Bug Summary

File:ccv_icf.c
Warning:line 1176, column 25
The left expression of the compound assignment is an uninitialized value. The computed value will also be garbage

Annotated Source Code

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clang -cc1 -cc1 -triple x86_64-unknown-linux-gnu -analyze -disable-free -clear-ast-before-backend -disable-llvm-verifier -discard-value-names -main-file-name ccv_icf.c -analyzer-checker=core -analyzer-checker=apiModeling -analyzer-checker=unix -analyzer-checker=deadcode -analyzer-checker=security.insecureAPI.UncheckedReturn -analyzer-checker=security.insecureAPI.getpw -analyzer-checker=security.insecureAPI.gets -analyzer-checker=security.insecureAPI.mktemp -analyzer-checker=security.insecureAPI.mkstemp -analyzer-checker=security.insecureAPI.vfork -analyzer-checker=nullability.NullPassedToNonnull -analyzer-checker=nullability.NullReturnedFromNonnull -analyzer-output plist -w -setup-static-analyzer -mrelocation-model pic -pic-level 2 -pic-is-pie -mframe-pointer=none -menable-no-infs -menable-no-nans -fapprox-func -funsafe-math-optimizations -fno-signed-zeros -mreassociate -freciprocal-math -ffp-contract=fast -fno-rounding-math -ffast-math -ffinite-math-only -complex-range=basic -mconstructor-aliases -funwind-tables=2 -target-cpu x86-64 -target-feature +sse2 -tune-cpu generic -debugger-tuning=gdb -fdebug-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib -fcoverage-compilation-dir=/home/liu/actions-runner/_work/ccv/ccv/lib -resource-dir /usr/local/lib/clang/19 -I . -I /usr/local/cuda/include -D HAVE_CBLAS -D HAVE_LIBPNG -D HAVE_LIBJPEG -D HAVE_FFTW3 -D HAVE_PTHREAD -D HAVE_LIBLINEAR -D HAVE_TESSERACT -D HAVE_AVCODEC -D HAVE_AVFORMAT -D HAVE_AVUTIL -D HAVE_SWSCALE -D HAVE_SSE2 -D HAVE_GSL -D HAVE_CUDA -D HAVE_CUDNN -D HAVE_NCCL -D USE_SYSTEM_CUB -I /usr/local/include -internal-isystem /usr/local/lib/clang/19/include -internal-isystem /usr/local/include -internal-isystem /usr/lib/gcc/x86_64-linux-gnu/12/../../../../x86_64-linux-gnu/include -internal-externc-isystem /usr/include/x86_64-linux-gnu -internal-externc-isystem /include -internal-externc-isystem /usr/include -O3 -ferror-limit 19 -fgnuc-version=4.2.1 -fskip-odr-check-in-gmf -vectorize-loops -vectorize-slp -analyzer-output=html -faddrsig -D__GCC_HAVE_DWARF2_CFI_ASM=1 -o /home/liu/actions-runner/_work/ccv/ccv/_analyze/2024-10-28-160422-343697-1 -x c ccv_icf.c
1#include "ccv.h"
2#include "ccv_internal.h"
3#ifdef HAVE_GSL1
4#include <gsl/gsl_rng.h>
5#include <gsl/gsl_randist.h>
6#endif
7#ifdef USE_OPENMP
8#include <omp.h>
9#endif
10#ifdef USE_DISPATCH
11#include <dispatch/dispatch.h>
12#endif
13
14const ccv_icf_param_t ccv_icf_default_params = {
15 .min_neighbors = 2,
16 .threshold = 0,
17 .step_through = 2,
18 .flags = 0,
19 .interval = 8,
20};
21
22// this uses a look up table for cubic root computation because rgb to luv only requires data within range of 0~1
23static inline float fast_cube_root(const float d)
24{
25 static const float cube_root[2048] = {
26 0.000000e+00, 7.875788e-02, 9.922871e-02, 1.135885e-01, 1.250203e-01, 1.346741e-01, 1.431126e-01, 1.506584e-01,
27 1.575158e-01, 1.638230e-01, 1.696787e-01, 1.751560e-01, 1.803105e-01, 1.851861e-01, 1.898177e-01, 1.942336e-01,
28 1.984574e-01, 2.025087e-01, 2.064040e-01, 2.101577e-01, 2.137818e-01, 2.172870e-01, 2.206827e-01, 2.239769e-01,
29 2.271770e-01, 2.302894e-01, 2.333199e-01, 2.362736e-01, 2.391553e-01, 2.419692e-01, 2.447191e-01, 2.474085e-01,
30 2.500407e-01, 2.526186e-01, 2.551450e-01, 2.576222e-01, 2.600528e-01, 2.624387e-01, 2.647821e-01, 2.670846e-01,
31 2.693482e-01, 2.715743e-01, 2.737645e-01, 2.759202e-01, 2.780428e-01, 2.801334e-01, 2.821933e-01, 2.842235e-01,
32 2.862251e-01, 2.881992e-01, 2.901465e-01, 2.920681e-01, 2.939647e-01, 2.958371e-01, 2.976862e-01, 2.995125e-01,
33 3.013168e-01, 3.030998e-01, 3.048621e-01, 3.066041e-01, 3.083267e-01, 3.100302e-01, 3.117152e-01, 3.133821e-01,
34 3.150315e-01, 3.166639e-01, 3.182795e-01, 3.198789e-01, 3.214625e-01, 3.230307e-01, 3.245837e-01, 3.261220e-01,
35 3.276460e-01, 3.291559e-01, 3.306521e-01, 3.321348e-01, 3.336045e-01, 3.350613e-01, 3.365056e-01, 3.379375e-01,
36 3.393574e-01, 3.407656e-01, 3.421622e-01, 3.435475e-01, 3.449216e-01, 3.462850e-01, 3.476377e-01, 3.489799e-01,
37 3.503119e-01, 3.516339e-01, 3.529460e-01, 3.542483e-01, 3.555412e-01, 3.568248e-01, 3.580992e-01, 3.593646e-01,
38 3.606211e-01, 3.618689e-01, 3.631082e-01, 3.643391e-01, 3.655617e-01, 3.667762e-01, 3.679827e-01, 3.691814e-01,
39 3.703723e-01, 3.715556e-01, 3.727314e-01, 3.738999e-01, 3.750610e-01, 3.762151e-01, 3.773621e-01, 3.785022e-01,
40 3.796354e-01, 3.807619e-01, 3.818818e-01, 3.829952e-01, 3.841021e-01, 3.852027e-01, 3.862970e-01, 3.873852e-01,
41 3.884673e-01, 3.895434e-01, 3.906136e-01, 3.916779e-01, 3.927365e-01, 3.937894e-01, 3.948367e-01, 3.958785e-01,
42 3.969149e-01, 3.979458e-01, 3.989714e-01, 3.999918e-01, 4.010071e-01, 4.020171e-01, 4.030222e-01, 4.040223e-01,
43 4.050174e-01, 4.060076e-01, 4.069931e-01, 4.079738e-01, 4.089499e-01, 4.099212e-01, 4.108880e-01, 4.118503e-01,
44 4.128081e-01, 4.137615e-01, 4.147105e-01, 4.156551e-01, 4.165955e-01, 4.175317e-01, 4.184637e-01, 4.193916e-01,
45 4.203153e-01, 4.212351e-01, 4.221508e-01, 4.230626e-01, 4.239704e-01, 4.248744e-01, 4.257746e-01, 4.266710e-01,
46 4.275636e-01, 4.284525e-01, 4.293377e-01, 4.302193e-01, 4.310973e-01, 4.319718e-01, 4.328427e-01, 4.337101e-01,
47 4.345741e-01, 4.354346e-01, 4.362918e-01, 4.371456e-01, 4.379961e-01, 4.388433e-01, 4.396872e-01, 4.405279e-01,
48 4.413654e-01, 4.421997e-01, 4.430309e-01, 4.438590e-01, 4.446840e-01, 4.455060e-01, 4.463249e-01, 4.471409e-01,
49 4.479539e-01, 4.487639e-01, 4.495711e-01, 4.503753e-01, 4.511767e-01, 4.519752e-01, 4.527710e-01, 4.535639e-01,
50 4.543541e-01, 4.551415e-01, 4.559263e-01, 4.567083e-01, 4.574877e-01, 4.582644e-01, 4.590385e-01, 4.598100e-01,
51 4.605789e-01, 4.613453e-01, 4.621091e-01, 4.628704e-01, 4.636292e-01, 4.643855e-01, 4.651394e-01, 4.658908e-01,
52 4.666398e-01, 4.673865e-01, 4.681307e-01, 4.688726e-01, 4.696122e-01, 4.703494e-01, 4.710843e-01, 4.718169e-01,
53 4.725473e-01, 4.732754e-01, 4.740013e-01, 4.747250e-01, 4.754464e-01, 4.761657e-01, 4.768828e-01, 4.775978e-01,
54 4.783106e-01, 4.790214e-01, 4.797300e-01, 4.804365e-01, 4.811410e-01, 4.818434e-01, 4.825437e-01, 4.832420e-01,
55 4.839384e-01, 4.846327e-01, 4.853250e-01, 4.860154e-01, 4.867038e-01, 4.873902e-01, 4.880748e-01, 4.887574e-01,
56 4.894381e-01, 4.901170e-01, 4.907939e-01, 4.914690e-01, 4.921423e-01, 4.928137e-01, 4.934832e-01, 4.941510e-01,
57 4.948170e-01, 4.954812e-01, 4.961436e-01, 4.968042e-01, 4.974631e-01, 4.981203e-01, 4.987757e-01, 4.994294e-01,
58 5.000814e-01, 5.007317e-01, 5.013803e-01, 5.020273e-01, 5.026726e-01, 5.033162e-01, 5.039582e-01, 5.045985e-01,
59 5.052372e-01, 5.058743e-01, 5.065099e-01, 5.071438e-01, 5.077761e-01, 5.084069e-01, 5.090362e-01, 5.096638e-01,
60 5.102900e-01, 5.109145e-01, 5.115376e-01, 5.121592e-01, 5.127792e-01, 5.133978e-01, 5.140148e-01, 5.146304e-01,
61 5.152445e-01, 5.158572e-01, 5.164684e-01, 5.170782e-01, 5.176865e-01, 5.182934e-01, 5.188988e-01, 5.195029e-01,
62 5.201056e-01, 5.207069e-01, 5.213068e-01, 5.219053e-01, 5.225024e-01, 5.230982e-01, 5.236927e-01, 5.242857e-01,
63 5.248775e-01, 5.254679e-01, 5.260570e-01, 5.266448e-01, 5.272312e-01, 5.278164e-01, 5.284002e-01, 5.289828e-01,
64 5.295641e-01, 5.301442e-01, 5.307229e-01, 5.313004e-01, 5.318767e-01, 5.324517e-01, 5.330254e-01, 5.335979e-01,
65 5.341693e-01, 5.347394e-01, 5.353082e-01, 5.358759e-01, 5.364423e-01, 5.370076e-01, 5.375717e-01, 5.381346e-01,
66 5.386963e-01, 5.392569e-01, 5.398163e-01, 5.403746e-01, 5.409316e-01, 5.414876e-01, 5.420424e-01, 5.425960e-01,
67 5.431486e-01, 5.437000e-01, 5.442503e-01, 5.447995e-01, 5.453476e-01, 5.458946e-01, 5.464405e-01, 5.469853e-01,
68 5.475290e-01, 5.480717e-01, 5.486133e-01, 5.491537e-01, 5.496932e-01, 5.502316e-01, 5.507689e-01, 5.513052e-01,
69 5.518404e-01, 5.523747e-01, 5.529078e-01, 5.534400e-01, 5.539711e-01, 5.545012e-01, 5.550303e-01, 5.555584e-01,
70 5.560855e-01, 5.566117e-01, 5.571368e-01, 5.576609e-01, 5.581840e-01, 5.587062e-01, 5.592273e-01, 5.597475e-01,
71 5.602668e-01, 5.607851e-01, 5.613024e-01, 5.618188e-01, 5.623342e-01, 5.628487e-01, 5.633622e-01, 5.638748e-01,
72 5.643865e-01, 5.648973e-01, 5.654072e-01, 5.659161e-01, 5.664241e-01, 5.669311e-01, 5.674374e-01, 5.679426e-01,
73 5.684470e-01, 5.689505e-01, 5.694531e-01, 5.699549e-01, 5.704557e-01, 5.709556e-01, 5.714548e-01, 5.719529e-01,
74 5.724503e-01, 5.729468e-01, 5.734424e-01, 5.739372e-01, 5.744311e-01, 5.749242e-01, 5.754164e-01, 5.759078e-01,
75 5.763984e-01, 5.768881e-01, 5.773770e-01, 5.778650e-01, 5.783523e-01, 5.788387e-01, 5.793243e-01, 5.798091e-01,
76 5.802931e-01, 5.807762e-01, 5.812586e-01, 5.817402e-01, 5.822210e-01, 5.827010e-01, 5.831801e-01, 5.836585e-01,
77 5.841362e-01, 5.846130e-01, 5.850891e-01, 5.855644e-01, 5.860389e-01, 5.865127e-01, 5.869856e-01, 5.874579e-01,
78 5.879294e-01, 5.884001e-01, 5.888700e-01, 5.893393e-01, 5.898077e-01, 5.902755e-01, 5.907425e-01, 5.912087e-01,
79 5.916742e-01, 5.921390e-01, 5.926031e-01, 5.930664e-01, 5.935290e-01, 5.939909e-01, 5.944521e-01, 5.949125e-01,
80 5.953723e-01, 5.958313e-01, 5.962896e-01, 5.967473e-01, 5.972042e-01, 5.976604e-01, 5.981160e-01, 5.985708e-01,
81 5.990250e-01, 5.994784e-01, 5.999312e-01, 6.003833e-01, 6.008347e-01, 6.012855e-01, 6.017355e-01, 6.021850e-01,
82 6.026337e-01, 6.030817e-01, 6.035291e-01, 6.039758e-01, 6.044219e-01, 6.048673e-01, 6.053120e-01, 6.057562e-01,
83 6.061996e-01, 6.066424e-01, 6.070846e-01, 6.075261e-01, 6.079670e-01, 6.084072e-01, 6.088468e-01, 6.092858e-01,
84 6.097241e-01, 6.101618e-01, 6.105989e-01, 6.110353e-01, 6.114712e-01, 6.119064e-01, 6.123410e-01, 6.127750e-01,
85 6.132084e-01, 6.136411e-01, 6.140732e-01, 6.145048e-01, 6.149357e-01, 6.153660e-01, 6.157957e-01, 6.162249e-01,
86 6.166534e-01, 6.170813e-01, 6.175086e-01, 6.179354e-01, 6.183616e-01, 6.187872e-01, 6.192122e-01, 6.196365e-01,
87 6.200604e-01, 6.204836e-01, 6.209063e-01, 6.213284e-01, 6.217499e-01, 6.221709e-01, 6.225913e-01, 6.230111e-01,
88 6.234304e-01, 6.238490e-01, 6.242672e-01, 6.246848e-01, 6.251017e-01, 6.255182e-01, 6.259341e-01, 6.263494e-01,
89 6.267643e-01, 6.271785e-01, 6.275922e-01, 6.280054e-01, 6.284180e-01, 6.288301e-01, 6.292416e-01, 6.296526e-01,
90 6.300631e-01, 6.304730e-01, 6.308824e-01, 6.312913e-01, 6.316996e-01, 6.321074e-01, 6.325147e-01, 6.329215e-01,
91 6.333277e-01, 6.337335e-01, 6.341386e-01, 6.345433e-01, 6.349475e-01, 6.353511e-01, 6.357543e-01, 6.361569e-01,
92 6.365590e-01, 6.369606e-01, 6.373618e-01, 6.377624e-01, 6.381625e-01, 6.385621e-01, 6.389612e-01, 6.393598e-01,
93 6.397579e-01, 6.401555e-01, 6.405526e-01, 6.409492e-01, 6.413454e-01, 6.417410e-01, 6.421362e-01, 6.425309e-01,
94 6.429250e-01, 6.433188e-01, 6.437120e-01, 6.441047e-01, 6.444970e-01, 6.448888e-01, 6.452801e-01, 6.456710e-01,
95 6.460613e-01, 6.464512e-01, 6.468406e-01, 6.472296e-01, 6.476181e-01, 6.480061e-01, 6.483937e-01, 6.487808e-01,
96 6.491674e-01, 6.495536e-01, 6.499393e-01, 6.503246e-01, 6.507094e-01, 6.510937e-01, 6.514776e-01, 6.518611e-01,
97 6.522441e-01, 6.526266e-01, 6.530087e-01, 6.533904e-01, 6.537716e-01, 6.541524e-01, 6.545327e-01, 6.549126e-01,
98 6.552920e-01, 6.556710e-01, 6.560495e-01, 6.564277e-01, 6.568054e-01, 6.571826e-01, 6.575595e-01, 6.579359e-01,
99 6.583118e-01, 6.586874e-01, 6.590625e-01, 6.594372e-01, 6.598114e-01, 6.601852e-01, 6.605586e-01, 6.609316e-01,
100 6.613042e-01, 6.616763e-01, 6.620481e-01, 6.624194e-01, 6.627903e-01, 6.631607e-01, 6.635308e-01, 6.639005e-01,
101 6.642697e-01, 6.646385e-01, 6.650070e-01, 6.653750e-01, 6.657426e-01, 6.661098e-01, 6.664766e-01, 6.668430e-01,
102 6.672090e-01, 6.675746e-01, 6.679398e-01, 6.683046e-01, 6.686690e-01, 6.690330e-01, 6.693966e-01, 6.697598e-01,
103 6.701226e-01, 6.704850e-01, 6.708471e-01, 6.712087e-01, 6.715700e-01, 6.719308e-01, 6.722913e-01, 6.726514e-01,
104 6.730111e-01, 6.733705e-01, 6.737294e-01, 6.740879e-01, 6.744461e-01, 6.748039e-01, 6.751614e-01, 6.755184e-01,
105 6.758750e-01, 6.762313e-01, 6.765872e-01, 6.769428e-01, 6.772979e-01, 6.776527e-01, 6.780071e-01, 6.783612e-01,
106 6.787149e-01, 6.790682e-01, 6.794212e-01, 6.797737e-01, 6.801260e-01, 6.804778e-01, 6.808293e-01, 6.811804e-01,
107 6.815312e-01, 6.818815e-01, 6.822316e-01, 6.825813e-01, 6.829306e-01, 6.832796e-01, 6.836282e-01, 6.839765e-01,
108 6.843244e-01, 6.846719e-01, 6.850191e-01, 6.853660e-01, 6.857125e-01, 6.860586e-01, 6.864043e-01, 6.867498e-01,
109 6.870949e-01, 6.874397e-01, 6.877841e-01, 6.881282e-01, 6.884719e-01, 6.888152e-01, 6.891583e-01, 6.895010e-01,
110 6.898433e-01, 6.901854e-01, 6.905270e-01, 6.908684e-01, 6.912094e-01, 6.915500e-01, 6.918904e-01, 6.922303e-01,
111 6.925700e-01, 6.929094e-01, 6.932484e-01, 6.935870e-01, 6.939254e-01, 6.942633e-01, 6.946011e-01, 6.949384e-01,
112 6.952754e-01, 6.956121e-01, 6.959485e-01, 6.962845e-01, 6.966202e-01, 6.969556e-01, 6.972907e-01, 6.976255e-01,
113 6.979599e-01, 6.982940e-01, 6.986278e-01, 6.989613e-01, 6.992944e-01, 6.996273e-01, 6.999598e-01, 7.002920e-01,
114 7.006239e-01, 7.009555e-01, 7.012867e-01, 7.016177e-01, 7.019483e-01, 7.022786e-01, 7.026086e-01, 7.029384e-01,
115 7.032678e-01, 7.035969e-01, 7.039256e-01, 7.042542e-01, 7.045823e-01, 7.049102e-01, 7.052377e-01, 7.055650e-01,
116 7.058919e-01, 7.062186e-01, 7.065449e-01, 7.068710e-01, 7.071967e-01, 7.075222e-01, 7.078474e-01, 7.081722e-01,
117 7.084967e-01, 7.088210e-01, 7.091449e-01, 7.094686e-01, 7.097920e-01, 7.101150e-01, 7.104378e-01, 7.107603e-01,
118 7.110825e-01, 7.114044e-01, 7.117260e-01, 7.120473e-01, 7.123684e-01, 7.126891e-01, 7.130095e-01, 7.133297e-01,
119 7.136496e-01, 7.139692e-01, 7.142885e-01, 7.146075e-01, 7.149262e-01, 7.152447e-01, 7.155629e-01, 7.158808e-01,
120 7.161984e-01, 7.165157e-01, 7.168328e-01, 7.171495e-01, 7.174660e-01, 7.177821e-01, 7.180981e-01, 7.184138e-01,
121 7.187291e-01, 7.190442e-01, 7.193590e-01, 7.196736e-01, 7.199879e-01, 7.203019e-01, 7.206156e-01, 7.209290e-01,
122 7.212422e-01, 7.215551e-01, 7.218677e-01, 7.221801e-01, 7.224922e-01, 7.228040e-01, 7.231156e-01, 7.234268e-01,
123 7.237378e-01, 7.240486e-01, 7.243591e-01, 7.246693e-01, 7.249793e-01, 7.252890e-01, 7.255983e-01, 7.259076e-01,
124 7.262164e-01, 7.265251e-01, 7.268335e-01, 7.271415e-01, 7.274494e-01, 7.277570e-01, 7.280643e-01, 7.283714e-01,
125 7.286782e-01, 7.289847e-01, 7.292911e-01, 7.295971e-01, 7.299029e-01, 7.302084e-01, 7.305137e-01, 7.308187e-01,
126 7.311234e-01, 7.314279e-01, 7.317322e-01, 7.320362e-01, 7.323400e-01, 7.326434e-01, 7.329467e-01, 7.332497e-01,
127 7.335525e-01, 7.338549e-01, 7.341572e-01, 7.344592e-01, 7.347609e-01, 7.350624e-01, 7.353637e-01, 7.356647e-01,
128 7.359655e-01, 7.362660e-01, 7.365662e-01, 7.368662e-01, 7.371660e-01, 7.374656e-01, 7.377649e-01, 7.380639e-01,
129 7.383628e-01, 7.386613e-01, 7.389597e-01, 7.392578e-01, 7.395556e-01, 7.398532e-01, 7.401506e-01, 7.404477e-01,
130 7.407446e-01, 7.410412e-01, 7.413377e-01, 7.416338e-01, 7.419298e-01, 7.422255e-01, 7.425209e-01, 7.428162e-01,
131 7.431112e-01, 7.434059e-01, 7.437005e-01, 7.439948e-01, 7.442889e-01, 7.445827e-01, 7.448763e-01, 7.451697e-01,
132 7.454628e-01, 7.457558e-01, 7.460485e-01, 7.463409e-01, 7.466331e-01, 7.469251e-01, 7.472169e-01, 7.475084e-01,
133 7.477998e-01, 7.480908e-01, 7.483817e-01, 7.486723e-01, 7.489627e-01, 7.492529e-01, 7.495428e-01, 7.498326e-01,
134 7.501221e-01, 7.504114e-01, 7.507005e-01, 7.509893e-01, 7.512779e-01, 7.515663e-01, 7.518545e-01, 7.521424e-01,
135 7.524302e-01, 7.527177e-01, 7.530050e-01, 7.532921e-01, 7.535789e-01, 7.538656e-01, 7.541520e-01, 7.544382e-01,
136 7.547241e-01, 7.550099e-01, 7.552955e-01, 7.555808e-01, 7.558660e-01, 7.561509e-01, 7.564356e-01, 7.567201e-01,
137 7.570043e-01, 7.572884e-01, 7.575722e-01, 7.578558e-01, 7.581393e-01, 7.584225e-01, 7.587055e-01, 7.589883e-01,
138 7.592708e-01, 7.595532e-01, 7.598354e-01, 7.601173e-01, 7.603990e-01, 7.606806e-01, 7.609619e-01, 7.612430e-01,
139 7.615239e-01, 7.618046e-01, 7.620851e-01, 7.623653e-01, 7.626454e-01, 7.629253e-01, 7.632049e-01, 7.634844e-01,
140 7.637637e-01, 7.640427e-01, 7.643216e-01, 7.646002e-01, 7.648786e-01, 7.651569e-01, 7.654349e-01, 7.657127e-01,
141 7.659904e-01, 7.662678e-01, 7.665451e-01, 7.668221e-01, 7.670989e-01, 7.673756e-01, 7.676520e-01, 7.679282e-01,
142 7.682042e-01, 7.684801e-01, 7.687557e-01, 7.690312e-01, 7.693064e-01, 7.695814e-01, 7.698563e-01, 7.701310e-01,
143 7.704054e-01, 7.706797e-01, 7.709538e-01, 7.712276e-01, 7.715013e-01, 7.717748e-01, 7.720481e-01, 7.723212e-01,
144 7.725941e-01, 7.728668e-01, 7.731394e-01, 7.734116e-01, 7.736838e-01, 7.739558e-01, 7.742275e-01, 7.744991e-01,
145 7.747704e-01, 7.750416e-01, 7.753126e-01, 7.755834e-01, 7.758540e-01, 7.761245e-01, 7.763947e-01, 7.766647e-01,
146 7.769346e-01, 7.772043e-01, 7.774737e-01, 7.777431e-01, 7.780122e-01, 7.782811e-01, 7.785498e-01, 7.788184e-01,
147 7.790868e-01, 7.793550e-01, 7.796230e-01, 7.798908e-01, 7.801584e-01, 7.804259e-01, 7.806932e-01, 7.809603e-01,
148 7.812271e-01, 7.814939e-01, 7.817604e-01, 7.820268e-01, 7.822930e-01, 7.825589e-01, 7.828248e-01, 7.830904e-01,
149 7.833558e-01, 7.836211e-01, 7.838862e-01, 7.841511e-01, 7.844158e-01, 7.846804e-01, 7.849448e-01, 7.852090e-01,
150 7.854730e-01, 7.857369e-01, 7.860005e-01, 7.862641e-01, 7.865273e-01, 7.867905e-01, 7.870535e-01, 7.873163e-01,
151 7.875788e-01, 7.878413e-01, 7.881036e-01, 7.883657e-01, 7.886276e-01, 7.888893e-01, 7.891509e-01, 7.894123e-01,
152 7.896735e-01, 7.899345e-01, 7.901954e-01, 7.904561e-01, 7.907166e-01, 7.909770e-01, 7.912372e-01, 7.914972e-01,
153 7.917571e-01, 7.920167e-01, 7.922763e-01, 7.925356e-01, 7.927948e-01, 7.930537e-01, 7.933126e-01, 7.935712e-01,
154 7.938297e-01, 7.940881e-01, 7.943462e-01, 7.946042e-01, 7.948620e-01, 7.951197e-01, 7.953772e-01, 7.956345e-01,
155 7.958916e-01, 7.961487e-01, 7.964054e-01, 7.966621e-01, 7.969186e-01, 7.971749e-01, 7.974311e-01, 7.976871e-01,
156 7.979429e-01, 7.981986e-01, 7.984541e-01, 7.987095e-01, 7.989646e-01, 7.992196e-01, 7.994745e-01, 7.997292e-01,
157 7.999837e-01, 8.002381e-01, 8.004923e-01, 8.007463e-01, 8.010002e-01, 8.012539e-01, 8.015075e-01, 8.017609e-01,
158 8.020141e-01, 8.022672e-01, 8.025202e-01, 8.027729e-01, 8.030255e-01, 8.032780e-01, 8.035302e-01, 8.037823e-01,
159 8.040344e-01, 8.042861e-01, 8.045378e-01, 8.047893e-01, 8.050406e-01, 8.052918e-01, 8.055428e-01, 8.057937e-01,
160 8.060444e-01, 8.062950e-01, 8.065454e-01, 8.067956e-01, 8.070457e-01, 8.072957e-01, 8.075454e-01, 8.077950e-01,
161 8.080446e-01, 8.082938e-01, 8.085430e-01, 8.087921e-01, 8.090409e-01, 8.092896e-01, 8.095381e-01, 8.097866e-01,
162 8.100348e-01, 8.102829e-01, 8.105308e-01, 8.107786e-01, 8.110263e-01, 8.112738e-01, 8.115211e-01, 8.117683e-01,
163 8.120154e-01, 8.122622e-01, 8.125089e-01, 8.127556e-01, 8.130020e-01, 8.132483e-01, 8.134944e-01, 8.137404e-01,
164 8.139862e-01, 8.142319e-01, 8.144775e-01, 8.147229e-01, 8.149682e-01, 8.152133e-01, 8.154582e-01, 8.157030e-01,
165 8.159477e-01, 8.161922e-01, 8.164365e-01, 8.166808e-01, 8.169249e-01, 8.171688e-01, 8.174126e-01, 8.176562e-01,
166 8.178997e-01, 8.181431e-01, 8.183863e-01, 8.186293e-01, 8.188722e-01, 8.191150e-01, 8.193576e-01, 8.196001e-01,
167 8.198425e-01, 8.200847e-01, 8.203267e-01, 8.205686e-01, 8.208104e-01, 8.210521e-01, 8.212935e-01, 8.215349e-01,
168 8.217760e-01, 8.220171e-01, 8.222581e-01, 8.224988e-01, 8.227395e-01, 8.229799e-01, 8.232203e-01, 8.234605e-01,
169 8.237006e-01, 8.239405e-01, 8.241804e-01, 8.244200e-01, 8.246595e-01, 8.248989e-01, 8.251381e-01, 8.253772e-01,
170 8.256162e-01, 8.258550e-01, 8.260937e-01, 8.263323e-01, 8.265706e-01, 8.268089e-01, 8.270471e-01, 8.272851e-01,
171 8.275229e-01, 8.277607e-01, 8.279983e-01, 8.282357e-01, 8.284730e-01, 8.287102e-01, 8.289472e-01, 8.291842e-01,
172 8.294209e-01, 8.296576e-01, 8.298941e-01, 8.301305e-01, 8.303667e-01, 8.306028e-01, 8.308387e-01, 8.310746e-01,
173 8.313103e-01, 8.315458e-01, 8.317813e-01, 8.320166e-01, 8.322517e-01, 8.324867e-01, 8.327217e-01, 8.329564e-01,
174 8.331911e-01, 8.334256e-01, 8.336599e-01, 8.338942e-01, 8.341283e-01, 8.343623e-01, 8.345962e-01, 8.348299e-01,
175 8.350635e-01, 8.352969e-01, 8.355302e-01, 8.357634e-01, 8.359964e-01, 8.362294e-01, 8.364622e-01, 8.366948e-01,
176 8.369274e-01, 8.371598e-01, 8.373921e-01, 8.376243e-01, 8.378563e-01, 8.380882e-01, 8.383200e-01, 8.385516e-01,
177 8.387831e-01, 8.390145e-01, 8.392458e-01, 8.394769e-01, 8.397079e-01, 8.399388e-01, 8.401695e-01, 8.404002e-01,
178 8.406307e-01, 8.408611e-01, 8.410913e-01, 8.413214e-01, 8.415514e-01, 8.417813e-01, 8.420110e-01, 8.422406e-01,
179 8.424702e-01, 8.426995e-01, 8.429288e-01, 8.431579e-01, 8.433869e-01, 8.436158e-01, 8.438445e-01, 8.440731e-01,
180 8.443016e-01, 8.445300e-01, 8.447582e-01, 8.449863e-01, 8.452144e-01, 8.454422e-01, 8.456700e-01, 8.458977e-01,
181 8.461251e-01, 8.463526e-01, 8.465798e-01, 8.468069e-01, 8.470340e-01, 8.472609e-01, 8.474877e-01, 8.477143e-01,
182 8.479409e-01, 8.481673e-01, 8.483936e-01, 8.486198e-01, 8.488458e-01, 8.490717e-01, 8.492976e-01, 8.495233e-01,
183 8.497488e-01, 8.499743e-01, 8.501996e-01, 8.504249e-01, 8.506500e-01, 8.508750e-01, 8.510998e-01, 8.513246e-01,
184 8.515491e-01, 8.517737e-01, 8.519981e-01, 8.522223e-01, 8.524465e-01, 8.526706e-01, 8.528944e-01, 8.531182e-01,
185 8.533419e-01, 8.535655e-01, 8.537889e-01, 8.540123e-01, 8.542355e-01, 8.544586e-01, 8.546816e-01, 8.549044e-01,
186 8.551272e-01, 8.553498e-01, 8.555723e-01, 8.557947e-01, 8.560170e-01, 8.562392e-01, 8.564612e-01, 8.566832e-01,
187 8.569050e-01, 8.571267e-01, 8.573483e-01, 8.575698e-01, 8.577912e-01, 8.580124e-01, 8.582336e-01, 8.584546e-01,
188 8.586755e-01, 8.588963e-01, 8.591169e-01, 8.593375e-01, 8.595580e-01, 8.597783e-01, 8.599985e-01, 8.602186e-01,
189 8.604387e-01, 8.606585e-01, 8.608783e-01, 8.610980e-01, 8.613176e-01, 8.615370e-01, 8.617563e-01, 8.619756e-01,
190 8.621947e-01, 8.624136e-01, 8.626326e-01, 8.628513e-01, 8.630700e-01, 8.632885e-01, 8.635070e-01, 8.637253e-01,
191 8.639436e-01, 8.641617e-01, 8.643796e-01, 8.645976e-01, 8.648154e-01, 8.650330e-01, 8.652506e-01, 8.654680e-01,
192 8.656853e-01, 8.659026e-01, 8.661197e-01, 8.663368e-01, 8.665537e-01, 8.667705e-01, 8.669872e-01, 8.672037e-01,
193 8.674202e-01, 8.676366e-01, 8.678529e-01, 8.680690e-01, 8.682851e-01, 8.685010e-01, 8.687168e-01, 8.689325e-01,
194 8.691481e-01, 8.693637e-01, 8.695791e-01, 8.697944e-01, 8.700095e-01, 8.702246e-01, 8.704396e-01, 8.706545e-01,
195 8.708693e-01, 8.710839e-01, 8.712984e-01, 8.715129e-01, 8.717272e-01, 8.719414e-01, 8.721556e-01, 8.723696e-01,
196 8.725836e-01, 8.727974e-01, 8.730111e-01, 8.732247e-01, 8.734382e-01, 8.736516e-01, 8.738649e-01, 8.740780e-01,
197 8.742912e-01, 8.745041e-01, 8.747170e-01, 8.749298e-01, 8.751425e-01, 8.753550e-01, 8.755675e-01, 8.757799e-01,
198 8.759921e-01, 8.762043e-01, 8.764163e-01, 8.766283e-01, 8.768401e-01, 8.770519e-01, 8.772635e-01, 8.774751e-01,
199 8.776865e-01, 8.778979e-01, 8.781091e-01, 8.783202e-01, 8.785312e-01, 8.787422e-01, 8.789530e-01, 8.791637e-01,
200 8.793744e-01, 8.795849e-01, 8.797953e-01, 8.800057e-01, 8.802159e-01, 8.804260e-01, 8.806360e-01, 8.808460e-01,
201 8.810558e-01, 8.812655e-01, 8.814751e-01, 8.816847e-01, 8.818941e-01, 8.821034e-01, 8.823127e-01, 8.825217e-01,
202 8.827308e-01, 8.829397e-01, 8.831486e-01, 8.833573e-01, 8.835659e-01, 8.837745e-01, 8.839829e-01, 8.841912e-01,
203 8.843995e-01, 8.846076e-01, 8.848156e-01, 8.850236e-01, 8.852314e-01, 8.854392e-01, 8.856469e-01, 8.858544e-01,
204 8.860618e-01, 8.862692e-01, 8.864765e-01, 8.866837e-01, 8.868908e-01, 8.870977e-01, 8.873046e-01, 8.875114e-01,
205 8.877181e-01, 8.879247e-01, 8.881311e-01, 8.883376e-01, 8.885438e-01, 8.887501e-01, 8.889562e-01, 8.891622e-01,
206 8.893681e-01, 8.895739e-01, 8.897797e-01, 8.899853e-01, 8.901908e-01, 8.903963e-01, 8.906016e-01, 8.908069e-01,
207 8.910121e-01, 8.912171e-01, 8.914221e-01, 8.916270e-01, 8.918318e-01, 8.920364e-01, 8.922410e-01, 8.924455e-01,
208 8.926499e-01, 8.928543e-01, 8.930585e-01, 8.932626e-01, 8.934667e-01, 8.936706e-01, 8.938744e-01, 8.940782e-01,
209 8.942819e-01, 8.944854e-01, 8.946889e-01, 8.948923e-01, 8.950956e-01, 8.952988e-01, 8.955019e-01, 8.957049e-01,
210 8.959078e-01, 8.961107e-01, 8.963134e-01, 8.965160e-01, 8.967186e-01, 8.969210e-01, 8.971235e-01, 8.973257e-01,
211 8.975279e-01, 8.977300e-01, 8.979320e-01, 8.981339e-01, 8.983358e-01, 8.985375e-01, 8.987392e-01, 8.989407e-01,
212 8.991421e-01, 8.993436e-01, 8.995448e-01, 8.997460e-01, 8.999471e-01, 9.001482e-01, 9.003491e-01, 9.005499e-01,
213 9.007506e-01, 9.009513e-01, 9.011519e-01, 9.013523e-01, 9.015527e-01, 9.017531e-01, 9.019532e-01, 9.021534e-01,
214 9.023534e-01, 9.025534e-01, 9.027532e-01, 9.029530e-01, 9.031526e-01, 9.033523e-01, 9.035518e-01, 9.037512e-01,
215 9.039505e-01, 9.041498e-01, 9.043489e-01, 9.045479e-01, 9.047469e-01, 9.049459e-01, 9.051446e-01, 9.053434e-01,
216 9.055420e-01, 9.057405e-01, 9.059390e-01, 9.061373e-01, 9.063356e-01, 9.065338e-01, 9.067319e-01, 9.069299e-01,
217 9.071279e-01, 9.073257e-01, 9.075235e-01, 9.077212e-01, 9.079187e-01, 9.081162e-01, 9.083136e-01, 9.085110e-01,
218 9.087082e-01, 9.089054e-01, 9.091024e-01, 9.092994e-01, 9.094964e-01, 9.096932e-01, 9.098899e-01, 9.100866e-01,
219 9.102831e-01, 9.104796e-01, 9.106760e-01, 9.108723e-01, 9.110685e-01, 9.112647e-01, 9.114607e-01, 9.116567e-01,
220 9.118526e-01, 9.120483e-01, 9.122441e-01, 9.124397e-01, 9.126353e-01, 9.128307e-01, 9.130261e-01, 9.132214e-01,
221 9.134166e-01, 9.136118e-01, 9.138068e-01, 9.140018e-01, 9.141967e-01, 9.143915e-01, 9.145862e-01, 9.147808e-01,
222 9.149753e-01, 9.151698e-01, 9.153642e-01, 9.155585e-01, 9.157528e-01, 9.159469e-01, 9.161409e-01, 9.163349e-01,
223 9.165288e-01, 9.167226e-01, 9.169164e-01, 9.171100e-01, 9.173036e-01, 9.174970e-01, 9.176905e-01, 9.178838e-01,
224 9.180770e-01, 9.182702e-01, 9.184632e-01, 9.186562e-01, 9.188492e-01, 9.190420e-01, 9.192348e-01, 9.194274e-01,
225 9.196200e-01, 9.198125e-01, 9.200049e-01, 9.201973e-01, 9.203895e-01, 9.205818e-01, 9.207739e-01, 9.209659e-01,
226 9.211578e-01, 9.213497e-01, 9.215415e-01, 9.217332e-01, 9.219248e-01, 9.221163e-01, 9.223078e-01, 9.224992e-01,
227 9.226905e-01, 9.228818e-01, 9.230729e-01, 9.232640e-01, 9.234550e-01, 9.236459e-01, 9.238367e-01, 9.240275e-01,
228 9.242182e-01, 9.244088e-01, 9.245993e-01, 9.247897e-01, 9.249801e-01, 9.251704e-01, 9.253606e-01, 9.255507e-01,
229 9.257408e-01, 9.259307e-01, 9.261206e-01, 9.263105e-01, 9.265002e-01, 9.266899e-01, 9.268795e-01, 9.270689e-01,
230 9.272584e-01, 9.274477e-01, 9.276370e-01, 9.278262e-01, 9.280154e-01, 9.282044e-01, 9.283934e-01, 9.285822e-01,
231 9.287710e-01, 9.289598e-01, 9.291484e-01, 9.293370e-01, 9.295255e-01, 9.297140e-01, 9.299023e-01, 9.300906e-01,
232 9.302788e-01, 9.304669e-01, 9.306549e-01, 9.308429e-01, 9.310308e-01, 9.312186e-01, 9.314064e-01, 9.315941e-01,
233 9.317816e-01, 9.319692e-01, 9.321566e-01, 9.323440e-01, 9.325313e-01, 9.327185e-01, 9.329057e-01, 9.330927e-01,
234 9.332797e-01, 9.334666e-01, 9.336535e-01, 9.338402e-01, 9.340270e-01, 9.342135e-01, 9.344001e-01, 9.345866e-01,
235 9.347730e-01, 9.349593e-01, 9.351455e-01, 9.353317e-01, 9.355178e-01, 9.357038e-01, 9.358898e-01, 9.360756e-01,
236 9.362615e-01, 9.364472e-01, 9.366328e-01, 9.368184e-01, 9.370039e-01, 9.371893e-01, 9.373747e-01, 9.375600e-01,
237 9.377452e-01, 9.379303e-01, 9.381154e-01, 9.383004e-01, 9.384854e-01, 9.386702e-01, 9.388550e-01, 9.390397e-01,
238 9.392243e-01, 9.394089e-01, 9.395934e-01, 9.397778e-01, 9.399621e-01, 9.401464e-01, 9.403306e-01, 9.405147e-01,
239 9.406988e-01, 9.408827e-01, 9.410667e-01, 9.412505e-01, 9.414343e-01, 9.416180e-01, 9.418016e-01, 9.419851e-01,
240 9.421686e-01, 9.423520e-01, 9.425353e-01, 9.427186e-01, 9.429018e-01, 9.430850e-01, 9.432680e-01, 9.434510e-01,
241 9.436339e-01, 9.438167e-01, 9.439995e-01, 9.441822e-01, 9.443648e-01, 9.445474e-01, 9.447299e-01, 9.449123e-01,
242 9.450946e-01, 9.452769e-01, 9.454591e-01, 9.456412e-01, 9.458233e-01, 9.460053e-01, 9.461872e-01, 9.463691e-01,
243 9.465508e-01, 9.467326e-01, 9.469142e-01, 9.470958e-01, 9.472773e-01, 9.474587e-01, 9.476401e-01, 9.478214e-01,
244 9.480026e-01, 9.481838e-01, 9.483649e-01, 9.485459e-01, 9.487268e-01, 9.489077e-01, 9.490886e-01, 9.492693e-01,
245 9.494500e-01, 9.496306e-01, 9.498111e-01, 9.499916e-01, 9.501719e-01, 9.503523e-01, 9.505326e-01, 9.507128e-01,
246 9.508929e-01, 9.510729e-01, 9.512529e-01, 9.514329e-01, 9.516127e-01, 9.517925e-01, 9.519722e-01, 9.521519e-01,
247 9.523315e-01, 9.525110e-01, 9.526904e-01, 9.528698e-01, 9.530491e-01, 9.532284e-01, 9.534075e-01, 9.535866e-01,
248 9.537657e-01, 9.539447e-01, 9.541236e-01, 9.543024e-01, 9.544812e-01, 9.546599e-01, 9.548386e-01, 9.550171e-01,
249 9.551957e-01, 9.553741e-01, 9.555525e-01, 9.557307e-01, 9.559090e-01, 9.560872e-01, 9.562653e-01, 9.564433e-01,
250 9.566213e-01, 9.567992e-01, 9.569771e-01, 9.571549e-01, 9.573326e-01, 9.575102e-01, 9.576878e-01, 9.578653e-01,
251 9.580427e-01, 9.582201e-01, 9.583974e-01, 9.585747e-01, 9.587519e-01, 9.589290e-01, 9.591061e-01, 9.592831e-01,
252 9.594600e-01, 9.596368e-01, 9.598137e-01, 9.599904e-01, 9.601671e-01, 9.603436e-01, 9.605201e-01, 9.606966e-01,
253 9.608730e-01, 9.610494e-01, 9.612256e-01, 9.614019e-01, 9.615780e-01, 9.617541e-01, 9.619301e-01, 9.621060e-01,
254 9.622819e-01, 9.624578e-01, 9.626336e-01, 9.628092e-01, 9.629849e-01, 9.631604e-01, 9.633359e-01, 9.635113e-01,
255 9.636867e-01, 9.638621e-01, 9.640373e-01, 9.642125e-01, 9.643876e-01, 9.645627e-01, 9.647377e-01, 9.649126e-01,
256 9.650874e-01, 9.652622e-01, 9.654370e-01, 9.656116e-01, 9.657863e-01, 9.659608e-01, 9.661353e-01, 9.663097e-01,
257 9.664841e-01, 9.666584e-01, 9.668326e-01, 9.670068e-01, 9.671809e-01, 9.673550e-01, 9.675289e-01, 9.677029e-01,
258 9.678767e-01, 9.680505e-01, 9.682242e-01, 9.683979e-01, 9.685715e-01, 9.687451e-01, 9.689186e-01, 9.690920e-01,
259 9.692653e-01, 9.694387e-01, 9.696119e-01, 9.697851e-01, 9.699582e-01, 9.701312e-01, 9.703043e-01, 9.704772e-01,
260 9.706500e-01, 9.708228e-01, 9.709955e-01, 9.711683e-01, 9.713409e-01, 9.715135e-01, 9.716859e-01, 9.718584e-01,
261 9.720308e-01, 9.722031e-01, 9.723753e-01, 9.725475e-01, 9.727197e-01, 9.728917e-01, 9.730637e-01, 9.732357e-01,
262 9.734076e-01, 9.735794e-01, 9.737512e-01, 9.739228e-01, 9.740945e-01, 9.742661e-01, 9.744377e-01, 9.746091e-01,
263 9.747805e-01, 9.749519e-01, 9.751231e-01, 9.752944e-01, 9.754655e-01, 9.756366e-01, 9.758077e-01, 9.759787e-01,
264 9.761496e-01, 9.763204e-01, 9.764913e-01, 9.766620e-01, 9.768327e-01, 9.770033e-01, 9.771739e-01, 9.773444e-01,
265 9.775148e-01, 9.776852e-01, 9.778556e-01, 9.780258e-01, 9.781960e-01, 9.783661e-01, 9.785362e-01, 9.787063e-01,
266 9.788762e-01, 9.790462e-01, 9.792160e-01, 9.793859e-01, 9.795555e-01, 9.797252e-01, 9.798949e-01, 9.800645e-01,
267 9.802339e-01, 9.804034e-01, 9.805728e-01, 9.807421e-01, 9.809114e-01, 9.810806e-01, 9.812497e-01, 9.814188e-01,
268 9.815878e-01, 9.817568e-01, 9.819257e-01, 9.820946e-01, 9.822634e-01, 9.824321e-01, 9.826008e-01, 9.827695e-01,
269 9.829381e-01, 9.831066e-01, 9.832750e-01, 9.834434e-01, 9.836118e-01, 9.837800e-01, 9.839482e-01, 9.841164e-01,
270 9.842845e-01, 9.844526e-01, 9.846206e-01, 9.847885e-01, 9.849564e-01, 9.851242e-01, 9.852920e-01, 9.854597e-01,
271 9.856274e-01, 9.857950e-01, 9.859625e-01, 9.861299e-01, 9.862974e-01, 9.864647e-01, 9.866320e-01, 9.867993e-01,
272 9.869665e-01, 9.871337e-01, 9.873008e-01, 9.874678e-01, 9.876347e-01, 9.878017e-01, 9.879685e-01, 9.881353e-01,
273 9.883021e-01, 9.884688e-01, 9.886354e-01, 9.888020e-01, 9.889685e-01, 9.891350e-01, 9.893014e-01, 9.894677e-01,
274 9.896340e-01, 9.898003e-01, 9.899665e-01, 9.901326e-01, 9.902986e-01, 9.904646e-01, 9.906306e-01, 9.907965e-01,
275 9.909624e-01, 9.911281e-01, 9.912939e-01, 9.914596e-01, 9.916252e-01, 9.917908e-01, 9.919563e-01, 9.921218e-01,
276 9.922872e-01, 9.924526e-01, 9.926178e-01, 9.927831e-01, 9.929483e-01, 9.931134e-01, 9.932785e-01, 9.934435e-01,
277 9.936085e-01, 9.937734e-01, 9.939383e-01, 9.941031e-01, 9.942678e-01, 9.944325e-01, 9.945971e-01, 9.947617e-01,
278 9.949263e-01, 9.950907e-01, 9.952552e-01, 9.954196e-01, 9.955838e-01, 9.957481e-01, 9.959123e-01, 9.960765e-01,
279 9.962406e-01, 9.964046e-01, 9.965686e-01, 9.967325e-01, 9.968964e-01, 9.970602e-01, 9.972240e-01, 9.973878e-01,
280 9.975514e-01, 9.977150e-01, 9.978786e-01, 9.980421e-01, 9.982055e-01, 9.983689e-01, 9.985323e-01, 9.986956e-01,
281 9.988588e-01, 9.990220e-01, 9.991851e-01, 9.993482e-01, 9.995112e-01, 9.996742e-01, 9.998372e-01, 1.000000e+00,
282 };
283 int i = (int)(d * 2047);
284 assert(i >= 0 && i < 2048)((void) sizeof ((i >= 0 && i < 2048) ? 1 : 0), __extension__
({ if (i >= 0 && i < 2048) ; else __assert_fail
("i >= 0 && i < 2048", "ccv_icf.c", 284, __extension__
__PRETTY_FUNCTION__); }));
285 return cube_root[i];
286}
287
288static inline void _ccv_rgb_to_luv(const float r, const float g, const float b, float* pl, float* pu, float* pv)
289{
290 const float x = 0.412453f * r + 0.35758f * g + 0.180423f * b;
291 const float y = 0.212671f * r + 0.71516f * g + 0.072169f * b;
292 const float z = 0.019334f * r + 0.119193f * g + 0.950227f * b;
293
294 const float x_n = 0.312713f, y_n = 0.329016f;
295 const float uv_n_divisor = -2.f * x_n + 12.f * y_n + 3.f;
296 const float u_n = 4.f * x_n / uv_n_divisor;
297 const float v_n = 9.f * y_n / uv_n_divisor;
298
299 const float uv_divisor = ccv_max((x + 15.f * y + 3.f * z), FLT_EPSILON)({ typeof ((x + 15.f * y + 3.f * z)) _a = ((x + 15.f * y + 3.f
* z)); typeof (1.19209290e-7F) _b = (1.19209290e-7F); (_a >
_b) ? _a : _b; });
300 const float u = 4.f * x / uv_divisor;
301 const float v = 9.f * y / uv_divisor;
302
303 const float y_cube_root = fast_cube_root(y);
304
305 const float l_value = ccv_max(0.f, ((116.f * y_cube_root) - 16.f))({ typeof (0.f) _a = (0.f); typeof (((116.f * y_cube_root) - 16.f
)) _b = (((116.f * y_cube_root) - 16.f)); (_a > _b) ? _a :
_b; });
306 const float u_value = 13.f * l_value * (u - u_n);
307 const float v_value = 13.f * l_value * (v - v_n);
308
309 // L in [0, 100], U in [-134, 220], V in [-140, 122]
310 *pl = l_value * (255.f / 100.f);
311 *pu = (u_value + 134.f) * (255.f / (220.f + 134.f));
312 *pv = (v_value + 140.f) * (255.f / (122.f + 140.f));
313}
314
315// generating the integrate channels features (which combines the grayscale, gradient magnitude, and 6-direction HOG)
316void ccv_icf(ccv_dense_matrix_t* a, ccv_dense_matrix_t** b, int type)
317{
318 int ch = CCV_GET_CHANNEL(a->type)((a->type) & 0xFFF);
319 assert(ch == 1 || ch == 3)((void) sizeof ((ch == 1 || ch == 3) ? 1 : 0), __extension__ (
{ if (ch == 1 || ch == 3) ; else __assert_fail ("ch == 1 || ch == 3"
, "ccv_icf.c", 319, __extension__ __PRETTY_FUNCTION__); }));
320 int nchr = (ch == 1) ? 8 : 10;
321 ccv_declare_derived_signature(sig, a->sig != 0, ccv_sign_with_literal("ccv_icf"), a->sig, CCV_EOF_SIGN)char _ccv_identifier_321[] = ("ccv_icf"); size_t _ccv_string_size_321
= sizeof(_ccv_identifier_321);; uint64_t sig = (a->sig !=
0) ? ccv_cache_generate_signature(_ccv_identifier_321, _ccv_string_size_321
, a->sig, ((uint64_t)0)) : 0;;
322 ccv_dense_matrix_t* db = *b = ccv_dense_matrix_renew(*b, a->rows, a->cols, CCV_32F | nchr, CCV_32F | nchr, sig);
323 ccv_object_return_if_cached(, db){ if ((!(db) || (((int*)(db))[0] & CCV_GARBAGE)) &&
(!(0) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0
) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (
((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int
*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0)
)[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] &
CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE
)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE)) &&
(!(0) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0
) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (
((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int
*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0)
)[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] &
CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE
)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE)) &&
(!(0) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0
) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (
((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int
*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0)
)[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] &
CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE
)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE)) &&
(!(0) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0
) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (
((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int
*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0)
)[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] &
CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE
)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE)) &&
(!(0) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0
) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (
((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int
*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0)
)[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] &
CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE
)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE)) &&
(!(0) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0
) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (
((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int
*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0)
)[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] &
CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE
)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE)) &&
(!(0) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0
) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (
((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int
*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0)
)[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] &
CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE
)) && (!(0) || (((int*)(0))[0] & CCV_GARBAGE)) &&
(!(0) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0
) || (((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (
((int*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int
*)(0))[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0)
)[0] & CCV_GARBAGE)) && (!(0) || (((int*)(0))[0] &
CCV_GARBAGE))) { (void)((db) && (((int*)(db))[0] &=
~CCV_GARBAGE));(void)((0) && (((int*)(0))[0] &= ~
CCV_GARBAGE));(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE
));(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE)
);(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE))
;(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));
(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(
void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void
)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)
((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)(
(0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)((
0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0
) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0)
&& (((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) &&
(((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (
((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && ((
(int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((
int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int
*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*
)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)
(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(
0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0
))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0)
)[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0))
[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0))[
0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0))[0
] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0))[0]
&= ~CCV_GARBAGE));(void)((0) && (((int*)(0))[0] &=
~CCV_GARBAGE));(void)((0) && (((int*)(0))[0] &= ~
CCV_GARBAGE));(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE
));(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE)
);(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE))
;(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));
(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(
void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void
)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)
((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)(
(0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)((
0) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0
) && (((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0)
&& (((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) &&
(((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (
((int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && ((
(int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((
int*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int
*)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*
)(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)
(0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(
0))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0
))[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0)
)[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0))
[0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0))[
0] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0))[0
] &= ~CCV_GARBAGE));(void)((0) && (((int*)(0))[0]
&= ~CCV_GARBAGE));(void)((0) && (((int*)(0))[0] &=
~CCV_GARBAGE));(void)((0) && (((int*)(0))[0] &= ~
CCV_GARBAGE));(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE
));(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE)
);(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE))
;(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));
(void)((0) && (((int*)(0))[0] &= ~CCV_GARBAGE));;
; return ; } };
324 ccv_dense_matrix_t* ag = 0;
325 ccv_dense_matrix_t* mg = 0;
326 ccv_gradient(a, &ag, 0, &mg, 0, 1, 1);
327 float* agp = ag->data.f32;
328 float* mgp = mg->data.f32;
329 float* dbp = db->data.f32;
330 ccv_zero(db);
331 int i, j, k;
332 unsigned char* a_ptr = a->data.u8;
333 float magnitude_scaling = 1 / sqrtf(2); // regularize it to 0~1
334 if (ch == 1)
335 {
336#define for_block(_, _for_get) \
337 for (i = 0; i < a->rows; i++) \
338 { \
339 for (j = 0; j < a->cols; j++) \
340 { \
341 dbp[0] = _for_get(a_ptr, j); \
342 dbp[1] = mgp[j] * magnitude_scaling; \
343 float agr = (ccv_clamp(agp[j] <= 180 ? agp[j] : agp[j] - 180, 0, 179.99)({ typeof (0) _a = (0); typeof (179.99) _b = (179.99); typeof
(agp[j] <= 180 ? agp[j] : agp[j] - 180) _x = (agp[j] <=
180 ? agp[j] : agp[j] - 180); (_x < _a) ? _a : ((_x > _b
) ? _b : _x); }) / 180.0) * 6; \
344 int ag0 = (int)agr; \
345 int ag1 = ag0 < 5 ? ag0 + 1 : 0; \
346 agr = agr - ag0; \
347 dbp[2 + ag0] = dbp[1] * (1 - agr); \
348 dbp[2 + ag1] = dbp[1] * agr; \
349 dbp += 8; \
350 } \
351 a_ptr += a->step; \
352 agp += a->cols; \
353 mgp += a->cols; \
354 }
355 ccv_matrix_getter(a->type, for_block){ switch (((a->type) & 0xFF000)) { case CCV_32S: { for_block
(, _ccv_get_32s_value); break; } case CCV_32F: { for_block(, _ccv_get_32f_value
); break; } case CCV_64S: { for_block(, _ccv_get_64s_value); break
; } case CCV_64F: { for_block(, _ccv_get_64f_value); break; }
default: { for_block(, _ccv_get_8u_value); } } };
356#undef for_block
357 } else {
358 // color one, luv, gradient magnitude, and 6-direction HOG
359#define for_block(_, _for_get) \
360 for (i = 0; i < a->rows; i++) \
361 { \
362 for (j = 0; j < a->cols; j++) \
363 { \
364 _ccv_rgb_to_luv(_for_get(a_ptr, j * ch) / 255.0, \
365 _for_get(a_ptr, j * ch + 1) / 255.0, \
366 _for_get(a_ptr, j * ch + 2) / 255.0, \
367 dbp, dbp + 1, dbp + 2); \
368 float agv = agp[j * ch]; \
369 float mgv = mgp[j * ch]; \
370 for (k = 1; k < ch; k++) \
371 { \
372 if (mgp[j * ch + k] > mgv) \
373 { \
374 mgv = mgp[j * ch + k]; \
375 agv = agp[j * ch + k]; \
376 } \
377 } \
378 dbp[3] = mgv * magnitude_scaling; \
379 float agr = (ccv_clamp(agv <= 180 ? agv : agv - 180, 0, 179.99)({ typeof (0) _a = (0); typeof (179.99) _b = (179.99); typeof
(agv <= 180 ? agv : agv - 180) _x = (agv <= 180 ? agv :
agv - 180); (_x < _a) ? _a : ((_x > _b) ? _b : _x); }) / 180.0) * 6; \
380 int ag0 = (int)agr; \
381 int ag1 = ag0 < 5 ? ag0 + 1 : 0; \
382 agr = agr - ag0; \
383 dbp[4 + ag0] = dbp[3] * (1 - agr); \
384 dbp[4 + ag1] = dbp[3] * agr; \
385 dbp += 10; \
386 } \
387 a_ptr += a->step; \
388 agp += a->cols * ch; \
389 mgp += a->cols * ch; \
390 }
391 ccv_matrix_getter(a->type, for_block){ switch (((a->type) & 0xFF000)) { case CCV_32S: { for_block
(, _ccv_get_32s_value); break; } case CCV_32F: { for_block(, _ccv_get_32f_value
); break; } case CCV_64S: { for_block(, _ccv_get_64s_value); break
; } case CCV_64F: { for_block(, _ccv_get_64f_value); break; }
default: { for_block(, _ccv_get_8u_value); } } };
392#undef for_block
393 }
394 ccv_matrix_free(ag);
395 ccv_matrix_free(mg);
396}
397
398static inline float _ccv_icf_run_feature(ccv_icf_feature_t* feature, float* ptr, int cols, int ch, int x, int y)
399{
400 float c = feature->beta;
401 int q;
402 for (q = 0; q < feature->count; q++)
403 c += (ptr[(feature->sat[q * 2 + 1].x + x + 1 + (feature->sat[q * 2 + 1].y + y + 1) * cols) * ch + feature->channel[q]] - ptr[(feature->sat[q * 2].x + x + (feature->sat[q * 2 + 1].y + y + 1) * cols) * ch + feature->channel[q]] + ptr[(feature->sat[q * 2].x + x + (feature->sat[q * 2].y + y) * cols) * ch + feature->channel[q]] - ptr[(feature->sat[q * 2 + 1].x + x + 1 + (feature->sat[q * 2].y + y) * cols) * ch + feature->channel[q]]) * feature->alpha[q];
404 return c;
405}
406
407static inline int _ccv_icf_run_weak_classifier(ccv_icf_decision_tree_t* weak_classifier, float* ptr, int cols, int ch, int x, int y)
408{
409 float c = _ccv_icf_run_feature(weak_classifier->features, ptr, cols, ch, x, y);
410 if (c > 0)
411 {
412 if (!(weak_classifier->pass & 0x1))
413 return 1;
414 return _ccv_icf_run_feature(weak_classifier->features + 2, ptr, cols, ch, x, y) > 0;
415 } else {
416 if (!(weak_classifier->pass & 0x2))
417 return 0;
418 return _ccv_icf_run_feature(weak_classifier->features + 1, ptr, cols, ch, x, y) > 0;
419 }
420}
421
422#ifdef HAVE_GSL1
423static void _ccv_icf_randomize_feature(gsl_rng* rng, ccv_size_t size, int minimum, ccv_icf_feature_t* feature, int grayscale)
424{
425 feature->count = gsl_rng_uniform_int(rng, CCV_ICF_SAT_MAX(2)) + 1;
426 assert(feature->count <= CCV_ICF_SAT_MAX)((void) sizeof ((feature->count <= (2)) ? 1 : 0), __extension__
({ if (feature->count <= (2)) ; else __assert_fail ("feature->count <= CCV_ICF_SAT_MAX"
, "ccv_icf.c", 426, __extension__ __PRETTY_FUNCTION__); }));
427 int i;
428 feature->beta = 0;
429 for (i = 0; i < feature->count; i++)
430 {
431 int x0, y0, x1, y1;
432 do {
433 x0 = gsl_rng_uniform_int(rng, size.width);
434 x1 = gsl_rng_uniform_int(rng, size.width);
435 y0 = gsl_rng_uniform_int(rng, size.height);
436 y1 = gsl_rng_uniform_int(rng, size.height);
437 } while ((ccv_max(x0, x1)({ typeof (x0) _a = (x0); typeof (x1) _b = (x1); (_a > _b)
? _a : _b; }) - ccv_min(x0, x1)({ typeof (x0) _a = (x0); typeof (x1) _b = (x1); (_a < _b)
? _a : _b; }) + 1) * (ccv_max(y0, y1)({ typeof (y0) _a = (y0); typeof (y1) _b = (y1); (_a > _b)
? _a : _b; }) - ccv_min(y0, y1)({ typeof (y0) _a = (y0); typeof (y1) _b = (y1); (_a < _b)
? _a : _b; }) + 1) < (minimum + 1) * (minimum + 1) ||
438 (ccv_max(x0, x1)({ typeof (x0) _a = (x0); typeof (x1) _b = (x1); (_a > _b)
? _a : _b; }) - ccv_min(x0, x1)({ typeof (x0) _a = (x0); typeof (x1) _b = (x1); (_a < _b)
? _a : _b; }) + 1) < minimum ||
439 (ccv_max(y0, y1)({ typeof (y0) _a = (y0); typeof (y1) _b = (y1); (_a > _b)
? _a : _b; }) - ccv_min(y0, y1)({ typeof (y0) _a = (y0); typeof (y1) _b = (y1); (_a < _b)
? _a : _b; }) + 1) < minimum);
440 feature->sat[i * 2].x = ccv_min(x0, x1)({ typeof (x0) _a = (x0); typeof (x1) _b = (x1); (_a < _b)
? _a : _b; });
441 feature->sat[i * 2].y = ccv_min(y0, y1)({ typeof (y0) _a = (y0); typeof (y1) _b = (y1); (_a < _b)
? _a : _b; });
442 feature->sat[i * 2 + 1].x = ccv_max(x0, x1)({ typeof (x0) _a = (x0); typeof (x1) _b = (x1); (_a > _b)
? _a : _b; });
443 feature->sat[i * 2 + 1].y = ccv_max(y0, y1)({ typeof (y0) _a = (y0); typeof (y1) _b = (y1); (_a > _b)
? _a : _b; });
444 feature->channel[i] = gsl_rng_uniform_int(rng, grayscale ? 8 : 10); // 8-channels for grayscale, and 10-channels for rgb
445 assert(feature->channel[i] >= 0 && feature->channel[i] < (grayscale ? 8 : 10))((void) sizeof ((feature->channel[i] >= 0 && feature
->channel[i] < (grayscale ? 8 : 10)) ? 1 : 0), __extension__
({ if (feature->channel[i] >= 0 && feature->
channel[i] < (grayscale ? 8 : 10)) ; else __assert_fail ("feature->channel[i] >= 0 && feature->channel[i] < (grayscale ? 8 : 10)"
, "ccv_icf.c", 445, __extension__ __PRETTY_FUNCTION__); }));
446 feature->alpha[i] = gsl_rng_uniform(rng) / (float)((feature->sat[i * 2 + 1].x - feature->sat[i * 2].x + 1) * (feature->sat[i * 2 + 1].y - feature->sat[i * 2].y + 1));
447 }
448}
449
450static void _ccv_icf_check_params(ccv_icf_new_param_t params)
451{
452 assert(params.size.width > 0 && params.size.height > 0)((void) sizeof ((params.size.width > 0 && params.size
.height > 0) ? 1 : 0), __extension__ ({ if (params.size.width
> 0 && params.size.height > 0) ; else __assert_fail
("params.size.width > 0 && params.size.height > 0"
, "ccv_icf.c", 452, __extension__ __PRETTY_FUNCTION__); }));
453 assert(params.deform_shift >= 0)((void) sizeof ((params.deform_shift >= 0) ? 1 : 0), __extension__
({ if (params.deform_shift >= 0) ; else __assert_fail ("params.deform_shift >= 0"
, "ccv_icf.c", 453, __extension__ __PRETTY_FUNCTION__); }));
454 assert(params.deform_angle >= 0)((void) sizeof ((params.deform_angle >= 0) ? 1 : 0), __extension__
({ if (params.deform_angle >= 0) ; else __assert_fail ("params.deform_angle >= 0"
, "ccv_icf.c", 454, __extension__ __PRETTY_FUNCTION__); }));
455 assert(params.deform_scale >= 0 && params.deform_scale < 1)((void) sizeof ((params.deform_scale >= 0 && params
.deform_scale < 1) ? 1 : 0), __extension__ ({ if (params.deform_scale
>= 0 && params.deform_scale < 1) ; else __assert_fail
("params.deform_scale >= 0 && params.deform_scale < 1"
, "ccv_icf.c", 455, __extension__ __PRETTY_FUNCTION__); }));
456 assert(params.feature_size > 0)((void) sizeof ((params.feature_size > 0) ? 1 : 0), __extension__
({ if (params.feature_size > 0) ; else __assert_fail ("params.feature_size > 0"
, "ccv_icf.c", 456, __extension__ __PRETTY_FUNCTION__); }));
457 assert(params.acceptance > 0 && params.acceptance < 1.0)((void) sizeof ((params.acceptance > 0 && params.acceptance
< 1.0) ? 1 : 0), __extension__ ({ if (params.acceptance >
0 && params.acceptance < 1.0) ; else __assert_fail
("params.acceptance > 0 && params.acceptance < 1.0"
, "ccv_icf.c", 457, __extension__ __PRETTY_FUNCTION__); }));
458}
459
460static ccv_dense_matrix_t* _ccv_icf_capture_feature(gsl_rng* rng, ccv_dense_matrix_t* image, ccv_decimal_pose_t pose, ccv_size_t size, ccv_margin_t margin, float deform_angle, float deform_scale, float deform_shift)
461{
462 float rotate_x = (deform_angle * 2 * gsl_rng_uniform(rng) - deform_angle) * CCV_PI(3.141592653589793) / 180 + pose.pitch;
463 float rotate_y = (deform_angle * 2 * gsl_rng_uniform(rng) - deform_angle) * CCV_PI(3.141592653589793) / 180 + pose.yaw;
464 float rotate_z = (deform_angle * 2 * gsl_rng_uniform(rng) - deform_angle) * CCV_PI(3.141592653589793) / 180 + pose.roll;
465 float scale = gsl_rng_uniform(rng);
466 // to make the scale evenly distributed, for example, when deforming of 1/2 ~ 2, we want it to distribute around 1, rather than any average of 1/2 ~ 2
467 scale = (1 + deform_scale * scale) / (1 + deform_scale * (1 - scale));
468 float scale_ratio = sqrtf((float)(size.width * size.height) / (pose.a * pose.b * 4));
469 float m00 = cosf(rotate_z) * scale;
470 float m01 = cosf(rotate_y) * sinf(rotate_z) * scale;
471 float m02 = (deform_shift * 2 * gsl_rng_uniform(rng) - deform_shift) / scale_ratio + pose.x + (margin.right - margin.left) / scale_ratio - image->cols * 0.5;
472 float m10 = (sinf(rotate_y) * cosf(rotate_z) - cosf(rotate_x) * sinf(rotate_z)) * scale;
473 float m11 = (sinf(rotate_y) * sinf(rotate_z) + cosf(rotate_x) * cosf(rotate_z)) * scale;
474 float m12 = (deform_shift * 2 * gsl_rng_uniform(rng) - deform_shift) / scale_ratio + pose.y + (margin.bottom - margin.top) / scale_ratio - image->rows * 0.5;
475 float m20 = (sinf(rotate_y) * cosf(rotate_z) + sinf(rotate_x) * sinf(rotate_z)) * scale;
476 float m21 = (sinf(rotate_y) * sinf(rotate_z) - sinf(rotate_x) * cosf(rotate_z)) * scale;
477 float m22 = cosf(rotate_x) * cosf(rotate_y);
478 ccv_dense_matrix_t* b = 0;
479 ccv_perspective_transform(image, &b, 0, m00, m01, m02, m10, m11, m12, m20, m21, m22);
480 ccv_dense_matrix_t* resize = 0;
481 // have 1px border around the grayscale image because we need these to compute correct gradient feature
482 ccv_size_t scale_size = {
483 .width = (int)((size.width + margin.left + margin.right + 2) / scale_ratio + 0.5),
484 .height = (int)((size.height + margin.top + margin.bottom + 2) / scale_ratio + 0.5),
485 };
486 assert(scale_size.width > 0 && scale_size.height > 0)((void) sizeof ((scale_size.width > 0 && scale_size
.height > 0) ? 1 : 0), __extension__ ({ if (scale_size.width
> 0 && scale_size.height > 0) ; else __assert_fail
("scale_size.width > 0 && scale_size.height > 0"
, "ccv_icf.c", 486, __extension__ __PRETTY_FUNCTION__); }));
487 ccv_slice(b, (ccv_matrix_t**)&resize, 0, (int)(b->rows * 0.5 - (size.height + margin.top + margin.bottom + 2) / scale_ratio * 0.5 + 0.5), (int)(b->cols * 0.5 - (size.width + margin.left + margin.right + 2) / scale_ratio * 0.5 + 0.5), scale_size.height, scale_size.width);
488 ccv_matrix_free(b);
489 b = 0;
490 if (scale_ratio > 1)
491 ccv_resample(resize, &b, 0, (double)(size.height + margin.top + margin.bottom + 2) / (double)resize->rows, (double)(size.width + margin.left + margin.right + 2) / (double)resize->cols, CCV_INTER_CUBIC);
492 else
493 ccv_resample(resize, &b, 0, (double)(size.height + margin.top + margin.bottom + 2) / (double)resize->rows, (double)(size.width + margin.left + margin.right + 2) / (double)resize->cols, CCV_INTER_AREA);
494 ccv_matrix_free(resize);
495 return b;
496}
497
498typedef struct {
499 uint8_t correct:1;
500 double weight;
501 float rate;
502} ccv_icf_example_state_t;
503
504typedef struct {
505 uint8_t classifier:1;
506 uint8_t positives:1;
507 uint8_t negatives:1;
508 uint8_t features:1;
509 uint8_t example_state:1;
510 uint8_t precomputed:1;
511} ccv_icf_classifier_cascade_persistence_state_t;
512
513typedef struct {
514 uint32_t index;
515 float value;
516} ccv_icf_value_index_t;
517
518typedef struct {
519 ccv_function_state_reserve_fieldint line_no;;
520 int i;
521 int bootstrap;
522 ccv_icf_new_param_t params;
523 ccv_icf_classifier_cascade_t* classifier;
524 ccv_array_t* positives;
525 ccv_array_t* negatives;
526 ccv_icf_feature_t* features;
527 ccv_size_t size;
528 ccv_margin_t margin;
529 ccv_icf_example_state_t* example_state;
530 uint8_t* precomputed;
531 ccv_icf_classifier_cascade_persistence_state_t x;
532} ccv_icf_classifier_cascade_state_t;
533
534static void _ccv_icf_write_classifier_cascade_state(ccv_icf_classifier_cascade_state_t* state, const char* directory)
535{
536 char filename[1024];
537 snprintf(filename, 1024, "%s/state", directory);
538 FILE* w = fopen(filename, "w+");
539 fprintf(w, "%d %d %d\n", state->line_no, state->i, state->bootstrap);
540 fprintf(w, "%d %d %d\n", state->params.feature_size, state->size.width, state->size.height);
541 fprintf(w, "%d %d %d %d\n", state->margin.left, state->margin.top, state->margin.right, state->margin.bottom);
542 fclose(w);
543 int i, q;
544 if (!state->x.positives)
545 {
546 snprintf(filename, 1024, "%s/positives", directory);
547 w = fopen(filename, "wb+");
548 fwrite(&state->positives->rnum, sizeof(state->positives->rnum), 1, w);
549 fwrite(&state->positives->rsize, sizeof(state->positives->rsize), 1, w);
550 for (i = 0; i < state->positives->rnum; i++)
551 {
552 ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(state->positives, i)((void*)(((char*)((state->positives)->data)) + (size_t)
(state->positives)->rsize * (size_t)(i)));
553 assert(a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2)((void) sizeof ((a->rows == state->size.height + state->
margin.top + state->margin.bottom + 2 && a->cols
== state->size.width + state->margin.left + state->
margin.right + 2) ? 1 : 0), __extension__ ({ if (a->rows ==
state->size.height + state->margin.top + state->margin
.bottom + 2 && a->cols == state->size.width + state
->margin.left + state->margin.right + 2) ; else __assert_fail
("a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2"
, "ccv_icf.c", 553, __extension__ __PRETTY_FUNCTION__); }));
554 fwrite(a, 1, state->positives->rsize, w);
555 }
556 fclose(w);
557 state->x.positives = 1;
558 }
559 if (!state->x.negatives)
560 {
561 assert(state->negatives->rsize == state->positives->rsize)((void) sizeof ((state->negatives->rsize == state->positives
->rsize) ? 1 : 0), __extension__ ({ if (state->negatives
->rsize == state->positives->rsize) ; else __assert_fail
("state->negatives->rsize == state->positives->rsize"
, "ccv_icf.c", 561, __extension__ __PRETTY_FUNCTION__); }));
562 snprintf(filename, 1024, "%s/negatives", directory);
563 w = fopen(filename, "wb+");
564 fwrite(&state->negatives->rnum, sizeof(state->negatives->rnum), 1, w);
565 fwrite(&state->negatives->rsize, sizeof(state->negatives->rsize), 1, w);
566 for (i = 0; i < state->negatives->rnum; i++)
567 {
568 ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(state->negatives, i)((void*)(((char*)((state->negatives)->data)) + (size_t)
(state->negatives)->rsize * (size_t)(i)));
569 assert(a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2)((void) sizeof ((a->rows == state->size.height + state->
margin.top + state->margin.bottom + 2 && a->cols
== state->size.width + state->margin.left + state->
margin.right + 2) ? 1 : 0), __extension__ ({ if (a->rows ==
state->size.height + state->margin.top + state->margin
.bottom + 2 && a->cols == state->size.width + state
->margin.left + state->margin.right + 2) ; else __assert_fail
("a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2"
, "ccv_icf.c", 569, __extension__ __PRETTY_FUNCTION__); }));
570 fwrite(a, 1, state->negatives->rsize, w);
571 }
572 fclose(w);
573 state->x.negatives = 1;
574 }
575 if (!state->x.features)
576 {
577 snprintf(filename, 1024, "%s/features", directory);
578 w = fopen(filename, "w+");
579 for (i = 0; i < state->params.feature_size; i++)
580 {
581 ccv_icf_feature_t* feature = state->features + i;
582 fprintf(w, "%d %a\n", feature->count, feature->beta);
583 for (q = 0; q < feature->count; q++)
584 fprintf(w, "%d %a %d %d %d %d\n", feature->channel[q], feature->alpha[q], feature->sat[q * 2].x, feature->sat[q * 2].y, feature->sat[q * 2 + 1].x, feature->sat[q * 2 + 1].y);
585 }
586 fclose(w);
587 state->x.features = 1;
588 }
589 if (!state->x.example_state)
590 {
591 snprintf(filename, 1024, "%s/example_state", directory);
592 w = fopen(filename, "w+");
593 for (i = 0; i < state->positives->rnum + state->negatives->rnum; i++)
594 fprintf(w, "%u %la %a\n", (uint32_t)state->example_state[i].correct, state->example_state[i].weight, state->example_state[i].rate);
595 fclose(w);
596 state->x.example_state = 1;
597 }
598 if (!state->x.precomputed)
599 {
600 size_t step = (3 * (state->positives->rnum + state->negatives->rnum) + 3) & -4;
601 snprintf(filename, 1024, "%s/precomputed", directory);
602 w = fopen(filename, "wb+");
603 fwrite(state->precomputed, 1, step * state->params.feature_size, w);
604 fclose(w);
605 state->x.precomputed = 1;
606 }
607 if (!state->x.classifier)
608 {
609 snprintf(filename, 1024, "%s/cascade", directory);
610 ccv_icf_write_classifier_cascade(state->classifier, filename);
611 state->x.classifier = 1;
612 }
613}
614
615static void _ccv_icf_read_classifier_cascade_state(const char* directory, ccv_icf_classifier_cascade_state_t* state)
616{
617 char filename[1024];
618 state->line_no = state->i = 0;
619 state->bootstrap = 0;
620 snprintf(filename, 1024, "%s/state", directory);
621 FILE* r = fopen(filename, "r");
622 if (r)
623 {
624 int feature_size;
625 fscanf(r, "%d %d %d", &state->line_no, &state->i, &state->bootstrap);
626 fscanf(r, "%d %d %d", &feature_size, &state->size.width, &state->size.height);
627 fscanf(r, "%d %d %d %d", &state->margin.left, &state->margin.top, &state->margin.right, &state->margin.bottom);
628 assert(feature_size == state->params.feature_size)((void) sizeof ((feature_size == state->params.feature_size
) ? 1 : 0), __extension__ ({ if (feature_size == state->params
.feature_size) ; else __assert_fail ("feature_size == state->params.feature_size"
, "ccv_icf.c", 628, __extension__ __PRETTY_FUNCTION__); }));
629 fclose(r);
630 }
631 int i, q;
632 snprintf(filename, 1024, "%s/positives", directory);
633 r = fopen(filename, "rb");
634 state->x.precomputed = state->x.features = state->x.example_state = state->x.classifier = state->x.positives = state->x.negatives = 1;
635 if (r)
636 {
637 int rnum, rsize;
638 fread(&rnum, sizeof(rnum), 1, r);
639 fread(&rsize, sizeof(rsize), 1, r);
640 state->positives = ccv_array_new(rsize, rnum, 0);
641 ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)alloca(rsize)__builtin_alloca (rsize);
642 for (i = 0; i < rnum; i++)
643 {
644 fread(a, 1, rsize, r);
645 assert(a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2)((void) sizeof ((a->rows == state->size.height + state->
margin.top + state->margin.bottom + 2 && a->cols
== state->size.width + state->margin.left + state->
margin.right + 2) ? 1 : 0), __extension__ ({ if (a->rows ==
state->size.height + state->margin.top + state->margin
.bottom + 2 && a->cols == state->size.width + state
->margin.left + state->margin.right + 2) ; else __assert_fail
("a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2"
, "ccv_icf.c", 645, __extension__ __PRETTY_FUNCTION__); }));
646 ccv_array_push(state->positives, a);
647 }
648 fclose(r);
649 }
650 snprintf(filename, 1024, "%s/negatives", directory);
651 r = fopen(filename, "rb");
652 if (r)
653 {
654 int rnum, rsize;
655 fread(&rnum, sizeof(rnum), 1, r);
656 fread(&rsize, sizeof(rsize), 1, r);
657 state->negatives = ccv_array_new(rsize, rnum, 0);
658 ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)alloca(rsize)__builtin_alloca (rsize);
659 for (i = 0; i < rnum; i++)
660 {
661 fread(a, 1, rsize, r);
662 assert(a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2)((void) sizeof ((a->rows == state->size.height + state->
margin.top + state->margin.bottom + 2 && a->cols
== state->size.width + state->margin.left + state->
margin.right + 2) ? 1 : 0), __extension__ ({ if (a->rows ==
state->size.height + state->margin.top + state->margin
.bottom + 2 && a->cols == state->size.width + state
->margin.left + state->margin.right + 2) ; else __assert_fail
("a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2"
, "ccv_icf.c", 662, __extension__ __PRETTY_FUNCTION__); }));
663 ccv_array_push(state->negatives, a);
664 }
665 fclose(r);
666 }
667 snprintf(filename, 1024, "%s/features", directory);
668 r = fopen(filename, "r");
669 if (r)
670 {
671 state->features = (ccv_icf_feature_t*)ccmallocmalloc(state->params.feature_size * sizeof(ccv_icf_feature_t));
672 for (i = 0; i < state->params.feature_size; i++)
673 {
674 ccv_icf_feature_t* feature = state->features + i;
675 fscanf(r, "%d %a", &feature->count, &feature->beta);
676 for (q = 0; q < feature->count; q++)
677 fscanf(r, "%d %a %d %d %d %d", &feature->channel[q], &feature->alpha[q], &feature->sat[q * 2].x, &feature->sat[q * 2].y, &feature->sat[q * 2 + 1].x, &feature->sat[q * 2 + 1].y);
678 }
679 fclose(r);
680 }
681 snprintf(filename, 1024, "%s/example_state", directory);
682 r = fopen(filename, "r");
683 if (r)
684 {
685 state->example_state = (ccv_icf_example_state_t*)ccmallocmalloc((state->positives->rnum + state->negatives->rnum) * sizeof(ccv_icf_example_state_t));
686 for (i = 0; i < state->positives->rnum + state->negatives->rnum; i++)
687 {
688 uint32_t correct;
689 double weight;
690 float rate;
691 fscanf(r, "%u %la %a", &correct, &weight, &rate);
692 state->example_state[i].correct = correct;
693 state->example_state[i].weight = weight;
694 state->example_state[i].rate = rate;
695 }
696 fclose(r);
697 } else
698 state->example_state = 0;
699 snprintf(filename, 1024, "%s/precomputed", directory);
700 r = fopen(filename, "rb");
701 if (r)
702 {
703 size_t step = (3 * (state->positives->rnum + state->negatives->rnum) + 3) & -4;
704 state->precomputed = (uint8_t*)ccmallocmalloc(sizeof(uint8_t) * state->params.feature_size * step);
705 fread(state->precomputed, 1, step * state->params.feature_size, r);
706 fclose(r);
707 } else
708 state->precomputed = 0;
709 snprintf(filename, 1024, "%s/cascade", directory);
710 state->classifier = ccv_icf_read_classifier_cascade(filename);
711 if (!state->classifier)
712 {
713 state->classifier = (ccv_icf_classifier_cascade_t*)ccmallocmalloc(sizeof(ccv_icf_classifier_cascade_t));
714 state->classifier->count = 0;
715 state->classifier->grayscale = state->params.grayscale;
716 state->classifier->weak_classifiers = (ccv_icf_decision_tree_t*)ccmallocmalloc(sizeof(ccv_icf_decision_tree_t) * state->params.weak_classifier);
717 } else {
718 if (state->classifier->count < state->params.weak_classifier)
719 state->classifier->weak_classifiers = (ccv_icf_decision_tree_t*)ccreallocrealloc(state->classifier->weak_classifiers, sizeof(ccv_icf_decision_tree_t) * state->params.weak_classifier);
720 }
721}
722
723#define less_than(s1, s2, aux) ((s1).value < (s2).value)
724static CCV_IMPLEMENT_QSORT(_ccv_icf_precomputed_ordering, ccv_icf_value_index_t, less_than)void _ccv_icf_precomputed_ordering(ccv_icf_value_index_t *array
, size_t total, int aux) { int isort_thresh = 7; ccv_icf_value_index_t
t; int sp = 0; struct { ccv_icf_value_index_t *lb; ccv_icf_value_index_t
*ub; } stack[48]; if( total <= 1 ) return; stack[0].lb = array
; stack[0].ub = array + (total - 1); while( sp >= 0 ) { ccv_icf_value_index_t
* left = stack[sp].lb; ccv_icf_value_index_t* right = stack[sp
--].ub; for(;;) { int i, n = (int)(right - left) + 1, m; ccv_icf_value_index_t
* ptr; ccv_icf_value_index_t* ptr2; if( n <= isort_thresh )
{ insert_sort: for( ptr = left + 1; ptr <= right; ptr++ )
{ for( ptr2 = ptr; ptr2 > left && less_than(ptr2[
0],ptr2[-1], aux); ptr2--) (((t)) = ((ptr2[0])), ((ptr2[0])) =
((ptr2[-1])), ((ptr2[-1])) = ((t))); } break; } else { ccv_icf_value_index_t
* left0; ccv_icf_value_index_t* left1; ccv_icf_value_index_t*
right0; ccv_icf_value_index_t* right1; ccv_icf_value_index_t
* pivot; ccv_icf_value_index_t* a; ccv_icf_value_index_t* b; ccv_icf_value_index_t
* c; int swap_cnt = 0; left0 = left; right0 = right; pivot = left
+ (n/2); if( n > 40 ) { int d = n / 8; a = left, b = left
+ d, c = left + 2*d; left = less_than(*a, *b, aux) ? (less_than
(*b, *c, aux) ? b : (less_than(*a, *c, aux) ? c : a)) : (less_than
(*c, *b, aux) ? b : (less_than(*a, *c, aux) ? a : c)); a = pivot
- d, b = pivot, c = pivot + d; pivot = less_than(*a, *b, aux
) ? (less_than(*b, *c, aux) ? b : (less_than(*a, *c, aux) ? c
: a)) : (less_than(*c, *b, aux) ? b : (less_than(*a, *c, aux
) ? a : c)); a = right - 2*d, b = right - d, c = right; right
= less_than(*a, *b, aux) ? (less_than(*b, *c, aux) ? b : (less_than
(*a, *c, aux) ? c : a)) : (less_than(*c, *b, aux) ? b : (less_than
(*a, *c, aux) ? a : c)); } a = left, b = pivot, c = right; pivot
= less_than(*a, *b, aux) ? (less_than(*b, *c, aux) ? b : (less_than
(*a, *c, aux) ? c : a)) : (less_than(*c, *b, aux) ? b : (less_than
(*a, *c, aux) ? a : c)); if( pivot != left0 ) { (((t)) = ((*pivot
)), ((*pivot)) = ((*left0)), ((*left0)) = ((t))); pivot = left0
; } left = left1 = left0 + 1; right = right1 = right0; for(;;
) { while( left <= right && !less_than(*pivot, *left
, aux) ) { if( !less_than(*left, *pivot, aux) ) { if( left >
left1 ) (((t)) = ((*left1)), ((*left1)) = ((*left)), ((*left
)) = ((t))); swap_cnt = 1; left1++; } left++; } while( left <=
right && !less_than(*right, *pivot, aux) ) { if( !less_than
(*pivot, *right, aux) ) { if( right < right1 ) (((t)) = ((
*right1)), ((*right1)) = ((*right)), ((*right)) = ((t))); swap_cnt
= 1; right1--; } right--; } if( left > right ) break; (((
t)) = ((*left)), ((*left)) = ((*right)), ((*right)) = ((t)));
swap_cnt = 1; left++; right--; } if( swap_cnt == 0 ) { left =
left0, right = right0; goto insert_sort; } n = ({ typeof ((int
)(left1 - left0)) _a = ((int)(left1 - left0)); typeof ((int)(
left - left1)) _b = ((int)(left - left1)); (_a < _b) ? _a :
_b; }); for( i = 0; i < n; i++ ) (((t)) = ((left0[i])), (
(left0[i])) = ((left[i-n])), ((left[i-n])) = ((t))); n = ({ typeof
((int)(right0 - right1)) _a = ((int)(right0 - right1)); typeof
((int)(right1 - right)) _b = ((int)(right1 - right)); (_a <
_b) ? _a : _b; }); for( i = 0; i < n; i++ ) (((t)) = ((left
[i])), ((left[i])) = ((right0[i-n+1])), ((right0[i-n+1])) = (
(t))); n = (int)(left - left1); m = (int)(right1 - right); if
( n > 1 ) { if( m > 1 ) { if( n > m ) { stack[++sp].
lb = left0; stack[sp].ub = left0 + n - 1; left = right0 - m +
1, right = right0; } else { stack[++sp].lb = right0 - m + 1;
stack[sp].ub = right0; left = left0, right = left0 + n - 1; }
} else left = left0, right = left0 + n - 1; } else if( m >
1 ) left = right0 - m + 1, right = right0; else break; } } }
}
725#undef less_than
726
727static inline void _ccv_icf_3_uint8_to_1_uint1_1_uint23(uint8_t* u8, uint8_t* u1, uint32_t* uint23)
728{
729 *u1 = (u8[0] >> 7);
730 *uint23 = (((uint32_t)(u8[0] & 0x7f)) << 16) | ((uint32_t)(u8[1]) << 8) | u8[2];
731}
732
733static inline uint32_t _ccv_icf_3_uint8_to_1_uint23(uint8_t* u8)
734{
735 return (((uint32_t)(u8[0] & 0x7f)) << 16) | ((uint32_t)(u8[1]) << 8) | u8[2];
736}
737
738static inline void _ccv_icf_1_uint1_1_uint23_to_3_uint8(uint8_t u1, uint32_t u23, uint8_t* u8)
739{
740 u8[0] = ((u1 << 7) | (u23 >> 16)) & 0xff;
741 u8[1] = (u23 >> 8) & 0xff;
742 u8[2] = u23 & 0xff;
743}
744
745static float _ccv_icf_run_feature_on_example(ccv_icf_feature_t* feature, ccv_dense_matrix_t* a)
746{
747 ccv_dense_matrix_t* icf = 0;
748 // we have 1px padding around the image
749 ccv_icf(a, &icf, 0);
750 ccv_dense_matrix_t* sat = 0;
751 ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO);
752 ccv_matrix_free(icf);
753 float* ptr = sat->data.f32;
754 int ch = CCV_GET_CHANNEL(sat->type)((sat->type) & 0xFFF);
755 float c = _ccv_icf_run_feature(feature, ptr, sat->cols, ch, 1, 1);
756 ccv_matrix_free(sat);
757 return c;
758}
759
760static uint8_t* _ccv_icf_precompute_features(ccv_icf_feature_t* features, int feature_size, ccv_array_t* positives, ccv_array_t* negatives)
761{
762 int i, j;
763 // we use 3 bytes to represent the sorted index, and compute feature result (float) on fly
764 size_t step = (3 * (positives->rnum + negatives->rnum) + 3) & -4;
765 uint8_t* precomputed = (uint8_t*)ccmallocmalloc(sizeof(uint8_t) * feature_size * step);
766 ccv_icf_value_index_t* sortkv = (ccv_icf_value_index_t*)ccmallocmalloc(sizeof(ccv_icf_value_index_t) * (positives->rnum + negatives->rnum));
767 PRINT(CCV_CLI_INFO, " - precompute features using %uM memory temporarily\n", (uint32_t)((sizeof(float) * (positives->rnum + negatives->rnum) * feature_size + sizeof(uint8_t) * feature_size * step) / (1024 * 1024)))do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - precompute features using %uM memory temporarily\n", (uint32_t
)((sizeof(float) * (positives->rnum + negatives->rnum) *
feature_size + sizeof(uint8_t) * feature_size * step) / (1024
* 1024))); fflush(stdout); } } while (0);
768 float* featval = (float*)ccmallocmalloc(sizeof(float) * feature_size * (positives->rnum + negatives->rnum));
769 ccv_disable_cache(); // clean up cache so we have enough space to run it
770#ifdef USE_DISPATCH
771 dispatch_semaphore_t sema = dispatch_semaphore_create(1);
772 dispatch_apply(positives->rnum + negatives->rnum, dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_DEFAULT, 0), ^(size_t i) {
773#else
774 for (i = 0; i < positives->rnum + negatives->rnum; i++)
775 {
776#endif
777#ifdef USE_DISPATCH
778 dispatch_semaphore_wait(sema, DISPATCH_TIME_FOREVER);
779#endif
780 if (i % 37 == 0 || i == positives->rnum + negatives->rnum - 1) // don't flush too fast
781 FLUSH(CCV_CLI_INFO, " - precompute %d features through %d%% (%d / %d) examples", feature_size, (int)(i + 1) * 100 / (positives->rnum + negatives->rnum), (int)i + 1, positives->rnum + negatives->rnum)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { for
(_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT;
_CCV_PRINT_LOOP++) printf("\b"); for (_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP
< _CCV_PRINT_COUNT; _CCV_PRINT_LOOP++) printf(" "); for (
_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT; _CCV_PRINT_LOOP
++) printf("\b"); _CCV_PRINT_COUNT = printf(" - precompute %d features through %d%% (%d / %d) examples"
, feature_size, (int)(i + 1) * 100 / (positives->rnum + negatives
->rnum), (int)i + 1, positives->rnum + negatives->rnum
); fflush(stdout); } } while (0);
782#ifdef USE_DISPATCH
783 dispatch_semaphore_signal(sema);
784 int j;
785#endif
786 ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(i < positives->rnum ? positives : negatives, i < positives->rnum ? i : i - positives->rnum)((void*)(((char*)((i < positives->rnum ? positives : negatives
)->data)) + (size_t)(i < positives->rnum ? positives
: negatives)->rsize * (size_t)(i < positives->rnum ?
i : i - positives->rnum)));
787 a->data.u8 = (unsigned char*)(a + 1); // re-host the pointer to the right place
788 ccv_dense_matrix_t* icf = 0;
789 // we have 1px padding around the image
790 ccv_icf(a, &icf, 0);
791 ccv_dense_matrix_t* sat = 0;
792 ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO);
793 ccv_matrix_free(icf);
794 float* ptr = sat->data.f32;
795 int ch = CCV_GET_CHANNEL(sat->type)((sat->type) & 0xFFF);
796 for (j = 0; j < feature_size; j++)
797 {
798 ccv_icf_feature_t* feature = features + j;
799 float c = _ccv_icf_run_feature(feature, ptr, sat->cols, ch, 1, 1);
800 assert(isfinite(c))((void) sizeof ((__builtin_isfinite (c)) ? 1 : 0), __extension__
({ if (__builtin_isfinite (c)) ; else __assert_fail ("isfinite(c)"
, "ccv_icf.c", 800, __extension__ __PRETTY_FUNCTION__); }));
801 featval[(size_t)j * (positives->rnum + negatives->rnum) + i] = c;
802 }
803 ccv_matrix_free(sat);
804#ifdef USE_DISPATCH
805 });
806 dispatch_release(sema);
807#else
808 }
809#endif
810 PRINT(CCV_CLI_INFO, "\n")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("\n"); fflush(stdout); } } while (0);
811 uint8_t* computed = precomputed;
812 float* pfeatval = featval;
813 for (i = 0; i < feature_size; i++)
814 {
815 if (i % 37 == 0 || i == feature_size - 1) // don't flush too fast
816 FLUSH(CCV_CLI_INFO, " - precompute %d examples through %d%% (%d / %d) features", positives->rnum + negatives->rnum, (i + 1) * 100 / feature_size, i + 1, feature_size)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { for
(_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT;
_CCV_PRINT_LOOP++) printf("\b"); for (_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP
< _CCV_PRINT_COUNT; _CCV_PRINT_LOOP++) printf(" "); for (
_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT; _CCV_PRINT_LOOP
++) printf("\b"); _CCV_PRINT_COUNT = printf(" - precompute %d examples through %d%% (%d / %d) features"
, positives->rnum + negatives->rnum, (i + 1) * 100 / feature_size
, i + 1, feature_size); fflush(stdout); } } while (0);
817 for (j = 0; j < positives->rnum + negatives->rnum; j++)
818 sortkv[j].value = pfeatval[j], sortkv[j].index = j;
819 _ccv_icf_precomputed_ordering(sortkv, positives->rnum + negatives->rnum, 0);
820 // the first flag denotes if the subsequent one are equal to the previous one (if so, we have to skip both of them)
821 for (j = 0; j < positives->rnum + negatives->rnum - 1; j++)
822 _ccv_icf_1_uint1_1_uint23_to_3_uint8(sortkv[j].value == sortkv[j + 1].value, sortkv[j].index, computed + j * 3);
823 j = positives->rnum + negatives->rnum - 1;
824 _ccv_icf_1_uint1_1_uint23_to_3_uint8(0, sortkv[j].index, computed + j * 3);
825 computed += step;
826 pfeatval += positives->rnum + negatives->rnum;
827 }
828 ccfreefree(featval);
829 ccfreefree(sortkv);
830 PRINT(CCV_CLI_INFO, "\n - features are precomputed on examples and will occupy %uM memory\n", (uint32_t)((feature_size * step) / (1024 * 1024)))do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("\n - features are precomputed on examples and will occupy %uM memory\n"
, (uint32_t)((feature_size * step) / (1024 * 1024))); fflush(
stdout); } } while (0);
831 return precomputed;
832}
833
834typedef struct {
835 uint32_t pass;
836 double weigh[4];
837 int first_feature;
838 uint8_t* lut;
839} ccv_icf_decision_tree_cache_t;
840
841static inline float _ccv_icf_compute_threshold_between(ccv_icf_feature_t* feature, uint8_t* computed, ccv_array_t* positives, ccv_array_t* negatives, int index0, int index1)
842{
843 float c[2];
844 uint32_t b[2] = {
845 _ccv_icf_3_uint8_to_1_uint23(computed + index0 * 3),
846 _ccv_icf_3_uint8_to_1_uint23(computed + index1 * 3),
847 };
848 ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(b[0] < positives->rnum ? positives : negatives, b[0] < positives->rnum ? b[0] : b[0] - positives->rnum)((void*)(((char*)((b[0] < positives->rnum ? positives :
negatives)->data)) + (size_t)(b[0] < positives->rnum
? positives : negatives)->rsize * (size_t)(b[0] < positives
->rnum ? b[0] : b[0] - positives->rnum)));
849 a->data.u8 = (unsigned char*)(a + 1); // re-host the pointer to the right place
850 c[0] = _ccv_icf_run_feature_on_example(feature, a);
851 a = (ccv_dense_matrix_t*)ccv_array_get(b[1] < positives->rnum ? positives : negatives, b[1] < positives->rnum ? b[1] : b[1] - positives->rnum)((void*)(((char*)((b[1] < positives->rnum ? positives :
negatives)->data)) + (size_t)(b[1] < positives->rnum
? positives : negatives)->rsize * (size_t)(b[1] < positives
->rnum ? b[1] : b[1] - positives->rnum)));
852 a->data.u8 = (unsigned char*)(a + 1); // re-host the pointer to the right place
853 c[1] = _ccv_icf_run_feature_on_example(feature, a);
854 return (c[0] + c[1]) * 0.5;
855}
856
857static inline void _ccv_icf_example_correct(ccv_icf_example_state_t* example_state, uint8_t* computed, uint8_t* lut, int leaf, ccv_array_t* positives, ccv_array_t* negatives, int start, int end)
858{
859 int i;
860 for (i = start; i <= end; i++)
861 {
862 uint32_t index = _ccv_icf_3_uint8_to_1_uint23(computed + i * 3);
863 if (!lut || lut[index] == leaf)
864 example_state[index].correct = (index < positives->rnum);
865 }
866}
867
868typedef struct {
869 int error_index;
870 double error_rate;
871 double weigh[2];
872 int count[2];
873} ccv_icf_first_feature_find_t;
874
875static ccv_icf_decision_tree_cache_t _ccv_icf_find_first_feature(ccv_icf_feature_t* features, int feature_size, ccv_array_t* positives, ccv_array_t* negatives, uint8_t* precomputed, ccv_icf_example_state_t* example_state, ccv_icf_feature_t* feature)
876{
877 int i;
878 assert(feature != 0)((void) sizeof ((feature != 0) ? 1 : 0), __extension__ ({ if (
feature != 0) ; else __assert_fail ("feature != 0", "ccv_icf.c"
, 878, __extension__ __PRETTY_FUNCTION__); }));
879 ccv_icf_decision_tree_cache_t intermediate_cache;
880 double aweigh0 = 0, aweigh1 = 0;
881 for (i = 0; i < positives->rnum; i++)
882 aweigh1 += example_state[i].weight, example_state[i].correct = 0; // assuming positive examples we get wrong
883 for (i = positives->rnum; i < positives->rnum + negatives->rnum; i++)
884 aweigh0 += example_state[i].weight, example_state[i].correct = 1; // assuming negative examples we get right
885 size_t step = (3 * (positives->rnum + negatives->rnum) + 3) & -4;
886 ccv_icf_first_feature_find_t* feature_find = (ccv_icf_first_feature_find_t*)ccmallocmalloc(sizeof(ccv_icf_first_feature_find_t) * feature_size);
887 parallel_for(i, feature_size){ int i; for ((i) = 0; (i) < (feature_size); (i)++) { {
888 ccv_icf_first_feature_find_t min_find = {
889 .error_rate = 1.0,
890 .error_index = 0,
891 .weigh = {0, 0},
892 .count = {0, 0},
893 };
894 double weigh[2] = {0, 0};
895 int count[2] = {0, 0};
896 int j;
897 uint8_t* computed = precomputed + step * i;
898 for (j = 0; j < positives->rnum + negatives->rnum; j++)
899 {
900 uint8_t skip;
901 uint32_t index;
902 _ccv_icf_3_uint8_to_1_uint1_1_uint23(computed + j * 3, &skip, &index);
903 conditional_assert(j == positives->rnum + negatives->rnum - 1, !skip)if ((j == positives->rnum + negatives->rnum - 1)) { ((void
) sizeof ((!skip) ? 1 : 0), __extension__ ({ if (!skip) ; else
__assert_fail ("!skip", "ccv_icf.c", 903, __extension__ __PRETTY_FUNCTION__
); })); };
904 assert(index >= 0 && index < positives->rnum + negatives->rnum)((void) sizeof ((index >= 0 && index < positives
->rnum + negatives->rnum) ? 1 : 0), __extension__ ({ if
(index >= 0 && index < positives->rnum + negatives
->rnum) ; else __assert_fail ("index >= 0 && index < positives->rnum + negatives->rnum"
, "ccv_icf.c", 904, __extension__ __PRETTY_FUNCTION__); }));
905 weigh[index < positives->rnum] += example_state[index].weight;
906 assert(example_state[index].weight > 0)((void) sizeof ((example_state[index].weight > 0) ? 1 : 0)
, __extension__ ({ if (example_state[index].weight > 0) ; else
__assert_fail ("example_state[index].weight > 0", "ccv_icf.c"
, 906, __extension__ __PRETTY_FUNCTION__); }));
907 assert(weigh[0] <= aweigh0 + 1e-10 && weigh[1] <= aweigh1 + 1e-10)((void) sizeof ((weigh[0] <= aweigh0 + 1e-10 && weigh
[1] <= aweigh1 + 1e-10) ? 1 : 0), __extension__ ({ if (weigh
[0] <= aweigh0 + 1e-10 && weigh[1] <= aweigh1 +
1e-10) ; else __assert_fail ("weigh[0] <= aweigh0 + 1e-10 && weigh[1] <= aweigh1 + 1e-10"
, "ccv_icf.c", 907, __extension__ __PRETTY_FUNCTION__); }));
908 ++count[index < positives->rnum];
909 if (skip) // the current index is equal to the next one, we cannot differentiate, therefore, skip
910 continue;
911 double error_rate = ccv_min(weigh[0] + aweigh1 - weigh[1], weigh[1] + aweigh0 - weigh[0])({ typeof (weigh[0] + aweigh1 - weigh[1]) _a = (weigh[0] + aweigh1
- weigh[1]); typeof (weigh[1] + aweigh0 - weigh[0]) _b = (weigh
[1] + aweigh0 - weigh[0]); (_a < _b) ? _a : _b; });
912 assert(error_rate > 0)((void) sizeof ((error_rate > 0) ? 1 : 0), __extension__ (
{ if (error_rate > 0) ; else __assert_fail ("error_rate > 0"
, "ccv_icf.c", 912, __extension__ __PRETTY_FUNCTION__); }));
913 if (error_rate < min_find.error_rate)
914 {
915 min_find.error_index = j;
916 min_find.error_rate = error_rate;
917 min_find.weigh[0] = weigh[0];
918 min_find.weigh[1] = weigh[1];
919 min_find.count[0] = count[0];
920 min_find.count[1] = count[1];
921 }
922 }
923 feature_find[i] = min_find;
924 } parallel_endfor} }
925 ccv_icf_first_feature_find_t best = {
926 .error_rate = 1.0,
927 .error_index = -1,
928 .weigh = {0, 0},
929 .count = {0, 0},
930 };
931 int feature_index = 0;
932 for (i = 0; i < feature_size; i++)
933 if (feature_find[i].error_rate < best.error_rate)
934 {
935 best = feature_find[i];
936 feature_index = i;
937 }
938 ccfreefree(feature_find);
939 *feature = features[feature_index];
940 uint8_t* computed = precomputed + step * feature_index;
941 intermediate_cache.lut = (uint8_t*)ccmallocmalloc(positives->rnum + negatives->rnum);
942 assert(best.error_index < positives->rnum + negatives->rnum - 1 && best.error_index >= 0)((void) sizeof ((best.error_index < positives->rnum + negatives
->rnum - 1 && best.error_index >= 0) ? 1 : 0), __extension__
({ if (best.error_index < positives->rnum + negatives->
rnum - 1 && best.error_index >= 0) ; else __assert_fail
("best.error_index < positives->rnum + negatives->rnum - 1 && best.error_index >= 0"
, "ccv_icf.c", 942, __extension__ __PRETTY_FUNCTION__); }));
943 if (best.weigh[0] + aweigh1 - best.weigh[1] < best.weigh[1] + aweigh0 - best.weigh[0])
944 {
945 for (i = 0; i < positives->rnum + negatives->rnum; i++)
946 intermediate_cache.lut[_ccv_icf_3_uint8_to_1_uint23(computed + i * 3)] = (i <= best.error_index);
947 feature->beta = _ccv_icf_compute_threshold_between(feature, computed, positives, negatives, best.error_index, best.error_index + 1);
948 // revert the sign of alpha, after threshold is computed
949 for (i = 0; i < feature->count; i++)
950 feature->alpha[i] = -feature->alpha[i];
951 intermediate_cache.weigh[0] = aweigh0 - best.weigh[0];
952 intermediate_cache.weigh[1] = aweigh1 - best.weigh[1];
953 intermediate_cache.weigh[2] = best.weigh[0];
954 intermediate_cache.weigh[3] = best.weigh[1];
955 intermediate_cache.pass = 3;
956 if (best.count[0] == 0)
957 intermediate_cache.pass &= 2; // only positive examples in the right, no need to build right leaf
958 if (best.count[1] == positives->rnum)
959 intermediate_cache.pass &= 1; // no positive examples in the left, no need to build left leaf
960 if (!(intermediate_cache.pass & 1)) // mark positives in the right as correct, if we don't have right leaf
961 _ccv_icf_example_correct(example_state, computed, 0, 0, positives, negatives, 0, best.error_index);
962 } else {
963 for (i = 0; i < positives->rnum + negatives->rnum; i++)
964 intermediate_cache.lut[_ccv_icf_3_uint8_to_1_uint23(computed + i * 3)] = (i > best.error_index);
965 feature->beta = -_ccv_icf_compute_threshold_between(feature, computed, positives, negatives, best.error_index, best.error_index + 1);
966 intermediate_cache.weigh[0] = best.weigh[0];
967 intermediate_cache.weigh[1] = best.weigh[1];
968 intermediate_cache.weigh[2] = aweigh0 - best.weigh[0];
969 intermediate_cache.weigh[3] = aweigh1 - best.weigh[1];
970 intermediate_cache.pass = 3;
971 if (best.count[0] == negatives->rnum)
972 intermediate_cache.pass &= 2; // only positive examples in the right, no need to build right leaf
973 if (best.count[1] == 0)
974 intermediate_cache.pass &= 1; // no positive examples in the left, no need to build left leaf
975 if (!(intermediate_cache.pass & 1)) // mark positives in the right as correct if we don't have right leaf
976 _ccv_icf_example_correct(example_state, computed, 0, 0, positives, negatives, best.error_index + 1, positives->rnum + negatives->rnum - 1);
977 }
978 intermediate_cache.first_feature = feature_index;
979 return intermediate_cache;
980}
981
982typedef struct {
983 int error_index;
984 double error_rate;
985 double weigh[2];
986} ccv_icf_second_feature_find_t;
987
988static double _ccv_icf_find_second_feature(ccv_icf_decision_tree_cache_t intermediate_cache, int leaf, ccv_icf_feature_t* features, int feature_size, ccv_array_t* positives, ccv_array_t* negatives, uint8_t* precomputed, ccv_icf_example_state_t* example_state, ccv_icf_feature_t* feature)
989{
990 size_t step = (3 * (positives->rnum + negatives->rnum) + 3) & -4;
991 uint8_t* lut = intermediate_cache.lut;
992 double* aweigh = intermediate_cache.weigh + leaf * 2;
993 ccv_icf_second_feature_find_t* feature_find = (ccv_icf_second_feature_find_t*)ccmallocmalloc(sizeof(ccv_icf_second_feature_find_t) * feature_size);
994 parallel_for(i, feature_size){ int i; for ((i) = 0; (i) < (feature_size); (i)++) { {
995 ccv_icf_second_feature_find_t min_find = {
996 .error_rate = 1.0,
997 .error_index = 0,
998 .weigh = {0, 0},
999 };
1000 double weigh[2] = {0, 0};
1001 uint8_t* computed = precomputed + step * i;
1002 int j, k;
1003 for (j = 0; j < positives->rnum + negatives->rnum; j++)
1004 {
1005 uint8_t skip;
1006 uint32_t index;
1007 _ccv_icf_3_uint8_to_1_uint1_1_uint23(computed + j * 3, &skip, &index);
1008 conditional_assert(j == positives->rnum + negatives->rnum - 1, !skip)if ((j == positives->rnum + negatives->rnum - 1)) { ((void
) sizeof ((!skip) ? 1 : 0), __extension__ ({ if (!skip) ; else
__assert_fail ("!skip", "ccv_icf.c", 1008, __extension__ __PRETTY_FUNCTION__
); })); };
1009 assert(index >= 0 && index < positives->rnum + negatives->rnum)((void) sizeof ((index >= 0 && index < positives
->rnum + negatives->rnum) ? 1 : 0), __extension__ ({ if
(index >= 0 && index < positives->rnum + negatives
->rnum) ; else __assert_fail ("index >= 0 && index < positives->rnum + negatives->rnum"
, "ccv_icf.c", 1009, __extension__ __PRETTY_FUNCTION__); }));
1010 // only care about part of the data
1011 if (lut[index] == leaf)
1012 {
1013 uint8_t leaf_skip = 0;
1014 for (k = j + 1; skip; k++)
1015 {
1016 uint32_t new_index;
1017 _ccv_icf_3_uint8_to_1_uint1_1_uint23(computed + j * 3, &skip, &new_index);
1018 // if the next equal one is the same leaf, we cannot distinguish them, skip
1019 if ((leaf_skip = (lut[new_index] == leaf)))
1020 break;
1021 conditional_assert(k == positives->rnum + negatives->rnum - 1, !skip)if ((k == positives->rnum + negatives->rnum - 1)) { ((void
) sizeof ((!skip) ? 1 : 0), __extension__ ({ if (!skip) ; else
__assert_fail ("!skip", "ccv_icf.c", 1021, __extension__ __PRETTY_FUNCTION__
); })); };
1022 }
1023 weigh[index < positives->rnum] += example_state[index].weight;
1024 if (leaf_skip)
1025 continue;
1026 assert(example_state[index].weight > 0)((void) sizeof ((example_state[index].weight > 0) ? 1 : 0)
, __extension__ ({ if (example_state[index].weight > 0) ; else
__assert_fail ("example_state[index].weight > 0", "ccv_icf.c"
, 1026, __extension__ __PRETTY_FUNCTION__); }));
1027 assert(weigh[0] <= aweigh[0] + 1e-10 && weigh[1] <= aweigh[1] + 1e-10)((void) sizeof ((weigh[0] <= aweigh[0] + 1e-10 && weigh
[1] <= aweigh[1] + 1e-10) ? 1 : 0), __extension__ ({ if (weigh
[0] <= aweigh[0] + 1e-10 && weigh[1] <= aweigh[
1] + 1e-10) ; else __assert_fail ("weigh[0] <= aweigh[0] + 1e-10 && weigh[1] <= aweigh[1] + 1e-10"
, "ccv_icf.c", 1027, __extension__ __PRETTY_FUNCTION__); }));
1028 double error_rate = ccv_min(weigh[0] + aweigh[1] - weigh[1], weigh[1] + aweigh[0] - weigh[0])({ typeof (weigh[0] + aweigh[1] - weigh[1]) _a = (weigh[0] + aweigh
[1] - weigh[1]); typeof (weigh[1] + aweigh[0] - weigh[0]) _b =
(weigh[1] + aweigh[0] - weigh[0]); (_a < _b) ? _a : _b; }
);
1029 if (error_rate < min_find.error_rate)
1030 {
1031 min_find.error_index = j;
1032 min_find.error_rate = error_rate;
1033 min_find.weigh[0] = weigh[0];
1034 min_find.weigh[1] = weigh[1];
1035 }
1036 }
1037 }
1038 feature_find[i] = min_find;
1039 } parallel_endfor} }
1040 ccv_icf_second_feature_find_t best = {
1041 .error_rate = 1.0,
1042 .error_index = -1,
1043 .weigh = {0, 0},
1044 };
1045 int i;
1046 int feature_index = 0;
1047 for (i = 0; i < feature_size; i++)
1048 if (feature_find[i].error_rate < best.error_rate)
1049 {
1050 best = feature_find[i];
1051 feature_index = i;
1052 }
1053 ccfreefree(feature_find);
1054 *feature = features[feature_index];
1055 uint8_t* computed = precomputed + step * feature_index;
1056 assert(best.error_index < positives->rnum + negatives->rnum - 1 && best.error_index >= 0)((void) sizeof ((best.error_index < positives->rnum + negatives
->rnum - 1 && best.error_index >= 0) ? 1 : 0), __extension__
({ if (best.error_index < positives->rnum + negatives->
rnum - 1 && best.error_index >= 0) ; else __assert_fail
("best.error_index < positives->rnum + negatives->rnum - 1 && best.error_index >= 0"
, "ccv_icf.c", 1056, __extension__ __PRETTY_FUNCTION__); }));
1057 if (best.weigh[0] + aweigh[1] - best.weigh[1] < best.weigh[1] + aweigh[0] - best.weigh[0])
1058 {
1059 feature->beta = _ccv_icf_compute_threshold_between(feature, computed, positives, negatives, best.error_index, best.error_index + 1);
1060 // revert the sign of alpha, after threshold is computed
1061 for (i = 0; i < feature->count; i++)
1062 feature->alpha[i] = -feature->alpha[i];
1063 // mark everything on the right properly
1064 _ccv_icf_example_correct(example_state, computed, lut, leaf, positives, negatives, 0, best.error_index);
1065 return best.weigh[1] + aweigh[0] - best.weigh[0];
1066 } else {
1067 feature->beta = -_ccv_icf_compute_threshold_between(feature, computed, positives, negatives, best.error_index, best.error_index + 1);
1068 // mark everything on the right properly
1069 _ccv_icf_example_correct(example_state, computed, lut, leaf, positives, negatives, best.error_index + 1, positives->rnum + negatives->rnum - 1);
1070 return best.weigh[0] + aweigh[1] - best.weigh[1];
1071 }
1072}
1073
1074static double _ccv_icf_find_best_weak_classifier(ccv_icf_feature_t* features, int feature_size, ccv_array_t* positives, ccv_array_t* negatives, uint8_t* precomputed, ccv_icf_example_state_t* example_state, ccv_icf_decision_tree_t* weak_classifier)
1075{
1076 // we are building the specific depth-2 decision tree
1077 ccv_icf_decision_tree_cache_t intermediate_cache = _ccv_icf_find_first_feature(features, feature_size, positives, negatives, precomputed, example_state, weak_classifier->features);
1078 // find the left feature
1079 // for the pass, 10 is the left branch, 01 is the right branch
1080 weak_classifier->pass = intermediate_cache.pass;
1081 double rate = 0;
1082 if (weak_classifier->pass & 0x2)
1083 rate += _ccv_icf_find_second_feature(intermediate_cache, 0, features, feature_size, positives, negatives, precomputed, example_state, weak_classifier->features + 1);
1084 else
1085 rate += intermediate_cache.weigh[0]; // the negative weights covered by first feature
1086 // find the right feature
1087 if (weak_classifier->pass & 0x1)
1088 rate += _ccv_icf_find_second_feature(intermediate_cache, 1, features, feature_size, positives, negatives, precomputed, example_state, weak_classifier->features + 2);
1089 else
1090 rate += intermediate_cache.weigh[3]; // the positive weights covered by first feature
1091 ccfreefree(intermediate_cache.lut);
1092 return rate;
1093}
1094
1095static ccv_array_t* _ccv_icf_collect_validates(gsl_rng* rng, ccv_size_t size, ccv_margin_t margin, ccv_array_t* validatefiles, int grayscale)
1096{
1097 ccv_array_t* validates = ccv_array_new(ccv_compute_dense_matrix_size(size.height + margin.top + margin.bottom + 2, size.width + margin.left + margin.right + 2, CCV_8U | (grayscale ? CCV_C1 : CCV_C3))(((sizeof(ccv_dense_matrix_t) + 63) & -64) + (((size.width
+ margin.left + margin.right + 2) * _ccv_get_data_type_size[
((CCV_8U | (grayscale ? CCV_C1 : CCV_C3)) & 0xFF000) >>
12] * ((CCV_8U | (grayscale ? CCV_C1 : CCV_C3)) & 0xFFF)
+ 3) & -4) * (size.height + margin.top + margin.bottom +
2)), validatefiles->rnum, 0);
1098 int i;
1099 // collect tests
1100 for (i = 0; i < validatefiles->rnum; i++)
1101 {
1102 ccv_file_info_t* file_info = (ccv_file_info_t*)ccv_array_get(validatefiles, i)((void*)(((char*)((validatefiles)->data)) + (size_t)(validatefiles
)->rsize * (size_t)(i)));
1103 ccv_dense_matrix_t* image = 0;
1104 ccv_read(file_info->filename, &image, CCV_IO_ANY_FILE | (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR))ccv_read_impl(file_info->filename, &image, CCV_IO_ANY_FILE
| (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR), 0, 0, 0);
1105 if (image == 0)
1106 {
1107 PRINT(CCV_CLI_ERROR, "\n - %s: cannot be open, possibly corrupted\n", file_info->filename)do { if ((CCV_CLI_ERROR & ccv_cli_get_output_levels())) {
printf("\n - %s: cannot be open, possibly corrupted\n", file_info
->filename); fflush(stdout); } } while (0);
1108 continue;
1109 }
1110 ccv_dense_matrix_t* feature = _ccv_icf_capture_feature(rng, image, file_info->pose, size, margin, 0, 0, 0);
1111 feature->sig = 0;
1112 ccv_array_push(validates, feature);
1113 ccv_matrix_free(feature);
1114 ccv_matrix_free(image);
1115 }
1116 return validates;
1117}
1118
1119static ccv_array_t* _ccv_icf_collect_positives(gsl_rng* rng, ccv_size_t size, ccv_margin_t margin, ccv_array_t* posfiles, int posnum, float deform_angle, float deform_scale, float deform_shift, int grayscale)
1120{
1121 ccv_array_t* positives = ccv_array_new(ccv_compute_dense_matrix_size(size.height + margin.top + margin.bottom + 2, size.width + margin.left + margin.right + 2, CCV_8U | (grayscale ? CCV_C1 : CCV_C3))(((sizeof(ccv_dense_matrix_t) + 63) & -64) + (((size.width
+ margin.left + margin.right + 2) * _ccv_get_data_type_size[
((CCV_8U | (grayscale ? CCV_C1 : CCV_C3)) & 0xFF000) >>
12] * ((CCV_8U | (grayscale ? CCV_C1 : CCV_C3)) & 0xFFF)
+ 3) & -4) * (size.height + margin.top + margin.bottom +
2)), posnum, 0);
1122 int i, j, q;
1123 // collect positives (with random deformation)
1124 for (i = 0; i < posnum;)
1125 {
1126 FLUSH(CCV_CLI_INFO, " - collect positives %d%% (%d / %d)", (i + 1) * 100 / posnum, i + 1, posnum)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { for
(_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT;
_CCV_PRINT_LOOP++) printf("\b"); for (_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP
< _CCV_PRINT_COUNT; _CCV_PRINT_LOOP++) printf(" "); for (
_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT; _CCV_PRINT_LOOP
++) printf("\b"); _CCV_PRINT_COUNT = printf(" - collect positives %d%% (%d / %d)"
, (i + 1) * 100 / posnum, i + 1, posnum); fflush(stdout); } }
while (0);
1127 double ratio = (double)(posnum - i) / posfiles->rnum;
1128 for (j = 0; j < posfiles->rnum && i < posnum; j++)
1129 {
1130 ccv_file_info_t* file_info = (ccv_file_info_t*)ccv_array_get(posfiles, j)((void*)(((char*)((posfiles)->data)) + (size_t)(posfiles)->
rsize * (size_t)(j)));
1131 ccv_dense_matrix_t* image = 0;
1132 ccv_read(file_info->filename, &image, CCV_IO_ANY_FILE | (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR))ccv_read_impl(file_info->filename, &image, CCV_IO_ANY_FILE
| (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR), 0, 0, 0);
1133 if (image == 0)
1134 {
1135 PRINT(CCV_CLI_ERROR, "\n - %s: cannot be open, possibly corrupted\n", file_info->filename)do { if ((CCV_CLI_ERROR & ccv_cli_get_output_levels())) {
printf("\n - %s: cannot be open, possibly corrupted\n", file_info
->filename); fflush(stdout); } } while (0);
1136 continue;
1137 }
1138 for (q = 0; q < ratio; q++)
1139 if (q < (int)ratio || gsl_rng_uniform(rng) <= ratio - (int)ratio)
1140 {
1141 FLUSH(CCV_CLI_INFO, " - collect positives %d%% (%d / %d)", (i + 1) * 100 / posnum, i + 1, posnum)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { for
(_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT;
_CCV_PRINT_LOOP++) printf("\b"); for (_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP
< _CCV_PRINT_COUNT; _CCV_PRINT_LOOP++) printf(" "); for (
_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT; _CCV_PRINT_LOOP
++) printf("\b"); _CCV_PRINT_COUNT = printf(" - collect positives %d%% (%d / %d)"
, (i + 1) * 100 / posnum, i + 1, posnum); fflush(stdout); } }
while (0);
1142 ccv_dense_matrix_t* feature = _ccv_icf_capture_feature(rng, image, file_info->pose, size, margin, deform_angle, deform_scale, deform_shift);
1143 feature->sig = 0;
1144 ccv_array_push(positives, feature);
1145 ccv_matrix_free(feature);
1146 ++i;
1147 if (i >= posnum)
1148 break;
1149 }
1150 ccv_matrix_free(image);
1151 }
1152 }
1153 PRINT(CCV_CLI_INFO, "\n")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("\n"); fflush(stdout); } } while (0);
1154 return positives;
1155}
1156
1157static uint64_t* _ccv_icf_precompute_classifier_cascade(ccv_icf_classifier_cascade_t* cascade, ccv_array_t* positives)
1158{
1159 int step = ((cascade->count - 1) >> 6) + 1;
1160 uint64_t* precomputed = (uint64_t*)ccmallocmalloc(sizeof(uint64_t) * positives->rnum * step);
18
←
Storing uninitialized value
→
1161 uint64_t* result = precomputed;
1162 int i, j;
1163 for (i = 0; i < positives->rnum; i++)
19
←
Assuming 'i' is < field 'rnum'
→
20
←
Loop condition is true. Entering loop body
→
1164 {
1165 ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)(ccv_array_get(positives, i)((void*)(((char*)((positives)->data)) + (size_t)(positives
)->rsize * (size_t)(i))));
1166 a->data.u8 = (uint8_t*)(a + 1);
1167 ccv_dense_matrix_t* icf = 0;
1168 ccv_icf(a, &icf, 0);
1169 ccv_dense_matrix_t* sat = 0;
1170 ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO);
1171 ccv_matrix_free(icf);
1172 float* ptr = sat->data.f32;
1173 int ch = CCV_GET_CHANNEL(sat->type)((sat->type) & 0xFFF);
1174 for (j = 0; j < cascade->count; j++)
21
←
Loop condition is true. Entering loop body
→
1175 if (_ccv_icf_run_weak_classifier(cascade->weak_classifiers + j, ptr, sat->cols, ch, 1, 1))
22
←
Taking true branch
→
1176 precomputed[j >> 6] |= (1UL << (j & 63));
23
←
The left expression of the compound assignment is an uninitialized value. The computed value will also be garbage
1177 else
1178 precomputed[j >> 6] &= ~(1UL << (j & 63));
1179 ccv_matrix_free(sat);
1180 precomputed += step;
1181 }
1182 return result;
1183}
1184
1185#define less_than(s1, s2, aux) ((s1) > (s2))
1186static CCV_IMPLEMENT_QSORT(_ccv_icf_threshold_rating, float, less_than)void _ccv_icf_threshold_rating(float *array, size_t total, int
aux) { int isort_thresh = 7; float t; int sp = 0; struct { float
*lb; float *ub; } stack[48]; if( total <= 1 ) return; stack
[0].lb = array; stack[0].ub = array + (total - 1); while( sp >=
0 ) { float* left = stack[sp].lb; float* right = stack[sp--]
.ub; for(;;) { int i, n = (int)(right - left) + 1, m; float* ptr
; float* ptr2; if( n <= isort_thresh ) { insert_sort: for(
ptr = left + 1; ptr <= right; ptr++ ) { for( ptr2 = ptr; ptr2
> left && less_than(ptr2[0],ptr2[-1], aux); ptr2--
) (((t)) = ((ptr2[0])), ((ptr2[0])) = ((ptr2[-1])), ((ptr2[-1
])) = ((t))); } break; } else { float* left0; float* left1; float
* right0; float* right1; float* pivot; float* a; float* b; float
* c; int swap_cnt = 0; left0 = left; right0 = right; pivot = left
+ (n/2); if( n > 40 ) { int d = n / 8; a = left, b = left
+ d, c = left + 2*d; left = less_than(*a, *b, aux) ? (less_than
(*b, *c, aux) ? b : (less_than(*a, *c, aux) ? c : a)) : (less_than
(*c, *b, aux) ? b : (less_than(*a, *c, aux) ? a : c)); a = pivot
- d, b = pivot, c = pivot + d; pivot = less_than(*a, *b, aux
) ? (less_than(*b, *c, aux) ? b : (less_than(*a, *c, aux) ? c
: a)) : (less_than(*c, *b, aux) ? b : (less_than(*a, *c, aux
) ? a : c)); a = right - 2*d, b = right - d, c = right; right
= less_than(*a, *b, aux) ? (less_than(*b, *c, aux) ? b : (less_than
(*a, *c, aux) ? c : a)) : (less_than(*c, *b, aux) ? b : (less_than
(*a, *c, aux) ? a : c)); } a = left, b = pivot, c = right; pivot
= less_than(*a, *b, aux) ? (less_than(*b, *c, aux) ? b : (less_than
(*a, *c, aux) ? c : a)) : (less_than(*c, *b, aux) ? b : (less_than
(*a, *c, aux) ? a : c)); if( pivot != left0 ) { (((t)) = ((*pivot
)), ((*pivot)) = ((*left0)), ((*left0)) = ((t))); pivot = left0
; } left = left1 = left0 + 1; right = right1 = right0; for(;;
) { while( left <= right && !less_than(*pivot, *left
, aux) ) { if( !less_than(*left, *pivot, aux) ) { if( left >
left1 ) (((t)) = ((*left1)), ((*left1)) = ((*left)), ((*left
)) = ((t))); swap_cnt = 1; left1++; } left++; } while( left <=
right && !less_than(*right, *pivot, aux) ) { if( !less_than
(*pivot, *right, aux) ) { if( right < right1 ) (((t)) = ((
*right1)), ((*right1)) = ((*right)), ((*right)) = ((t))); swap_cnt
= 1; right1--; } right--; } if( left > right ) break; (((
t)) = ((*left)), ((*left)) = ((*right)), ((*right)) = ((t)));
swap_cnt = 1; left++; right--; } if( swap_cnt == 0 ) { left =
left0, right = right0; goto insert_sort; } n = ({ typeof ((int
)(left1 - left0)) _a = ((int)(left1 - left0)); typeof ((int)(
left - left1)) _b = ((int)(left - left1)); (_a < _b) ? _a :
_b; }); for( i = 0; i < n; i++ ) (((t)) = ((left0[i])), (
(left0[i])) = ((left[i-n])), ((left[i-n])) = ((t))); n = ({ typeof
((int)(right0 - right1)) _a = ((int)(right0 - right1)); typeof
((int)(right1 - right)) _b = ((int)(right1 - right)); (_a <
_b) ? _a : _b; }); for( i = 0; i < n; i++ ) (((t)) = ((left
[i])), ((left[i])) = ((right0[i-n+1])), ((right0[i-n+1])) = (
(t))); n = (int)(left - left1); m = (int)(right1 - right); if
( n > 1 ) { if( m > 1 ) { if( n > m ) { stack[++sp].
lb = left0; stack[sp].ub = left0 + n - 1; left = right0 - m +
1, right = right0; } else { stack[++sp].lb = right0 - m + 1;
stack[sp].ub = right0; left = left0, right = left0 + n - 1; }
} else left = left0, right = left0 + n - 1; } else if( m >
1 ) left = right0 - m + 1, right = right0; else break; } } }
}
1187#undef less_than
1188
1189static void _ccv_icf_classifier_cascade_soft_with_validates(ccv_array_t* validates, ccv_icf_classifier_cascade_t* cascade, double min_accept)
1190{
1191 int i, j;
1192 int step = ((cascade->count - 1) >> 6) + 1;
1193 uint64_t* precomputed = _ccv_icf_precompute_classifier_cascade(cascade, validates);
17
←
Calling '_ccv_icf_precompute_classifier_cascade'
→
1194 float* positive_rate = (float*)ccmallocmalloc(sizeof(float) * validates->rnum);
1195 uint64_t* computed = precomputed;
1196 for (i = 0; i < validates->rnum; i++)
1197 {
1198 positive_rate[i] = 0;
1199 for (j = 0; j < cascade->count; j++)
1200 {
1201 uint64_t accept = computed[j >> 6] & (1UL << (j & 63));
1202 positive_rate[i] += cascade->weak_classifiers[j].weigh[!!accept];
1203 }
1204 computed += step;
1205 }
1206 _ccv_icf_threshold_rating(positive_rate, validates->rnum, 0);
1207 float threshold = positive_rate[ccv_min((int)(min_accept * (validates->rnum + 0.5) - 0.5), validates->rnum - 1)({ typeof ((int)(min_accept * (validates->rnum + 0.5) - 0.5
)) _a = ((int)(min_accept * (validates->rnum + 0.5) - 0.5)
); typeof (validates->rnum - 1) _b = (validates->rnum -
1); (_a < _b) ? _a : _b; })];
1208 ccfreefree(positive_rate);
1209 computed = precomputed;
1210 // compute the final acceptance per validates / negatives with final threshold
1211 uint64_t* acceptance = (uint64_t*)cccalloccalloc(((validates->rnum - 1) >> 6) + 1, sizeof(uint64_t));
1212 int true_positives = 0;
1213 for (i = 0; i < validates->rnum; i++)
1214 {
1215 float rate = 0;
1216 for (j = 0; j < cascade->count; j++)
1217 {
1218 uint64_t accept = computed[j >> 6] & (1UL << (j & 63));
1219 rate += cascade->weak_classifiers[j].weigh[!!accept];
1220 }
1221 if (rate >= threshold)
1222 {
1223 acceptance[i >> 6] |= (1UL << (i & 63));
1224 ++true_positives;
1225 } else
1226 acceptance[i >> 6] &= ~(1UL << (i & 63));
1227 computed += step;
1228 }
1229 PRINT(CCV_CLI_INFO, " - at threshold %f, true positive rate: %f%%\n", threshold, (float)true_positives * 100 / validates->rnum)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - at threshold %f, true positive rate: %f%%\n", threshold,
(float)true_positives * 100 / validates->rnum); fflush(stdout
); } } while (0);
1230 float* rate = (float*)cccalloccalloc(validates->rnum, sizeof(float));
1231 for (j = 0; j < cascade->count; j++)
1232 {
1233 computed = precomputed;
1234 for (i = 0; i < validates->rnum; i++)
1235 {
1236 uint64_t correct = computed[j >> 6] & (1UL << (j & 63));
1237 rate[i] += cascade->weak_classifiers[j].weigh[!!correct];
1238 computed += step;
1239 }
1240 float threshold = FLT_MAX3.40282347e+38F;
1241 // find a threshold that keeps all accepted validates still acceptable
1242 for (i = 0; i < validates->rnum; i++)
1243 {
1244 uint64_t correct = acceptance[i >> 6] & (1UL << (i & 63));
1245 if (correct && rate[i] < threshold)
1246 threshold = rate[i];
1247 }
1248 cascade->weak_classifiers[j].threshold = threshold - 1e-10;
1249 }
1250 ccfreefree(rate);
1251 ccfreefree(acceptance);
1252 ccfreefree(precomputed);
1253}
1254
1255typedef struct {
1256 ccv_point_t point;
1257 float sum;
1258} ccv_point_with_sum_t;
1259
1260static void _ccv_icf_bootstrap_negatives(ccv_icf_classifier_cascade_t* cascade, ccv_array_t* negatives, gsl_rng* rng, ccv_array_t* bgfiles, int negnum, int grayscale, int spread, ccv_icf_param_t params)
1261{
1262#ifdef USE_DISPATCH
1263 __block int i;
1264#else
1265 int i;
1266#endif
1267#ifdef USE_DISPATCH
1268 __block int fppi = 0, is = 0;
1269#else
1270 int fppi = 0, is = 0;
1271#endif
1272 int t = 0;
1273 for (i = 0; i < negnum;)
1274 {
1275 double ratio = (double)(negnum - i) / bgfiles->rnum;
1276#ifdef USE_DISPATCH
1277 dispatch_semaphore_t sem = dispatch_semaphore_create(1);
1278 dispatch_apply(bgfiles->rnum, dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_DEFAULT, 0), ^(size_t j) {
1279#else
1280 size_t j;
1281 for (j = 0; j < bgfiles->rnum; j++)
1282 {
1283#endif
1284 int k, x, y, q, p;
1285 ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccmallocmalloc(ccv_compute_dense_matrix_size(cascade->size.height + 2, cascade->size.width + 2, (grayscale ? CCV_C1 : CCV_C3) | CCV_8U)(((sizeof(ccv_dense_matrix_t) + 63) & -64) + (((cascade->
size.width + 2) * _ccv_get_data_type_size[(((grayscale ? CCV_C1
: CCV_C3) | CCV_8U) & 0xFF000) >> 12] * (((grayscale
? CCV_C1 : CCV_C3) | CCV_8U) & 0xFFF) + 3) & -4) * (
cascade->size.height + 2)));
1286#ifdef USE_DISPATCH
1287 dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER);
1288#endif
1289 if (i >= negnum || (spread && ratio < 1 && gsl_rng_uniform(rng) > ratio))
1290 {
1291 ccfreefree(a);
1292#ifdef USE_DISPATCH
1293 dispatch_semaphore_signal(sem);
1294 return;
1295#else
1296 continue;
1297#endif
1298 }
1299 FLUSH(CCV_CLI_INFO, " - bootstrap negatives %d%% (%d / %d) [%u / %d] %s", (i + 1) * 100 / negnum, i + 1, negnum, (uint32_t)(j + 1), bgfiles->rnum, spread ? "" : "without statistic balancing")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { for
(_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT;
_CCV_PRINT_LOOP++) printf("\b"); for (_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP
< _CCV_PRINT_COUNT; _CCV_PRINT_LOOP++) printf(" "); for (
_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT; _CCV_PRINT_LOOP
++) printf("\b"); _CCV_PRINT_COUNT = printf(" - bootstrap negatives %d%% (%d / %d) [%u / %d] %s"
, (i + 1) * 100 / negnum, i + 1, negnum, (uint32_t)(j + 1), bgfiles
->rnum, spread ? "" : "without statistic balancing"); fflush
(stdout); } } while (0);
1300#ifdef USE_DISPATCH
1301 gsl_rng* crng = gsl_rng_alloc(gsl_rng_default);
1302 gsl_rng_set(crng, gsl_rng_get(rng));
1303 dispatch_semaphore_signal(sem);
1304#else
1305 gsl_rng* crng = rng;
1306#endif
1307 ccv_file_info_t* file_info = (ccv_file_info_t*)ccv_array_get(bgfiles, j)((void*)(((char*)((bgfiles)->data)) + (size_t)(bgfiles)->
rsize * (size_t)(j)));
1308 ccv_dense_matrix_t* image = 0;
1309 ccv_read(file_info->filename, &image, CCV_IO_ANY_FILE | (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR))ccv_read_impl(file_info->filename, &image, CCV_IO_ANY_FILE
| (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR), 0, 0, 0);
1310 if (image == 0)
1311 {
1312 PRINT(CCV_CLI_ERROR, "\n - %s: cannot be open, possibly corrupted\n", file_info->filename)do { if ((CCV_CLI_ERROR & ccv_cli_get_output_levels())) {
printf("\n - %s: cannot be open, possibly corrupted\n", file_info
->filename); fflush(stdout); } } while (0);
1313 ccfreefree(a);
1314#ifdef USE_DISPATCH
1315 gsl_rng_free(crng);
1316 return;
1317#else
1318 continue;
1319#endif
1320 }
1321 if (ccv_max(image->rows, image->cols)({ typeof (image->rows) _a = (image->rows); typeof (image
->cols) _b = (image->cols); (_a > _b) ? _a : _b; }) < 800 ||
1322 image->rows <= (cascade->size.height - cascade->margin.top - cascade->margin.bottom) ||
1323 image->cols <= (cascade->size.width - cascade->margin.left - cascade->margin.right)) // background is too small, blow it up to next scale
1324 {
1325 ccv_dense_matrix_t* blowup = 0;
1326 ccv_sample_up(image, &blowup, 0, 0, 0);
1327 ccv_matrix_free(image);
1328 image = blowup;
1329 }
1330 if (image->rows <= (cascade->size.height - cascade->margin.top - cascade->margin.bottom) ||
1331 image->cols <= (cascade->size.width - cascade->margin.left - cascade->margin.right)) // background is still too small, abort
1332 {
1333 ccv_matrix_free(image);
1334 ccfreefree(a);
1335#ifdef USE_DISPATCH
1336 gsl_rng_free(crng);
1337 return;
1338#else
1339 continue;
1340#endif
1341 }
1342 double scale = pow(2., 1. / (params.interval + 1.));
1343 int next = params.interval + 1;
1344 int scale_upto = (int)(log(ccv_min((double)image->rows / (cascade->size.height - cascade->margin.top - cascade->margin.bottom), (double)image->cols / (cascade->size.width - cascade->margin.left - cascade->margin.right))({ typeof ((double)image->rows / (cascade->size.height -
cascade->margin.top - cascade->margin.bottom)) _a = ((
double)image->rows / (cascade->size.height - cascade->
margin.top - cascade->margin.bottom)); typeof ((double)image
->cols / (cascade->size.width - cascade->margin.left
- cascade->margin.right)) _b = ((double)image->cols / (
cascade->size.width - cascade->margin.left - cascade->
margin.right)); (_a < _b) ? _a : _b; })) / log(scale) - DBL_MIN2.2250738585072014e-308) + 1;
1345 ccv_dense_matrix_t** pyr = (ccv_dense_matrix_t**)ccmallocmalloc(scale_upto * sizeof(ccv_dense_matrix_t*));
1346 memset(pyr, 0, scale_upto * sizeof(ccv_dense_matrix_t*));
1347#ifdef USE_DISPATCH
1348 dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER);
1349#endif
1350 ++is; // how many images are scanned
1351#ifdef USE_DISPATCH
1352 dispatch_semaphore_signal(sem);
1353#endif
1354 if (t % 2 != 0)
1355 ccv_flip(image, 0, 0, CCV_FLIP_X);
1356 if (t % 4 >= 2)
1357 ccv_flip(image, 0, 0, CCV_FLIP_Y);
1358 pyr[0] = image;
1359 for (q = 1; q < ccv_min(params.interval + 1, scale_upto)({ typeof (params.interval + 1) _a = (params.interval + 1); typeof
(scale_upto) _b = (scale_upto); (_a < _b) ? _a : _b; }); q++)
1360 ccv_resample(pyr[0], &pyr[q], 0, (double)(int)(pyr[0]->rows / pow(scale, q)) / (double)pyr[0]->rows, (double)(int)(pyr[0]->cols / pow(scale, q)) / (double)pyr[0]->cols, CCV_INTER_AREA);
1361 for (q = next; q < scale_upto; q++)
1362 ccv_sample_down(pyr[q - next], &pyr[q], 0, 0, 0);
1363 for (q = 0; q < scale_upto; q++)
1364 {
1365#ifdef USE_DISPATCH
1366 dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER);
1367#endif
1368 if (i >= negnum)
1369 {
1370#ifdef USE_DISPATCH
1371 dispatch_semaphore_signal(sem);
1372#endif
1373 ccv_matrix_free(pyr[q]);
1374 continue;
1375 }
1376#ifdef USE_DISPATCH
1377 dispatch_semaphore_signal(sem);
1378#endif
1379 ccv_dense_matrix_t* bordered = 0;
1380 ccv_border(pyr[q], (ccv_matrix_t**)&bordered, 0, cascade->margin);
1381 ccv_matrix_free(pyr[q]);
1382 ccv_dense_matrix_t* icf = 0;
1383 ccv_icf(bordered, &icf, 0);
1384 ccv_dense_matrix_t* sat = 0;
1385 ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO);
1386 ccv_matrix_free(icf);
1387 assert(sat->rows == bordered->rows + 1 && sat->cols == bordered->cols + 1)((void) sizeof ((sat->rows == bordered->rows + 1 &&
sat->cols == bordered->cols + 1) ? 1 : 0), __extension__
({ if (sat->rows == bordered->rows + 1 && sat->
cols == bordered->cols + 1) ; else __assert_fail ("sat->rows == bordered->rows + 1 && sat->cols == bordered->cols + 1"
, "ccv_icf.c", 1387, __extension__ __PRETTY_FUNCTION__); }));
1388 int ch = CCV_GET_CHANNEL(sat->type)((sat->type) & 0xFFF);
1389 float* ptr = sat->data.f32 + sat->cols * ch;
1390 ccv_array_t* seq = ccv_array_new(sizeof(ccv_point_with_sum_t), 64, 0);
1391 for (y = 1; y < sat->rows - cascade->size.height - 2; y += params.step_through)
1392 {
1393 for (x = 1; x < sat->cols - cascade->size.width - 2; x += params.step_through)
1394 {
1395 int pass = 1;
1396 float sum = 0;
1397 for (p = 0; p < cascade->count; p++)
1398 {
1399 ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + p;
1400 int c = _ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, x, 0);
1401 sum += weak_classifier->weigh[c];
1402 if (sum < weak_classifier->threshold)
1403 {
1404 pass = 0;
1405 break;
1406 }
1407 }
1408 if (pass)
1409 {
1410 ccv_point_with_sum_t point;
1411 point.point = ccv_point(x - 1, y - 1);
1412 point.sum = sum;
1413 ccv_array_push(seq, &point);
1414 }
1415 }
1416 ptr += sat->cols * ch * params.step_through;
1417 }
1418 ccv_matrix_free(sat);
1419 // shuffle negatives so that we don't have too biased negatives
1420#ifdef USE_DISPATCH
1421 dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER);
1422#endif
1423 fppi += seq->rnum; // how many detections we have in total
1424#ifdef USE_DISPATCH
1425 dispatch_semaphore_signal(sem);
1426#endif
1427 if (seq->rnum > 0)
1428 {
1429 gsl_ran_shuffle(crng, ccv_array_get(seq, 0)((void*)(((char*)((seq)->data)) + (size_t)(seq)->rsize *
(size_t)(0))), seq->rnum, seq->rsize);
1430 /* so that we at least collect 10 from each scale */
1431 for (p = 0; p < (spread ? ccv_min(10, seq->rnum)({ typeof (10) _a = (10); typeof (seq->rnum) _b = (seq->
rnum); (_a < _b) ? _a : _b; }) : seq->rnum); p++) // collect enough negatives from this scale
1432 {
1433 a = ccv_dense_matrix_new(cascade->size.height + 2, cascade->size.width + 2, (grayscale ? CCV_C1 : CCV_C3) | CCV_8U, a, 0);
1434 ccv_point_with_sum_t* point = (ccv_point_with_sum_t*)ccv_array_get(seq, p)((void*)(((char*)((seq)->data)) + (size_t)(seq)->rsize *
(size_t)(p)));
1435 ccv_slice(bordered, (ccv_matrix_t**)&a, 0, point->point.y, point->point.x, a->rows, a->cols);
1436 assert(bordered->rows >= point->point.y + a->rows && bordered->cols >= point->point.x + a->cols)((void) sizeof ((bordered->rows >= point->point.y + a
->rows && bordered->cols >= point->point.
x + a->cols) ? 1 : 0), __extension__ ({ if (bordered->rows
>= point->point.y + a->rows && bordered->
cols >= point->point.x + a->cols) ; else __assert_fail
("bordered->rows >= point->point.y + a->rows && bordered->cols >= point->point.x + a->cols"
, "ccv_icf.c", 1436, __extension__ __PRETTY_FUNCTION__); }));
1437 a->sig = 0;
1438 // verify the data we sliced is worthy negative
1439 ccv_dense_matrix_t* icf = 0;
1440 ccv_icf(a, &icf, 0);
1441 ccv_dense_matrix_t* sat = 0;
1442 ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO);
1443 ccv_matrix_free(icf);
1444 float* ptr = sat->data.f32;
1445 int ch = CCV_GET_CHANNEL(sat->type)((sat->type) & 0xFFF);
1446 int pass = 1;
1447 float sum = 0;
1448 for (k = 0; k < cascade->count; k++)
1449 {
1450 ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + k;
1451 int c = _ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, 1, 1);
1452 sum += weak_classifier->weigh[c];
1453 if (sum < weak_classifier->threshold)
1454 {
1455 pass = 0;
1456 break;
1457 }
1458 }
1459 ccv_matrix_free(sat);
1460 if (pass)
1461 {
1462#ifdef USE_DISPATCH
1463 dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER);
1464#endif
1465 if (i < negnum)
1466 ccv_array_push(negatives, a);
1467 ++i;
1468 if (i >= negnum)
1469 {
1470#ifdef USE_DISPATCH
1471 dispatch_semaphore_signal(sem);
1472#endif
1473 break;
1474 }
1475#ifdef USE_DISPATCH
1476 dispatch_semaphore_signal(sem);
1477#endif
1478 }
1479 }
1480 }
1481 ccv_array_free(seq);
1482 ccv_matrix_free(bordered);
1483 }
1484 ccfreefree(pyr);
1485 ccfreefree(a);
1486#ifdef USE_DISPATCH
1487 gsl_rng_free(crng);
1488 });
1489 dispatch_release(sem);
1490#else
1491 }
1492#endif
1493 if ((double)fppi / is <= (double)negnum / bgfiles->rnum) // if the targeted negative per image is bigger than our fppi, we don't prob anymore
1494 spread = 0;
1495 ++t;
1496 if (t > (spread ? 4 : 3) && !spread) // we've go over 4 or 3 transformations (original, flip x, flip y, flip x & y, [and original again]), and nothing we can do now
1497 break;
1498 }
1499 PRINT(CCV_CLI_INFO, "\n")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("\n"); fflush(stdout); } } while (0);
1500}
1501
1502static ccv_array_t* _ccv_icf_collect_negatives(gsl_rng* rng, ccv_size_t size, ccv_margin_t margin, ccv_array_t* bgfiles, int negnum, float deform_angle, float deform_scale, float deform_shift, int grayscale)
1503{
1504 ccv_array_t* negatives = ccv_array_new(ccv_compute_dense_matrix_size(size.height + margin.top + margin.bottom + 2, size.width + margin.left + margin.right + 2, CCV_8U | (grayscale ? CCV_C1 : CCV_C3))(((sizeof(ccv_dense_matrix_t) + 63) & -64) + (((size.width
+ margin.left + margin.right + 2) * _ccv_get_data_type_size[
((CCV_8U | (grayscale ? CCV_C1 : CCV_C3)) & 0xFF000) >>
12] * ((CCV_8U | (grayscale ? CCV_C1 : CCV_C3)) & 0xFFF)
+ 3) & -4) * (size.height + margin.top + margin.bottom +
2)), negnum, 0);
1505 int i, j, q;
1506 // randomly collect negatives (with random deformation)
1507 for (i = 0; i < negnum;)
1508 {
1509 FLUSH(CCV_CLI_INFO, " - collect negatives %d%% (%d / %d)", (i + 1) * 100 / negnum, i + 1, negnum)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { for
(_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT;
_CCV_PRINT_LOOP++) printf("\b"); for (_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP
< _CCV_PRINT_COUNT; _CCV_PRINT_LOOP++) printf(" "); for (
_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT; _CCV_PRINT_LOOP
++) printf("\b"); _CCV_PRINT_COUNT = printf(" - collect negatives %d%% (%d / %d)"
, (i + 1) * 100 / negnum, i + 1, negnum); fflush(stdout); } }
while (0);
1510 double ratio = (double)(negnum - i) / bgfiles->rnum;
1511 for (j = 0; j < bgfiles->rnum && i < negnum; j++)
1512 {
1513 ccv_file_info_t* file_info = (ccv_file_info_t*)ccv_array_get(bgfiles, j)((void*)(((char*)((bgfiles)->data)) + (size_t)(bgfiles)->
rsize * (size_t)(j)));
1514 ccv_dense_matrix_t* image = 0;
1515 ccv_read(file_info->filename, &image, CCV_IO_ANY_FILE | (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR))ccv_read_impl(file_info->filename, &image, CCV_IO_ANY_FILE
| (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR), 0, 0, 0);
1516 if (image == 0)
1517 {
1518 PRINT(CCV_CLI_ERROR, "\n - %s: cannot be open, possibly corrupted\n", file_info->filename)do { if ((CCV_CLI_ERROR & ccv_cli_get_output_levels())) {
printf("\n - %s: cannot be open, possibly corrupted\n", file_info
->filename); fflush(stdout); } } while (0);
1519 continue;
1520 }
1521 double max_scale_ratio = ccv_min((double)image->rows / size.height, (double)image->cols / size.width)({ typeof ((double)image->rows / size.height) _a = ((double
)image->rows / size.height); typeof ((double)image->cols
/ size.width) _b = ((double)image->cols / size.width); (_a
< _b) ? _a : _b; });
1522 if (max_scale_ratio <= 0.5) // too small to be interesting
1523 continue;
1524 for (q = 0; q < ratio; q++)
1525 if (q < (int)ratio || gsl_rng_uniform(rng) <= ratio - (int)ratio)
1526 {
1527 FLUSH(CCV_CLI_INFO, " - collect negatives %d%% (%d / %d)", (i + 1) * 100 / negnum, i + 1, negnum)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { for
(_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT;
_CCV_PRINT_LOOP++) printf("\b"); for (_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP
< _CCV_PRINT_COUNT; _CCV_PRINT_LOOP++) printf(" "); for (
_CCV_PRINT_LOOP = 0; _CCV_PRINT_LOOP < _CCV_PRINT_COUNT; _CCV_PRINT_LOOP
++) printf("\b"); _CCV_PRINT_COUNT = printf(" - collect negatives %d%% (%d / %d)"
, (i + 1) * 100 / negnum, i + 1, negnum); fflush(stdout); } }
while (0);
1528 ccv_decimal_pose_t pose;
1529 double scale_ratio = gsl_rng_uniform(rng) * (max_scale_ratio - 0.5) + 0.5;
1530 pose.a = size.width * 0.5 * scale_ratio;
1531 pose.b = size.height * 0.5 * scale_ratio;
1532 pose.x = gsl_rng_uniform_int(rng, ccv_max((int)(image->cols - pose.a * 2 + 1.5), 1)({ typeof ((int)(image->cols - pose.a * 2 + 1.5)) _a = ((int
)(image->cols - pose.a * 2 + 1.5)); typeof (1) _b = (1); (
_a > _b) ? _a : _b; })) + pose.a;
1533 pose.y = gsl_rng_uniform_int(rng, ccv_max((int)(image->rows - pose.b * 2 + 1.5), 1)({ typeof ((int)(image->rows - pose.b * 2 + 1.5)) _a = ((int
)(image->rows - pose.b * 2 + 1.5)); typeof (1) _b = (1); (
_a > _b) ? _a : _b; })) + pose.b;
1534 pose.roll = pose.pitch = pose.yaw = 0;
1535 ccv_dense_matrix_t* feature = _ccv_icf_capture_feature(rng, image, pose, size, margin, deform_angle, deform_scale, deform_shift);
1536 feature->sig = 0;
1537 ccv_array_push(negatives, feature);
1538 ccv_matrix_free(feature);
1539 ++i;
1540 if (i >= negnum)
1541 break;
1542 }
1543 ccv_matrix_free(image);
1544 }
1545 }
1546 PRINT(CCV_CLI_INFO, "\n")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("\n"); fflush(stdout); } } while (0);
1547 return negatives;
1548}
1549
1550#ifdef USE_SANITY_ASSERTION
1551static double _ccv_icf_rate_weak_classifier(ccv_icf_decision_tree_t* weak_classifier, ccv_array_t* positives, ccv_array_t* negatives, ccv_icf_example_state_t* example_state)
1552{
1553 int i;
1554 double rate = 0;
1555 for (i = 0; i < positives->rnum + negatives->rnum; i++)
1556 {
1557 ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(i < positives->rnum ? positives : negatives, i < positives->rnum ? i : i - positives->rnum)((void*)(((char*)((i < positives->rnum ? positives : negatives
)->data)) + (size_t)(i < positives->rnum ? positives
: negatives)->rsize * (size_t)(i < positives->rnum ?
i : i - positives->rnum)));
1558 a->data.u8 = (uint8_t*)(a + 1); // re-host the pointer to the right place
1559 ccv_dense_matrix_t* icf = 0;
1560 // we have 1px padding around the image
1561 ccv_icf(a, &icf, 0);
1562 ccv_dense_matrix_t* sat = 0;
1563 ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO);
1564 ccv_matrix_free(icf);
1565 float* ptr = sat->data.f32;
1566 int ch = CCV_GET_CHANNEL(sat->type)((sat->type) & 0xFFF);
1567 if (i < positives->rnum)
1568 {
1569 if (_ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, 1, 1))
1570 {
1571 assert(example_state[i].correct)((void) sizeof ((example_state[i].correct) ? 1 : 0), __extension__
({ if (example_state[i].correct) ; else __assert_fail ("example_state[i].correct"
, "ccv_icf.c", 1571, __extension__ __PRETTY_FUNCTION__); }));
1572 rate += example_state[i].weight;
1573 } else {
1574 assert(!example_state[i].correct)((void) sizeof ((!example_state[i].correct) ? 1 : 0), __extension__
({ if (!example_state[i].correct) ; else __assert_fail ("!example_state[i].correct"
, "ccv_icf.c", 1574, __extension__ __PRETTY_FUNCTION__); }));
1575 }
1576 } else {
1577 if (!_ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, 1, 1))
1578 {
1579 assert(example_state[i].correct)((void) sizeof ((example_state[i].correct) ? 1 : 0), __extension__
({ if (example_state[i].correct) ; else __assert_fail ("example_state[i].correct"
, "ccv_icf.c", 1579, __extension__ __PRETTY_FUNCTION__); }));
1580 rate += example_state[i].weight;
1581 } else {
1582 assert(!example_state[i].correct)((void) sizeof ((!example_state[i].correct) ? 1 : 0), __extension__
({ if (!example_state[i].correct) ; else __assert_fail ("!example_state[i].correct"
, "ccv_icf.c", 1582, __extension__ __PRETTY_FUNCTION__); }));
1583 }
1584 }
1585 ccv_matrix_free(sat);
1586 }
1587 return rate;
1588}
1589#endif
1590#endif
1591
1592ccv_icf_classifier_cascade_t* ccv_icf_classifier_cascade_new(ccv_array_t* posfiles, int posnum, ccv_array_t* bgfiles, int negnum, ccv_array_t* validatefiles, const char* dir, ccv_icf_new_param_t params)
1593{
1594#ifdef HAVE_GSL1
1595 _ccv_icf_check_params(params);
1596 assert(posfiles->rnum > 0)((void) sizeof ((posfiles->rnum > 0) ? 1 : 0), __extension__
({ if (posfiles->rnum > 0) ; else __assert_fail ("posfiles->rnum > 0"
, "ccv_icf.c", 1596, __extension__ __PRETTY_FUNCTION__); }));
1597 assert(bgfiles->rnum > 0)((void) sizeof ((bgfiles->rnum > 0) ? 1 : 0), __extension__
({ if (bgfiles->rnum > 0) ; else __assert_fail ("bgfiles->rnum > 0"
, "ccv_icf.c", 1597, __extension__ __PRETTY_FUNCTION__); }));
1598 assert(posnum > 0 && negnum > 0)((void) sizeof ((posnum > 0 && negnum > 0) ? 1 :
0), __extension__ ({ if (posnum > 0 && negnum >
0) ; else __assert_fail ("posnum > 0 && negnum > 0"
, "ccv_icf.c", 1598, __extension__ __PRETTY_FUNCTION__); }));
1599 PRINT(CCV_CLI_INFO, "with %d positive examples and %d negative examples\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("with %d positive examples and %d negative examples\n" "positive examples are going to be collected from %d positive images\n"
"negative examples are are going to be collected from %d background images\n"
, posnum, negnum, posfiles->rnum, bgfiles->rnum); fflush
(stdout); } } while (0)
1600 "positive examples are going to be collected from %d positive images\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("with %d positive examples and %d negative examples\n" "positive examples are going to be collected from %d positive images\n"
"negative examples are are going to be collected from %d background images\n"
, posnum, negnum, posfiles->rnum, bgfiles->rnum); fflush
(stdout); } } while (0)
1601 "negative examples are are going to be collected from %d background images\n",do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("with %d positive examples and %d negative examples\n" "positive examples are going to be collected from %d positive images\n"
"negative examples are are going to be collected from %d background images\n"
, posnum, negnum, posfiles->rnum, bgfiles->rnum); fflush
(stdout); } } while (0)
1602 posnum, negnum, posfiles->rnum, bgfiles->rnum)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("with %d positive examples and %d negative examples\n" "positive examples are going to be collected from %d positive images\n"
"negative examples are are going to be collected from %d background images\n"
, posnum, negnum, posfiles->rnum, bgfiles->rnum); fflush
(stdout); } } while (0);
1603 PRINT(CCV_CLI_INFO, "use color? %s\n", params.grayscale ? "no" : "yes")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("use color? %s\n", params.grayscale ? "no" : "yes"); fflush(
stdout); } } while (0);
1604 PRINT(CCV_CLI_INFO, "feature pool size : %d\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1605 "weak classifier count : %d\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1606 "soft cascade acceptance : %lf\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1607 "minimum dimension of ICF feature : %d\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1608 "number of bootstrap : %d\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1609 "distortion on translation : %f\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1610 "distortion on rotation : %f\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1611 "distortion on scale : %f\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1612 "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1613 "------------------------\n",do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0)
1614 params.feature_size, params.weak_classifier, params.acceptance, params.min_dimension, params.bootstrap, params.deform_shift, params.deform_angle, params.deform_scale, params.size.width, params.size.height, params.margin.left, params.margin.top, params.margin.right, params.margin.bottom)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n"
"minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n"
"distortion on translation : %f\n" "distortion on rotation : %f\n"
"distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", params.feature_size, params.weak_classifier
, params.acceptance, params.min_dimension, params.bootstrap, params
.deform_shift, params.deform_angle, params.deform_scale, params
.size.width, params.size.height, params.margin.left, params.margin
.top, params.margin.right, params.margin.bottom); fflush(stdout
); } } while (0);
1615 gsl_rng_env_setup();
1616 gsl_rng* rng = gsl_rng_alloc(gsl_rng_default);
1617 // we will keep all states inside this structure for easier save / resume across process
1618 // this should work better than ad-hoc one we used in DPM / BBF implementation
1619 ccv_icf_classifier_cascade_state_t z;
1620 z.params = params;
1621 ccv_function_state_begin(_ccv_icf_read_classifier_cascade_state, z, dir)(_ccv_icf_read_classifier_cascade_state)((dir), &(z)); switch
((z).line_no) { case 0:;;
1622 z.classifier->grayscale = params.grayscale;
1623 z.size = params.size;
1624 z.margin = params.margin;
1625 z.classifier->size = ccv_size(z.size.width + z.margin.left + z.margin.right, z.size.height + z.margin.top + z.margin.bottom);
1626 z.features = (ccv_icf_feature_t*)ccmallocmalloc(sizeof(ccv_icf_feature_t) * params.feature_size);
1627 // generate random features
1628 for (z.i = 0; z.i < params.feature_size; z.i++)
1629 _ccv_icf_randomize_feature(rng, z.classifier->size, params.min_dimension, z.features + z.i, params.grayscale);
1630 z.x.features = 0;
1631 ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir)do { (z).line_no = 1631; (_ccv_icf_write_classifier_cascade_state
)(&(z), (dir)); case 1631:; } while (0);
1632 z.positives = _ccv_icf_collect_positives(rng, z.size, z.margin, posfiles, posnum, params.deform_angle, params.deform_scale, params.deform_shift, params.grayscale);
1633 z.x.positives = 0;
1634 ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir)do { (z).line_no = 1634; (_ccv_icf_write_classifier_cascade_state
)(&(z), (dir)); case 1634:; } while (0);
1635 z.negatives = _ccv_icf_collect_negatives(rng, z.size, z.margin, bgfiles, negnum, params.deform_angle, params.deform_scale, params.deform_shift, params.grayscale);
1636 z.x.negatives = 0;
1637 ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir)do { (z).line_no = 1637; (_ccv_icf_write_classifier_cascade_state
)(&(z), (dir)); case 1637:; } while (0);
1638 for (z.bootstrap = 0; z.bootstrap <= params.bootstrap; z.bootstrap++)
1639 {
1640 z.example_state = (ccv_icf_example_state_t*)ccmallocmalloc(sizeof(ccv_icf_example_state_t) * (z.negatives->rnum + z.positives->rnum));
1641 memset(z.example_state, 0, sizeof(ccv_icf_example_state_t) * (z.negatives->rnum + z.positives->rnum));
1642 for (z.i = 0; z.i < z.positives->rnum + z.negatives->rnum; z.i++)
1643 z.example_state[z.i].weight = (z.i < z.positives->rnum) ? 0.5 / z.positives->rnum : 0.5 / z.negatives->rnum;
1644 z.x.example_state = 0;
1645 ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir)do { (z).line_no = 1645; (_ccv_icf_write_classifier_cascade_state
)(&(z), (dir)); case 1645:; } while (0);
1646 z.precomputed = _ccv_icf_precompute_features(z.features, params.feature_size, z.positives, z.negatives);
1647 z.x.precomputed = 0;
1648 ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir)do { (z).line_no = 1648; (_ccv_icf_write_classifier_cascade_state
)(&(z), (dir)); case 1648:; } while (0);
1649 for (z.i = 0; z.i < params.weak_classifier; z.i++)
1650 {
1651 z.classifier->count = z.i + 1;
1652 PRINT(CCV_CLI_INFO, " - boost weak classifier %d of %d\n", z.i + 1, params.weak_classifier)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - boost weak classifier %d of %d\n", z.i + 1, params.weak_classifier
); fflush(stdout); } } while (0);
1653 int j;
1654 ccv_icf_decision_tree_t weak_classifier;
1655 double rate = _ccv_icf_find_best_weak_classifier(z.features, params.feature_size, z.positives, z.negatives, z.precomputed, z.example_state, &weak_classifier);
1656 assert(rate > 0.5)((void) sizeof ((rate > 0.5) ? 1 : 0), __extension__ ({ if
(rate > 0.5) ; else __assert_fail ("rate > 0.5", "ccv_icf.c"
, 1656, __extension__ __PRETTY_FUNCTION__); })); // it has to be better than random chance
1657#ifdef USE_SANITY_ASSERTION
1658 double confirm_rate = _ccv_icf_rate_weak_classifier(&weak_classifier, z.positives, z.negatives, z.example_state);
1659#endif
1660 double alpha = sqrt((1 - rate) / rate);
1661 double beta = 1.0 / alpha;
1662 double c = log(rate / (1 - rate));
1663 weak_classifier.weigh[0] = -c;
1664 weak_classifier.weigh[1] = c;
1665 weak_classifier.threshold = 0;
1666 double reweigh = 0;
1667 for (j = 0; j < z.positives->rnum + z.negatives->rnum; j++)
1668 {
1669 z.example_state[j].weight *= (z.example_state[j].correct) ? alpha : beta;
1670 z.example_state[j].rate += weak_classifier.weigh[!((j < z.positives->rnum) ^ z.example_state[j].correct)];
1671 reweigh += z.example_state[j].weight;
1672 }
1673 reweigh = 1.0 / reweigh;
1674#ifdef USE_SANITY_ASSERTION
1675 PRINT(CCV_CLI_INFO, " - on all examples, best feature at rate %lf, confirm rate %lf\n", rate, confirm_rate)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - on all examples, best feature at rate %lf, confirm rate %lf\n"
, rate, confirm_rate); fflush(stdout); } } while (0);
1676#else
1677 PRINT(CCV_CLI_INFO, " - on all examples, best feature at rate %lf\n", rate)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - on all examples, best feature at rate %lf\n", rate); fflush
(stdout); } } while (0);
1678#endif
1679 // balancing the weight to sum 1.0
1680 for (j = 0; j < z.positives->rnum + z.negatives->rnum; j++)
1681 z.example_state[j].weight *= reweigh;
1682 z.classifier->weak_classifiers[z.i] = weak_classifier;
1683 // compute the threshold at given acceptance
1684 float threshold = z.example_state[0].rate;
1685 for (j = 1; j < z.positives->rnum; j++)
1686 if (z.example_state[j].rate < threshold)
1687 threshold = z.example_state[j].rate;
1688 int true_positives = 0, false_positives = 0;
1689 for (j = 0; j < z.positives->rnum; j++)
1690 if (z.example_state[j].rate >= threshold)
1691 ++true_positives;
1692 for (j = z.positives->rnum; j < z.positives->rnum + z.negatives->rnum; j++)
1693 if (z.example_state[j].rate >= threshold)
1694 ++false_positives;
1695 PRINT(CCV_CLI_INFO, " - at threshold %f, true positive rate: %f%%, false positive rate: %f%% (%d)\n", threshold, (float)true_positives * 100 / z.positives->rnum, (float)false_positives * 100 / z.negatives->rnum, false_positives)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - at threshold %f, true positive rate: %f%%, false positive rate: %f%% (%d)\n"
, threshold, (float)true_positives * 100 / z.positives->rnum
, (float)false_positives * 100 / z.negatives->rnum, false_positives
); fflush(stdout); } } while (0);
1696 PRINT(CCV_CLI_INFO, " - first feature :\n")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - first feature :\n"); fflush(stdout); } } while (0);
1697 for (j = 0; j < weak_classifier.features[0].count; j++)
1698 PRINT(CCV_CLI_INFO, " - %d - (%d, %d) - (%d, %d)\n", weak_classifier.features[0].channel[j], weak_classifier.features[0].sat[j * 2].x, weak_classifier.features[0].sat[j * 2].y, weak_classifier.features[0].sat[j * 2 + 1].x, weak_classifier.features[0].sat[j * 2 + 1].y)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - %d - (%d, %d) - (%d, %d)\n", weak_classifier.features[0]
.channel[j], weak_classifier.features[0].sat[j * 2].x, weak_classifier
.features[0].sat[j * 2].y, weak_classifier.features[0].sat[j *
2 + 1].x, weak_classifier.features[0].sat[j * 2 + 1].y); fflush
(stdout); } } while (0);
1699 if (weak_classifier.pass & 0x2)
1700 {
1701 PRINT(CCV_CLI_INFO, " - second feature, on left :\n")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - second feature, on left :\n"); fflush(stdout); } } while
(0);
1702 for (j = 0; j < weak_classifier.features[1].count; j++)
1703 PRINT(CCV_CLI_INFO, " - | - %d - (%d, %d) - (%d, %d)\n", weak_classifier.features[1].channel[j], weak_classifier.features[1].sat[j * 2].x, weak_classifier.features[1].sat[j * 2].y, weak_classifier.features[1].sat[j * 2 + 1].x, weak_classifier.features[1].sat[j * 2 + 1].y)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - | - %d - (%d, %d) - (%d, %d)\n", weak_classifier.features
[1].channel[j], weak_classifier.features[1].sat[j * 2].x, weak_classifier
.features[1].sat[j * 2].y, weak_classifier.features[1].sat[j *
2 + 1].x, weak_classifier.features[1].sat[j * 2 + 1].y); fflush
(stdout); } } while (0);
1704 }
1705 if (weak_classifier.pass & 0x1)
1706 {
1707 PRINT(CCV_CLI_INFO, " - second feature, on right :\n")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - second feature, on right :\n"); fflush(stdout); } } while
(0);
1708 for (j = 0; j < weak_classifier.features[2].count; j++)
1709 PRINT(CCV_CLI_INFO, " - | - %d - (%d, %d) - (%d, %d)\n", weak_classifier.features[2].channel[j], weak_classifier.features[2].sat[j * 2].x, weak_classifier.features[2].sat[j * 2].y, weak_classifier.features[2].sat[j * 2 + 1].x, weak_classifier.features[2].sat[j * 2 + 1].y)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - | - %d - (%d, %d) - (%d, %d)\n", weak_classifier.features
[2].channel[j], weak_classifier.features[2].sat[j * 2].x, weak_classifier
.features[2].sat[j * 2].y, weak_classifier.features[2].sat[j *
2 + 1].x, weak_classifier.features[2].sat[j * 2 + 1].y); fflush
(stdout); } } while (0);
1710 }
1711 z.classifier->count = z.i + 1; // update count
1712 z.classifier->size = ccv_size(z.size.width + z.margin.left + z.margin.right, z.size.height + z.margin.top + z.margin.bottom);
1713 z.classifier->margin = z.margin;
1714 if (z.i + 1 < params.weak_classifier)
1715 {
1716 z.x.example_state = 0;
1717 z.x.classifier = 0;
1718 ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir)do { (z).line_no = 1718; (_ccv_icf_write_classifier_cascade_state
)(&(z), (dir)); case 1718:; } while (0);
1719 }
1720 }
1721 if (z.bootstrap < params.bootstrap) // collecting negatives, again
1722 {
1723 // free expensive memory
1724 ccfreefree(z.example_state);
1725 z.example_state = 0;
1726 ccfreefree(z.precomputed);
1727 z.precomputed = 0;
1728 _ccv_icf_classifier_cascade_soft_with_validates(z.positives, z.classifier, 1); // assuming perfect score, what's the soft cascading will be
1729 int exists = z.negatives->rnum;
1730 int spread_policy = z.bootstrap < 2; // we don't spread bootstrapping anymore after the first two bootstrappings
1731 // try to boostrap half negatives from perfect scoring
1732 _ccv_icf_bootstrap_negatives(z.classifier, z.negatives, rng, bgfiles, (negnum + 1) / 2, params.grayscale, spread_policy, params.detector);
1733 int leftover = negnum - (z.negatives->rnum - exists);
1734 if (leftover > 0)
1735 {
1736 // if we cannot get enough negative examples, now will use the validates data set to extract more
1737 ccv_array_t* validates = _ccv_icf_collect_validates(rng, z.size, z.margin, validatefiles, params.grayscale);
1738 _ccv_icf_classifier_cascade_soft_with_validates(validates, z.classifier, params.acceptance);
1739 ccv_array_free(validates);
1740 _ccv_icf_bootstrap_negatives(z.classifier, z.negatives, rng, bgfiles, leftover, params.grayscale, spread_policy, params.detector);
1741 }
1742 PRINT(CCV_CLI_INFO, " - after %d bootstrapping, learn with %d positives and %d negatives\n", z.bootstrap + 1, z.positives->rnum, z.negatives->rnum)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
(" - after %d bootstrapping, learn with %d positives and %d negatives\n"
, z.bootstrap + 1, z.positives->rnum, z.negatives->rnum
); fflush(stdout); } } while (0);
1743 z.classifier->count = 0; // reset everything
1744 z.x.negatives = 0;
1745 } else {
1746 z.x.example_state = 0;
1747 z.x.classifier = 0;
1748 ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir)do { (z).line_no = 1748; (_ccv_icf_write_classifier_cascade_state
)(&(z), (dir)); case 1748:; } while (0);
1749 }
1750 }
1751 if (z.precomputed)
1752 ccfreefree(z.precomputed);
1753 if (z.example_state)
1754 ccfreefree(z.example_state);
1755 ccfreefree(z.features);
1756 ccv_array_free(z.positives);
1757 ccv_array_free(z.negatives);
1758 gsl_rng_free(rng);
1759 ccv_function_state_finish()};
1760 return z.classifier;
1761#else
1762 assert(0 && "ccv_icf_classifier_cascade_new requires GSL library support")((void) sizeof ((0 && "ccv_icf_classifier_cascade_new requires GSL library support"
) ? 1 : 0), __extension__ ({ if (0 && "ccv_icf_classifier_cascade_new requires GSL library support"
) ; else __assert_fail ("0 && \"ccv_icf_classifier_cascade_new requires GSL library support\""
, "ccv_icf.c", 1762, __extension__ __PRETTY_FUNCTION__); }));
1763 return 0;
1764#endif
1765}
1766
1767void ccv_icf_classifier_cascade_soft(ccv_icf_classifier_cascade_t* cascade, ccv_array_t* posfiles, double acceptance)
1768{
1769#ifdef HAVE_GSL1
1770 PRINT(CCV_CLI_INFO, "with %d positive examples\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("with %d positive examples\n" "going to accept %.2lf%% positive examples\n"
, posfiles->rnum, acceptance * 100); fflush(stdout); } } while
(0)
1
Assuming the condition is false
→
2
←
Taking false branch
→
3
←
Loop condition is false. Exiting loop
→
1771 "going to accept %.2lf%% positive examples\n",do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("with %d positive examples\n" "going to accept %.2lf%% positive examples\n"
, posfiles->rnum, acceptance * 100); fflush(stdout); } } while
(0)
1772 posfiles->rnum, acceptance * 100)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("with %d positive examples\n" "going to accept %.2lf%% positive examples\n"
, posfiles->rnum, acceptance * 100); fflush(stdout); } } while
(0);
1773 ccv_size_t size = ccv_size(cascade->size.width - cascade->margin.left - cascade->margin.right, cascade->size.height - cascade->margin.top - cascade->margin.bottom);
1774 PRINT(CCV_CLI_INFO, "use color? %s\n", cascade->grayscale ? "no" : "yes")do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("use color? %s\n", cascade->grayscale ? "no" : "yes"); fflush
(stdout); } } while (0);
4
←
Assuming the condition is false
→
5
←
Taking false branch
→
6
←
Loop condition is false. Exiting loop
→
1775 PRINT(CCV_CLI_INFO, "compute soft cascading thresholds for ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("compute soft cascading thresholds for ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", size.width, size.height, cascade
->margin.left, cascade->margin.top, cascade->margin.
right, cascade->margin.bottom); fflush(stdout); } } while (
0)
7
←
Assuming the condition is false
→
8
←
Taking false branch
→
9
←
Loop condition is false. Exiting loop
→
1776 "------------------------\n",do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("compute soft cascading thresholds for ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", size.width, size.height, cascade
->margin.left, cascade->margin.top, cascade->margin.
right, cascade->margin.bottom); fflush(stdout); } } while (
0)
1777 size.width, size.height, cascade->margin.left, cascade->margin.top, cascade->margin.right, cascade->margin.bottom)do { if ((CCV_CLI_INFO & ccv_cli_get_output_levels())) { printf
("compute soft cascading thresholds for ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n"
"------------------------\n", size.width, size.height, cascade
->margin.left, cascade->margin.top, cascade->margin.
right, cascade->margin.bottom); fflush(stdout); } } while (
0);
1778 gsl_rng_env_setup();
1779 gsl_rng* rng = gsl_rng_alloc(gsl_rng_default);
1780 /* collect positives */
1781 double weigh[2] = {
1782 0, 0
1783 };
1784 int i;
1785 for (i = 0; i < cascade->count; i++)
10
←
Assuming 'i' is < field 'count'
→
11
←
Loop condition is true. Entering loop body
→
12
←
Assuming 'i' is >= field 'count'
→
13
←
Loop condition is false. Execution continues on line 1791
→
1786 {
1787 ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + i;
1788 weigh[0] += weak_classifier->weigh[0];
1789 weigh[1] += weak_classifier->weigh[1];
1790 }
1791 weigh[0] = 1 / fabs(weigh[0]), weigh[1] = 1 / fabs(weigh[1]);
1792 for (i = 0; i < cascade->count; i++)
14
←
Loop condition is true. Entering loop body
→
15
←
Loop condition is false. Execution continues on line 1798
→
1793 {
1794 ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + i;
1795 weak_classifier->weigh[0] = weak_classifier->weigh[0] * weigh[0];
1796 weak_classifier->weigh[1] = weak_classifier->weigh[1] * weigh[1];
1797 }
1798 ccv_array_t* validates = _ccv_icf_collect_validates(rng, size, cascade->margin, posfiles, cascade->grayscale);
1799 /* compute soft cascading thresholds */
1800 _ccv_icf_classifier_cascade_soft_with_validates(validates, cascade, acceptance);
16
←
Calling '_ccv_icf_classifier_cascade_soft_with_validates'
→
1801 ccv_array_free(validates);
1802 gsl_rng_free(rng);
1803#else
1804 assert(0 && "ccv_icf_classifier_cascade_soft requires GSL library support")((void) sizeof ((0 && "ccv_icf_classifier_cascade_soft requires GSL library support"
) ? 1 : 0), __extension__ ({ if (0 && "ccv_icf_classifier_cascade_soft requires GSL library support"
) ; else __assert_fail ("0 && \"ccv_icf_classifier_cascade_soft requires GSL library support\""
, "ccv_icf.c", 1804, __extension__ __PRETTY_FUNCTION__); }));
1805#endif
1806}
1807
1808static void _ccv_icf_read_classifier_cascade_with_fd(FILE* r, ccv_icf_classifier_cascade_t* cascade)
1809{
1810 cascade->type = CCV_ICF_CLASSIFIER_TYPE_A;
1811 fscanf(r, "%d %d %d %d", &cascade->count, &cascade->size.width, &cascade->size.height, &cascade->grayscale);
1812 fscanf(r, "%d %d %d %d", &cascade->margin.left, &cascade->margin.top, &cascade->margin.right, &cascade->margin.bottom);
1813 cascade->weak_classifiers = (ccv_icf_decision_tree_t*)ccmallocmalloc(sizeof(ccv_icf_decision_tree_t) * cascade->count);
1814 int i, q;
1815 for (i = 0; i < cascade->count; i++)
1816 {
1817 ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + i;
1818 fscanf(r, "%u %a %a %a", &weak_classifier->pass, &weak_classifier->weigh[0], &weak_classifier->weigh[1], &weak_classifier->threshold);
1819 fscanf(r, "%d %a", &weak_classifier->features[0].count, &weak_classifier->features[0].beta);
1820 for (q = 0; q < weak_classifier->features[0].count; q++)
1821 fscanf(r, "%d %a %d %d %d %d", &weak_classifier->features[0].channel[q], &weak_classifier->features[0].alpha[q], &weak_classifier->features[0].sat[q * 2].x, &weak_classifier->features[0].sat[q * 2].y, &weak_classifier->features[0].sat[q * 2 + 1].x, &weak_classifier->features[0].sat[q * 2 + 1].y);
1822 if (weak_classifier->pass & 0x2)
1823 {
1824 fscanf(r, "%d %a", &weak_classifier->features[1].count, &weak_classifier->features[1].beta);
1825 for (q = 0; q < weak_classifier->features[1].count; q++)
1826 fscanf(r, "%d %a %d %d %d %d", &weak_classifier->features[1].channel[q], &weak_classifier->features[1].alpha[q], &weak_classifier->features[1].sat[q * 2].x, &weak_classifier->features[1].sat[q * 2].y, &weak_classifier->features[1].sat[q * 2 + 1].x, &weak_classifier->features[1].sat[q * 2 + 1].y);
1827 }
1828 if (weak_classifier->pass & 0x1)
1829 {
1830 fscanf(r, "%d %a", &weak_classifier->features[2].count, &weak_classifier->features[2].beta);
1831 for (q = 0; q < weak_classifier->features[2].count; q++)
1832 fscanf(r, "%d %a %d %d %d %d", &weak_classifier->features[2].channel[q], &weak_classifier->features[2].alpha[q], &weak_classifier->features[2].sat[q * 2].x, &weak_classifier->features[2].sat[q * 2].y, &weak_classifier->features[2].sat[q * 2 + 1].x, &weak_classifier->features[2].sat[q * 2 + 1].y);
1833 }
1834 }
1835}
1836
1837static void _ccv_icf_write_classifier_cascade_with_fd(ccv_icf_classifier_cascade_t* cascade, FILE* w)
1838{
1839 int i, q;
1840 fprintf(w, "%d %d %d %d\n", cascade->count, cascade->size.width, cascade->size.height, cascade->grayscale);
1841 fprintf(w, "%d %d %d %d\n", cascade->margin.left, cascade->margin.top, cascade->margin.right, cascade->margin.bottom);
1842 for (i = 0; i < cascade->count; i++)
1843 {
1844 ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + i;
1845 fprintf(w, "%u %a %a %a\n", weak_classifier->pass, weak_classifier->weigh[0], weak_classifier->weigh[1], weak_classifier->threshold);
1846 fprintf(w, "%d %a\n", weak_classifier->features[0].count, weak_classifier->features[0].beta);
1847 for (q = 0; q < weak_classifier->features[0].count; q++)
1848 fprintf(w, "%d %a\n%d %d %d %d\n", weak_classifier->features[0].channel[q], weak_classifier->features[0].alpha[q], weak_classifier->features[0].sat[q * 2].x, weak_classifier->features[0].sat[q * 2].y, weak_classifier->features[0].sat[q * 2 + 1].x, weak_classifier->features[0].sat[q * 2 + 1].y);
1849 if (weak_classifier->pass & 0x2)
1850 {
1851 fprintf(w, "%d %a\n", weak_classifier->features[1].count, weak_classifier->features[1].beta);
1852 for (q = 0; q < weak_classifier->features[1].count; q++)
1853 fprintf(w, "%d %a\n%d %d %d %d\n", weak_classifier->features[1].channel[q], weak_classifier->features[1].alpha[q], weak_classifier->features[1].sat[q * 2].x, weak_classifier->features[1].sat[q * 2].y, weak_classifier->features[1].sat[q * 2 + 1].x, weak_classifier->features[1].sat[q * 2 + 1].y);
1854 }
1855 if (weak_classifier->pass & 0x1)
1856 {
1857 fprintf(w, "%d %a\n", weak_classifier->features[2].count, weak_classifier->features[2].beta);
1858 for (q = 0; q < weak_classifier->features[2].count; q++)
1859 fprintf(w, "%d %a\n%d %d %d %d\n", weak_classifier->features[2].channel[q], weak_classifier->features[2].alpha[q], weak_classifier->features[2].sat[q * 2].x, weak_classifier->features[2].sat[q * 2].y, weak_classifier->features[2].sat[q * 2 + 1].x, weak_classifier->features[2].sat[q * 2 + 1].y);
1860 }
1861 }
1862}
1863
1864ccv_icf_classifier_cascade_t* ccv_icf_read_classifier_cascade(const char* filename)
1865{
1866 FILE* r = fopen(filename, "r");
1867 ccv_icf_classifier_cascade_t* cascade = 0;
1868 if (r)
1869 {
1870 cascade = (ccv_icf_classifier_cascade_t*)ccmallocmalloc(sizeof(ccv_icf_classifier_cascade_t));
1871 _ccv_icf_read_classifier_cascade_with_fd(r, cascade);
1872 fclose(r);
1873 }
1874 return cascade;
1875}
1876
1877void ccv_icf_write_classifier_cascade(ccv_icf_classifier_cascade_t* cascade, const char* filename)
1878{
1879 FILE* w = fopen(filename, "w+");
1880 if (w)
1881 {
1882 _ccv_icf_write_classifier_cascade_with_fd(cascade, w);
1883 fclose(w);
1884 }
1885}
1886
1887void ccv_icf_classifier_cascade_free(ccv_icf_classifier_cascade_t* classifier)
1888{
1889 ccfreefree(classifier->weak_classifiers);
1890 ccfreefree(classifier);
1891}
1892
1893ccv_icf_multiscale_classifier_cascade_t* ccv_icf_read_multiscale_classifier_cascade(const char* directory)
1894{
1895 char filename[1024];
1896 snprintf(filename, 1024, "%s/multiscale", directory);
1897 FILE* r = fopen(filename, "r");
1898 if (r)
1899 {
1900 int octave = 0, count = 0, grayscale = 0;
1901 fscanf(r, "%d %d %d", &octave, &count, &grayscale);
1902 fclose(r);
1903 ccv_icf_multiscale_classifier_cascade_t* classifier = (ccv_icf_multiscale_classifier_cascade_t*)ccmallocmalloc(sizeof(ccv_icf_multiscale_classifier_cascade_t) + sizeof(ccv_icf_classifier_cascade_t) * count);
1904 classifier->type = CCV_ICF_CLASSIFIER_TYPE_B;
1905 classifier->octave = octave;
1906 classifier->count = count;
1907 classifier->grayscale = grayscale;
1908 classifier->cascade = (ccv_icf_classifier_cascade_t*)(classifier + 1);
1909 int i;
1910 for (i = 0; i < count; i++)
1911 {
1912 snprintf(filename, 1024, "%s/cascade-%d", directory, i + 1);
1913 r = fopen(filename, "r");
1914 if (r)
1915 {
1916 ccv_icf_classifier_cascade_t* cascade = classifier->cascade + i;
1917 _ccv_icf_read_classifier_cascade_with_fd(r, cascade);
1918 fclose(r);
1919 }
1920 }
1921 return classifier;
1922 }
1923 return 0;
1924}
1925
1926void ccv_icf_write_multiscale_classifier_cascade(ccv_icf_multiscale_classifier_cascade_t* classifier, const char* directory)
1927{
1928 char filename[1024];
1929 snprintf(filename, 1024, "%s/multiscale", directory);
1930 FILE* w = fopen(filename, "w+");
1931 fprintf(w, "%d %d %d\n", classifier->octave, classifier->count, classifier->grayscale);
1932 fclose(w);
1933 int i;
1934 for (i = 0; i < classifier->count; i++)
1935 {
1936 snprintf(filename, 1024, "%s/cascade-%d", directory, i + 1);
1937 w = fopen(filename, "w+");
1938 _ccv_icf_write_classifier_cascade_with_fd(classifier->cascade + i, w);
1939 fclose(w);
1940 }
1941}
1942
1943void ccv_icf_multiscale_classifier_cascade_free(ccv_icf_multiscale_classifier_cascade_t* classifier)
1944{
1945 int i;
1946 for (i = 0; i < classifier->count; i++)
1947 ccfreefree(classifier->cascade[i].weak_classifiers);
1948 ccfreefree(classifier);
1949}
1950
1951static int _ccv_is_equal_same_class(const void* _r1, const void* _r2, void* data)
1952{
1953 const ccv_comp_t* r1 = (const ccv_comp_t*)_r1;
1954 const ccv_comp_t* r2 = (const ccv_comp_t*)_r2;
1955 int distance = (int)(ccv_min(r1->rect.width, r1->rect.height)({ typeof (r1->rect.width) _a = (r1->rect.width); typeof
(r1->rect.height) _b = (r1->rect.height); (_a < _b)
? _a : _b; }) * 0.25 + 0.5);
1956
1957 return r2->classification.id == r1->classification.id &&
1958 r2->rect.x <= r1->rect.x + distance &&
1959 r2->rect.x >= r1->rect.x - distance &&
1960 r2->rect.y <= r1->rect.y + distance &&
1961 r2->rect.y >= r1->rect.y - distance &&
1962 r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) &&
1963 (int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width &&
1964 r2->rect.height <= (int)(r1->rect.height * 1.5 + 0.5) &&
1965 (int)(r2->rect.height * 1.5 + 0.5) >= r1->rect.height;
1966}
1967
1968static void _ccv_icf_detect_objects_with_classifier_cascade(ccv_dense_matrix_t* a, ccv_icf_classifier_cascade_t** cascades, int count, ccv_icf_param_t params, ccv_array_t* seq[])
1969{
1970 int i, j, k, q, x, y;
1971 int scale_upto = 1;
1972 for (i = 0; i < count; i++)
1973 scale_upto = ccv_max(scale_upto, (int)(log(ccv_min((double)a->rows / (cascades[i]->size.height - cascades[i]->margin.top - cascades[i]->margin.bottom), (double)a->cols / (cascades[i]->size.width - cascades[i]->margin.left - cascades[i]->margin.right))) / log(2.) - DBL_MIN) + 1)({ typeof (scale_upto) _a = (scale_upto); typeof ((int)(log((
{ typeof ((double)a->rows / (cascades[i]->size.height -
cascades[i]->margin.top - cascades[i]->margin.bottom))
_a = ((double)a->rows / (cascades[i]->size.height - cascades
[i]->margin.top - cascades[i]->margin.bottom)); typeof (
(double)a->cols / (cascades[i]->size.width - cascades[i
]->margin.left - cascades[i]->margin.right)) _b = ((double
)a->cols / (cascades[i]->size.width - cascades[i]->margin
.left - cascades[i]->margin.right)); (_a < _b) ? _a : _b
; })) / log(2.) - 2.2250738585072014e-308) + 1) _b = ((int)(log
(({ typeof ((double)a->rows / (cascades[i]->size.height
- cascades[i]->margin.top - cascades[i]->margin.bottom
)) _a = ((double)a->rows / (cascades[i]->size.height - cascades
[i]->margin.top - cascades[i]->margin.bottom)); typeof (
(double)a->cols / (cascades[i]->size.width - cascades[i
]->margin.left - cascades[i]->margin.right)) _b = ((double
)a->cols / (cascades[i]->size.width - cascades[i]->margin
.left - cascades[i]->margin.right)); (_a < _b) ? _a : _b
; })) / log(2.) - 2.2250738585072014e-308) + 1); (_a > _b)
? _a : _b; });
1974 ccv_dense_matrix_t** pyr = (ccv_dense_matrix_t**)alloca(sizeof(ccv_dense_matrix_t*) * scale_upto)__builtin_alloca (sizeof(ccv_dense_matrix_t*) * scale_upto);
1975 pyr[0] = a;
1976 for (i = 1; i < scale_upto; i++)
1977 {
1978 pyr[i] = 0;
1979 ccv_sample_down(pyr[i - 1], &pyr[i], 0, 0, 0);
1980 }
1981 for (i = 0; i < scale_upto; i++)
1982 {
1983 // run it
1984 for (j = 0; j < count; j++)
1985 {
1986 double scale_ratio = pow(2., 1. / (params.interval + 1));
1987 double scale = 1;
1988 ccv_icf_classifier_cascade_t* cascade = cascades[j];
1989 for (k = 0; k <= params.interval; k++)
1990 {
1991 int rows = (int)(pyr[i]->rows / scale + 0.5);
1992 int cols = (int)(pyr[i]->cols / scale + 0.5);
1993 if (rows < cascade->size.height || cols < cascade->size.width)
1994 break;
1995 ccv_dense_matrix_t* image = k == 0 ? pyr[i] : 0;
1996 if (k > 0)
1997 ccv_resample(pyr[i], &image, 0, (double)rows / (double)pyr[i]->rows, (double)cols / (double)pyr[i]->cols, CCV_INTER_AREA);
1998 ccv_dense_matrix_t* bordered = 0;
1999 ccv_border(image, (ccv_matrix_t**)&bordered, 0, cascade->margin);
2000 if (k > 0)
2001 ccv_matrix_free(image);
2002 rows = bordered->rows;
2003 cols = bordered->cols;
2004 ccv_dense_matrix_t* icf = 0;
2005 ccv_icf(bordered, &icf, 0);
2006 ccv_matrix_free(bordered);
2007 ccv_dense_matrix_t* sat = 0;
2008 ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO);
2009 ccv_matrix_free(icf);
2010 int ch = CCV_GET_CHANNEL(sat->type)((sat->type) & 0xFFF);
2011 float* ptr = sat->data.f32;
2012 for (y = 0; y < rows; y += params.step_through)
2013 {
2014 if (y >= sat->rows - cascade->size.height - 1)
2015 break;
2016 for (x = 0; x < cols; x += params.step_through)
2017 {
2018 if (x >= sat->cols - cascade->size.width - 1)
2019 break;
2020 int pass = 1;
2021 float sum = 0;
2022 for (q = 0; q < cascade->count; q++)
2023 {
2024 ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + q;
2025 int c = _ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, x, 0);
2026 sum += weak_classifier->weigh[c];
2027 if (sum < weak_classifier->threshold)
2028 {
2029 pass = 0;
2030 break;
2031 }
2032 }
2033 if (pass)
2034 {
2035 ccv_comp_t comp;
2036 comp.rect = ccv_rect((int)((x + 0.5) * scale * (1 << i) - 0.5), (int)((y + 0.5) * scale * (1 << i) - 0.5), (cascade->size.width - cascade->margin.left - cascade->margin.right) * scale * (1 << i), (cascade->size.height - cascade->margin.top - cascade->margin.bottom) * scale * (1 << i));
2037 comp.neighbors = 1;
2038 comp.classification.id = j + 1;
2039 comp.classification.confidence = sum;
2040 ccv_array_push(seq[j], &comp);
2041 }
2042 }
2043 ptr += sat->cols * ch * params.step_through;
2044 }
2045 ccv_matrix_free(sat);
2046 scale *= scale_ratio;
2047 }
2048 }
2049 }
2050
2051 for (i = 1; i < scale_upto; i++)
2052 ccv_matrix_free(pyr[i]);
2053}
2054
2055static void _ccv_icf_detect_objects_with_multiscale_classifier_cascade(ccv_dense_matrix_t* a, ccv_icf_multiscale_classifier_cascade_t** multiscale_cascade, int count, ccv_icf_param_t params, ccv_array_t* seq[])
2056{
2057 int i, j, k, q, x, y, ix, iy, py;
2058 assert(multiscale_cascade[0]->count % multiscale_cascade[0]->octave == 0)((void) sizeof ((multiscale_cascade[0]->count % multiscale_cascade
[0]->octave == 0) ? 1 : 0), __extension__ ({ if (multiscale_cascade
[0]->count % multiscale_cascade[0]->octave == 0) ; else
__assert_fail ("multiscale_cascade[0]->count % multiscale_cascade[0]->octave == 0"
, "ccv_icf.c", 2058, __extension__ __PRETTY_FUNCTION__); }));
2059 ccv_margin_t margin = multiscale_cascade[0]->cascade[multiscale_cascade[0]->count - 1].margin;
2060 for (i = 1; i < count; i++)
2061 {
2062 assert(multiscale_cascade[i]->count % multiscale_cascade[i]->octave == 0)((void) sizeof ((multiscale_cascade[i]->count % multiscale_cascade
[i]->octave == 0) ? 1 : 0), __extension__ ({ if (multiscale_cascade
[i]->count % multiscale_cascade[i]->octave == 0) ; else
__assert_fail ("multiscale_cascade[i]->count % multiscale_cascade[i]->octave == 0"
, "ccv_icf.c", 2062, __extension__ __PRETTY_FUNCTION__); }));
2063 assert(multiscale_cascade[i - 1]->grayscale == multiscale_cascade[i]->grayscale)((void) sizeof ((multiscale_cascade[i - 1]->grayscale == multiscale_cascade
[i]->grayscale) ? 1 : 0), __extension__ ({ if (multiscale_cascade
[i - 1]->grayscale == multiscale_cascade[i]->grayscale)
; else __assert_fail ("multiscale_cascade[i - 1]->grayscale == multiscale_cascade[i]->grayscale"
, "ccv_icf.c", 2063, __extension__ __PRETTY_FUNCTION__); }));
2064 assert(multiscale_cascade[i - 1]->count == multiscale_cascade[i]->count)((void) sizeof ((multiscale_cascade[i - 1]->count == multiscale_cascade
[i]->count) ? 1 : 0), __extension__ ({ if (multiscale_cascade
[i - 1]->count == multiscale_cascade[i]->count) ; else __assert_fail
("multiscale_cascade[i - 1]->count == multiscale_cascade[i]->count"
, "ccv_icf.c", 2064, __extension__ __PRETTY_FUNCTION__); }));
2065 assert(multiscale_cascade[i - 1]->octave == multiscale_cascade[i]->octave)((void) sizeof ((multiscale_cascade[i - 1]->octave == multiscale_cascade
[i]->octave) ? 1 : 0), __extension__ ({ if (multiscale_cascade
[i - 1]->octave == multiscale_cascade[i]->octave) ; else
__assert_fail ("multiscale_cascade[i - 1]->octave == multiscale_cascade[i]->octave"
, "ccv_icf.c", 2065, __extension__ __PRETTY_FUNCTION__); }));
2066 ccv_icf_classifier_cascade_t* cascade = multiscale_cascade[i]->cascade + multiscale_cascade[i]->count - 1;
2067 margin.top = ccv_max(margin.top, cascade->margin.top)({ typeof (margin.top) _a = (margin.top); typeof (cascade->
margin.top) _b = (cascade->margin.top); (_a > _b) ? _a :
_b; });
2068 margin.right = ccv_max(margin.right, cascade->margin.right)({ typeof (margin.right) _a = (margin.right); typeof (cascade
->margin.right) _b = (cascade->margin.right); (_a > _b
) ? _a : _b; });
2069 margin.bottom = ccv_max(margin.bottom, cascade->margin.bottom)({ typeof (margin.bottom) _a = (margin.bottom); typeof (cascade
->margin.bottom) _b = (cascade->margin.bottom); (_a >
_b) ? _a : _b; });
2070 margin.left = ccv_max(margin.left, cascade->margin.left)({ typeof (margin.left) _a = (margin.left); typeof (cascade->
margin.left) _b = (cascade->margin.left); (_a > _b) ? _a
: _b; });
2071 }
2072 int scale_upto = 1;
2073 for (i = 0; i < count; i++)
2074 scale_upto = ccv_max(scale_upto, (int)(log(ccv_min((double)a->rows / (multiscale_cascade[i]->cascade[0].size.height - multiscale_cascade[i]->cascade[0].margin.top - multiscale_cascade[i]->cascade[0].margin.bottom), (double)a->cols / (multiscale_cascade[i]->cascade[0].size.width - multiscale_cascade[i]->cascade[0].margin.left - multiscale_cascade[i]->cascade[0].margin.right))) / log(2.) - DBL_MIN) + 2 - multiscale_cascade[i]->octave)({ typeof (scale_upto) _a = (scale_upto); typeof ((int)(log((
{ typeof ((double)a->rows / (multiscale_cascade[i]->cascade
[0].size.height - multiscale_cascade[i]->cascade[0].margin
.top - multiscale_cascade[i]->cascade[0].margin.bottom)) _a
= ((double)a->rows / (multiscale_cascade[i]->cascade[0
].size.height - multiscale_cascade[i]->cascade[0].margin.top
- multiscale_cascade[i]->cascade[0].margin.bottom)); typeof
((double)a->cols / (multiscale_cascade[i]->cascade[0].
size.width - multiscale_cascade[i]->cascade[0].margin.left
- multiscale_cascade[i]->cascade[0].margin.right)) _b = (
(double)a->cols / (multiscale_cascade[i]->cascade[0].size
.width - multiscale_cascade[i]->cascade[0].margin.left - multiscale_cascade
[i]->cascade[0].margin.right)); (_a < _b) ? _a : _b; })
) / log(2.) - 2.2250738585072014e-308) + 2 - multiscale_cascade
[i]->octave) _b = ((int)(log(({ typeof ((double)a->rows
/ (multiscale_cascade[i]->cascade[0].size.height - multiscale_cascade
[i]->cascade[0].margin.top - multiscale_cascade[i]->cascade
[0].margin.bottom)) _a = ((double)a->rows / (multiscale_cascade
[i]->cascade[0].size.height - multiscale_cascade[i]->cascade
[0].margin.top - multiscale_cascade[i]->cascade[0].margin.
bottom)); typeof ((double)a->cols / (multiscale_cascade[i]
->cascade[0].size.width - multiscale_cascade[i]->cascade
[0].margin.left - multiscale_cascade[i]->cascade[0].margin
.right)) _b = ((double)a->cols / (multiscale_cascade[i]->
cascade[0].size.width - multiscale_cascade[i]->cascade[0].
margin.left - multiscale_cascade[i]->cascade[0].margin.right
)); (_a < _b) ? _a : _b; })) / log(2.) - 2.2250738585072014e-308
) + 2 - multiscale_cascade[i]->octave); (_a > _b) ? _a :
_b; });
2075 ccv_dense_matrix_t** pyr = (ccv_dense_matrix_t**)alloca(sizeof(ccv_dense_matrix_t*) * scale_upto)__builtin_alloca (sizeof(ccv_dense_matrix_t*) * scale_upto);
2076 pyr[0] = a;
2077 for (i = 1; i < scale_upto; i++)
2078 {
2079 pyr[i] = 0;
2080 ccv_sample_down(pyr[i - 1], &pyr[i], 0, 0, 0);
2081 }
2082 for (i = 0; i < scale_upto; i++)
2083 {
2084 ccv_dense_matrix_t* bordered = 0;
2085 ccv_border(pyr[i], (ccv_matrix_t**)&bordered, 0, margin);
2086 ccv_dense_matrix_t* icf = 0;
2087 ccv_icf(bordered, &icf, 0);
2088 ccv_matrix_free(bordered);
2089 ccv_dense_matrix_t* sat = 0;
2090 ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO);
2091 ccv_matrix_free(icf);
2092 int ch = CCV_GET_CHANNEL(sat->type)((sat->type) & 0xFFF);
2093 assert(CCV_GET_DATA_TYPE(sat->type) == CCV_32F)((void) sizeof ((((sat->type) & 0xFF000) == CCV_32F) ?
1 : 0), __extension__ ({ if (((sat->type) & 0xFF000) ==
CCV_32F) ; else __assert_fail ("CCV_GET_DATA_TYPE(sat->type) == CCV_32F"
, "ccv_icf.c", 2093, __extension__ __PRETTY_FUNCTION__); }));
2094 // run it
2095 for (j = 0; j < count; j++)
2096 {
2097 double scale_ratio = pow(2., (double)multiscale_cascade[j]->octave / multiscale_cascade[j]->count);
2098 int starter = i > 0 ? multiscale_cascade[j]->count - (multiscale_cascade[j]->count / multiscale_cascade[j]->octave) : 0;
2099 double scale = pow(scale_ratio, starter);
2100 for (k = starter; k < multiscale_cascade[j]->count; k++)
2101 {
2102 ccv_icf_classifier_cascade_t* cascade = multiscale_cascade[j]->cascade + k;
2103 int rows = (int)(pyr[i]->rows / scale + cascade->margin.top + 0.5);
2104 int cols = (int)(pyr[i]->cols / scale + cascade->margin.left + 0.5);
2105 int top = margin.top - cascade->margin.top;
2106 int right = margin.right - cascade->margin.right;
2107 int bottom = margin.bottom - cascade->margin.bottom;
2108 int left = margin.left - cascade->margin.left;
2109 if (sat->rows - top - bottom <= cascade->size.height || sat->cols - left - right <= cascade->size.width)
2110 break;
2111 float* ptr = sat->data.f32 + top * sat->cols * ch;
2112 for (y = 0, iy = py = top; y < rows; y += params.step_through)
2113 {
2114 iy = (int)((y + 0.5) * scale + top);
2115 if (iy >= sat->rows - cascade->size.height - 1)
2116 break;
2117 if (iy > py)
2118 {
2119 ptr += sat->cols * ch * (iy - py);
2120 py = iy;
2121 }
2122 for (x = 0; x < cols; x += params.step_through)
2123 {
2124 ix = (int)((x + 0.5) * scale + left);
2125 if (ix >= sat->cols - cascade->size.width - 1)
2126 break;
2127 int pass = 1;
2128 float sum = 0;
2129 for (q = 0; q < cascade->count; q++)
2130 {
2131 ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + q;
2132 int c = _ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, ix, 0);
2133 sum += weak_classifier->weigh[c];
2134 if (sum < weak_classifier->threshold)
2135 {
2136 pass = 0;
2137 break;
2138 }
2139 }
2140 if (pass)
2141 {
2142 ccv_comp_t comp;
2143 comp.rect = ccv_rect((int)((x + 0.5) * scale * (1 << i)), (int)((y + 0.5) * scale * (1 << i)), (cascade->size.width - cascade->margin.left - cascade->margin.right) << i, (cascade->size.height - cascade->margin.top - cascade->margin.bottom) << i);
2144 comp.neighbors = 1;
2145 comp.classification.id = j + 1;
2146 comp.classification.confidence = sum;
2147 ccv_array_push(seq[j], &comp);
2148 }
2149 }
2150 }
2151 scale *= scale_ratio;
2152 }
2153 }
2154 ccv_matrix_free(sat);
2155 }
2156
2157 for (i = 1; i < scale_upto; i++)
2158 ccv_matrix_free(pyr[i]);
2159}
2160
2161ccv_array_t* ccv_icf_detect_objects(ccv_dense_matrix_t* a, void* cascade, int count, ccv_icf_param_t params)
2162{
2163 assert(count > 0)((void) sizeof ((count > 0) ? 1 : 0), __extension__ ({ if (
count > 0) ; else __assert_fail ("count > 0", "ccv_icf.c"
, 2163, __extension__ __PRETTY_FUNCTION__); }));
2164 int i, j, k;
2165 int type = *(((int**)cascade)[0]);
2166 for (i = 1; i < count; i++)
2167 {
2168 // check all types to be the same
2169 assert(*(((int**)cascade)[i]) == type)((void) sizeof ((*(((int**)cascade)[i]) == type) ? 1 : 0), __extension__
({ if (*(((int**)cascade)[i]) == type) ; else __assert_fail (
"*(((int**)cascade)[i]) == type", "ccv_icf.c", 2169, __extension__
__PRETTY_FUNCTION__); }));
2170 }
2171 ccv_array_t** seq = (ccv_array_t**)alloca(sizeof(ccv_array_t*) * count)__builtin_alloca (sizeof(ccv_array_t*) * count);
2172 for (i = 0; i < count; i++)
2173 seq[i] = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
2174 switch (type)
2175 {
2176 case CCV_ICF_CLASSIFIER_TYPE_A:
2177 _ccv_icf_detect_objects_with_classifier_cascade(a, (ccv_icf_classifier_cascade_t**)cascade, count, params, seq);
2178 break;
2179 case CCV_ICF_CLASSIFIER_TYPE_B:
2180 _ccv_icf_detect_objects_with_multiscale_classifier_cascade(a, (ccv_icf_multiscale_classifier_cascade_t**)cascade, count, params, seq);
2181 break;
2182 }
2183 ccv_array_t* result_seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
2184 ccv_array_t* seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
2185 for (k = 0; k < count; k++)
2186 {
2187 /* the following code from OpenCV's haar feature implementation */
2188 if(params.min_neighbors == 0)
2189 {
2190 for (i = 0; i < seq[k]->rnum; i++)
2191 {
2192 ccv_comp_t* comp = (ccv_comp_t*)ccv_array_get(seq[k], i)((void*)(((char*)((seq[k])->data)) + (size_t)(seq[k])->
rsize * (size_t)(i)));
2193 ccv_array_push(result_seq, comp);
2194 }
2195 } else {
2196 ccv_array_t* idx_seq = 0;
2197 ccv_array_clear(seq2);
2198 // group retrieved rectangles in order to filter out noise
2199 int ncomp = ccv_array_group(seq[k], &idx_seq, _ccv_is_equal_same_class, 0);
2200 ccv_comp_t* comps = (ccv_comp_t*)cccalloccalloc(ncomp + 1, sizeof(ccv_comp_t));
2201
2202 // count number of neighbors
2203 for (i = 0; i < seq[k]->rnum; i++)
2204 {
2205 ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(seq[k], i)((void*)(((char*)((seq[k])->data)) + (size_t)(seq[k])->
rsize * (size_t)(i)));
2206 int idx = *(int*)ccv_array_get(idx_seq, i)((void*)(((char*)((idx_seq)->data)) + (size_t)(idx_seq)->
rsize * (size_t)(i)));
2207
2208 comps[idx].classification.id = r1.classification.id;
2209 if (r1.classification.confidence > comps[idx].classification.confidence || comps[idx].neighbors == 0)
2210 {
2211 comps[idx].rect = r1.rect;
2212 comps[idx].classification.confidence = r1.classification.confidence;
2213 }
2214
2215 ++comps[idx].neighbors;
2216 }
2217
2218 // calculate average bounding box
2219 for (i = 0; i < ncomp; i++)
2220 {
2221 int n = comps[i].neighbors;
2222 if (n >= params.min_neighbors)
2223 ccv_array_push(seq2, comps + i);
2224 }
2225
2226 // filter out large object rectangles contains small object rectangles
2227 for (i = 0; i < seq2->rnum; i++)
2228 {
2229 ccv_comp_t* r2 = (ccv_comp_t*)ccv_array_get(seq2, i)((void*)(((char*)((seq2)->data)) + (size_t)(seq2)->rsize
* (size_t)(i)));
2230 int distance = (int)(ccv_min(r2->rect.width, r2->rect.height)({ typeof (r2->rect.width) _a = (r2->rect.width); typeof
(r2->rect.height) _b = (r2->rect.height); (_a < _b)
? _a : _b; }) * 0.25 + 0.5);
2231 for (j = 0; j < seq2->rnum; j++)
2232 {
2233 ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(seq2, j)((void*)(((char*)((seq2)->data)) + (size_t)(seq2)->rsize
* (size_t)(j)));
2234 if (i != j &&
2235 abs(r1.classification.id) == r2->classification.id &&
2236 r1.rect.x >= r2->rect.x - distance &&
2237 r1.rect.y >= r2->rect.y - distance &&
2238 r1.rect.x + r1.rect.width <= r2->rect.x + r2->rect.width + distance &&
2239 r1.rect.y + r1.rect.height <= r2->rect.y + r2->rect.height + distance &&
2240 // if r1 (the smaller one) is better, mute r2
2241 (r2->classification.confidence <= r1.classification.confidence && r2->neighbors < r1.neighbors))
2242 {
2243 r2->classification.id = -r2->classification.id;
2244 break;
2245 }
2246 }
2247 }
2248
2249 // filter out small object rectangles inside large object rectangles
2250 for (i = 0; i < seq2->rnum; i++)
2251 {
2252 ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(seq2, i)((void*)(((char*)((seq2)->data)) + (size_t)(seq2)->rsize
* (size_t)(i)));
2253 if (r1.classification.id > 0)
2254 {
2255 int flag = 1;
2256
2257 for (j = 0; j < seq2->rnum; j++)
2258 {
2259 ccv_comp_t r2 = *(ccv_comp_t*)ccv_array_get(seq2, j)((void*)(((char*)((seq2)->data)) + (size_t)(seq2)->rsize
* (size_t)(j)));
2260 int distance = (int)(ccv_min(r2.rect.width, r2.rect.height)({ typeof (r2.rect.width) _a = (r2.rect.width); typeof (r2.rect
.height) _b = (r2.rect.height); (_a < _b) ? _a : _b; }) * 0.25 + 0.5);
2261
2262 if (i != j &&
2263 abs(r1.classification.id) == abs(r2.classification.id) &&
2264 r1.rect.x >= r2.rect.x - distance &&
2265 r1.rect.y >= r2.rect.y - distance &&
2266 r1.rect.x + r1.rect.width <= r2.rect.x + r2.rect.width + distance &&
2267 r1.rect.y + r1.rect.height <= r2.rect.y + r2.rect.height + distance &&
2268 // if r2 is better, we mute r1
2269 (r2.classification.confidence > r1.classification.confidence || r2.neighbors >= r1.neighbors))
2270 {
2271 flag = 0;
2272 break;
2273 }
2274 }
2275
2276 if (flag)
2277 ccv_array_push(result_seq, &r1);
2278 }
2279 }
2280 ccv_array_free(idx_seq);
2281 ccfreefree(comps);
2282 }
2283 ccv_array_free(seq[k]);
2284 }
2285 ccv_array_free(seq2);
2286
2287 return result_seq;
2288}