add test for feature extraction using CNN

modules/cnn_3dobj/README.md

@@ -21,7 +21,7 @@ $ cmake -D CMAKE_INSTALL_PREFIX=/usr/local ..
 $ make all -j4
 $ sudo make install
 ```
-###After all these steps, the headers and libs of CAFFE will be set on /usr/local/ path, and when you compiling opencv with opencv_contrib modules as below, the protobuf and caffe will be recognized as already installed while building. Protobuf is 
+###After all these steps, the headers and libs of CAFFE will be set on /usr/local/ path, and when you compiling opencv with opencv_contrib modules as below, the protobuf and caffe will be recognized as already installed while building. Protobuf is needed.
 
 #Compiling OpenCV
 ```
@@ -57,7 +57,7 @@ $ make
 ```
 $ ./sphereview_test -plymodel=../3Dmodel/ape.ply -label_class=0
 ```
-###press 'Q' to start 2D image genaration 
+###press 'Q' to start 2D image genaration
 ```
 $ ./sphereview_test -plymodel=../3Dmodel/ant.ply -label_class=1
 ```

modules/cnn_3dobj/test/test_cnn_3dobj_feature_extract.cpp

+/*
+ *  Created on: Aug 14, 2015
+ *      Author: yidawang
+ */
+
+#include "test_precomp.hpp"
+
+using namespace cv;
+using namespace cv::cnn_3dobj;
+
+class CV_CNN_Feature_Test : public cvtest::BaseTest
+{
+public:
+    CV_CNN_Feature_Test();
+protected:
+    void run(int);
+};
+
+CV_CNN_Feature_Test::CV_CNN_Feature_Test()
+{
+}
+
+/**
+ * This test checks the following:
+ * Feature extraction by the triplet trained CNN model
+ */
+void CV_CNN_Feature_Test::run(int)
+{
+    string caffemodel = ts->get_data_path() + "cnn_3dobj/samples/data/3d_triplet_iter_20000.caffemodel";
+    string network_forIMG   = ts->get_data_path() + "cnn_3dobj/samples/data/3d_triplet_testIMG.prototxt";
+    string mean_file    = "no";
+    string target_img   = ts->get_data_path() + "cnn_3dobj/samples/data/images_all/2_24.png";
+    string feature_blob = "feat";
+    string device = "CPU";
+    int dev_id = 0;
+
+    cv::cnn_3dobj::descriptorExtractor descriptor;
+    bool set_succeed = descriptor.setNet(device, dev_id);
+    if (!set_succeed) {
+      ts->printf(cvtest::TS::LOG, "Net parameters which is GPU or CPU could not be set");
+      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
+      return;
+    }
+    int net_ready;
+    if (strcmp(mean_file.c_str(), "no") == 0)
+        net_ready = descriptor.loadNet(set_succeed, network_forIMG, caffemodel);
+    else
+        net_ready = descriptor.loadNet(set_succeed, network_forIMG, caffemodel, mean_file);
+    if (!net_ready) {
+      ts->printf(cvtest::TS::LOG, "No model loaded");
+      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
+      return;
+    }
+    cv::Mat img = cv::imread(target_img, -1);
+    if (img.empty()) {
+      ts->printf(cvtest::TS::LOG, "could not read image %s\n", target_img.c_str());
+      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
+      return;
+    }
+    cv::Mat feature_test;
+    descriptor.extract(net_ready, img, feature_test, feature_blob);
+    if (feature_test.empty()) {
+      ts->printf(cvtest::TS::LOG, "could not extract feature from image %s\n", target_img.c_str());
+      ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
+      return;
+    }
+
+}
+
+TEST(VIDEO_BGSUBGMG, accuracy) { CV_CNN_Feature_Test test; test.safe_run(); }

modules/cnn_3dobj/test/test_main.cpp

+#include "test_precomp.hpp"
+
+CV_TEST_MAIN("cv")

modules/cnn_3dobj/test/test_precomp.hpp

+#ifdef __GNUC__
+#  pragma GCC diagnostic ignored "-Wmissing-declarations"
+#  if defined __clang__ || defined __APPLE__
+#    pragma GCC diagnostic ignored "-Wmissing-prototypes"
+#    pragma GCC diagnostic ignored "-Wextra"
+#  endif
+#endif
+
+#ifndef __OPENCV_TEST_PRECOMP_HPP__
+#define __OPENCV_TEST_PRECOMP_HPP__
+
+#include <iostream>
+#include "opencv2/ts.hpp"
+#include "opencv2/imgproc.hpp"
+#include "opencv2/cnn_3dobj_config.hpp"
+#include "opencv2/cnn_3dobj.hpp"
+
+#endif