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}"
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@@ -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");
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@@ -100,15 +123,16 @@ int main(int argc, char** argv)
constStringkeys="{help | | demo :$ ./sphereview_test -ite_depth=2 -plymodel=../data/3Dmodel/ape.ply -imagedir=../data/images_all/ -labeldir=../data/label_all.txt -num_class=6 -label_class=0, then press 'q' to run the demo for images generation when you see the gray background and a coordinate.}"
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@@ -51,17 +87,20 @@ int main(int argc, char *argv[])
"{plymodel | ../data/3Dmodel/ape.ply | Path of the '.ply' file for image rendering. }"
"{imagedir | ../data/images_all/ | Path of the generated images for one particular .ply model. }"
"{labeldir | ../data/label_all.txt | Path of the generated images for one particular .ply model. }"
"{bakgrdir | | Path of the backgroud images sets. }"
"{cam_head_x | 0 | Head of the camera. }"
"{cam_head_y | 0 | Head of the camera. }"
"{cam_head_z | -1 | Head of the camera. }"
"{semisphere | 1 | Camera only has positions on half of the whole sphere. }"
"{z_range | 0.6 | Maximum camera position on z axis. }"
"{center_gen | 0 | Find center from all points. }"
"{image_size | 128 | Size of captured images. }"
"{label_class | 1 | Class label of current .ply model. }"
"{label_item | 1 | Item label of current .ply model. }"
"{label_class | | Class label of current .ply model. }"
"{label_item | | Item label of current .ply model. }"
"{rgb_use | 0 | Use RGB image or grayscale. }"
"{num_class | 6 | Total number of classes of models. }"
"{binary_out | 0 | Produce binaryfiles for images and label. }";
"{binary_out | 0 | Produce binaryfiles for images and label. }"
"{front_view | 0 | Take a special view of front angle}";
/* Get parameters from comand line. */
cv::CommandLineParserparser(argc,argv,keys);
parser.about("Generating training data for CNN with triplet loss");
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@@ -71,33 +110,78 @@ int main(int argc, char *argv[])
