您将学习如何分析一个训练过的模型的性能。
在本节教程中，我们将学习如何如何分析一个训练过的模型的性能，主要内容包括：
- 如何从特定图像中提取特征
- 如何对提取的特征进行有意义的对比
代码清单6-18
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#include <iostream>
#include "opencv2/imgproc.hpp"
#include "opencv2/cnn_3dobj.hpp"
using namespace cv;
using namespace cv::cnn_3dobj;
int main(int argc, char** argv)
{
const String keys = "{help | | this demo will have an analysis on the trained model, it will print information about whether the model is suit for set different classes apart and also discriminant on object pose at the same time.}"
"{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 in reference.}"
"{ref_img1 | ../data/images_all/4_79.png | Path of closest image.}"
"{ref_img2 | ../data/images_all/4_87.png | Path of less closer image in the same class with reference image.}"
"{ref_img3 | ../data/images_all/3_78.png | Path of image with the same pose in another class.}"
"{feature_blob | feat | Name of layer which will represent as the feature, in this network, ip1 or feat is well.}"
"{device | CPU | device}"
"{dev_id | 0 | dev_id}";
/* Get parameters from comand line. */
cv::CommandLineParser parser(argc, argv, keys);
parser.about("Demo for object data classification and pose estimation");
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
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 ref_img1 = parser.get<String>("ref_img1");
String ref_img2 = parser.get<String>("ref_img2");
String ref_img3 = parser.get<String>("ref_img3");
String feature_blob = parser.get<String>("feature_blob");
String device = parser.get<String>("device");
int dev_id = parser.get<int>("dev_id");
std::vector<String> ref_img;
/* Sample which is most closest in pose to reference image
*and also the same class.
*/
ref_img.push_back(ref_img1);
/* Sample which is less closest in pose to reference image
*and also the same class.
*/
ref_img.push_back(ref_img2);
/* Sample which is very close in pose to reference image
*but not the same class.
*/
ref_img.push_back(ref_img3);
/* Initialize a net work with Device. */
cv::cnn_3dobj::descriptorExtractor descriptor(device, dev_id);
/* 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);
cv::Mat img_base = cv::imread(target_img, -1);
if (img_base.empty())
{
printf("could not read reference image %s\n, make sure the path of images are set properly.", target_img.c_str());
}
std::vector<cv::Mat> img;
for (unsigned int i = 0; i < ref_img.size(); i++)
{
img.push_back(cv::imread(ref_img[i], -1));
if (img[i].empty()) {
printf("could not read reference image %s\n, make sure the path of images are set properly.", ref_img[i].c_str());
}
}
cv::Mat feature_test;
descriptor.extract(img_base, feature_test, feature_blob);
if (feature_test.empty()) {
printf("could not extract feature from test image which is read into cv::Mat.");
}
cv::Mat feature_reference;
descriptor.extract(img, feature_reference, feature_blob);
if (feature_reference.empty()) {
printf("could not extract feature from reference images which is already stored in vector<cv::Mat>.");
}
std::vector<float> matches;
for (int i = 0; i < feature_reference.rows; i++)
{
cv::Mat distance = feature_test-feature_reference.row(i);
matches.push_back(cv::norm(distance));
}
bool pose_pass = false;
bool class_pass = false;
/* Have comparations on the distance between reference image and 3 other images
*distance between closest sample and reference image should be smallest and
*distance between sample in another class and reference image should be largest.
*/
if (matches[0] < matches[1] && matches[0] < matches[2])
pose_pass = true;
if (matches[1] < matches[2])
class_pass = true;
if (!pose_pass)
{
printf("\n =========== Model %s ========== \nIs not trained properly that the similar pose could not be tell from a cluster of features.\n", caffemodel.c_str());
}
else if (!class_pass)
{
printf("\n =========== Model %s ========== \nIs not trained properly that feature from the same class is not discriminant from the one of another class with similar pose.\n", caffemodel.c_str());
}
else
{
printf("\n =========== Model %s ========== \nSuits for setting different classes apart and also discriminant on object pose at the same time.\n", caffemodel.c_str());
}
return 0;
}接下来对代码清单6-18中的程序进行详细介绍.
- 样例最接近参考图像且属于同一类别的样本。
代码清单6-19：最相似
ref_img.push_back(ref_img1);- 样例与参考图像最不相似但属于同一类别的样本。
代码清单6-20：同类不相似
ref_img.push_back(ref_img2);- 样例与参考图像非常接近，但不是同一类的样本。
代码清单6-21：相似不同类
ref_img.push_back(ref_img3);- 使用Device初始化一个网络。
代码清单6-22：初始化网络
cv::cnn_3dobj::descriptorExtractor descriptor(device, dev_id);- 加载用caffe模型训练过的网络参数和结构。
代码清单6-23：加载结构
if (strcmp(mean_file.c_str(), "no") == 0)
descriptor.loadNet(network_forIMG, caffemodel);
else
descriptor.loadNet(network_forIMG, caffemodel, mean_file);- 比较参考图像和其他3个样本图像之间的距离，最接近参考图像的样本和参考图像之间的距离应最小，另一个样本和参考图像之间的距离应最大。
代码清单6-24：比较结果
if (matches[0] < matches[1] && matches[0] < matches[2])
pose_pass = true;
if (matches[1] < matches[2])
class_pass = true;