constStringkeys="{help | | This sample will extract featrues from reference images and target image for classification. You can add a mean_file if there little variance in data such as human faces, otherwise it is not so useful}"
constStringkeys="{help | | This sample will extract features from reference images and target image for classification. You can add a mean_file if there little variance in data such as human faces, otherwise it is not so useful}"
"{src_dir | ../data/images_all/ | Source direction of the images ready for being used for extract feature as gallery.}"
"{caffemodel | ../../testdata/cv/3d_triplet_iter_30000.caffemodel | caffe model for feature exrtaction.}"
"{network_forIMG | ../../testdata/cv/3d_triplet_testIMG.prototxt | Network definition file used for extracting feature from a single image and making a classification}"
...
...
@@ -91,7 +113,8 @@ int main(int argc, char** argv)
"{feature_blob | feat | Name of layer which will represent as the feature, in this network, ip1 or feat is well.}"
"{num_candidate | 15 | Number of candidates in gallery as the prediction result.}"
"{device | CPU | Device type: CPU or GPU}"
"{dev_id | 0 | Device id}";
"{dev_id | 0 | Device id}"
"{gallery_out | 0 | Option on output binary features on gallery images}";
/* get parameters from comand line */
cv::CommandLineParserparser(argc,argv,keys);
parser.about("Feature extraction and classification");
...
...
@@ -100,15 +123,16 @@ int main(int argc, char** argv)
