This document describes how to train and -use a cascade of boosted classifiers for rapid object detection. A large set of -over-complete haar-like features provide the basis for the simple individual -classifiers. Examples of object detection tasks are face, eye and nose -detection, as well as logo detection.
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The sample detection task in this document -is logo detection, since logo detection does not require the collection of -large set of registered and carefully marked object samples. Instead we assume -that from one prototype image, a very large set of derived object examples can -be derived (createsamples utility, see below).
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A detailed description of the training/evaluation -algorithm can be found in [1] and [2].
- -For training a training samples must be -collected. There are two sample types: negative samples and positive samples. -Negative samples correspond to non-object images. Positive samples correspond -to object images.
- -Negative samples are taken from arbitrary -images. These images must not contain object representations. Negative samples -are passed through background description file. It is a text file in which each -text line contains the filename (relative to the directory of the description -file) of negative sample image. This file must be created manually. Note that -the negative samples and sample images are also called background samples or -background samples images, and are used interchangeably in this document
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Example of negative description file:
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Directory structure:
- -/img
- -img1.jpg
- -img2.jpg
- -bg.txt
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File bg.txt:
- -img/img1.jpg
- -img/img2.jpg
- -Positive samples are created by createsamples
-utility. They may be created from single object image or from collection of
-previously marked up images.
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The single object image may for instance -contain a company logo. Then are large set of positive samples are created from -the given object image by randomly rotating, changing the logo color as well as -placing the logo on arbitrary background.
- -The amount and range of randomness can be -controlled by command line arguments.
- -Command line arguments:
- -- vec <vec_file_name>
- -name of the -output file containing the positive samples for training
- -- img <image_file_name>
- -source object -image (e.g., a company logo)
- -- bg <background_file_name>
- -background -description file; contains a list of images into which randomly distorted -versions of the object are pasted for positive sample generation
- -- num <number_of_samples>
- -number of -positive samples to generate
- -- bgcolor <background_color>
- -background color (currently grayscale images are assumed); the -background color denotes the transparent color. Since there might be -compression artifacts, the amount of color tolerance can be specified by –bgthresh. All pixels between bgcolor-bgthresh and bgcolor+bgthresh are regarded as transparent.
- -- bgthresh <background_color_threshold>
- -- inv
- -if specified, the colors will be inverted
- -- randinv
- -if specified, the colors will be inverted randomly
- -- maxidev <max_intensity_deviation>
- -maximal -intensity deviation of foreground samples pixels
- -- maxxangle <max_x_rotation_angle>,
- -- maxyangle <max_y_rotation_angle>,
- -- maxzangle <max_z_rotation_angle>
- -maximum rotation angles in radians
- --show
- -if specified, each sample will be shown. Pressing ‘Esc’ will -continue creation process without samples showing. Useful debugging option.
- -- w <sample_width>
- -width (in -pixels) of the output samples
- -- h <sample_height>
- -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 random around -all three axes. The chosen angle is limited my -max?angle. Next pixels of -intensities in the range of [bg_color-bg_color_threshold; -bg_color+bg_color_threshold] are regarded as -transparent. White noise is added to the intensities of the foreground. If –inv key is -specified then foreground pixel intensities are inverted. If –randinv key is -specified then it is randomly selected whether for this sample inversion will -be applied. Finally, the obtained image is placed onto arbitrary background -from the background description file, resized to the pixel size specified by –w and –h and stored -into the file specified by the –vec command line parameter.
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Positive samples also may be obtained from -a collection of previously marked up images. This collection is described by -text file similar to background description file. Each line of this file -corresponds to collection image. The first element of the line is image file -name. It is followed by number of object instances. The following numbers are -the coordinates of bounding rectangles (x, y, width, height).
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Example of description file:
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Directory structure:
- -/img
- -img1.jpg
- -img2.jpg
- -info.dat
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File info.dat:
- -img/img1.jpg 1 140 -100 45 45
- -img/img2.jpg 2 100 -200 50 50 50 30 25 25
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Image img1.jpg contains single object -instance with bounding rectangle (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 of –img:
- -- info <collection_file_name>
- -description file -of marked up images collection
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The scheme of sample creation in this case -is as follows. The object instances are taken from images. Then they are -resized to samples size and stored in output file. No distortion is applied, so -the only affecting arguments are –w, -h, -show and –num.
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createsamples utility may be used for examining samples stored in positive samples -file. In 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 positive samples files are generated. So the createsamples utility is only one way -to collect/create a vector file of positive samples.
- -The next step after samples creation is -training of classifier. It is performed by the haartraining utility.
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Command line arguments:
- -- data <dir_name>
- -directory name in which the trained classifier is stored
- -- vec <vec_file_name>
- -file name of positive sample file (created by trainingsamples utility or by any other means)
- -- bg <background_file_name>
- -background description file
- -- npos <number_of_positive_samples>,
- -- nneg <number_of_negative_samples>
- -number of positive/negative samples used in training of each -classifier stage. Reasonable values are npos = 7000 and nneg = 3000.
- -- nstages <number_of_stages>
- -number of -stages to be trained
- -- nsplits <number_of_splits>
- -determines the weak classifier used in stage classifiers. If 1, then a simple stump classifier is used, if 2 and more, then CART classifier with number_of_splits internal (split) -nodes is used
- -- mem <memory_in_MB>
- -Available memory in MB for precalculation. The more memory you -have the faster the training process
- -- sym (default),
- -- nonsym
- -specifies whether the object class under training has vertical -symmetry or not. Vertical symmetry speeds up training process. For instance, -frontal faces show off vertical symmetry
- -- minhitrate <min_hit_rate>
- -minimal desired hit rate for each stage classifier. Overall hit -rate may be estimated as (min_hit_rate^number_of_stages)
- -- maxfalsealarm <max_false_alarm_rate>
- -maximal desired false alarm rate for each stage classifier. Overall -false alarm rate may be estimated as (max_false_alarm_rate^number_of_stages)
- -- weighttrimming <weight_trimming>
- -Specifies -wheter and how much weight trimming should be used. A decent choice is 0.90.
- -- eqw
- -- mode <BASIC (default) | CORE | ALL>
- -selects the type of haar features set used in training. BASIC use -only upright features, while ALL uses the full set of upright and 45 degree -rotated feature set. See [1] for more details.
- -- w <sample_width>,
- -- h <sample_height>
- -Size of training samples (in pixels). Must have exactly the same -values as used during training samples creation (utility trainingsamples)
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Note: in order to use multiprocessor -advantage a compiler that supports OpenMP 1.0 standard should be used.
- -OpenCV cvHaarDetectObjects() function (in -particular haarFaceDetect demo) is used for detection.
- -In order to evaluate the performance of -trained classifier a collection of marked up images is needed. When such -collection is not available test samples may be created from single object -image by createsamples utility. The scheme of test samples creation in this case is -similar to training samples creation since each test sample is a background -image into which a randomly distorted and randomly scaled instance of the -object picture is pasted at a random position.
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If both –img and –info arguments are specified then -test samples will be created by createsamples utility. The sample image is arbitrary distorted as it was -described below, then it is placed at random location to background image and -stored. The corresponding description line is added to the file specified by –info argument.
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The –w and –h keys determine the minimal size of -placed object picture.
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The test image file name format is as -follows:
- -imageOrderNumber_x_y_width_height.jpg, where x, y, width and height are the coordinates of placed object bounding rectangle.
- -Note that you should use a background -images set different from the background image set used during training.
- -In order to evaluate the performance of the -classifier performance utility may be used. It takes a collection of marked up images, -applies the classifier and outputs the performance, i.e. number of found -objects, number of missed objects, number of false alarms and other -information.
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Command line arguments:
- -- data <dir_name>
- -directory name in which the trained classifier is stored
- -- info <collection_file_name>
- -file with test samples description
- -- maxSizeDiff <max_size_difference>,
- -- maxPosDiff <max_position_difference>
- -determine the criterion of reference and detected rectangles -coincidence. Default values are 1.5 and 0.3 respectively.
- -- sf <scale_factor>,
- -detection parameter. Default value is 1.2.
- -- w <sample_width>,
- -- h <sample_height>
- -Size of training samples (in pixels). Must have exactly the same -values as used during training (utility haartraining)
- -[1] Rainer Lienhart and Jochen Maydt. An -Extended Set of Haar-like Features for Rapid Object Detection. Submitted to -ICIP2002.
- -[2] Alexander Kuranov, Rainer Lienhart, and -Vadim Pisarevsky. An Empirical Analysis of Boosting Algorithms for Rapid -Objects With an Extended Set of Haar-like Features. Intel Technical Report -MRL-TR-July02-01, 2002.
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