| OpenCV 4.3.0 Open Source Computer Vision

Classify

## Goal

In this tutorial you will learn how to

- How to extract feature from an image
- How to extract features from images under a given root path
- How to make a prediction using reference images and target image

## Code

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#include <opencv2/cnn_3dobj.hpp>
#include <opencv2/features2d.hpp>
#include <iomanip>
using namespace cv;
using namespace std;
using namespace cv::cnn_3dobj;
static void listDir(const char *path, std::vector<String>& files, bool r)
{
DIR *pDir;
struct dirent *ent;
char childpath[512];
pDir = opendir(path);
memset(childpath, 0, sizeof(childpath));
while ((ent = readdir(pDir)) != NULL)
{
if (ent->d_type & DT_DIR)
{
if (strcmp(ent->d_name, ".") == 0 || strcmp(ent->d_name, "..") == 0 || strcmp(ent->d_name, ".DS_Store") == 0)
{
continue;
}
if (r)
{
sprintf(childpath, "%s/%s", path, ent->d_name);
listDir(childpath,files,false);
}
}
else
{
if (strcmp(ent->d_name, ".DS_Store") != 0)
files.push_back(ent->d_name);
}
}
sort(files.begin(),files.end());
};
static int featureWrite(const Mat &features, const String &fname)
{
ofstream ouF;
ouF.open(fname.c_str(), std::ofstream::binary);
if (!ouF)
{
cerr << "failed to open the file : " << fname << endl;
return 0;
}
for (int r = 0; r < features.rows; r++)
{
ouF.write(reinterpret_cast<const char*>(features.ptr(r)), features.cols*features.elemSize());
}
ouF.close();
return 1;
}
int main(int argc, char** argv)
{
const String keys = "{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}"
"{mean_file | no | The mean file generated by Caffe from all gallery images, this could be used for mean value substraction from all images. If you want to use the mean file, you can set this as ../data/images_mean/triplet_mean.binaryproto.}"
"{target_img | ../data/images_all/4_78.png | Path of image waiting to be classified.}"
"{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}"
"{gallery_out | 0 | Option on output binary features on gallery images}";
/* get parameters from comand line */
cv::CommandLineParser parser(argc, argv, keys);
parser.about("Feature extraction and classification");
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 target_img = parser.get<String>("target_img");
String feature_blob = parser.get<String>("feature_blob");
int num_candidate = parser.get<int>("num_candidate");
String device = parser.get<String>("device");
int gallery_out = parser.get<int>("gallery_out");
/* Initialize a net work with Device */
cv::cnn_3dobj::descriptorExtractor descriptor(device);
std::cout << "Using" << descriptor.getDeviceType() << std::endl;
/* Load net with the caffe trained net work parameter and structure */
if (strcmp(mean_file.c_str(), "no") == 0)
descriptor.loadNet(network_forIMG, caffemodel);
else
descriptor.loadNet(network_forIMG, caffemodel, mean_file);
std::vector<String> name_gallery;
/* List the file names under a given path */
listDir(src_dir.c_str(), name_gallery, false);
if (gallery_out)
{
ofstream namelist_out("gallelist.txt");
/* Writing name of the reference images. */
for (unsigned int i = 0; i < name_gallery.size(); i++)
namelist_out << name_gallery.at(i) << endl;
}
for (unsigned int i = 0; i < name_gallery.size(); i++)
{
name_gallery[i] = src_dir + name_gallery[i];
}
std::vector<cv::Mat> img_gallery;
cv::Mat feature_reference;
for (unsigned int i = 0; i < name_gallery.size(); i++)
{
img_gallery.push_back(cv::imread(name_gallery[i]));
}
/* Extract feature from a set of images */
descriptor.extract(img_gallery, feature_reference, feature_blob);
if (gallery_out)
{
std::cout << std::endl << "---------- Features of gallery images ----------" << std::endl;
/* Print features of the reference images. */
for (int i = 0; i < feature_reference.rows; i++)
std::cout << feature_reference.row(i) << endl;
std::cout << std::endl << "---------- Saving features of gallery images into feature.bin ----------" << std::endl;
featureWrite(feature_reference, "feature.bin");
}
else
{
std::cout << std::endl << "---------- Prediction for " << target_img << " ----------" << std::endl;
cv::Mat img = cv::imread(target_img);
std::cout << std::endl << "---------- Features of gallery images ----------" << std::endl;
std::vector<std::pair<String, float> > prediction;
/* Print features of the reference images. */
for (int i = 0; i < feature_reference.rows; i++)
std::cout << feature_reference.row(i) << endl;
cv::Mat feature_test;
descriptor.extract(img, feature_test, feature_blob);
/* Initialize a matcher which using L2 distance. */
cv::BFMatcher matcher(NORM_L2);
std::vector<std::vector<cv::DMatch> > matches;
/* Have a KNN match on the target and reference images. */
matcher.knnMatch(feature_test, feature_reference, matches, num_candidate);
/* Print feature of the target image waiting to be classified. */
std::cout << std::endl << "---------- Features of target image: " << target_img << "----------" << endl << feature_test << std::endl;
/* Print the top N prediction. */
std::cout << std::endl << "---------- Prediction result(Distance - File Name in Gallery) ----------" << std::endl;
for (size_t i = 0; i < matches[0].size(); ++i)
{
std::cout << i << " - " << std::fixed << std::setprecision(2) << name_gallery[matches[0][i].trainIdx] << " - \"" << matches[0][i].distance << "\"" << std::endl;
}
}
return 0;
}

## Explanation

Here is the general structure of the program:

- Initialize a net work with Device.
  cv::cnn_3dobj::descriptorExtractor descriptor(device);
- Load net with the caffe trained net work parameter and structure.
  if (strcmp(mean_file.c_str(), "no") == 0)
  descriptor.loadNet(network_forIMG, caffemodel);
  else
  descriptor.loadNet(network_forIMG, caffemodel, mean_file);
- List the file names under a given path.
  listDir(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];
  }
- Extract feature from a set of images.
  descriptor.extract(img_gallery, feature_reference, feature_blob);
- Initialize a matcher which using L2 distance.
  cv::BFMatcher matcher(NORM_L2);
  std::vector<std::vector<cv::DMatch> > matches;
- Have a KNN match on the target and reference images.
  matcher.knnMatch(feature_test, feature_reference, matches, num_candidate);
- Print features of the reference images.
  std::cout << std::endl << "---------- Features of target image: " << target_img << "----------" << endl << feature_test << std::endl;

  ## Results