@@ -36,7 +36,7 @@ gives us a feature vector containing 64 values. This is the feature vector we us
Finally, as in the previous case, we start by splitting our big dataset into individual cells. For
every digit, 250 cells are reserved for training data and remaining 250 data is reserved for
testing. Full code is given below, you also can download it from [here](https://github.com/opencv/opencv/tree/master/samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py):
testing. Full code is given below, you also can download it from [here](https://github.com/opencv/opencv/tree/3.4/samples/python/tutorial_code/ml/py_svm_opencv/hogsvm.py):
Now that you have the basics done [here's](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
Now that you have the basics done [here's](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/core/interoperability_with_OpenCV_1/interoperability_with_OpenCV_1.cpp)
an example that mixes the usage of the C interface with the C++ one. You will also find it in the
sample directory of the OpenCV source code library at the
You may also find the source code and the video file in the
`samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity` directory of the OpenCV
source library or download it from [here](https://github.com/opencv/opencv/tree/master/samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp).
source library or download it from [here](https://github.com/opencv/opencv/tree/3.4/samples/cpp/tutorial_code/gpu/gpu-basics-similarity/gpu-basics-similarity.cpp).
The full source code is quite long (due to the controlling of the application via the command line
arguments and performance measurement). Therefore, to avoid cluttering up these sections with those
@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/master/samples/cpp/pca.cpp)
@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)
Working with a boosted cascade of weak classifiers includes two major stages: the training and the detection stage. The detection stage using either HAAR or LBP based models, is described in the @ref tutorial_cascade_classifier "object detection tutorial". This documentation gives an overview of the functionality needed to train your own boosted cascade of weak classifiers. The current guide will walk through all the different stages: collecting training data, preparation of the training data and executing the actual model training.
To support this tutorial, several official OpenCV applications will be used: [opencv_createsamples](https://github.com/opencv/opencv/tree/master/apps/createsamples), [opencv_annotation](https://github.com/opencv/opencv/tree/master/apps/annotation), [opencv_traincascade](https://github.com/opencv/opencv/tree/master/apps/traincascade) and [opencv_visualisation](https://github.com/opencv/opencv/tree/master/apps/visualisation).
To support this tutorial, several official OpenCV applications will be used: [opencv_createsamples](https://github.com/opencv/opencv/tree/3.4/apps/createsamples), [opencv_annotation](https://github.com/opencv/opencv/tree/3.4/apps/annotation), [opencv_traincascade](https://github.com/opencv/opencv/tree/3.4/apps/traincascade) and [opencv_visualisation](https://github.com/opencv/opencv/tree/3.4/apps/visualisation).
