EVOLUTION-MANAGER

Edit File: 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> <stages> <!-- stage 0 --> <_> <maxWeakCount>16</maxWeakCount> <stageThreshold>-1.4806525707244873e+00</stageThreshold> <weakClassifiers> <_> <internalNodes> 0 -1 472 -1.5126220881938934e-02</internalNodes> <leafValues> 7.5887596607208252e-01 -3.4230688214302063e-01</leafValues></_> <_> <internalNodes> 0 -1 839 3.9337221533060074e-03</internalNodes> <leafValues> 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