mirror of
https://github.com/opencv/opencv_contrib.git
synced 2025-10-18 17:24:28 +08:00
Doc files reorganized
This commit is contained in:
Maksim Shabunin
0
modules/face/doc/etc/at.txt
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modules/face/doc/etc/at.txt
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modules/face/doc/src/facerec_demo.cpp
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modules/face/doc/src/facerec_demo.cpp
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modules/face/samples/etc/at.txt
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modules/face/samples/etc/at.txt
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/home/philipp/facerec/data/at/s11/4.pgm;10
|
||||
/home/philipp/facerec/data/at/s11/10.pgm;10
|
||||
/home/philipp/facerec/data/at/s11/8.pgm;10
|
||||
/home/philipp/facerec/data/at/s11/1.pgm;10
|
||||
/home/philipp/facerec/data/at/s26/2.pgm;25
|
||||
/home/philipp/facerec/data/at/s26/7.pgm;25
|
||||
/home/philipp/facerec/data/at/s26/6.pgm;25
|
||||
/home/philipp/facerec/data/at/s26/9.pgm;25
|
||||
/home/philipp/facerec/data/at/s26/5.pgm;25
|
||||
/home/philipp/facerec/data/at/s26/3.pgm;25
|
||||
/home/philipp/facerec/data/at/s26/4.pgm;25
|
||||
/home/philipp/facerec/data/at/s26/10.pgm;25
|
||||
/home/philipp/facerec/data/at/s26/8.pgm;25
|
||||
/home/philipp/facerec/data/at/s26/1.pgm;25
|
||||
/home/philipp/facerec/data/at/s18/2.pgm;17
|
||||
/home/philipp/facerec/data/at/s18/7.pgm;17
|
||||
/home/philipp/facerec/data/at/s18/6.pgm;17
|
||||
/home/philipp/facerec/data/at/s18/9.pgm;17
|
||||
/home/philipp/facerec/data/at/s18/5.pgm;17
|
||||
/home/philipp/facerec/data/at/s18/3.pgm;17
|
||||
/home/philipp/facerec/data/at/s18/4.pgm;17
|
||||
/home/philipp/facerec/data/at/s18/10.pgm;17
|
||||
/home/philipp/facerec/data/at/s18/8.pgm;17
|
||||
/home/philipp/facerec/data/at/s18/1.pgm;17
|
||||
/home/philipp/facerec/data/at/s29/2.pgm;28
|
||||
/home/philipp/facerec/data/at/s29/7.pgm;28
|
||||
/home/philipp/facerec/data/at/s29/6.pgm;28
|
||||
/home/philipp/facerec/data/at/s29/9.pgm;28
|
||||
/home/philipp/facerec/data/at/s29/5.pgm;28
|
||||
/home/philipp/facerec/data/at/s29/3.pgm;28
|
||||
/home/philipp/facerec/data/at/s29/4.pgm;28
|
||||
/home/philipp/facerec/data/at/s29/10.pgm;28
|
||||
/home/philipp/facerec/data/at/s29/8.pgm;28
|
||||
/home/philipp/facerec/data/at/s29/1.pgm;28
|
||||
/home/philipp/facerec/data/at/s33/2.pgm;32
|
||||
/home/philipp/facerec/data/at/s33/7.pgm;32
|
||||
/home/philipp/facerec/data/at/s33/6.pgm;32
|
||||
/home/philipp/facerec/data/at/s33/9.pgm;32
|
||||
/home/philipp/facerec/data/at/s33/5.pgm;32
|
||||
/home/philipp/facerec/data/at/s33/3.pgm;32
|
||||
/home/philipp/facerec/data/at/s33/4.pgm;32
|
||||
/home/philipp/facerec/data/at/s33/10.pgm;32
|
||||
/home/philipp/facerec/data/at/s33/8.pgm;32
|
||||
/home/philipp/facerec/data/at/s33/1.pgm;32
|
||||
/home/philipp/facerec/data/at/s12/2.pgm;11
|
||||
/home/philipp/facerec/data/at/s12/7.pgm;11
|
||||
/home/philipp/facerec/data/at/s12/6.pgm;11
|
||||
/home/philipp/facerec/data/at/s12/9.pgm;11
|
||||
/home/philipp/facerec/data/at/s12/5.pgm;11
|
||||
/home/philipp/facerec/data/at/s12/3.pgm;11
|
||||
/home/philipp/facerec/data/at/s12/4.pgm;11
|
||||
/home/philipp/facerec/data/at/s12/10.pgm;11
|
||||
/home/philipp/facerec/data/at/s12/8.pgm;11
|
||||
/home/philipp/facerec/data/at/s12/1.pgm;11
|
||||
/home/philipp/facerec/data/at/s6/2.pgm;5
|
||||
/home/philipp/facerec/data/at/s6/7.pgm;5
|
||||
/home/philipp/facerec/data/at/s6/6.pgm;5
|
||||
/home/philipp/facerec/data/at/s6/9.pgm;5
|
||||
/home/philipp/facerec/data/at/s6/5.pgm;5
|
||||
/home/philipp/facerec/data/at/s6/3.pgm;5
|
||||
/home/philipp/facerec/data/at/s6/4.pgm;5
|
||||
/home/philipp/facerec/data/at/s6/10.pgm;5
|
||||
/home/philipp/facerec/data/at/s6/8.pgm;5
|
||||
/home/philipp/facerec/data/at/s6/1.pgm;5
|
||||
/home/philipp/facerec/data/at/s22/2.pgm;21
|
||||
/home/philipp/facerec/data/at/s22/7.pgm;21
|
||||
/home/philipp/facerec/data/at/s22/6.pgm;21
|
||||
/home/philipp/facerec/data/at/s22/9.pgm;21
|
||||
/home/philipp/facerec/data/at/s22/5.pgm;21
|
||||
/home/philipp/facerec/data/at/s22/3.pgm;21
|
||||
/home/philipp/facerec/data/at/s22/4.pgm;21
|
||||
/home/philipp/facerec/data/at/s22/10.pgm;21
|
||||
/home/philipp/facerec/data/at/s22/8.pgm;21
|
||||
/home/philipp/facerec/data/at/s22/1.pgm;21
|
||||
/home/philipp/facerec/data/at/s15/2.pgm;14
|
||||
/home/philipp/facerec/data/at/s15/7.pgm;14
|
||||
/home/philipp/facerec/data/at/s15/6.pgm;14
|
||||
/home/philipp/facerec/data/at/s15/9.pgm;14
|
||||
/home/philipp/facerec/data/at/s15/5.pgm;14
|
||||
/home/philipp/facerec/data/at/s15/3.pgm;14
|
||||
/home/philipp/facerec/data/at/s15/4.pgm;14
|
||||
/home/philipp/facerec/data/at/s15/10.pgm;14
|
||||
/home/philipp/facerec/data/at/s15/8.pgm;14
|
||||
/home/philipp/facerec/data/at/s15/1.pgm;14
|
||||
/home/philipp/facerec/data/at/s2/2.pgm;1
|
||||
/home/philipp/facerec/data/at/s2/7.pgm;1
|
||||
/home/philipp/facerec/data/at/s2/6.pgm;1
|
||||
/home/philipp/facerec/data/at/s2/9.pgm;1
|
||||
/home/philipp/facerec/data/at/s2/5.pgm;1
|
||||
/home/philipp/facerec/data/at/s2/3.pgm;1
|
||||
/home/philipp/facerec/data/at/s2/4.pgm;1
|
||||
/home/philipp/facerec/data/at/s2/10.pgm;1
|
||||
/home/philipp/facerec/data/at/s2/8.pgm;1
|
||||
/home/philipp/facerec/data/at/s2/1.pgm;1
|
||||
/home/philipp/facerec/data/at/s31/2.pgm;30
|
||||
/home/philipp/facerec/data/at/s31/7.pgm;30
|
||||
/home/philipp/facerec/data/at/s31/6.pgm;30
|
||||
/home/philipp/facerec/data/at/s31/9.pgm;30
|
||||
/home/philipp/facerec/data/at/s31/5.pgm;30
|
||||
/home/philipp/facerec/data/at/s31/3.pgm;30
|
||||
/home/philipp/facerec/data/at/s31/4.pgm;30
|
||||
/home/philipp/facerec/data/at/s31/10.pgm;30
|
||||
/home/philipp/facerec/data/at/s31/8.pgm;30
|
||||
/home/philipp/facerec/data/at/s31/1.pgm;30
|
||||
/home/philipp/facerec/data/at/s28/2.pgm;27
|
||||
/home/philipp/facerec/data/at/s28/7.pgm;27
|
||||
/home/philipp/facerec/data/at/s28/6.pgm;27
|
||||
/home/philipp/facerec/data/at/s28/9.pgm;27
|
||||
/home/philipp/facerec/data/at/s28/5.pgm;27
|
||||
/home/philipp/facerec/data/at/s28/3.pgm;27
|
||||
/home/philipp/facerec/data/at/s28/4.pgm;27
|
||||
/home/philipp/facerec/data/at/s28/10.pgm;27
|
||||
/home/philipp/facerec/data/at/s28/8.pgm;27
|
||||
/home/philipp/facerec/data/at/s28/1.pgm;27
|
||||
/home/philipp/facerec/data/at/s40/2.pgm;39
|
||||
/home/philipp/facerec/data/at/s40/7.pgm;39
|
||||
/home/philipp/facerec/data/at/s40/6.pgm;39
|
||||
/home/philipp/facerec/data/at/s40/9.pgm;39
|
||||
/home/philipp/facerec/data/at/s40/5.pgm;39
|
||||
/home/philipp/facerec/data/at/s40/3.pgm;39
|
||||
/home/philipp/facerec/data/at/s40/4.pgm;39
|
||||
/home/philipp/facerec/data/at/s40/10.pgm;39
|
||||
/home/philipp/facerec/data/at/s40/8.pgm;39
|
||||
/home/philipp/facerec/data/at/s40/1.pgm;39
|
||||
/home/philipp/facerec/data/at/s3/2.pgm;2
|
||||
/home/philipp/facerec/data/at/s3/7.pgm;2
|
||||
/home/philipp/facerec/data/at/s3/6.pgm;2
|
||||
/home/philipp/facerec/data/at/s3/9.pgm;2
|
||||
/home/philipp/facerec/data/at/s3/5.pgm;2
|
||||
/home/philipp/facerec/data/at/s3/3.pgm;2
|
||||
/home/philipp/facerec/data/at/s3/4.pgm;2
|
||||
/home/philipp/facerec/data/at/s3/10.pgm;2
|
||||
/home/philipp/facerec/data/at/s3/8.pgm;2
|
||||
/home/philipp/facerec/data/at/s3/1.pgm;2
|
||||
/home/philipp/facerec/data/at/s38/2.pgm;37
|
||||
/home/philipp/facerec/data/at/s38/7.pgm;37
|
||||
/home/philipp/facerec/data/at/s38/6.pgm;37
|
||||
/home/philipp/facerec/data/at/s38/9.pgm;37
|
||||
/home/philipp/facerec/data/at/s38/5.pgm;37
|
||||
/home/philipp/facerec/data/at/s38/3.pgm;37
|
||||
/home/philipp/facerec/data/at/s38/4.pgm;37
|
||||
/home/philipp/facerec/data/at/s38/10.pgm;37
|
||||
/home/philipp/facerec/data/at/s38/8.pgm;37
|
||||
/home/philipp/facerec/data/at/s38/1.pgm;37
|
||||
169
modules/face/samples/src/facerec_demo.cpp
Executable file
169
modules/face/samples/src/facerec_demo.cpp
Executable file
@@ -0,0 +1,169 @@
|
||||
/*
|
||||
* Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
|
||||
* Released to public domain under terms of the BSD Simplified license.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions are met:
|
||||
* * Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.
|
||||
* * Redistributions in binary form must reproduce the above copyright
|
||||
* notice, this list of conditions and the following disclaimer in the
|
||||
* documentation and/or other materials provided with the distribution.
|
||||
* * Neither the name of the organization nor the names of its contributors
|
||||
* may be used to endorse or promote products derived from this software
|
||||
* without specific prior written permission.
|
||||
*
|
||||
* See <http://www.opensource.org/licenses/bsd-license>
|
||||
*/
|
||||
|
||||
#include "opencv2/core.hpp"
|
||||
#include "opencv2/face.hpp"
|
||||
#include "opencv2/highgui.hpp"
|
||||
|
||||
#include <iostream>
|
||||
#include <fstream>
|
||||
#include <sstream>
|
||||
|
||||
using namespace cv;
|
||||
using namespace cv::face;
|
||||
using namespace std;
|
||||
|
||||
static Mat norm_0_255(InputArray _src) {
|
||||
Mat src = _src.getMat();
|
||||
// Create and return normalized image:
|
||||
Mat dst;
|
||||
switch(src.channels()) {
|
||||
case 1:
|
||||
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC1);
|
||||
break;
|
||||
case 3:
|
||||
cv::normalize(_src, dst, 0, 255, NORM_MINMAX, CV_8UC3);
|
||||
break;
|
||||
default:
|
||||
src.copyTo(dst);
|
||||
break;
|
||||
}
|
||||
return dst;
|
||||
}
|
||||
|
||||
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 != 2) {
|
||||
cout << "usage: " << argv[0] << " <csv.ext>" << endl;
|
||||
exit(1);
|
||||
}
|
||||
// Get the path to your CSV.
|
||||
string fn_csv = string(argv[1]);
|
||||
// These vectors hold the images and corresponding labels.
|
||||
vector<Mat> images;
|
||||
vector<int> labels;
|
||||
// Read in the data. This can fail if no valid
|
||||
// input filename is given.
|
||||
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);
|
||||
}
|
||||
// Quit if there are not enough images for this demo.
|
||||
if(images.size() <= 1) {
|
||||
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
|
||||
CV_Error(CV_StsError, error_message);
|
||||
}
|
||||
// Get the height from the first image. We'll need this
|
||||
// later in code to reshape the images to their original
|
||||
// size:
|
||||
int height = images[0].rows;
|
||||
// The following lines simply get the last images from
|
||||
// your dataset and remove it from the vector. This is
|
||||
// done, so that the training data (which we learn the
|
||||
// cv::FaceRecognizer on) and the test data we test
|
||||
// the model with, do not overlap.
|
||||
Mat testSample = images[images.size() - 1];
|
||||
int testLabel = labels[labels.size() - 1];
|
||||
images.pop_back();
|
||||
labels.pop_back();
|
||||
// The following lines create an Eigenfaces model for
|
||||
// face recognition and train it with the images and
|
||||
// labels read from the given CSV file.
|
||||
// This here is a full PCA, if you just want to keep
|
||||
// 10 principal components (read Eigenfaces), then call
|
||||
// the factory method like this:
|
||||
//
|
||||
// cv::createEigenFaceRecognizer(10);
|
||||
//
|
||||
// If you want to create a FaceRecognizer with a
|
||||
// confidennce threshold, call it with:
|
||||
//
|
||||
// cv::createEigenFaceRecognizer(10, 123.0);
|
||||
//
|
||||
Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
|
||||
model->train(images, labels);
|
||||
// The following line predicts the label of a given
|
||||
// test image:
|
||||
int predictedLabel = model->predict(testSample);
|
||||
//
|
||||
// To get the confidence of a prediction call the model with:
|
||||
//
|
||||
// int predictedLabel = -1;
|
||||
// double confidence = 0.0;
|
||||
// model->predict(testSample, predictedLabel, confidence);
|
||||
//
|
||||
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
|
||||
cout << result_message << endl;
|
||||
// Sometimes you'll need to get/set internal model data,
|
||||
// which isn't exposed by the public cv::FaceRecognizer.
|
||||
// Since each cv::FaceRecognizer is derived from a
|
||||
// cv::Algorithm, you can query the data.
|
||||
//
|
||||
// First we'll use it to set the threshold of the FaceRecognizer
|
||||
// to 0.0 without retraining the model. This can be useful if
|
||||
// you are evaluating the model:
|
||||
//
|
||||
model->set("threshold", 0.0);
|
||||
// Now the threshold of this model is set to 0.0. A prediction
|
||||
// now returns -1, as it's impossible to have a distance below
|
||||
// it
|
||||
predictedLabel = model->predict(testSample);
|
||||
cout << "Predicted class = " << predictedLabel << endl;
|
||||
// Here is how to get the eigenvalues of this Eigenfaces model:
|
||||
Mat eigenvalues = model->getMat("eigenvalues");
|
||||
// And we can do the same to display the Eigenvectors (read Eigenfaces):
|
||||
Mat W = model->getMat("eigenvectors");
|
||||
// From this we will display the (at most) first 10 Eigenfaces:
|
||||
for (int i = 0; i < min(10, W.cols); i++) {
|
||||
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
|
||||
cout << msg << endl;
|
||||
// get eigenvector #i
|
||||
Mat ev = W.col(i).clone();
|
||||
// Reshape to original size & normalize to [0...255] for imshow.
|
||||
Mat grayscale = norm_0_255(ev.reshape(1, height));
|
||||
// Show the image & apply a Jet colormap for better sensing.
|
||||
Mat cgrayscale;
|
||||
applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
|
||||
imshow(format("%d", i), cgrayscale);
|
||||
}
|
||||
waitKey(0);
|
||||
|
||||
return 0;
|
||||
}
|
||||
@@ -658,7 +658,7 @@ at/s17/3.pgm;1
|
||||
|
||||
Here is the script, if you can't find it:
|
||||
|
||||
@verbinclude face/doc/src/create_csv.py
|
||||
@verbinclude face/samples/src/create_csv.py
|
||||
|
||||
### Aligning Face Images {#tutorial_face_appendix_align}
|
||||
|
||||
@@ -677,7 +677,7 @@ where:
|
||||
If you are using the same *offset_pct* and *dest_sz* for your images, they are all aligned at the
|
||||
eyes.
|
||||
|
||||
@verbinclude face/doc/src/crop_face.py
|
||||
@verbinclude face/samples/src/crop_face.py
|
||||
|
||||
Imagine we are given [this photo of Arnold
|
||||
Schwarzenegger](http://en.wikipedia.org/wiki/File:Arnold_Schwarzenegger_edit%28ws%29.jpg), which is
|
||||
@@ -689,11 +689,11 @@ Here are some examples:
|
||||
|
||||
Configuration | Cropped, Scaled, Rotated Face
|
||||
--------------------------------|------------------------------------------------------------------
|
||||
0.1 (10%), 0.1 (10%), (200,200) | 
|
||||
0.2 (20%), 0.2 (20%), (200,200) | 
|
||||
0.3 (30%), 0.3 (30%), (200,200) | 
|
||||
0.2 (20%), 0.2 (20%), (70,70) | 
|
||||
0.1 (10%), 0.1 (10%), (200,200) | 
|
||||
0.2 (20%), 0.2 (20%), (200,200) | 
|
||||
0.3 (30%), 0.3 (30%), (200,200) | 
|
||||
0.2 (20%), 0.2 (20%), (70,70) | 
|
||||
|
||||
### CSV for the AT&T Facedatabase {#tutorial_face_appendix_attcsv}
|
||||
|
||||
@verbinclude face/doc/etc/at.txt
|
||||
@verbinclude face/samples/etc/at.txt
|
||||
|
||||
|
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@@ -1,146 +0,0 @@
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2014, OpenCV Foundation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
// Author: Tolga Birdal <tbirdal AT gmail.com>
|
||||
|
||||
#include "opencv2/surface_matching.hpp"
|
||||
#include <iostream>
|
||||
#include "opencv2/surface_matching/ppf_helpers.hpp"
|
||||
#include "opencv2/core/utility.hpp"
|
||||
|
||||
using namespace std;
|
||||
using namespace cv;
|
||||
using namespace ppf_match_3d;
|
||||
|
||||
static void help(const string& errorMessage)
|
||||
{
|
||||
cout << "Program init error : "<< errorMessage << endl;
|
||||
cout << "\nUsage : ppf_matching [input model file] [input scene file]"<< endl;
|
||||
cout << "\nPlease start again with new parameters"<< endl;
|
||||
}
|
||||
|
||||
int main(int argc, char** argv)
|
||||
{
|
||||
// welcome message
|
||||
cout << "****************************************************" << endl;
|
||||
cout << "* Surface Matching demonstration : demonstrates the use of surface matching"
|
||||
" using point pair features." << endl;
|
||||
cout << "* The sample loads a model and a scene, where the model lies in a different"
|
||||
" pose than the training.\n* It then trains the model and searches for it in the"
|
||||
" input scene. The detected poses are further refined by ICP\n* and printed to the "
|
||||
" standard output." << endl;
|
||||
cout << "****************************************************" << endl;
|
||||
|
||||
if (argc < 3)
|
||||
{
|
||||
help("Not enough input arguments");
|
||||
exit(1);
|
||||
}
|
||||
|
||||
#if (defined __x86_64__ || defined _M_X64)
|
||||
cout << "Running on 64 bits" << endl;
|
||||
#else
|
||||
cout << "Running on 32 bits" << endl;
|
||||
#endif
|
||||
|
||||
#ifdef _OPENMP
|
||||
cout << "Running with OpenMP" << endl;
|
||||
#else
|
||||
cout << "Running without OpenMP and without TBB" << endl;
|
||||
#endif
|
||||
|
||||
string modelFileName = (string)argv[1];
|
||||
string sceneFileName = (string)argv[2];
|
||||
|
||||
Mat pc = loadPLYSimple(modelFileName.c_str(), 1);
|
||||
|
||||
// Now train the model
|
||||
cout << "Training..." << endl;
|
||||
int64 tick1 = cv::getTickCount();
|
||||
ppf_match_3d::PPF3DDetector detector(0.025, 0.05);
|
||||
detector.trainModel(pc);
|
||||
int64 tick2 = cv::getTickCount();
|
||||
cout << endl << "Training complete in "
|
||||
<< (double)(tick2-tick1)/ cv::getTickFrequency()
|
||||
<< " sec" << endl << "Loading model..." << endl;
|
||||
|
||||
// Read the scene
|
||||
Mat pcTest = loadPLYSimple(sceneFileName.c_str(), 1);
|
||||
|
||||
// Match the model to the scene and get the pose
|
||||
cout << endl << "Starting matching..." << endl;
|
||||
vector<Pose3DPtr> results;
|
||||
tick1 = cv::getTickCount();
|
||||
detector.match(pcTest, results, 1.0/40.0, 0.05);
|
||||
tick2 = cv::getTickCount();
|
||||
cout << endl << "PPF Elapsed Time " <<
|
||||
(tick2-tick1)/cv::getTickFrequency() << " sec" << endl;
|
||||
|
||||
// Get only first N results
|
||||
int N = 2;
|
||||
vector<Pose3DPtr> resultsSub(results.begin(),results.begin()+N);
|
||||
|
||||
// Create an instance of ICP
|
||||
ICP icp(100, 0.005f, 2.5f, 8);
|
||||
int64 t1 = cv::getTickCount();
|
||||
|
||||
// Register for all selected poses
|
||||
cout << endl << "Performing ICP on " << N << " poses..." << endl;
|
||||
icp.registerModelToScene(pc, pcTest, resultsSub);
|
||||
int64 t2 = cv::getTickCount();
|
||||
|
||||
cout << endl << "ICP Elapsed Time " <<
|
||||
(t2-t1)/cv::getTickFrequency() << " sec" << endl;
|
||||
|
||||
cout << "Poses: " << endl;
|
||||
// debug first five poses
|
||||
for (size_t i=0; i<resultsSub.size(); i++)
|
||||
{
|
||||
Pose3DPtr result = resultsSub[i];
|
||||
cout << "Pose Result " << i << endl;
|
||||
result->printPose();
|
||||
if (i==0)
|
||||
{
|
||||
Mat pct = transformPCPose(pc, result->pose);
|
||||
writePLY(pct, "para6700PCTrans.ply");
|
||||
}
|
||||
}
|
||||
|
||||
return 0;
|
||||
|
||||
}
|
||||
@@ -111,7 +111,7 @@ point sampling, I will be leaving that aside now in order to respect the general
|
||||
methods (Typically for such algorithms training on a CAD model is not needed, and a point cloud
|
||||
would be sufficient). Below is the outline of the entire algorithm:
|
||||
|
||||
|
||||
|
||||
|
||||
As explained, the algorithm relies on the extraction and indexing of point pair features, which are
|
||||
defined as follows:
|
||||
@@ -337,11 +337,11 @@ Results
|
||||
This section is dedicated to the results of surface matching (point-pair-feature matching and a
|
||||
following ICP refinement):
|
||||
|
||||
|
||||
|
||||
|
||||
Matches of different models for Mian dataset is presented below:
|
||||
|
||||
|
||||
|
||||
|
||||
You might checkout the video on [youTube here](http://www.youtube.com/watch?v=uFnqLFznuZU).
|
||||
|
||||
|
||||
Reference in New Issue
Block a user