haarcascade_frontalcatface.xml

<?xml version="1.0"?>
<!----------------------------------------------------------------------------
 A frontal cat face detector using the basic set of Haar features, i.e.
 horizontal and vertical features but not diagonal features.

 Contributed by Joseph Howse (josephhowse@nummist.com).

 More information can be found in the following publications and
 presentations:

 Joseph Howse. OpenCV for Secret Agents (book). Packt Publishing, January
   2015.
 Joseph Howse. "Training Detectors and Recognizers in Python and OpenCV"
   (tutorial). ISMAR 2014. September 9, 2014.
   http://nummist.com/opencv/Howse_ISMAR_20140909.pdf
 Joseph Howse. "Training Intelligent Camera Systems with Python and OpenCV"
   (webcast). O’Reilly Media. June 17, 2014.
   http://www.oreilly.com/pub/e/3077

 Build scripts and demo applications can be found in the following repository:
 https://bitbucket.org/Joe_Howse/angora-blue

 KNOWN LIMITATIONS:

 An upright subject is assumed. In situations where the cat's face might be
 sideways or upside down (e.g. the cat is rolling over), try various rotations
 of the input image.

 CHANGELOG:

 2016-08-06: Re-trained with more negative samples and more stages. False
   positives are much rarer now. If you tailored your code for the cascade's
   previous version, now you should re-adjust the arguments of
   CascadeClassifier::detectMultiScale. For example, decrease the value of the
   minNeighbors argument. You do not need to use a human face detector to
   cross-check the positives anymore.
 2014-04-25: First release (at https://bitbucket.org/Joe_Howse/angora-blue)

 //////////////////////////////////////////////////////////////////////////
 | Contributors License Agreement
 | IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
 |   By downloading, copying, installing or using the software you agree
 |   to this license.
 |   If you do not agree to this license, do not download, install,
 |   copy or use the software.
 |
 | Copyright (c) 2014-2016, Joseph Howse (Nummist Media Corporation Limited,
 | Halifax, Nova Scotia, Canada). All rights reserved.
 |
 | Redistribution and use in source and binary forms, with or without
 | modification, are permitted provided that the following conditions are
 | met:
 |
 |    * Redistributions of source code must retain the above copyright
 |       notice, this list of conditions and the following disclaimer.
 |    * Redistributions in binary form must reproduce the above
 |      copyright notice, this list of conditions and the following
 |      disclaimer in the documentation and/or other materials provided
 |      with the distribution.
 |    * The name of Contributor may not used to endorse or promote products
 |      derived from this software without specific prior written permission.
 |
 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 | "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 | LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
 | A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
 | CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
 | EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
 | PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
 | PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
 | LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
 | NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 | SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.  Back to
 | Top
 //////////////////////////////////////////////////////////////////////////
 ---------------------------------------------------------------------------->
<opencv_storage>
<cascade>
  <stageType>BOOST</stageType>
  <featureType>HAAR</featureType>
  <height>24</height>
  <width>24</width>
  <stageParams>
    <boostType>GAB</boostType>
    <minHitRate>9.9500000476837158e-01</minHitRate>
    <maxFalseAlarm>5.0000000000000000e-01</maxFalseAlarm>
    <weightTrimRate>9.4999999999999996e-01</weightTrimRate>
    <maxDepth>1</maxDepth>
    <maxWeakCount>100</maxWeakCount></stageParams>
  <featureParams>
    <maxCatCount>0</maxCatCount>
    <featSize>1</featSize>
    <mode>BASIC</mode></featureParams>
  <stageNum>20</stageNum>
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