remove leveldb dependency, using Input/OutputArray for feature extraction, add the newest model, format fix for OpenCV
Wangyida
9 years ago
parent
4fe5498a45
commit
b831fc3bad
14 changed files with 509 additions and 1118 deletions
@ -1,3 +1,3 @@ |
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set(the_description "CNN for 3D object recognition and pose estimation including a completed Sphere View on 3D objects") |
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ocv_define_module(cnn_3dobj opencv_core opencv_imgproc opencv_viz opencv_highgui caffe protobuf leveldb glog OPTIONAL WRAP python) |
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target_link_libraries(opencv_cnn_3dobj caffe protobuf leveldb glog) |
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ocv_define_module(cnn_3dobj opencv_core opencv_imgproc opencv_viz opencv_highgui caffe protobuf glog OPTIONAL WRAP python) |
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target_link_libraries(opencv_cnn_3dobj caffe protobuf glog) |
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|
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@ -1,112 +0,0 @@ |
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/*
|
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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, |
||||
* 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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@ -1,94 +0,0 @@ |
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name: "3d_triplet" |
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layer { |
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name: "data" |
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type: "Data" |
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top: "data" |
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top: "label" |
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include { |
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phase: TEST |
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} |
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data_param { |
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source: "/home/wangyida/Desktop/opencv_contrib/modules/nouse_test/samples/data/dbfile" |
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batch_size: 69 |
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} |
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} |
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layer { |
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name: "conv1" |
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type: "Convolution" |
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bottom: "data" |
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top: "conv1" |
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convolution_param { |
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num_output: 16 |
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kernel_size: 8 |
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stride: 1 |
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} |
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} |
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layer { |
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name: "pool1" |
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type: "Pooling" |
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bottom: "conv1" |
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top: "pool1" |
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pooling_param { |
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pool: MAX |
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kernel_size: 2 |
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stride: 2 |
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} |
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} |
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layer { |
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name: "relu1" |
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type: "ReLU" |
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bottom: "pool1" |
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top: "pool1" |
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} |
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layer { |
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name: "conv2" |
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type: "Convolution" |
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bottom: "pool1" |
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top: "conv2" |
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convolution_param { |
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num_output: 7 |
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kernel_size: 5 |
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stride: 1 |
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} |
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} |
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layer { |
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name: "pool2" |
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type: "Pooling" |
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bottom: "conv2" |
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top: "pool2" |
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pooling_param { |
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pool: MAX |
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kernel_size: 2 |
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stride: 2 |
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} |
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} |
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layer { |
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name: "relu2" |
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type: "ReLU" |
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bottom: "pool2" |
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top: "pool2" |
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} |
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layer { |
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name: "ip1" |
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type: "InnerProduct" |
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bottom: "pool2" |
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top: "ip1" |
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inner_product_param { |
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num_output: 256 |
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} |
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} |
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layer { |
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name: "relu3" |
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type: "ReLU" |
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bottom: "ip1" |
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top: "ip1" |
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} |
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layer { |
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name: "feat" |
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type: "InnerProduct" |
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bottom: "ip1" |
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top: "feat" |
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inner_product_param { |
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num_output: 4 |
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} |
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} |
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@ -1,64 +0,0 @@ |
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/*
|
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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 |
||||
* 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 |
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* with the distribution. |
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* * 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. |
||||
* |
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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 |
||||
* 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, |
||||
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, |
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; |
||||
* 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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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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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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cv::cnn_3dobj::DataTrans Trans; |
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Trans.convert(src_dir,src_dst,attach_dir,channel,width,height); |
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std::cout << std::endl << "All featrues of images in: " << std::endl << src_dir << std::endl << "have been converted to levelDB data in: " << std::endl << src_dst << std::endl << "for extracting feature of gallery images in classification efficiently, this convertion is not needed in feature extraction of test image" << std::endl; |
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} |
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@ -1,99 +0,0 @@ |
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/*
|
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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 |
||||
* 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 |
||||
* 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, |
||||
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; |
||||
* 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 <stdio.h> // for snprintf |
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#include <tr1/memory> |
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#include <string> |
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#include <vector> |
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#include "google/protobuf/text_format.h" |
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#include <opencv2/opencv.hpp> |
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#include <opencv2/core/core.hpp> |
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#define CPU_ONLY |
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#include "caffe/blob.hpp" |
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#include "caffe/common.hpp" |
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#include "caffe/net.hpp" |
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#include "caffe/proto/caffe.pb.h" |
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#include "caffe/util/io.hpp" |
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#include "caffe/vision_layers.hpp" |
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using caffe::Blob; |
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using caffe::Caffe; |
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using caffe::Datum; |
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using caffe::Net; |
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//using boost::shared_ptr;
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using std::string; |
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//namespace db = caffe::db;
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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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"{pretrained_binary_proto | ../data/3d_triplet_iter_10000.caffemodel | caffe model for feature exrtaction.}" |
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"{feature_extraction_proto | ../data/3d_triplet_train_test.prototxt | network definition in .prototxt the path of the 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 | the 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 | the layer used for feature extraction in CNN.}" |
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"{num_mini_batches | 6 | 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}" |
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"{dev_id | 0 | dev_id}"; |
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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 pretrained_binary_proto = parser.get<string>("pretrained_binary_proto"); |
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string feature_extraction_proto = parser.get<string>("feature_extraction_proto"); |
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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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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> extractedFeature = transTemp.feature_extraction_pipeline(pretrained_binary_proto, feature_extraction_proto, save_feature_dataset_names, extract_feature_blob_names, num_mini_batches, device, dev_id); |
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} |
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@ -1,237 +0,0 @@ |
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#include "precomp.hpp" |
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using std::string; |
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using namespace std; |
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|
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namespace cv |
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{ |
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namespace cnn_3dobj |
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{ |
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DataTrans::DataTrans() |
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{ |
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}; |
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void DataTrans::list_dir(const char *path,vector<string>& files,bool r) |
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{ |
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DIR *pDir; |
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struct dirent *ent; |
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char childpath[512]; |
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pDir = opendir(path); |
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memset(childpath, 0, sizeof(childpath)); |
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while ((ent = readdir(pDir)) != NULL) |
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{ |
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if (ent->d_type & DT_DIR) |
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{ |
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|
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if (strcmp(ent->d_name, ".") == 0 || strcmp(ent->d_name, "..") == 0) |
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{ |
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continue; |
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} |
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if(r) |
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{ |
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sprintf(childpath, "%s/%s", path, ent->d_name); |
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DataTrans::list_dir(childpath,files,false); |
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} |
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} |
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else |
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{ |
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files.push_back(ent->d_name); |
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} |
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} |
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sort(files.begin(),files.end()); |
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|
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}; |
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|
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string DataTrans::get_classname(string path) |
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{ |
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int index = path.find_last_of('_'); |
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return path.substr(0, index); |
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} |
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|
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|
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int DataTrans::get_labelid(string fileName) |
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{ |
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string class_name_tmp = get_classname(fileName); |
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all_class_name.insert(class_name_tmp); |
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map<string,int>::iterator name_iter_tmp = class2id.find(class_name_tmp); |
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if (name_iter_tmp == class2id.end()) |
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{ |
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int id = class2id.size(); |
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class2id.insert(name_iter_tmp, std::make_pair(class_name_tmp, id)); |
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return id; |
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} |
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else |
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{ |
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return name_iter_tmp->second; |
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} |
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} |
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|
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void DataTrans::loadimg(string path,char* buffer,const bool is_color) |
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{ |
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cv::Mat img = cv::imread(path, is_color); |
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string val; |
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int rows = img.rows; |
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int cols = img.cols; |
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int pos=0; |
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int channel; |
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if (is_color == 0) |
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{ |
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channel = 1; |
||||
}else{ |
||||
channel = 3; |
||||
} |
||||
for (int c = 0; c < channel; c++) |
||||
{ |
||||
for (int row = 0; row < rows; row++) |
||||
{ |
||||
for (int col = 0; col < cols; col++) |
||||
{ |
||||
buffer[pos++]=img.at<cv::Vec3b>(row,col)[c]; |
||||
} |
||||
} |
||||
} |
||||
|
||||
}; |
||||
|
||||
void DataTrans::convert(string imgdir,string outputdb,string attachdir,int channel,int width,int height) |
||||
{ |
||||
leveldb::DB* db; |
||||
leveldb::Options options; |
||||
options.create_if_missing = true; |
||||
// options.error_if_exists = true;
|
||||
caffe::Datum datum; |
||||
datum.set_channels(channel); |
||||
datum.set_height(height); |
||||
datum.set_width(width); |
||||
int image_size = channel*width*height; |
||||
char buffer[image_size]; |
||||
|
||||
string value; |
||||
CHECK(leveldb::DB::Open(options, outputdb, &db).ok()); |
||||
vector<string> filenames; |
||||
list_dir(imgdir.c_str(),filenames, false); |
||||
string img_log = attachdir+"image_filename"; |
||||
ofstream writefile(img_log.c_str()); |
||||
for(int i=0;i<(int)filenames.size();i++) |
||||
{ |
||||
string path= imgdir; |
||||
path.append(filenames[i]); |
||||
|
||||
loadimg(path,buffer,false); |
||||
|
||||
int labelid = get_labelid(filenames[i]); |
||||
|
||||
datum.set_label(labelid); |
||||
datum.set_data(buffer,image_size); |
||||
datum.SerializeToString(&value); |
||||
snprintf(buffer, image_size, "%05d", i); |
||||
printf("\nclassid:%d classname:%s abspath:%s",labelid,get_classname(filenames[i]).c_str(),path.c_str()); |
||||
db->Put(leveldb::WriteOptions(),string(buffer),value); |
||||
//printf("%d %s\n",i,fileNames[i].c_str());
|
||||
|
||||
assert(writefile.is_open()); |
||||
writefile<<i<<" "<<filenames[i]<<"\n"; |
||||
|
||||
} |
||||
delete db; |
||||
writefile.close(); |
||||
|
||||
img_log = attachdir+"image_classname"; |
||||
writefile.open(img_log.c_str()); |
||||
set<string>::iterator iter = all_class_name.begin(); |
||||
while(iter != all_class_name.end()) |
||||
{ |
||||
assert(writefile.is_open()); |
||||
writefile<<(*iter)<<"\n"; |
||||
//printf("%s\n",(*iter).c_str());
|
||||
iter++; |
||||
} |
||||
writefile.close(); |
||||
|
||||
}; |
||||
|
||||
std::vector<cv::Mat> DataTrans::feature_extraction_pipeline(std::string pretrained_binary_proto, std::string feature_extraction_proto, std::string save_feature_dataset_names, std::string extract_feature_blob_names, int num_mini_batches, std::string device, int dev_id) { |
||||
if (strcmp(device.c_str(), "GPU") == 0) { |
||||
LOG(ERROR)<< "Using GPU"; |
||||
int device_id = 0; |
||||
if (strcmp(device.c_str(), "GPU") == 0) { |
||||
device_id = dev_id; |
||||
CHECK_GE(device_id, 0); |
||||
} |
||||
LOG(ERROR) << "Using Device_id=" << device_id; |
||||
Caffe::SetDevice(device_id); |
||||
Caffe::set_mode(Caffe::GPU); |
||||
} else { |
||||
LOG(ERROR) << "Using CPU"; |
||||
Caffe::set_mode(Caffe::CPU); |
||||
} |
||||
boost::shared_ptr<Net<float> > feature_extraction_net( |
||||
new Net<float>(feature_extraction_proto, caffe::TEST)); |
||||
feature_extraction_net->CopyTrainedLayersFrom(pretrained_binary_proto); |
||||
std::vector<std::string> blob_names; |
||||
blob_names.push_back(extract_feature_blob_names); |
||||
std::vector<std::string> dataset_names; |
||||
dataset_names.push_back(save_feature_dataset_names); |
||||
CHECK_EQ(blob_names.size(), dataset_names.size()) << |
||||
" the number of blob names and dataset names must be equal"; |
||||
size_t num_features = blob_names.size(); |
||||
|
||||
for (size_t i = 0; i < num_features; i++) { |
||||
CHECK(feature_extraction_net->has_blob(blob_names[i])) |
||||
<< "Unknown feature blob name " << blob_names[i] |
||||
<< " in the network " << feature_extraction_proto; |
||||
} |
||||
std::vector<FILE*> files; |
||||
for (size_t i = 0; i < num_features; ++i) |
||||
{ |
||||
LOG(INFO) << "Opening file " << dataset_names[i]; |
||||
FILE * temp = fopen(dataset_names[i].c_str(), "wb"); |
||||
files.push_back(temp); |
||||
} |
||||
|
||||
|
||||
LOG(ERROR)<< "Extacting Features"; |
||||
|
||||
Datum datum; |
||||
std::vector<cv::Mat> featureVec; |
||||
std::vector<Blob<float>*> input_vec; |
||||
std::vector<int> image_indices(num_features, 0); |
||||
for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index) { |
||||
feature_extraction_net->Forward(input_vec); |
||||
for (size_t i = 0; i < num_features; ++i) { |
||||
const boost::shared_ptr<Blob<float> > feature_blob = feature_extraction_net |
||||
->blob_by_name(blob_names[i]); |
||||
int batch_size = feature_blob->num(); |
||||
int dim_features = feature_blob->count() / batch_size; |
||||
if (batch_index == 0) |
||||
{ |
||||
int fea_num = batch_size*num_mini_batches; |
||||
fwrite(&dim_features, sizeof(int), 1, files[i]); |
||||
fwrite(&fea_num, sizeof(int), 1, files[i]); |
||||
} |
||||
const float* feature_blob_data; |
||||
for (int n = 0; n < batch_size; ++n) { |
||||
|
||||
feature_blob_data = feature_blob->cpu_data() + |
||||
feature_blob->offset(n); |
||||
fwrite(feature_blob_data, sizeof(float), dim_features, files[i]); |
||||
cv::Mat tempfeat = cv::Mat(1, dim_features, CV_32FC1); |
||||
for (int dim = 0; dim < dim_features; dim++) { |
||||
tempfeat.at<float>(0,dim) = *(feature_blob_data++); |
||||
} |
||||
featureVec.push_back(tempfeat); |
||||
++image_indices[i]; |
||||
if (image_indices[i] % 1000 == 0) { |
||||
LOG(ERROR)<< "Extracted features of " << image_indices[i] << |
||||
" query images for feature blob " << blob_names[i]; |
||||
} |
||||
} // for (int n = 0; n < batch_size; ++n)
|
||||
} // for (int i = 0; i < num_features; ++i)
|
||||
} // for (int batch_index = 0; batch_index < num_mini_batches; ++batch_index)
|
||||
// write the last batch
|
||||
for (size_t i = 0; i < num_features; ++i) { |
||||
fclose(files[i]); |
||||
} |
||||
|
||||
LOG(ERROR)<< "Successfully extracted the features!"; |
||||
return featureVec; |
||||
}; |
||||
}} |
||||
Loading…
Reference in new issue