CoCalc -- haarcascade_frontalcatface.xml

GitHub Repository: Tetragramm/opencv
Path: blob/master/data/haarcascades/haarcascade_frontalcatface.xml
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<?xml version="1.0"?>
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<!--
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 A frontal cat face detector using the basic set of Haar features, i.e.
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 horizontal and vertical features but not diagonal features.
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 Contributed by Joseph Howse ([email protected]).
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 More information can be found in the following publications and
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 presentations:
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 Joseph Howse. OpenCV for Secret Agents (book). Packt Publishing, January
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   2015.
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 Joseph Howse. "Training Detectors and Recognizers in Python and OpenCV"
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   (tutorial). ISMAR 2014. September 9, 2014.
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   http://nummist.com/opencv/Howse_ISMAR_20140909.pdf
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 Joseph Howse. "Training Intelligent Camera Systems with Python and OpenCV"
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   (webcast). O’Reilly Media. June 17, 2014.
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   http://www.oreilly.com/pub/e/3077
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 Build scripts and demo applications can be found in the following repository:
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 https://bitbucket.org/Joe_Howse/angora-blue
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 KNOWN LIMITATIONS:
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 An upright subject is assumed. In situations where the cat's face might be
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 sideways or upside down (e.g. the cat is rolling over), try various rotations
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 of the input image.
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 CHANGELOG:
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 2016-08-06: Re-trained with more negative samples and more stages. False
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   positives are much rarer now. If you tailored your code for the cascade's
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   previous version, now you should re-adjust the arguments of
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   CascadeClassifier::detectMultiScale. For example, decrease the value of the
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   minNeighbors argument. You do not need to use a human face detector to
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   cross-check the positives anymore.
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 2014-04-25: First release (at https://bitbucket.org/Joe_Howse/angora-blue)
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 //////////////////////////////////////////////////////////////////////////
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 | Contributors License Agreement
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 | IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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 |   By downloading, copying, installing or using the software you agree
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 |   to this license.
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 |   If you do not agree to this license, do not download, install,
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 |   copy or use the software.
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 |
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 | Copyright (c) 2014-2016, Joseph Howse (Nummist Media Corporation Limited,
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 | Halifax, Nova Scotia, Canada). All rights reserved.
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 |
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 | Redistribution and use in source and binary forms, with or without
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 | modification, are permitted provided that the following conditions are
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 | met:
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 |
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 |    * Redistributions of source code must retain the above copyright
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 |       notice, this list of conditions and the following disclaimer.
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 |    * Redistributions in binary form must reproduce the above
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 |      copyright notice, this list of conditions and the following
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 |      disclaimer in the documentation and/or other materials provided
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 |      with the distribution.
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 |    * The name of Contributor may not used to endorse or promote products
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 |      derived from this software without specific prior written permission.
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 |
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 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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 | "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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 | LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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 | A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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 | CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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 | EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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 | PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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 | PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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 | LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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 | NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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 | SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.  Back to
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 | Top
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 //////////////////////////////////////////////////////////////////////////
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 -->
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<opencv_storage>
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<cascade>
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  <stageType>BOOST</stageType>
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  <featureType>HAAR</featureType>
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  <height>24</height>
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  <width>24</width>
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  <stageParams>
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    <boostType>GAB</boostType>
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    <minHitRate>9.9500000476837158e-01</minHitRate>
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    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
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    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
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    <maxDepth>1</maxDepth>
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    <maxWeakCount>100</maxWeakCount></stageParams>
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  <featureParams>
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    <maxCatCount>0</maxCatCount>
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    <featSize>1</featSize>
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    <mode>BASIC</mode></featureParams>
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