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authorN.N. <sevyves@users.sourceforge.net>2009-08-15 12:57:31 +0000
committerN.N. <sevyves@users.sourceforge.net>2009-08-15 12:57:31 +0000
commit7cabbe3dcaa0ef520f0caaf4009bd9add5474011 (patch)
treecdd14992168f67d7888a30a37ef51f03ccb20a7d /haarcascades/haarcascade_lowerbody.xml
parent9952f0e57d7d6b18e8a83257ec89746365c5a0cd (diff)
added threshold
svn path=/trunk/externals/pix_opencv/; revision=11922
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diff --git a/haarcascades/haarcascade_lowerbody.xml b/haarcascades/haarcascade_lowerbody.xml
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+<?xml version="1.0"?>
+<!--
+ 19x23 lowerbody detector (see the detailed description below).
+
+//////////////////////////////////////////////////////////////////////////
+| 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) 2004, Hannes Kruppa and Bernt Schiele (ETH Zurich, Switzerland).
+| 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
+//////////////////////////////////////////////////////////////////////////
+
+"Haar"-based Detectors For Pedestrian Detection
+===============================================
+by Hannes Kruppa and Bernt Schiele, ETH Zurich, Switzerland
+
+This archive provides the following three detectors:
+- upper body detector (most fun, useful in many scenarios!)
+- lower body detector
+- full body detector
+
+These detectors have been successfully applied to pedestrian detection
+in still images. They can be directly passed as parameters to the
+program HaarFaceDetect.
+NOTE: These detectors deal with frontal and backside views but not
+with side views (also see "Known limitations" below).
+
+RESEARCHERS:
+If you are using any of the detectors or involved ideas please cite
+this paper (available at www.vision.ethz.ch/publications/):
+
+@InProceedings{Kruppa03-bmvc,
+ author = "Hannes Kruppa, Modesto Castrillon-Santana and Bernt Schiele",
+ title = "Fast and Robust Face Finding via Local Context."
+ booktitle = "Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance"
+ year = "2003",
+ month = "October"
+}
+
+COMMERCIAL:
+If you have any commercial interest in this work please contact
+hkruppa@inf.ethz.ch
+
+
+ADDITIONAL INFORMATION
+======================
+Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player
+that can play back .mpg movies.
+Under Linux that's:
+> ffplay demo.mpg
+or:
+> mplayer demo.mpg
+
+The movie shows a person walking towards the camera in a realistic
+indoor setting. Using ffplay or mplayer you can pause and continue the
+movie by pressing the space bar.
+
+Detections coming from the different detectors are visualized using
+different line styles:
+upper body : dotted line
+lower body : dashed line
+full body : solid line
+
+You will notice that successful detections containing the target do
+not sit tightly on the body but also include some of the background
+left and right. This is not a bug but accurately reflects the
+employed training data which also includes portions of the background
+to ensure proper silhouette representation. If you want to get a
+feeling for the training data check out the CBCL data set:
+http://www.ai.mit.edu/projects/cbcl/software-datasets/PedestrianData.html
+
+There is also a small number of false alarms in this sequence.
+NOTE: This is per frame detection, not tracking (which is also one of
+the reasons why it is not mislead by the person's shadow on the back
+wall).
+
+On an Intel Xeon 1.7GHz machine the detectors operate at something
+between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the
+detector. The detectors work as well on much lower image resolutions
+which is always an interesting possibility for speed-ups or
+"coarse-to-fine" search strategies.
+
+Additional information e.g. on training parameters, detector
+combination, detecting other types of objects (e.g. cars) etc. is
+available in my PhD thesis report (available end of June). Check out
+www.vision.ethz.ch/kruppa/
+
+
+KNOWN LIMITATIONS
+==================
+1) the detectors only support frontal and back views but not sideviews.
+ Sideviews are trickier and it makes a lot of sense to include additional
+ modalities for their detection, e.g. motion information. I recommend
+ Viola and Jones' ICCV 2003 paper if this further interests you.
+
+2) dont expect these detectors to be as accurate as a frontal face detector.
+ A frontal face as a pattern is pretty distinct with respect to other
+ patterns occuring in the world (i.e. image "background"). This is not so
+ for upper, lower and especially full bodies, because they have to rely
+ on fragile silhouette information rather than internal (facial) features.
+ Still, we found especially the upper body detector to perform amazingly well.
+ In contrast to a face detector these detectors will also work at very low
+ image resolutions
+
+Acknowledgements
+================
+Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.
+-->
+<opencv_storage>
+<haarcascade_lowerbody type_id="opencv-haar-classifier">
+ <size>19 23</size>
+ <stages>
+ <_>
+ <!-- stage 0 -->
+ <trees>
+ <_>
+ <!-- tree 0 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>3 4 12 16 -1.</_>
+ <_>7 4 4 16 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0168698690831661</threshold>
+ <left_val>0.5465741753578186</left_val>
+ <right_val>-0.6367803812026978</right_val></_></_>
+ <_>
+ <!-- tree 1 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>11 0 2 20 -1.</_>
+ <_>11 10 2 10 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>2.5349899660795927e-003</threshold>
+ <left_val>-0.3760549128055573</left_val>
+ <right_val>0.3237810134887695</right_val></_></_>
+ <_>
+ <!-- tree 2 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>4 1 4 22 -1.</_>
+ <_>4 12 4 11 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0247094593942165</threshold>
+ <left_val>-0.6797912716865540</left_val>
+ <right_val>0.2050105929374695</right_val></_></_>
+ <_>
+ <!-- tree 3 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>9 8 7 12 -1.</_>
+ <_>9 14 7 6 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0824368596076965</threshold>
+ <left_val>0.2058863937854767</left_val>
+ <right_val>-0.8493843078613281</right_val></_></_>
+ <_>
+ <!-- tree 4 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>6 0 6 10 -1.</_>
+ <_>6 0 3 5 2.</_>
+ <_>9 5 3 5 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-8.2128931535407901e-004</threshold>
+ <left_val>0.3189192116260529</left_val>
+ <right_val>-0.4646945893764496</right_val></_></_>
+ <_>
+ <!-- tree 5 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>1 18 18 5 -1.</_>
+ <_>1 18 9 5 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0230169594287872</threshold>
+ <left_val>0.1867029964923859</left_val>
+ <right_val>-0.7033089995384216</right_val></_></_>
+ <_>
+ <!-- tree 6 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>4 20 10 3 -1.</_>
+ <_>9 20 5 3 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>6.6386149264872074e-003</threshold>
+ <left_val>0.1637049019336700</left_val>
+ <right_val>-0.8460472226142883</right_val></_></_>
+ <_>
+ <!-- tree 7 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>6 17 10 6 -1.</_>
+ <_>6 20 10 3 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>7.6682120561599731e-004</threshold>
+ <left_val>-0.3985269069671631</left_val>
+ <right_val>0.2311332970857620</right_val></_></_>
+ <_>
+ <!-- tree 8 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>0 0 4 20 -1.</_>
+ <_>0 10 4 10 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.1173167973756790</threshold>
+ <left_val>0.1044503971934319</left_val>
+ <right_val>-0.8851094245910645</right_val></_></_>
+ <_>
+ <!-- tree 9 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>3 0 16 14 -1.</_>
+ <_>3 7 16 7 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0154212303459644</threshold>
+ <left_val>-0.2785950899124146</left_val>
+ <right_val>0.2892192006111145</right_val></_></_>
+ <_>
+ <!-- tree 10 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>5 1 4 13 -1.</_>
+ <_>7 1 2 13 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0340189486742020</threshold>
+ <left_val>-0.1428766995668411</left_val>
+ <right_val>0.7780153155326843</right_val></_></_>
+ <_>
+ <!-- tree 11 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>1 8 18 12 -1.</_>
+ <_>10 8 9 6 2.</_>
+ <_>1 14 9 6 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0346388705074787</threshold>
+ <left_val>0.1864407956600189</left_val>
+ <right_val>-0.6032484173774719</right_val></_></_>
+ <_>
+ <!-- tree 12 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>2 0 15 21 -1.</_>
+ <_>7 0 5 21 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.3750365972518921</threshold>
+ <left_val>0.9278184175491333</left_val>
+ <right_val>-0.1542160063982010</right_val></_></_>
+ <_>
+ <!-- tree 13 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>1 5 18 18 -1.</_>
+ <_>10 5 9 9 2.</_>
+ <_>1 14 9 9 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0560119710862637</threshold>
+ <left_val>-0.5859106779098511</left_val>
+ <right_val>0.1954751014709473</right_val></_></_>
+ <_>
+ <!-- tree 14 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>2 19 15 3 -1.</_>
+ <_>7 19 5 3 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-1.4878909569233656e-003</threshold>
+ <left_val>0.2813934981822968</left_val>
+ <right_val>-0.4185301065444946</right_val></_></_>
+ <_>
+ <!-- tree 15 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>7 20 12 3 -1.</_>
+ <_>7 20 6 3 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0144956996664405</threshold>
+ <left_val>-0.7227396965026856</left_val>
+ <right_val>0.0942884609103203</right_val></_></_>
+ <_>
+ <!-- tree 16 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>1 21 14 2 -1.</_>
+ <_>8 21 7 2 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-5.6178281083703041e-003</threshold>
+ <left_val>-0.5955196022987366</left_val>
+ <right_val>0.1520265042781830</right_val></_></_></trees>
+ <stage_threshold>-1.4308550357818604</stage_threshold>
+ <parent>-1</parent>
+ <next>-1</next></_>
+ <_>
+ <!-- stage 1 -->
+ <trees>
+ <_>
+ <!-- tree 0 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>0 16 18 6 -1.</_>
+ <_>6 16 6 6 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-3.1839120201766491e-003</threshold>
+ <left_val>0.4002513885498047</left_val>
+ <right_val>-0.6847316026687622</right_val></_></_>
+ <_>
+ <!-- tree 1 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>8 3 4 20 -1.</_>
+ <_>8 13 4 10 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>3.5989920143038034e-003</threshold>
+ <left_val>-0.5189595222473145</left_val>
+ <right_val>0.3010114133358002</right_val></_></_>
+ <_>
+ <!-- tree 2 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>0 19 18 3 -1.</_>
+ <_>9 19 9 3 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0188046302646399</threshold>
+ <left_val>0.1555491983890533</left_val>
+ <right_val>-0.8047717213630676</right_val></_></_>
+ <_>
+ <!-- tree 3 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>5 21 14 2 -1.</_>
+ <_>5 21 7 2 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>5.2497140131890774e-003</threshold>
+ <left_val>0.1378080993890762</left_val>
+ <right_val>-0.6076750755310059</right_val></_></_>
+ <_>
+ <!-- tree 4 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>2 0 9 5 -1.</_>
+ <_>5 0 3 5 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-1.4204799663275480e-003</threshold>
+ <left_val>0.3231942951679230</left_val>
+ <right_val>-0.4340746104717255</right_val></_></_>
+ <_>
+ <!-- tree 5 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>3 20 15 3 -1.</_>
+ <_>8 20 5 3 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0251743495464325</threshold>
+ <left_val>-0.7078087925910950</left_val>
+ <right_val>0.0931063294410706</right_val></_></_>
+ <_>
+ <!-- tree 6 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>3 9 6 14 -1.</_>
+ <_>5 9 2 14 3.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>3.2285219058394432e-003</threshold>
+ <left_val>-0.3251047134399414</left_val>
+ <right_val>0.3357169926166534</right_val></_></_>
+ <_>
+ <!-- tree 7 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>12 3 3 18 -1.</_>
+ <_>12 12 3 9 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0949934124946594</threshold>
+ <left_val>0.0824390873312950</left_val>
+ <right_val>-0.8754953742027283</right_val></_></_>
+ <_>
+ <!-- tree 8 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>1 14 4 9 -1.</_>
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+ <tilted>0</tilted></feature>
+ <threshold>-6.5919090993702412e-003</threshold>
+ <left_val>-0.7380419969558716</left_val>
+ <right_val>0.1385374963283539</right_val></_></_>
+ <_>
+ <!-- tree 9 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>7 15 11 8 -1.</_>
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+ <tilted>0</tilted></feature>
+ <threshold>-1.1146620381623507e-003</threshold>
+ <left_val>0.1791726946830750</left_val>
+ <right_val>-0.2795585989952087</right_val></_></_>
+ <_>
+ <!-- tree 10 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>0 7 6 10 -1.</_>
+ <_>0 7 3 5 2.</_>
+ <_>3 12 3 5 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0133490199223161</threshold>
+ <left_val>0.1305782943964005</left_val>
+ <right_val>-0.6980267167091370</right_val></_></_>
+ <_>
+ <!-- tree 11 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>10 6 4 13 -1.</_>
+ <_>10 6 2 13 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>-0.0351814515888691</threshold>
+ <left_val>0.4653536081314087</left_val>
+ <right_val>-0.1069877967238426</right_val></_></_>
+ <_>
+ <!-- tree 12 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>5 6 4 13 -1.</_>
+ <_>7 6 2 13 2.</_></rects>
+ <tilted>0</tilted></feature>
+ <threshold>0.0318745896220207</threshold>
+ <left_val>-0.1356538981199265</left_val>
+ <right_val>0.7904788851737976</right_val></_></_></trees>
+ <stage_threshold>-1.1907930374145508</stage_threshold>
+ <parent>0</parent>
+ <next>-1</next></_>
+ <_>
+ <!-- stage 2 -->
+ <trees>
+ <_>
+ <!-- tree 0 -->
+ <_>
+ <!-- root node -->
+ <feature>
+ <rects>
+ <_>8 2 6 8 -1.</_>
+ <_>8 2 6 4 2.</_></rects>
+ <tilted>1</tilted></feature>
+ <threshold>-0.0106474300846457</threshold>
+ <left_val>0.3807902932167053</left_val>
+ <right_val>-0.5867233872413635</right_val></_></_>
+ <_>
+ <!-- tree 1 -->
+ <_>
+ <!-- root node -->
+ <feature>
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+ <_>0 11 19 12 -1.</_>
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+ <tilted>0</tilted></feature>
+ <threshold>-0.0732144936919212</threshold>
+ <left_val>-0.7955095171928406</left_val>
+ <right_val>0.1722325980663300</right_val></_></_>
+ <_>
+ <!-- tree 2 -->
+ <_>
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+ <threshold>6.0464427806437016e-003</threshold>
+ <left_val>0.1653216034173966</left_val>
+ <right_val>-0.6937664747238159</right_val></_></_>
+ <_>
+ <!-- tree 3 -->
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+ <threshold>7.3225022060796618e-004</threshold>
+ <left_val>-0.3324716091156006</left_val>
+ <right_val>0.2366997003555298</right_val></_></_>
+ <_>
+ <!-- tree 4 -->
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+ <!-- root node -->
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+ <tilted>0</tilted></feature>
+ <threshold>-0.0109900804236531</threshold>
+ <left_val>-0.6913688778877258</left_val>
+ <right_val>0.2105827033519745</right_val></_></_>
+ <_>
+ <!-- tree 5 -->
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+ <!-- root node -->
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+ <threshold>-1.5282750246115029e-004</threshold>
+ <left_val>0.2030584961175919</left_val>
+ <right_val>-0.4655165970325470</right_val></_></_>
+ <_>
+ <!-- tree 6 -->
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+ <!-- root node -->
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+ <threshold>2.4822261184453964e-004</threshold>
+ <left_val>-0.4212292134761810</left_val>
+ <right_val>0.2733530998229981</right_val></_></_>
+ <_>
+ <!-- tree 7 -->
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+ <!-- root node -->
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+ <threshold>-8.4205856546759605e-003</threshold>
+ <left_val>-0.4374446868896484</left_val>
+ <right_val>0.0588318482041359</right_val></_></_>
+ <_>
+ <!-- tree 8 -->
+ <_>
+ <!-- root node -->
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+ <rects>
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+ <tilted>0</tilted></feature>
+ <threshold>-0.3699279129505158</threshold>
+ <left_val>0.9107081890106201</left_val>
+ <right_val>-0.0872075408697128</right_val></_></_>
+ <_>
+ <!-- tree 9 -->
+ <_>
+ <!-- root node -->
+ <feature>
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+ <_>14 17 5 6 -1.</_>
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+ <tilted>0</tilted></feature>
+ <threshold>6.1259930953383446e-003</threshold>
+ <left_val>0.1188673004508019</left_val>
+ <right_val>-0.1852017045021057</right_val></_></_>
+ <_>
+ <!-- tree 10 -->
+ <_>
+ <!-- root node -->
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+ <stage_threshold>-1.2834340333938599</stage_threshold>
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generated by cgit v1.2.1 (git 2.18.0) at 2024-06-02 03:32:47 +0000