << endl;
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return 0;
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}
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