mirror of
https://github.com/opencv/opencv_contrib.git
synced 2025-10-19 11:21:39 +08:00
99 lines
5.0 KiB
C++
99 lines
5.0 KiB
C++
/*
|
|
* Software License Agreement (BSD License)
|
|
*
|
|
* Copyright (c) 2009, Willow Garage, Inc.
|
|
* All rights reserved.
|
|
*
|
|
* 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 Willow Garage, Inc. nor the names of its
|
|
* contributors may 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
|
|
* COPYRIGHT OWNER 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.
|
|
*
|
|
*/
|
|
#include <opencv2/cnn_3dobj.hpp>
|
|
#include <iomanip>
|
|
using namespace cv;
|
|
using namespace std;
|
|
using namespace cv::cnn_3dobj;
|
|
int main(int argc, char** argv)
|
|
{
|
|
const String keys = "{help | | this demo will convert a set of images in a particular path into leveldb database for feature extraction using Caffe.}"
|
|
"{src_dir | ../data/images_all/ | Source direction of the images ready for being used for extract feature as gallery.}"
|
|
"{caffemodel | ../data/3d_triplet_iter_10000.caffemodel | caffe model for feature exrtaction.}"
|
|
"{network_forIMG | ../data/3d_triplet_testIMG.prototxt | Network definition file used for extracting feature from a single image and making a classification}"
|
|
"{mean_file | ../data/images_mean/triplet_mean.binaryproto | The mean file generated by Caffe from all gallery images, this could be used for mean value substraction from all images.}"
|
|
"{label_file | ../data/label_all.txt | A namelist including all gallery images.}"
|
|
"{target_img | ../data/images_all/2_13.png | Path of image waiting to be classified.}"
|
|
"{num_candidate | 6 | Number of candidates in gallery as the prediction result.}";
|
|
cv::CommandLineParser parser(argc, argv, keys);
|
|
parser.about("Demo for Sphere View data generation");
|
|
if (parser.has("help"))
|
|
{
|
|
parser.printMessage();
|
|
return 0;
|
|
}
|
|
string src_dir = parser.get<string>("src_dir");
|
|
string caffemodel = parser.get<string>("caffemodel");
|
|
string network_forIMG = parser.get<string>("network_forIMG");
|
|
string mean_file = parser.get<string>("mean_file");
|
|
string label_file = parser.get<string>("label_file");
|
|
string target_img = parser.get<string>("target_img");
|
|
int num_candidate = parser.get<int>("num_candidate");
|
|
cv::cnn_3dobj::DataTrans transTemp;
|
|
std::vector<string> name_gallery;
|
|
transTemp.list_dir(src_dir.c_str(), name_gallery, false);
|
|
for (unsigned int i = 0; i < name_gallery.size(); i++) {
|
|
name_gallery[i] = src_dir + name_gallery[i];
|
|
}
|
|
////start another demo
|
|
cv::cnn_3dobj::Classification classifier(network_forIMG, caffemodel, mean_file, label_file);
|
|
std::vector<cv::Mat> feature_reference;
|
|
for (unsigned int i = 0; i < name_gallery.size(); i++) {
|
|
cv::Mat img_gallery = cv::imread(name_gallery[i], -1);
|
|
feature_reference.push_back(classifier.feature_extract(img_gallery, false));
|
|
}
|
|
|
|
std::cout << std::endl << "---------- Prediction for "
|
|
<< target_img << " ----------" << std::endl;
|
|
|
|
cv::Mat img = cv::imread(target_img, -1);
|
|
// CHECK(!img.empty()) << "Unable to decode image " << target_img;
|
|
std::cout << std::endl << "---------- Featrue of gallery images ----------" << std::endl;
|
|
std::vector<std::pair<string, float> > prediction;
|
|
for (unsigned int i = 0; i < feature_reference.size(); i++)
|
|
std::cout << feature_reference[i].t() << endl;
|
|
cv::Mat feature_test = classifier.feature_extract(img, false);
|
|
std::cout << std::endl << "---------- Featrue of target image: " << target_img << "----------" << endl << feature_test.t() << std::endl;
|
|
prediction = classifier.Classify(feature_reference, img, num_candidate, false);
|
|
// Print the top N prediction.
|
|
std::cout << std::endl << "---------- Prediction result(distance - file name in gallery) ----------" << std::endl;
|
|
for (size_t i = 0; i < prediction.size(); ++i) {
|
|
std::pair<string, float> p = prediction[i];
|
|
std::cout << std::fixed << std::setprecision(2) << p.second << " - \""
|
|
<< p.first << "\"" << std::endl;
|
|
}
|
|
return 0;
|
|
}
|