Merge pull request #2320 from xerus:separate

1. Update the README.md file under the modules folder. Here you add your model with a single line description.

2. Add a README.md inside your own module folder. This README explains which functionality (seperate functions) is available, links to the corresponding samples and explains in somewhat more detail what the module is expected to do. If any extra requirements are needed to build the module without problems, add them here also.

2. Add a README.md inside your own module folder. This README explains which functionality (separate functions) is available, links to the corresponding samples and explains in somewhat more detail what the module is expected to do. If any extra requirements are needed to build the module without problems, add them here also.

```

===========================================================

##Demo3: Model performance test

####This demo will run a performance test of a trained CNN model on several images. If the the model fails on telling different samples from seperate classes apart, or is confused on samples with similar pose but from different classes, this will give some information for model analysis.

####This demo will run a performance test of a trained CNN model on several images. If the the model fails on telling different samples from separate classes apart, or is confused on samples with similar pose but from different classes, this will give some information for model analysis.

```

$ ./example_cnn_3dobj_model_analysis

```

As CNN based learning algorithm shows better performance on the classification issues,

the rich labeled data could be more useful in the training stage. 3D object classification and pose estimation

is a jointed mission aimming at seperate different posed apart in the descriptor form.

is a jointed mission aiming at separate different posed apart in the descriptor form.

In the training stage, we prepare 2D training images generated from our module with their

class label and pose label. We fully exploit the information lies in their labels

enum SceneSettings

{

    /// the window will use a seperate scene. The scene will be shared otherwise.

    SCENE_SEPERATE = 1,

    /// the window will use a separate scene. The scene will be shared otherwise.

    SCENE_SEPARATE = 1,

    /// allow the user to control the camera.

    SCENE_INTERACTIVE = 2,

    /// draw coordinate system crosses for debugging

owin.createLightEntity("sun", (0, 0, -100))

# interaction scene

iwin = cv.ovis.createWindow("AR", imsize, cv.ovis.SCENE_SEPERATE | cv.ovis.SCENE_INTERACTIVE)

iwin = cv.ovis.createWindow("AR", imsize, cv.ovis.SCENE_SEPARATE | cv.ovis.SCENE_INTERACTIVE)

iwin.createEntity("figure", "Sinbad.mesh", tvec=(0, -5, 0), rot=(np.pi, 0, 0))

iwin.createLightEntity("sun", (0, 0, -100))

iwin.setCameraIntrinsics(K, imsize)