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330 lines
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330 lines
13 KiB
Markdown
Cascade Classifier Training {#tutorial_traincascade}
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===========================
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Introduction
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------------
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The work with a cascade classifier inlcudes two major stages: training and detection. Detection
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stage is described in a documentation of objdetect module of general OpenCV documentation.
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This documentation gives some basic information about cascade classifier. Current guide is describing how
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to train a cascade classifier: preparation of the training data and running the training application.
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### Important notes
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There are two applications in OpenCV to train cascade classifier: opencv_haartraining and
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opencv_traincascade. opencv_traincascade is a newer version, written in C++ in accordance to
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OpenCV 2.x API. But the main difference between this two applications is that opencv_traincascade
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supports both Haar @cite Viola01 and LBP (Local Binary Patterns) @cite Liao2007 features. LBP features
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are integer in contrast to Haar features, so both training and detection with LBP are several times
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faster then with Haar features. Regarding the LBP and Haar detection quality, it depends on
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training: the quality of training dataset first of all and training parameters too. It's possible to
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train a LBP-based classifier that will provide almost the same quality as Haar-based one.
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opencv_traincascade and opencv_haartraining store the trained classifier in different file
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formats. Note, the newer cascade detection interface (see CascadeClassifier class in objdetect
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module) support both formats. opencv_traincascade can save (export) a trained cascade in the older
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format. But opencv_traincascade and opencv_haartraining can not load (import) a classifier in
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another format for the futher training after interruption.
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Note that opencv_traincascade application can use TBB for multi-threading. To use it in multicore
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mode OpenCV must be built with TBB.
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Also there are some auxilary utilities related to the training.
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- opencv_createsamples is used to prepare a training dataset of positive and test samples.
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opencv_createsamples produces dataset of positive samples in a format that is supported by
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both opencv_haartraining and opencv_traincascade applications. The output is a file
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with \*.vec extension, it is a binary format which contains images.
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- opencv_performance may be used to evaluate the quality of classifiers, but for trained by
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opencv_haartraining only. It takes a collection of marked up images, runs the classifier and
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reports the performance, i.e. number of found objects, number of missed objects, number of
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false alarms and other information.
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Since opencv_haartraining is an obsolete application, only opencv_traincascade will be described
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futher. opencv_createsamples utility is needed to prepare a training data for opencv_traincascade,
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so it will be described too.
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Training data preparation
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-------------------------
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For training we need a set of samples. There are two types of samples: negative and positive.
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Negative samples correspond to non-object images. Positive samples correspond to images with
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detected objects. Set of negative samples must be prepared manually, whereas set of positive samples
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is created using opencv_createsamples utility.
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### Negative Samples
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Negative samples are taken from arbitrary images. These images must not contain detected objects.
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Negative samples are enumerated in a special file. It is a text file in which each line contains an
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image filename (relative to the directory of the description file) of negative sample image. This
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file must be created manually. Note that negative samples and sample images are also called
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background samples or background images, and are used interchangeably in this document.
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Described images may be of different sizes. But each image should be (but not nessesarily) larger
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than a training window size, because these images are used to subsample negative image to the
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training size.
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An example of description file:
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Directory structure:
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@code{.text}
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/img
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img1.jpg
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img2.jpg
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bg.txt
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@endcode
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File bg.txt:
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@code{.text}
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img/img1.jpg
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img/img2.jpg
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@endcode
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### Positive Samples
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Positive samples are created by opencv_createsamples utility. They may be created from a single
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image with object or from a collection of previously marked up images.
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Please note that you need a large dataset of positive samples before you give it to the mentioned
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utility, because it only applies perspective transformation. For example you may need only one
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positive sample for absolutely rigid object like an OpenCV logo, but you definetely need hundreds
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and even thousands of positive samples for faces. In the case of faces you should consider all the
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race and age groups, emotions and perhaps beard styles.
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So, a single object image may contain a company logo. Then a large set of positive samples is
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created from the given object image by random rotating, changing the logo intensity as well as
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placing the logo on arbitrary background. The amount and range of randomness can be controlled by
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command line arguments of opencv_createsamples utility.
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Command line arguments:
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- -vec \<vec_file_name\>
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Name of the output file containing the positive samples for training.
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- -img \<image_file_name\>
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Source object image (e.g., a company logo).
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- -bg \<background_file_name\>
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Background description file; contains a list of images which are used as a background for
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randomly distorted versions of the object.
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- -num \<number_of_samples\>
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Number of positive samples to generate.
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- -bgcolor \<background_color\>
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Background color (currently grayscale images are assumed); the background color denotes the
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transparent color. Since there might be compression artifacts, the amount of color tolerance
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can be specified by -bgthresh. All pixels withing bgcolor-bgthresh and bgcolor+bgthresh range
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are interpreted as transparent.
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- -bgthresh \<background_color_threshold\>
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- -inv
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If specified, colors will be inverted.
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- -randinv
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If specified, colors will be inverted randomly.
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- -maxidev \<max_intensity_deviation\>
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Maximal intensity deviation of pixels in foreground samples.
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- -maxxangle \<max_x_rotation_angle\>
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- -maxyangle \<max_y_rotation_angle\>
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- -maxzangle \<max_z_rotation_angle\>
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Maximum rotation angles must be given in radians.
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- -show
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Useful debugging option. If specified, each sample will be shown. Pressing Esc will continue
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the samples creation process without.
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- -w \<sample_width\>
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Width (in pixels) of the output samples.
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- -h \<sample_height\>
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Height (in pixels) of the output samples.
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For following procedure is used to create a sample object instance: The source image is rotated
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randomly around all three axes. The chosen angle is limited by -maxxangle, -maxyangle and maxzangle. Then pixels having the
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intensity from [bg_color-bg_color_threshold; bg_color+bg_color_threshold] range are
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interpreted as transparent. White noise is added to the intensities of the foreground. If the -inv
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key is specified then foreground pixel intensities are inverted. If -randinv key is specified then
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algorithm randomly selects whether inversion should be applied to this sample. Finally, the obtained
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image is placed onto an arbitrary background from the background description file, resized to the
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desired size specified by -w and -h and stored to the vec-file, specified by the -vec command line
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option.
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Positive samples also may be obtained from a collection of previously marked up images. This
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collection is described by a text file similar to background description file. Each line of this
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file corresponds to an image. The first element of the line is the filename. It is followed by the
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number of object instances. The following numbers are the coordinates of objects bounding rectangles
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(x, y, width, height).
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An example of description file:
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Directory structure:
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@code{.text}
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/img
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img1.jpg
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img2.jpg
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info.dat
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@endcode
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File info.dat:
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@code{.text}
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img/img1.jpg 1 140 100 45 45
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img/img2.jpg 2 100 200 50 50 50 30 25 25
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@endcode
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Image img1.jpg contains single object instance with the following coordinates of bounding rectangle:
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(140, 100, 45, 45). Image img2.jpg contains two object instances.
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In order to create positive samples from such collection, -info argument should be specified instead
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of \`-img\`:
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- -info \<collection_file_name\>
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Description file of marked up images collection.
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The scheme of samples creation in this case is as follows. The object instances are taken from
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images. Then they are resized to target samples size and stored in output vec-file. No distortion is
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applied, so the only affecting arguments are -w, -h, -show and -num.
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opencv_createsamples utility may be used for examining samples stored in positive samples file. In
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order to do this only -vec, -w and -h parameters should be specified.
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Note that for training, it does not matter how vec-files with positive samples are generated. But
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opencv_createsamples utility is the only one way to collect/create a vector file of positive
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samples, provided by OpenCV.
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Example of vec-file is available here opencv/data/vec_files/trainingfaces_24-24.vec. It can be
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used to train a face detector with the following window size: -w 24 -h 24.
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Cascade Training
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----------------
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The next step is the training of classifier. As mentioned above opencv_traincascade or
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opencv_haartraining may be used to train a cascade classifier, but only the newer
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opencv_traincascade will be described futher.
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Command line arguments of opencv_traincascade application grouped by purposes:
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-# Common arguments:
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- -data \<cascade_dir_name\>
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Where the trained classifier should be stored.
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- -vec \<vec_file_name\>
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vec-file with positive samples (created by opencv_createsamples utility).
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- -bg \<background_file_name\>
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Background description file.
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- -numPos \<number_of_positive_samples\>
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- -numNeg \<number_of_negative_samples\>
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Number of positive/negative samples used in training for every classifier stage.
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- -numStages \<number_of_stages\>
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Number of cascade stages to be trained.
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- -precalcValBufSize \<precalculated_vals_buffer_size_in_Mb\>
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Size of buffer for precalculated feature values (in Mb).
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- -precalcIdxBufSize \<precalculated_idxs_buffer_size_in_Mb\>
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Size of buffer for precalculated feature indices (in Mb). The more memory you have the
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faster the training process.
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- -baseFormatSave
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This argument is actual in case of Haar-like features. If it is specified, the cascade will
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be saved in the old format.
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- -numThreads \<max_number_of_threads\>
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Maximum number of threads to use during training. Notice that the actual number of used
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threads may be lower, depending on your machine and compilation options.
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- -acceptanceRatioBreakValue \<break_value\>
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This argument is used to determine how precise your model should keep learning and when to stop.
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A good guideline is to train not further than 10e-5, to ensure the model does not overtrain on your training data.
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By default this value is set to -1 to disable this feature.
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-# Cascade parameters:
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- -stageType \<BOOST(default)\>
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Type of stages. Only boosted classifier are supported as a stage type at the moment.
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- -featureType\<{HAAR(default), LBP}\>
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Type of features: HAAR - Haar-like features, LBP - local binary patterns.
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- -w \<sampleWidth\>
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- -h \<sampleHeight\>
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Size of training samples (in pixels). Must have exactly the same values as used during
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training samples creation (opencv_createsamples utility).
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-# Boosted classifer parameters:
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- -bt \<{DAB, RAB, LB, GAB(default)}\>
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Type of boosted classifiers: DAB - Discrete AdaBoost, RAB - Real AdaBoost, LB - LogitBoost,
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GAB - Gentle AdaBoost.
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- -minHitRate \<min_hit_rate\>
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Minimal desired hit rate for each stage of the classifier. Overall hit rate may be estimated
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as (min_hit_rate ^ number_of_stages), @cite Viola04 §4.1.
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- -maxFalseAlarmRate \<max_false_alarm_rate\>
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Maximal desired false alarm rate for each stage of the classifier. Overall false alarm rate
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may be estimated as (max_false_alarm_rate ^ number_of_stages), @cite Viola04 §4.1.
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- -weightTrimRate \<weight_trim_rate\>
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Specifies whether trimming should be used and its weight. A decent choice is 0.95.
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- -maxDepth \<max_depth_of_weak_tree\>
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Maximal depth of a weak tree. A decent choice is 1, that is case of stumps.
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- -maxWeakCount \<max_weak_tree_count\>
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Maximal count of weak trees for every cascade stage. The boosted classifier (stage) will
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have so many weak trees (\<=maxWeakCount), as needed to achieve the
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given -maxFalseAlarmRate.
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-# Haar-like feature parameters:
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- -mode \<BASIC (default) | CORE | ALL\>
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Selects the type of Haar features set used in training. BASIC use only upright features,
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while ALL uses the full set of upright and 45 degree rotated feature set. See @cite Lienhart02
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for more details.
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-# Local Binary Patterns parameters:
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Local Binary Patterns don't have parameters.
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After the opencv_traincascade application has finished its work, the trained cascade will be saved
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in cascade.xml file in the folder, which was passed as -data parameter. Other files in this folder
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are created for the case of interrupted training, so you may delete them after completion of
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training.
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Training is finished and you can test your cascade classifier!
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