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Add Java and Python code for ML tutorials.
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@@ -91,37 +91,112 @@ __Find the eigenvectors and eigenvalues of the covariance matrix__
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Source Code
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-----------
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This tutorial code's is shown lines below. You can also download it from
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[here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp).
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@include cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
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@add_toggle_cpp
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp)
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- **Code at glance:**
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@include samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp
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@end_toggle
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@add_toggle_java
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java)
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- **Code at glance:**
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@include samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java
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@end_toggle
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@add_toggle_python
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py)
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- **Code at glance:**
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@include samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py
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@end_toggle
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@note Another example using PCA for dimensionality reduction while maintaining an amount of variance can be found at [opencv_source_code/samples/cpp/pca.cpp](https://github.com/opencv/opencv/tree/3.4/samples/cpp/pca.cpp)
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Explanation
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-----------
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-# __Read image and convert it to binary__
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- __Read image and convert it to binary__
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Here we apply the necessary pre-processing procedures in order to be able to detect the objects of interest.
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp pre-process
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Here we apply the necessary pre-processing procedures in order to be able to detect the objects of interest.
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-# __Extract objects of interest__
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp pre-process
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@end_toggle
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Then find and filter contours by size and obtain the orientation of the remaining ones.
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp contours
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java pre-process
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@end_toggle
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-# __Extract orientation__
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py pre-process
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@end_toggle
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Orientation is extracted by the call of getOrientation() function, which performs all the PCA procedure.
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp pca
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- __Extract objects of interest__
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First the data need to be arranged in a matrix with size n x 2, where n is the number of data points we have. Then we can perform that PCA analysis. The calculated mean (i.e. center of mass) is stored in the _cntr_ variable and the eigenvectors and eigenvalues are stored in the corresponding std::vector’s.
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Then find and filter contours by size and obtain the orientation of the remaining ones.
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-# __Visualize result__
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp contours
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@end_toggle
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The final result is visualized through the drawAxis() function, where the principal components are drawn in lines, and each eigenvector is multiplied by its eigenvalue and translated to the mean position.
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp visualization
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp visualization1
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java contours
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py contours
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@end_toggle
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- __Extract orientation__
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Orientation is extracted by the call of getOrientation() function, which performs all the PCA procedure.
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp pca
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java pca
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py pca
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@end_toggle
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First the data need to be arranged in a matrix with size n x 2, where n is the number of data points we have. Then we can perform that PCA analysis. The calculated mean (i.e. center of mass) is stored in the _cntr_ variable and the eigenvectors and eigenvalues are stored in the corresponding std::vector’s.
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- __Visualize result__
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The final result is visualized through the drawAxis() function, where the principal components are drawn in lines, and each eigenvector is multiplied by its eigenvalue and translated to the mean position.
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp visualization
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java visualization
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py visualization
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@end_toggle
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_pca/introduction_to_pca.cpp visualization1
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java visualization1
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_pca/introduction_to_pca.py visualization1
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@end_toggle
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Results
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-------
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@@ -96,25 +96,67 @@ Source Code
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@note The following code has been implemented with OpenCV 3.0 classes and functions. An equivalent version of the code using OpenCV 2.4 can be found in [this page.](http://docs.opencv.org/2.4/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html#introductiontosvms)
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@include cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp
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@add_toggle_cpp
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp)
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- **Code at glance:**
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@include samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp
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@end_toggle
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@add_toggle_java
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java)
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- **Code at glance:**
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@include samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java
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@end_toggle
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@add_toggle_python
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py)
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- **Code at glance:**
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@include samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py
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@end_toggle
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Explanation
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-----------
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-# **Set up the training data**
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- **Set up the training data**
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The training data of this exercise is formed by a set of labeled 2D-points that belong to one of
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two different classes; one of the classes consists of one point and the other of three points.
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The training data of this exercise is formed by a set of labeled 2D-points that belong to one of
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two different classes; one of the classes consists of one point and the other of three points.
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@snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp setup1
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp setup1
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@end_toggle
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The function @ref cv::ml::SVM::train that will be used afterwards requires the training data to be
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stored as @ref cv::Mat objects of floats. Therefore, we create these objects from the arrays
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defined above:
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java setup1
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@end_toggle
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@snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp setup2
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py setup1
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@end_toggle
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-# **Set up SVM's parameters**
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The function @ref cv::ml::SVM::train that will be used afterwards requires the training data to be
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stored as @ref cv::Mat objects of floats. Therefore, we create these objects from the arrays
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defined above:
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp setup2
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java setup2
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py setup1
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@end_toggle
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- **Set up SVM's parameters**
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In this tutorial we have introduced the theory of SVMs in the most simple case, when the
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training examples are spread into two classes that are linearly separable. However, SVMs can be
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@@ -123,35 +165,55 @@ Explanation
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we have to define some parameters before training the SVM. These parameters are stored in an
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object of the class @ref cv::ml::SVM.
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@snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp init
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp init
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@end_toggle
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Here:
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- *Type of SVM*. We choose here the type @ref cv::ml::SVM::C_SVC "C_SVC" that can be used for
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n-class classification (n \f$\geq\f$ 2). The important feature of this type is that it deals
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with imperfect separation of classes (i.e. when the training data is non-linearly separable).
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This feature is not important here since the data is linearly separable and we chose this SVM
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type only for being the most commonly used.
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java init
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@end_toggle
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- *Type of SVM kernel*. We have not talked about kernel functions since they are not
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interesting for the training data we are dealing with. Nevertheless, let's explain briefly now
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the main idea behind a kernel function. It is a mapping done to the training data to improve
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its resemblance to a linearly separable set of data. This mapping consists of increasing the
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dimensionality of the data and is done efficiently using a kernel function. We choose here the
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type @ref cv::ml::SVM::LINEAR "LINEAR" which means that no mapping is done. This parameter is
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defined using cv::ml::SVM::setKernel.
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py init
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@end_toggle
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- *Termination criteria of the algorithm*. The SVM training procedure is implemented solving a
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constrained quadratic optimization problem in an **iterative** fashion. Here we specify a
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maximum number of iterations and a tolerance error so we allow the algorithm to finish in
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less number of steps even if the optimal hyperplane has not been computed yet. This
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parameter is defined in a structure @ref cv::TermCriteria .
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Here:
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- *Type of SVM*. We choose here the type @ref cv::ml::SVM::C_SVC "C_SVC" that can be used for
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n-class classification (n \f$\geq\f$ 2). The important feature of this type is that it deals
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with imperfect separation of classes (i.e. when the training data is non-linearly separable).
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This feature is not important here since the data is linearly separable and we chose this SVM
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type only for being the most commonly used.
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-# **Train the SVM**
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- *Type of SVM kernel*. We have not talked about kernel functions since they are not
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interesting for the training data we are dealing with. Nevertheless, let's explain briefly now
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the main idea behind a kernel function. It is a mapping done to the training data to improve
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its resemblance to a linearly separable set of data. This mapping consists of increasing the
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dimensionality of the data and is done efficiently using a kernel function. We choose here the
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type @ref cv::ml::SVM::LINEAR "LINEAR" which means that no mapping is done. This parameter is
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defined using cv::ml::SVM::setKernel.
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- *Termination criteria of the algorithm*. The SVM training procedure is implemented solving a
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constrained quadratic optimization problem in an **iterative** fashion. Here we specify a
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maximum number of iterations and a tolerance error so we allow the algorithm to finish in
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less number of steps even if the optimal hyperplane has not been computed yet. This
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parameter is defined in a structure @ref cv::TermCriteria .
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- **Train the SVM**
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We call the method @ref cv::ml::SVM::train to build the SVM model.
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@snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp train
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp train
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@end_toggle
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-# **Regions classified by the SVM**
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java train
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py train
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@end_toggle
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- **Regions classified by the SVM**
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The method @ref cv::ml::SVM::predict is used to classify an input sample using a trained SVM. In
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this example we have used this method in order to color the space depending on the prediction done
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@@ -159,16 +221,36 @@ Explanation
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Cartesian plane. Each of the points is colored depending on the class predicted by the SVM; in
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green if it is the class with label 1 and in blue if it is the class with label -1.
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@snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp show
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp show
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@end_toggle
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-# **Support vectors**
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java show
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py show
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@end_toggle
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- **Support vectors**
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We use here a couple of methods to obtain information about the support vectors.
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The method @ref cv::ml::SVM::getSupportVectors obtain all of the support
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vectors. We have used this methods here to find the training examples that are
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support vectors and highlight them.
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@snippet cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp show_vectors
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/introduction_to_svm/introduction_to_svm.cpp show_vectors
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@end_toggle
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@add_toggle_java
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@snippet samples/java/tutorial_code/ml/introduction_to_svm/IntroductionToSVMDemo.java show_vectors
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@end_toggle
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@add_toggle_python
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@snippet samples/python/tutorial_code/ml/introduction_to_svm/introduction_to_svm.py show_vectors
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@end_toggle
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Results
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-------
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@@ -92,81 +92,175 @@ You may also find the source code in `samples/cpp/tutorial_code/ml/non_linear_sv
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@note The following code has been implemented with OpenCV 3.0 classes and functions. An equivalent version of the code
|
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using OpenCV 2.4 can be found in [this page.](http://docs.opencv.org/2.4/doc/tutorials/ml/non_linear_svms/non_linear_svms.html#nonlinearsvms)
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@include cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
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@add_toggle_cpp
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- **Downloadable code**: Click
|
||||
[here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp)
|
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- **Code at glance:**
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||||
@include samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp
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@end_toggle
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||||
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||||
@add_toggle_java
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- **Downloadable code**: Click
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[here](https://github.com/opencv/opencv/tree/3.4/samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java)
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- **Code at glance:**
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@include samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java
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@end_toggle
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||||
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@add_toggle_python
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- **Downloadable code**: Click
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||||
[here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py)
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- **Code at glance:**
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||||
@include samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py
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@end_toggle
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||||
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||||
Explanation
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||||
-----------
|
||||
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||||
-# __Set up the training data__
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||||
- __Set up the training data__
|
||||
|
||||
The training data of this exercise is formed by a set of labeled 2D-points that belong to one of
|
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two different classes. To make the exercise more appealing, the training data is generated
|
||||
randomly using a uniform probability density functions (PDFs).
|
||||
The training data of this exercise is formed by a set of labeled 2D-points that belong to one of
|
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two different classes. To make the exercise more appealing, the training data is generated
|
||||
randomly using a uniform probability density functions (PDFs).
|
||||
|
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We have divided the generation of the training data into two main parts.
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We have divided the generation of the training data into two main parts.
|
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In the first part we generate data for both classes that is linearly separable.
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@snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp setup1
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In the first part we generate data for both classes that is linearly separable.
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In the second part we create data for both classes that is non-linearly separable, data that
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overlaps.
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@snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp setup2
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@add_toggle_cpp
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@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp setup1
|
||||
@end_toggle
|
||||
|
||||
-# __Set up SVM's parameters__
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java setup1
|
||||
@end_toggle
|
||||
|
||||
@note In the previous tutorial @ref tutorial_introduction_to_svm there is an explanation of the
|
||||
attributes of the class @ref cv::ml::SVM that we configure here before training the SVM.
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py setup1
|
||||
@end_toggle
|
||||
|
||||
@snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp init
|
||||
In the second part we create data for both classes that is non-linearly separable, data that
|
||||
overlaps.
|
||||
|
||||
There are just two differences between the configuration we do here and the one that was done in
|
||||
the previous tutorial (@ref tutorial_introduction_to_svm) that we use as reference.
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp setup2
|
||||
@end_toggle
|
||||
|
||||
- _C_. We chose here a small value of this parameter in order not to punish too much the
|
||||
misclassification errors in the optimization. The idea of doing this stems from the will of
|
||||
obtaining a solution close to the one intuitively expected. However, we recommend to get a
|
||||
better insight of the problem by making adjustments to this parameter.
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java setup2
|
||||
@end_toggle
|
||||
|
||||
@note In this case there are just very few points in the overlapping region between classes.
|
||||
By giving a smaller value to __FRAC_LINEAR_SEP__ the density of points can be incremented and the
|
||||
impact of the parameter _C_ explored deeply.
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py setup2
|
||||
@end_toggle
|
||||
|
||||
- _Termination Criteria of the algorithm_. The maximum number of iterations has to be
|
||||
increased considerably in order to solve correctly a problem with non-linearly separable
|
||||
training data. In particular, we have increased in five orders of magnitude this value.
|
||||
- __Set up SVM's parameters__
|
||||
|
||||
-# __Train the SVM__
|
||||
@note In the previous tutorial @ref tutorial_introduction_to_svm there is an explanation of the
|
||||
attributes of the class @ref cv::ml::SVM that we configure here before training the SVM.
|
||||
|
||||
We call the method @ref cv::ml::SVM::train to build the SVM model. Watch out that the training
|
||||
process may take a quite long time. Have patiance when your run the program.
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp init
|
||||
@end_toggle
|
||||
|
||||
@snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp train
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java init
|
||||
@end_toggle
|
||||
|
||||
-# __Show the Decision Regions__
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py init
|
||||
@end_toggle
|
||||
|
||||
The method @ref cv::ml::SVM::predict is used to classify an input sample using a trained SVM. In
|
||||
this example we have used this method in order to color the space depending on the prediction done
|
||||
by the SVM. In other words, an image is traversed interpreting its pixels as points of the
|
||||
Cartesian plane. Each of the points is colored depending on the class predicted by the SVM; in
|
||||
dark green if it is the class with label 1 and in dark blue if it is the class with label 2.
|
||||
There are just two differences between the configuration we do here and the one that was done in
|
||||
the previous tutorial (@ref tutorial_introduction_to_svm) that we use as reference.
|
||||
|
||||
@snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show
|
||||
- _C_. We chose here a small value of this parameter in order not to punish too much the
|
||||
misclassification errors in the optimization. The idea of doing this stems from the will of
|
||||
obtaining a solution close to the one intuitively expected. However, we recommend to get a
|
||||
better insight of the problem by making adjustments to this parameter.
|
||||
|
||||
-# __Show the training data__
|
||||
@note In this case there are just very few points in the overlapping region between classes.
|
||||
By giving a smaller value to __FRAC_LINEAR_SEP__ the density of points can be incremented and the
|
||||
impact of the parameter _C_ explored deeply.
|
||||
|
||||
The method @ref cv::circle is used to show the samples that compose the training data. The samples
|
||||
of the class labeled with 1 are shown in light green and in light blue the samples of the class
|
||||
labeled with 2.
|
||||
- _Termination Criteria of the algorithm_. The maximum number of iterations has to be
|
||||
increased considerably in order to solve correctly a problem with non-linearly separable
|
||||
training data. In particular, we have increased in five orders of magnitude this value.
|
||||
|
||||
@snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show_data
|
||||
- __Train the SVM__
|
||||
|
||||
-# __Support vectors__
|
||||
We call the method @ref cv::ml::SVM::train to build the SVM model. Watch out that the training
|
||||
process may take a quite long time. Have patiance when your run the program.
|
||||
|
||||
We use here a couple of methods to obtain information about the support vectors. The method
|
||||
@ref cv::ml::SVM::getSupportVectors obtain all support vectors. We have used this methods here
|
||||
to find the training examples that are support vectors and highlight them.
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp train
|
||||
@end_toggle
|
||||
|
||||
@snippet cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show_vectors
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java train
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py train
|
||||
@end_toggle
|
||||
|
||||
- __Show the Decision Regions__
|
||||
|
||||
The method @ref cv::ml::SVM::predict is used to classify an input sample using a trained SVM. In
|
||||
this example we have used this method in order to color the space depending on the prediction done
|
||||
by the SVM. In other words, an image is traversed interpreting its pixels as points of the
|
||||
Cartesian plane. Each of the points is colored depending on the class predicted by the SVM; in
|
||||
dark green if it is the class with label 1 and in dark blue if it is the class with label 2.
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java show
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py show
|
||||
@end_toggle
|
||||
|
||||
- __Show the training data__
|
||||
|
||||
The method @ref cv::circle is used to show the samples that compose the training data. The samples
|
||||
of the class labeled with 1 are shown in light green and in light blue the samples of the class
|
||||
labeled with 2.
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show_data
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java show_data
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py show_data
|
||||
@end_toggle
|
||||
|
||||
- __Support vectors__
|
||||
|
||||
We use here a couple of methods to obtain information about the support vectors. The method
|
||||
@ref cv::ml::SVM::getSupportVectors obtain all support vectors. We have used this methods here
|
||||
to find the training examples that are support vectors and highlight them.
|
||||
|
||||
@add_toggle_cpp
|
||||
@snippet samples/cpp/tutorial_code/ml/non_linear_svms/non_linear_svms.cpp show_vectors
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_java
|
||||
@snippet samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java show_vectors
|
||||
@end_toggle
|
||||
|
||||
@add_toggle_python
|
||||
@snippet samples/python/tutorial_code/ml/non_linear_svms/non_linear_svms.py show_vectors
|
||||
@end_toggle
|
||||
|
||||
Results
|
||||
-------
|
||||
|
||||
@@ -6,6 +6,8 @@ of data.
|
||||
|
||||
- @subpage tutorial_introduction_to_svm
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Fernando Iglesias García
|
||||
@@ -14,6 +16,8 @@ of data.
|
||||
|
||||
- @subpage tutorial_non_linear_svms
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Fernando Iglesias García
|
||||
@@ -23,6 +27,8 @@ of data.
|
||||
|
||||
- @subpage tutorial_introduction_to_pca
|
||||
|
||||
*Languages:* C++, Java, Python
|
||||
|
||||
*Compatibility:* \> OpenCV 2.0
|
||||
|
||||
*Author:* Theodore Tsesmelis
|
||||
|
||||
Reference in New Issue
Block a user