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1 solid logo-32 facet normal 5.299990e-01 -8.479983e-01 0.000000e+004 vertex -1.809197e+00 -1.071657e+00 5.000000e-015 vertex -1.809197e+00 -1.071657e+00 -5.000000e-016 vertex -3.710588e-01 -1.728205e-01 5.000000e-019 facet normal 5.299990e-01 -8.479983e-01 0.000000e+0011 vertex -3.710588e-01 -1.728205e-01 5.000000e-0112 vertex -1.809197e+00 -1.071657e+00 -5.000000e-0113 vertex -3.710588e-01 -1.728205e-01 -5.000000e-0116 facet normal 5.995555e-01 -8.003332e-01 0.000000e+00[all …]
13 // Copyright (C) 2000-2008, Intel Corporation, all rights reserved.57 #define CV_DESCALE(x, n) (((x) + (1 << ((n)-1))) >> (n))106 ////////////////// Various 3/4-channel to 3/4-channel RGB transformations /////////////////158 /////////// Transforming 16-bit (565 or 555) RGB to/from 24/32-bit (888[8]) RGB //////////493 ///////////////////////////////////// RGB <-> YUV //////////////////////////////////////505 const int Cr = CV_DESCALE((src[bidx^2] - Y) * c_RGB2YUVCoeffs_i[3] + delta, yuv_shift); in RGB2YUVConvert()506 const int Cb = CV_DESCALE((src[bidx] - Y) * c_RGB2YUVCoeffs_i[4] + delta, yuv_shift); in RGB2YUVConvert()516 dst.y = (src[bidx^2] - dst.x) * c_RGB2YUVCoeffs_f[3] + ColorChannel<float>::half(); in RGB2YUVConvert()517 dst.z = (src[bidx] - dst.x) * c_RGB2YUVCoeffs_f[4] + ColorChannel<float>::half(); in RGB2YUVConvert()546 __constant__ float c_YUV2RGBCoeffs_f[5] = { 2.032f, -0.395f, -0.581f, 1.140f };[all …]
1 // SPDX-License-Identifier: GPL-2.012 * on mips), so this wastes a bit of space on those - though we15 #define E(err) [err + BUILD_BUG_ON_ZERO(err <= 0 || err > 300)] = "-" #err macro17 E(E2BIG),18 E(EACCES),19 E(EADDRINUSE),20 E(EADDRNOTAVAIL),21 E(EADV),22 E(EAFNOSUPPORT),23 E(EALREADY),[all …]
... 1# ncurses 6.1 - patch 20191109 - Thomas E. Dickey 2# 3# ----- ...
2 <!--3 Tree-based 20x20 gentle adaboost frontal face detector.44 -->46 <cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>58 <stageThreshold>3.5069230198860168e-01</stageThreshold>62 0 1 0 4.3272329494357109e-03 -1 -2 1 1.3076160103082657e-02</internalNodes>64 3.8381900638341904e-02 8.9652568101882935e-0165 2.6293140649795532e-01</leafValues></_>68 0 1 2 5.2434601821005344e-04 -1 -2 3 4.4573000632226467e-03</internalNodes>70 1.0216630250215530e-01 1.2384019792079926e-01[all …]
2 <!--3 A frontal cat face detector using the full set of Haar features, i.e.18 http://www.oreilly.com/pub/e/307721 https://bitbucket.org/Joe_Howse/angora-blue26 sideways or upside down (e.g. the cat is rolling over), try various rotations31 2016-08-06: Re-trained with more negative samples and more stages. False33 previous version, now you should re-adjust the arguments of36 cross-check the positives anymore.37 2014-04-25: First release (at https://bitbucket.org/Joe_Howse/angora-blue)47 | Copyright (c) 2014-2016, Joseph Howse (Nummist Media Corporation Limited,[all …]
2 <!--3 Stump-based 20x20 gentle adaboost frontal face detector.44 -->46 <cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>58 <stageThreshold>8.2268941402435303e-01</stageThreshold>62 0 -1 0 4.0141958743333817e-03</internalNodes>64 3.3794190734624863e-02 8.3781069517135620e-01</leafValues></_>67 0 -1 1 1.5151339583098888e-02</internalNodes>69 1.5141320228576660e-01 7.4888122081756592e-01</leafValues></_>72 0 -1 2 4.2109931819140911e-03</internalNodes>[all …]
2 <!--3 Stump-based 20x20 gentle adaboost frontal face detector.45 -->47 <cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>59 <stageThreshold>-1.3442519903182983e+00</stageThreshold>63 0 -1 0 3.7895569112151861e-03</internalNodes>65 -9.2945802211761475e-01 6.4119851589202881e-01</leafValues></_>68 0 -1 1 1.2098110280930996e-02</internalNodes>70 -7.1810090541839600e-01 4.7141009569168091e-01</leafValues></_>73 0 -1 2 1.2138449819758534e-03</internalNodes>[all …]
2 <!--3 Stump-based 20x20 frontal eye detector.44 -->46 <cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>58 <stageThreshold>-1.4562760591506958e+00</stageThreshold>62 0 -1 0 1.2963959574699402e-01</internalNodes>64 -7.7304208278656006e-01 6.8350148200988770e-01</leafValues></_>67 0 -1 1 -4.6326808631420135e-02</internalNodes>69 5.7352751493453979e-01 -4.9097689986228943e-01</leafValues></_>72 0 -1 2 -1.6173090785741806e-02</internalNodes>[all …]
2 <!--3 A frontal cat face detector using the basic set of Haar features, i.e.18 http://www.oreilly.com/pub/e/307721 https://bitbucket.org/Joe_Howse/angora-blue26 sideways or upside down (e.g. the cat is rolling over), try various rotations31 2016-08-06: Re-trained with more negative samples and more stages. False33 previous version, now you should re-adjust the arguments of36 cross-check the positives anymore.37 2014-04-25: First release (at https://bitbucket.org/Joe_Howse/angora-blue)47 | Copyright (c) 2014-2016, Joseph Howse (Nummist Media Corporation Limited,[all …]
2 <!--3 Stump-based 24x24 discrete(?) adaboost frontal face detector.44 -->46 <cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>58 <stageThreshold>-5.0425500869750977e+00</stageThreshold>62 0 -1 0 -3.1511999666690826e-02</internalNodes>64 2.0875380039215088e+00 -2.2172100543975830e+00</leafValues></_>67 0 -1 1 1.2396000325679779e-02</internalNodes>69 -1.8633940219879150e+00 1.3272049427032471e+00</leafValues></_>72 0 -1 2 2.1927999332547188e-02</internalNodes>[all …]