vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

2026-08-22 00:11:13 +08:00
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+Training data generation using Icosphere {#tutorial_data_generation}
+=============
+
+Goal
+----
+
+In this tutorial you will learn how to
+
+-   Conduct a point cloud of camera view on sphere.
+-   Generate training images using 3D model.
+
+Code
+----
+
+@include cnn_3dobj/samples/sphereview_data.cpp
+
+Explanation
+-----------
+
+Here is the general structure of the program:
+
+-   Create a window.
+    @code{.cpp}
+    viz::Viz3d myWindow("Coordinate Frame");
+    @endcode
+
+-   Set window size as 64*64, we use this scale as default.
+    @code{.cpp}
+    myWindow.setWindowSize(Size(64,64));
+    @endcode
+
+-   Add coordinate axes.
+    @code{.cpp}
+    myWindow.showWidget("Coordinate Widget", viz::WCoordinateSystem());
+    myWindow.setBackgroundColor(viz::Color::gray());
+    myWindow.spin();
+    @endcode
+
+-   Create a Mesh widget, loading .ply models.
+    @code{.cpp}
+    viz::Mesh objmesh = viz::Mesh::load(plymodel);
+    @endcode
+-   Get the center of the generated mesh widget, cause some .ply files.
+    @code{.cpp}
+    Point3d cam_focal_point = ViewSphere.getCenter(objmesh.cloud);
+    @endcode
+
+-   Get the pose of the camera using makeCameraPoses.
+    @code{.cpp}
+    Affine3f cam_pose = viz::makeCameraPose(campos.at(pose)*radius+cam_focal_point, cam_focal_point, cam_y_dir*radius+cam_focal_point);
+    @endcode
+
+-   Get the transformation matrix from camera coordinate system to global.
+    @code{.cpp}
+    Affine3f transform = viz::makeTransformToGlobal(Vec3f(1.0f,0.0f,0.0f), Vec3f(0.0f,1.0f,0.0f), Vec3f(0.0f,0.0f,1.0f), campos.at(pose));
+    viz::WMesh mesh_widget(objmesh);
+    @endcode
+
+-   Save screen shot as images.
+    @code{.cpp}
+    myWindow.saveScreenshot(filename);
+    @endcode
+
+-   Write images into binary files for further using in CNN training.
+    @code{.cpp}
+    ViewSphere.writeBinaryfile(filename, binaryPath, headerPath,(int)campos.size()*num_class, label_class, (int)(campos.at(pose).x*100), (int)(campos.at(pose).y*100), (int)(campos.at(pose).z*100), rgb_use);
+    @endcode
+
+Results
+-------
+
+Here is collection images created by this demo using 4 model.
+
+![](images_all/1_8.png)
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+Classify {#tutorial_feature_classification}
+===============
+
+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
+----
+
+@include cnn_3dobj/samples/classify.cpp
+
+Explanation
+-----------
+
+Here is the general structure of the program:
+
+-   Initialize a net work with Device.
+    @code{.cpp}
+    cv::cnn_3dobj::descriptorExtractor descriptor(device);
+    @endcode
+
+-   Load net with the caffe trained net work parameter and structure.
+    @code{.cpp}
+    if (strcmp(mean_file.c_str(), "no") == 0)
+        descriptor.loadNet(network_forIMG, caffemodel);
+    else
+        descriptor.loadNet(network_forIMG, caffemodel, mean_file);
+    @endcode
+
+-   List the file names under a given path.
+    @code{.cpp}
+    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];
+    }
+    @endcode
+
+-   Extract feature from a set of images.
+    @code{.cpp}
+    descriptor.extract(img_gallery, feature_reference, feature_blob);
+    @endcode
+
+-   Initialize a matcher which using L2 distance.
+    @code{.cpp}
+    cv::BFMatcher matcher(NORM_L2);
+    std::vector<std::vector<cv::DMatch> > matches;
+    @endcode
+
+-   Have a KNN match on the target and reference images.
+    @code{.cpp}
+    matcher.knnMatch(feature_test, feature_reference, matches, num_candidate);
+    @endcode
+
+-   Print features of the reference images.
+    @code{.cpp}std::cout << std::endl << "---------- Features of target image: " << target_img << "----------" << endl << feature_test << std::endl;
+    @endcode
+Results
+-------
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+Training Model Analysis {#tutorial_model_analysis}
+=============
+
+Goal
+----
+
+In this tutorial you will learn how to
+
+-   Extract feature from particular image.
+-   Have a meaningful comparation on the extracted feature.
+
+Code
+----
+
+@include cnn_3dobj/samples/model_analysis.cpp
+
+Explanation
+-----------
+
+Here is the general structure of the program:
+
+-   Sample which is most closest in pose to reference image and also the same class.
+    @code{.cpp}
+    ref_img.push_back(ref_img1);
+    @endcode
+
+-   Sample which is less closest in pose to reference image and also the same class.
+    @code{.cpp}
+    ref_img.push_back(ref_img2);
+    @endcode
+
+-   Sample which is very close in pose to reference image but not the same class.
+    @code{.cpp}
+    ref_img.push_back(ref_img3);
+    @endcode
+
+-   Initialize a net work with Device.
+    @code{.cpp}
+    cv::cnn_3dobj::descriptorExtractor descriptor(device, dev_id);
+    @endcode
+-   Load net with the caffe trained net work parameter and structure.
+    @code{.cpp}
+    if (strcmp(mean_file.c_str(), "no") == 0)
+        descriptor.loadNet(network_forIMG, caffemodel);
+    else
+        descriptor.loadNet(network_forIMG, caffemodel, mean_file);
+    @endcode
+
+-   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.
+    @code{.cpp}
+    if (matches[0] < matches[1] && matches[0] < matches[2])
+        pose_pass = true;
+    if (matches[1] < matches[2])
+        class_pass = true;
+    @endcode
+Results
+-------
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+CNN for 3D Object Classification and Pose Estimation {#tutorial_table_of_content_cnn_3dobj}
+==========
+
+-   @subpage tutorial_data_generation
+
+    *Compatibility:* \> OpenCV 3.0.0
+
+    *Author:* Yida Wang
+
+    You will learn how to generate training images from 3D models with proper poses for CNN training.
+
+-   @subpage tutorial_feature_classification
+
+    *Compatibility:* \> OpenCV 3.0.0
+
+    *Author:* Yida Wang
+
+    You will learn how to extract features from images and make a prediction using descriptor.
+
+-   @subpage tutorial_model_analysis
+
+    *Compatibility:* \> OpenCV 3.0.0
+
+    *Author:* Yida Wang
+
+    You will learn how to analyze performance of a trained Model.