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113 lines
6.5 KiB
C++
113 lines
6.5 KiB
C++
/*
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* Software License Agreement (BSD License)
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*
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* Copyright (c) 2009, Willow Garage, Inc.
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of Willow Garage, Inc. nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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*/
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#include <opencv2/cnn_3dobj.hpp>
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#include <iomanip>
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using namespace cv;
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using namespace std;
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using namespace cv::cnn_3dobj;
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int main(int argc, char** argv)
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{
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const String keys = "{help | | this demo will convert a set of images in a particular path into leveldb database for feature extraction using Caffe.}"
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"{src_dir | ../data/images_all/ | Source direction of the images ready for being converted to leveldb dataset.}"
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"{src_dst | ../data/dbfile | Aim direction of the converted to leveldb dataset. }"
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"{attach_dir | ../data/dbfile | Path for saving additional files which describe the transmission results. }"
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"{channel | 1 | Channel of the images. }"
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"{width | 64 | Width of images}"
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"{height | 64 | Height of images}"
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"{caffemodel | ../data/3d_triplet_iter_10000.caffemodel | caffe model for feature exrtaction.}"
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"{network_forDB | ../data/3d_triplet_galleryIMG.prototxt | Network definition file used for extracting feature from levelDB data, causion: the path of levelDB training samples must be wrotten in in .prototxt files in Phase TEST}"
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"{save_feature_dataset_names | ../data/feature/feature_iter_10000.bin | Output of the extracted feature in form of binary files together with the vector<cv::Mat> features as the feature.}"
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"{extract_feature_blob_names | feat | Layer used for feature extraction in CNN.}"
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"{num_mini_batches | 4 | Batches suit for the batches defined in the .proto for the aim of extracting feature from all images.}"
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"{device | CPU | Device: CPU or GPU.}"
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"{dev_id | 0 | ID of GPU.}"
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"{network_forIMG | ../data/3d_triplet_testIMG.prototxt | Network definition file used for extracting feature from a single image and making a classification}"
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"{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.}"
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"{label_file | ../data/dbfileimage_filename | A namelist including all gallery images.}"
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"{target_img | ../data/images_all/2_13.png | Path of image waiting to be classified.}"
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"{num_candidate | 6 | Number of candidates in gallery as the prediction result.}";
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cv::CommandLineParser parser(argc, argv, keys);
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parser.about("Demo for Sphere View data generation");
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if (parser.has("help"))
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{
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parser.printMessage();
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return 0;
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}
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string src_dir = parser.get<string>("src_dir");
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string src_dst = parser.get<string>("src_dst");
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string attach_dir = parser.get<string>("attach_dir");
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int channel = parser.get<int>("channel");
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int width = parser.get<int>("width");
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int height = parser.get<int>("height");
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string caffemodel = parser.get<string>("caffemodel");
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string network_forDB = parser.get<string>("network_forDB");
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string save_feature_dataset_names = parser.get<string>("save_feature_dataset_names");
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string extract_feature_blob_names = parser.get<string>("extract_feature_blob_names");
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int num_mini_batches = parser.get<int>("num_mini_batches");
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string device = parser.get<string>("device");
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int dev_id = parser.get<int>("dev_id");
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string network_forIMG = parser.get<string>("network_forIMG");
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string mean_file = parser.get<string>("mean_file");
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string label_file = parser.get<string>("label_file");
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string target_img = parser.get<string>("target_img");
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int num_candidate = parser.get<int>("num_candidate");
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cv::cnn_3dobj::DataTrans transTemp;
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transTemp.convert(src_dir,src_dst,attach_dir,channel,width,height);
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std::vector<cv::Mat> feature_reference = transTemp.feature_extraction_pipeline(caffemodel, network_forDB, save_feature_dataset_names, extract_feature_blob_names, num_mini_batches, device, dev_id);
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////start another demo
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cv::cnn_3dobj::Classification classifier(network_forIMG, caffemodel, mean_file, label_file);
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std::cout << std::endl << "---------- Prediction for "
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<< target_img << " ----------" << std::endl;
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cv::Mat img = cv::imread(target_img, -1);
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// CHECK(!img.empty()) << "Unable to decode image " << target_img;
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std::cout << std::endl << "---------- Featrue of gallery images ----------" << std::endl;
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std::vector<std::pair<string, float> > prediction;
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for (unsigned int i = 0; i < feature_reference.size(); i++)
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std::cout << feature_reference[i] << endl;
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cv::Mat feature_test = classifier.feature_extract(img, false);
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std::cout << std::endl << "---------- Featrue of target image: " << target_img << "----------" << endl << feature_test.t() << std::endl;
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prediction = classifier.Classify(feature_reference, img, num_candidate, false);
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// Print the top N prediction.
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std::cout << std::endl << "---------- Prediction result(distance - file name in gallery) ----------" << std::endl;
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for (size_t i = 0; i < prediction.size(); ++i) {
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std::pair<string, float> p = prediction[i];
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std::cout << std::fixed << std::setprecision(2) << p.second << " - \""
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<< p.first << "\"" << std::endl;
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}
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return 0;
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}
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