| File: | ccv_icf.c |
| Warning: | line 2112, column 17 Although the value stored to 'iy' is used in the enclosing expression, the value is never actually read from 'iy' |
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| 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 | |
| 14 | const 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 |
| 23 | static 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 | |
| 288 | static 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) |
| 316 | void 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 | |
| 398 | static 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 | |
| 407 | static 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 |
| 423 | static 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 | |
| 450 | static 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 | |
| 460 | static 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 | |
| 498 | typedef struct { |
| 499 | uint8_t correct:1; |
| 500 | double weight; |
| 501 | float rate; |
| 502 | } ccv_icf_example_state_t; |
| 503 | |
| 504 | typedef 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 | |
| 513 | typedef struct { |
| 514 | uint32_t index; |
| 515 | float value; |
| 516 | } ccv_icf_value_index_t; |
| 517 | |
| 518 | typedef 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 | |
| 534 | static 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 | |
| 615 | static 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) |
| 724 | static 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 | |
| 727 | static 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 | |
| 733 | static 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 | |
| 738 | static 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 | |
| 745 | static 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 | |
| 760 | static 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 | |
| 834 | typedef 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 | |
| 841 | static 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 | |
| 857 | static 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 | |
| 868 | typedef 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 | |
| 875 | static 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 | |
| 982 | typedef struct { |
| 983 | int error_index; |
| 984 | double error_rate; |
| 985 | double weigh[2]; |
| 986 | } ccv_icf_second_feature_find_t; |
| 987 | |
| 988 | static 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 | |
| 1074 | static 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 | |
| 1095 | static 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 | |
| 1119 | static 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 | |
| 1157 | static 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); |
| 1161 | uint64_t* result = precomputed; |
| 1162 | int i, j; |
| 1163 | for (i = 0; i < positives->rnum; i++) |
| 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++) |
| 1175 | if (_ccv_icf_run_weak_classifier(cascade->weak_classifiers + j, ptr, sat->cols, ch, 1, 1)) |
| 1176 | precomputed[j >> 6] |= (1UL << (j & 63)); |
| 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)) |
| 1186 | static 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 | |
| 1189 | static 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); |
| 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 | |
| 1255 | typedef struct { |
| 1256 | ccv_point_t point; |
| 1257 | float sum; |
| 1258 | } ccv_point_with_sum_t; |
| 1259 | |
| 1260 | static 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 | |
| 1502 | static 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 |
| 1551 | static 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 | |
| 1592 | ccv_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 | |
| 1767 | void 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) |
| 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); |
| 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) |
| 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++) |
| 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++) |
| 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); |
| 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 | |
| 1808 | static 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 | |
| 1837 | static 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 | |
| 1864 | ccv_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 | |
| 1877 | void 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 | |
| 1887 | void ccv_icf_classifier_cascade_free(ccv_icf_classifier_cascade_t* classifier) |
| 1888 | { |
| 1889 | ccfreefree(classifier->weak_classifiers); |
| 1890 | ccfreefree(classifier); |
| 1891 | } |
| 1892 | |
| 1893 | ccv_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 | |
| 1926 | void 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 | |
| 1943 | void 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 | |
| 1951 | static 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 | |
| 1968 | static 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 | |
| 2055 | static 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) |
Although the value stored to 'iy' is used in the enclosing expression, the value is never actually read from 'iy' | |
| 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 | |
| 2161 | ccv_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 | } |