/* * SPDX-License-Identifier: BSD-3-Clause * * Copyright (c) 2009, Willow Garage, Inc. * Copyright (C) 2013-2024 OpenMV, LLC. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * 3. Neither the name of the Willow Garage nor the names of its * contributors may be used to endorse or promote products derived * from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * * ORB keypoints descriptor based on OpenCV ORB detector. */ #include "imlib.h" #if defined(IMLIB_ENABLE_FIND_KEYPOINTS) #include "fmath.h" #include "arm_math.h" #include "fb_alloc.h" #include "file_utils.h" #define PATCH_SIZE (31) // 31x31 pixels #define KDESC_SIZE (32) // 32 bytes #define MAX_KP_DIST (KDESC_SIZE * 8) typedef struct { int x; int y; } sample_point_t; const static int u_max[] = { 15, 15, 15, 15, 14, 14, 14, 13, 13, 12, 11, 10, 9, 8, 6, 3, 0 }; const static int sample_pattern[256 * 4] = { 8, -3, 9, 5 /*mean (0), correlation (0)*/, 4, 2, 7, -12 /*mean (1.12461e-05), correlation (0.0437584)*/, -11, 9, -8, 2 /*mean (3.37382e-05), correlation (0.0617409)*/, 7, -12, 12, -13 /*mean (5.62303e-05), correlation (0.0636977)*/, 2, -13, 2, 12 /*mean (0.000134953), correlation (0.085099)*/, 1, -7, 1, 6 /*mean (0.000528565), correlation (0.0857175)*/, -2, -10, -2, -4 /*mean (0.0188821), correlation (0.0985774)*/, -13, -13, -11, -8 /*mean (0.0363135), correlation (0.0899616)*/, -13, -3, -12, -9 /*mean (0.121806), correlation (0.099849)*/, 10, 4, 11, 9 /*mean (0.122065), correlation (0.093285)*/, -13, -8, -8, -9 /*mean (0.162787), correlation (0.0942748)*/, -11, 7, -9, 12 /*mean (0.21561), correlation (0.0974438)*/, 7, 7, 12, 6 /*mean (0.160583), correlation (0.130064)*/, -4, -5, -3, 0 /*mean (0.228171), correlation (0.132998)*/, -13, 2, -12, -3 /*mean (0.00997526), correlation (0.145926)*/, -9, 0, -7, 5 /*mean (0.198234), correlation (0.143636)*/, 12, -6, 12, -1 /*mean (0.0676226), correlation (0.16689)*/, -3, 6, -2, 12 /*mean (0.166847), correlation (0.171682)*/, -6, -13, -4, -8 /*mean (0.101215), correlation (0.179716)*/, 11, -13, 12, -8 /*mean (0.200641), correlation (0.192279)*/, 4, 7, 5, 1 /*mean (0.205106), correlation (0.186848)*/, 5, -3, 10, -3 /*mean (0.234908), correlation (0.192319)*/, 3, -7, 6, 12 /*mean (0.0709964), correlation (0.210872)*/, -8, -7, -6, -2 /*mean (0.0939834), correlation (0.212589)*/, -2, 11, -1, -10 /*mean (0.127778), correlation (0.20866)*/, -13, 12, -8, 10 /*mean (0.14783), correlation (0.206356)*/, -7, 3, -5, -3 /*mean (0.182141), correlation (0.198942)*/, -4, 2, -3, 7 /*mean (0.188237), correlation (0.21384)*/, -10, -12, -6, 11 /*mean (0.14865), correlation (0.23571)*/, 5, -12, 6, -7 /*mean (0.222312), correlation (0.23324)*/, 5, -6, 7, -1 /*mean (0.229082), correlation (0.23389)*/, 1, 0, 4, -5 /*mean (0.241577), correlation (0.215286)*/, 9, 11, 11, -13 /*mean (0.00338507), correlation (0.251373)*/, 4, 7, 4, 12 /*mean (0.131005), correlation (0.257622)*/, 2, -1, 4, 4 /*mean (0.152755), correlation (0.255205)*/, -4, -12, -2, 7 /*mean (0.182771), correlation (0.244867)*/, -8, -5, -7, -10 /*mean (0.186898), correlation (0.23901)*/, 4, 11, 9, 12 /*mean (0.226226), correlation (0.258255)*/, 0, -8, 1, -13 /*mean (0.0897886), correlation (0.274827)*/, -13, -2, -8, 2 /*mean (0.148774), correlation (0.28065)*/, -3, -2, -2, 3 /*mean (0.153048), correlation (0.283063)*/, -6, 9, -4, -9 /*mean (0.169523), correlation (0.278248)*/, 8, 12, 10, 7 /*mean (0.225337), correlation (0.282851)*/, 0, 9, 1, 3 /*mean (0.226687), correlation (0.278734)*/, 7, -5, 11, -10 /*mean (0.00693882), correlation (0.305161)*/, -13, -6, -11, 0 /*mean (0.0227283), correlation (0.300181)*/, 10, 7, 12, 1 /*mean (0.125517), correlation (0.31089)*/, -6, -3, -6, 12 /*mean (0.131748), correlation (0.312779)*/, 10, -9, 12, -4 /*mean (0.144827), correlation (0.292797)*/, -13, 8, -8, -12 /*mean (0.149202), correlation (0.308918)*/, -13, 0, -8, -4 /*mean (0.160909), correlation (0.310013)*/, 3, 3, 7, 8 /*mean (0.177755), correlation (0.309394)*/, 5, 7, 10, -7 /*mean (0.212337), correlation (0.310315)*/, -1, 7, 1, -12 /*mean (0.214429), correlation (0.311933)*/, 3, -10, 5, 6 /*mean (0.235807), correlation (0.313104)*/, 2, -4, 3, -10 /*mean (0.00494827), correlation (0.344948)*/, -13, 0, -13, 5 /*mean (0.0549145), correlation (0.344675)*/, -13, -7, -12, 12 /*mean (0.103385), correlation (0.342715)*/, -13, 3, -11, 8 /*mean (0.134222), correlation (0.322922)*/, -7, 12, -4, 7 /*mean (0.153284), correlation (0.337061)*/, 6, -10, 12, 8 /*mean (0.154881), correlation (0.329257)*/, -9, -1, -7, -6 /*mean (0.200967), correlation (0.33312)*/, -2, -5, 0, 12 /*mean (0.201518), correlation (0.340635)*/, -12, 5, -7, 5 /*mean (0.207805), correlation (0.335631)*/, 3, -10, 8, -13 /*mean (0.224438), correlation (0.34504)*/, -7, -7, -4, 5 /*mean (0.239361), correlation (0.338053)*/, -3, -2, -1, -7 /*mean (0.240744), correlation (0.344322)*/, 2, 9, 5, -11 /*mean (0.242949), correlation (0.34145)*/, -11, -13, -5, -13 /*mean (0.244028), correlation (0.336861)*/, -1, 6, 0, -1 /*mean (0.247571), correlation (0.343684)*/, 5, -3, 5, 2 /*mean (0.000697256), correlation (0.357265)*/, -4, -13, -4, 12 /*mean (0.00213675), correlation (0.373827)*/, -9, -6, -9, 6 /*mean (0.0126856), correlation (0.373938)*/, -12, -10, -8, -4 /*mean (0.0152497), correlation (0.364237)*/, 10, 2, 12, -3 /*mean (0.0299933), correlation (0.345292)*/, 7, 12, 12, 12 /*mean (0.0307242), correlation (0.366299)*/, -7, -13, -6, 5 /*mean (0.0534975), correlation (0.368357)*/, -4, 9, -3, 4 /*mean (0.099865), correlation (0.372276)*/, 7, -1, 12, 2 /*mean (0.117083), correlation (0.364529)*/, -7, 6, -5, 1 /*mean (0.126125), correlation (0.369606)*/, -13, 11, -12, 5 /*mean (0.130364), correlation (0.358502)*/, -3, 7, -2, -6 /*mean (0.131691), correlation (0.375531)*/, 7, -8, 12, -7 /*mean (0.160166), correlation (0.379508)*/, -13, -7, -11, -12 /*mean (0.167848), correlation (0.353343)*/, 1, -3, 12, 12 /*mean (0.183378), correlation (0.371916)*/, 2, -6, 3, 0 /*mean (0.228711), correlation (0.371761)*/, -4, 3, -2, -13 /*mean (0.247211), correlation (0.364063)*/, -1, -13, 1, 9 /*mean (0.249325), correlation (0.378139)*/, 7, 1, 8, -6 /*mean (0.000652272), correlation (0.411682)*/, 1, -1, 3, 12 /*mean (0.00248538), correlation (0.392988)*/, 9, 1, 12, 6 /*mean (0.0206815), correlation (0.386106)*/, -1, -9, -1, 3 /*mean (0.0364485), correlation (0.410752)*/, -13, -13, -10, 5 /*mean (0.0376068), correlation (0.398374)*/, 7, 7, 10, 12 /*mean (0.0424202), correlation (0.405663)*/, 12, -5, 12, 9 /*mean (0.0942645), correlation (0.410422)*/, 6, 3, 7, 11 /*mean (0.1074), correlation (0.413224)*/, 5, -13, 6, 10 /*mean (0.109256), correlation (0.408646)*/, 2, -12, 2, 3 /*mean (0.131691), correlation (0.416076)*/, 3, 8, 4, -6 /*mean (0.165081), correlation (0.417569)*/, 2, 6, 12, -13 /*mean (0.171874), correlation (0.408471)*/, 9, -12, 10, 3 /*mean (0.175146), correlation (0.41296)*/, -8, 4, -7, 9 /*mean (0.183682), correlation (0.402956)*/, -11, 12, -4, -6 /*mean (0.184672), correlation (0.416125)*/, 1, 12, 2, -8 /*mean (0.191487), correlation (0.386696)*/, 6, -9, 7, -4 /*mean (0.192668), correlation (0.394771)*/, 2, 3, 3, -2 /*mean (0.200157), correlation (0.408303)*/, 6, 3, 11, 0 /*mean (0.204588), correlation (0.411762)*/, 3, -3, 8, -8 /*mean (0.205904), correlation (0.416294)*/, 7, 8, 9, 3 /*mean (0.213237), correlation (0.409306)*/, -11, -5, -6, -4 /*mean (0.243444), correlation (0.395069)*/, -10, 11, -5, 10 /*mean (0.247672), correlation (0.413392)*/, -5, -8, -3, 12 /*mean (0.24774), correlation (0.411416)*/, -10, 5, -9, 0 /*mean (0.00213675), correlation (0.454003)*/, 8, -1, 12, -6 /*mean (0.0293635), correlation (0.455368)*/, 4, -6, 6, -11 /*mean (0.0404971), correlation (0.457393)*/, -10, 12, -8, 7 /*mean (0.0481107), correlation (0.448364)*/, 4, -2, 6, 7 /*mean (0.050641), correlation (0.455019)*/, -2, 0, -2, 12 /*mean (0.0525978), correlation (0.44338)*/, -5, -8, -5, 2 /*mean (0.0629667), correlation (0.457096)*/, 7, -6, 10, 12 /*mean (0.0653846), correlation (0.445623)*/, -9, -13, -8, -8 /*mean (0.0858749), correlation (0.449789)*/, -5, -13, -5, -2 /*mean (0.122402), correlation (0.450201)*/, 8, -8, 9, -13 /*mean (0.125416), correlation (0.453224)*/, -9, -11, -9, 0 /*mean (0.130128), correlation (0.458724)*/, 1, -8, 1, -2 /*mean (0.132467), correlation (0.440133)*/, 7, -4, 9, 1 /*mean (0.132692), correlation (0.454)*/, -2, 1, -1, -4 /*mean (0.135695), correlation (0.455739)*/, 11, -6, 12, -11 /*mean (0.142904), correlation (0.446114)*/, -12, -9, -6, 4 /*mean (0.146165), correlation (0.451473)*/, 3, 7, 7, 12 /*mean (0.147627), correlation (0.456643)*/, 5, 5, 10, 8 /*mean (0.152901), correlation (0.455036)*/, 0, -4, 2, 8 /*mean (0.167083), correlation (0.459315)*/, -9, 12, -5, -13 /*mean (0.173234), correlation (0.454706)*/, 0, 7, 2, 12 /*mean (0.18312), correlation (0.433855)*/, -1, 2, 1, 7 /*mean (0.185504), correlation (0.443838)*/, 5, 11, 7, -9 /*mean (0.185706), correlation (0.451123)*/, 3, 5, 6, -8 /*mean (0.188968), correlation (0.455808)*/, -13, -4, -8, 9 /*mean (0.191667), correlation (0.459128)*/, -5, 9, -3, -3 /*mean (0.193196), correlation (0.458364)*/, -4, -7, -3, -12 /*mean (0.196536), correlation (0.455782)*/, 6, 5, 8, 0 /*mean (0.1972), correlation (0.450481)*/, -7, 6, -6, 12 /*mean (0.199438), correlation (0.458156)*/, -13, 6, -5, -2 /*mean (0.211224), correlation (0.449548)*/, 1, -10, 3, 10 /*mean (0.211718), correlation (0.440606)*/, 4, 1, 8, -4 /*mean (0.213034), correlation (0.443177)*/, -2, -2, 2, -13 /*mean (0.234334), correlation (0.455304)*/, 2, -12, 12, 12 /*mean (0.235684), correlation (0.443436)*/, -2, -13, 0, -6 /*mean (0.237674), correlation (0.452525)*/, 4, 1, 9, 3 /*mean (0.23962), correlation (0.444824)*/, -6, -10, -3, -5 /*mean (0.248459), correlation (0.439621)*/, -3, -13, -1, 1 /*mean (0.249505), correlation (0.456666)*/, 7, 5, 12, -11 /*mean (0.00119208), correlation (0.495466)*/, 4, -2, 5, -7 /*mean (0.00372245), correlation (0.484214)*/, -13, 9, -9, -5 /*mean (0.00741116), correlation (0.499854)*/, 7, 1, 8, 6 /*mean (0.0208952), correlation (0.499773)*/, 7, -8, 7, 6 /*mean (0.0220085), correlation (0.501609)*/, -7, -4, -7, 1 /*mean (0.0233806), correlation (0.496568)*/, -8, 11, -7, -8 /*mean (0.0236505), correlation (0.489719)*/, -13, 6, -12, -8 /*mean (0.0268781), correlation (0.503487)*/, 2, 4, 3, 9 /*mean (0.0323324), correlation (0.501938)*/, 10, -5, 12, 3 /*mean (0.0399235), correlation (0.494029)*/, -6, -5, -6, 7 /*mean (0.0420153), correlation (0.486579)*/, 8, -3, 9, -8 /*mean (0.0548021), correlation (0.484237)*/, 2, -12, 2, 8 /*mean (0.0616622), correlation (0.496642)*/, -11, -2, -10, 3 /*mean (0.0627755), correlation (0.498563)*/, -12, -13, -7, -9 /*mean (0.0829622), correlation (0.495491)*/, -11, 0, -10, -5 /*mean (0.0843342), correlation (0.487146)*/, 5, -3, 11, 8 /*mean (0.0929937), correlation (0.502315)*/, -2, -13, -1, 12 /*mean (0.113327), correlation (0.48941)*/, -1, -8, 0, 9 /*mean (0.132119), correlation (0.467268)*/, -13, -11, -12, -5 /*mean (0.136269), correlation (0.498771)*/, -10, -2, -10, 11 /*mean (0.142173), correlation (0.498714)*/, -3, 9, -2, -13 /*mean (0.144141), correlation (0.491973)*/, 2, -3, 3, 2 /*mean (0.14892), correlation (0.500782)*/, -9, -13, -4, 0 /*mean (0.150371), correlation (0.498211)*/, -4, 6, -3, -10 /*mean (0.152159), correlation (0.495547)*/, -4, 12, -2, -7 /*mean (0.156152), correlation (0.496925)*/, -6, -11, -4, 9 /*mean (0.15749), correlation (0.499222)*/, 6, -3, 6, 11 /*mean (0.159211), correlation (0.503821)*/, -13, 11, -5, 5 /*mean (0.162427), correlation (0.501907)*/, 11, 11, 12, 6 /*mean (0.16652), correlation (0.497632)*/, 7, -5, 12, -2 /*mean (0.169141), correlation (0.484474)*/, -1, 12, 0, 7 /*mean (0.169456), correlation (0.495339)*/, -4, -8, -3, -2 /*mean (0.171457), correlation (0.487251)*/, -7, 1, -6, 7 /*mean (0.175), correlation (0.500024)*/, -13, -12, -8, -13 /*mean (0.175866), correlation (0.497523)*/, -7, -2, -6, -8 /*mean (0.178273), correlation (0.501854)*/, -8, 5, -6, -9 /*mean (0.181107), correlation (0.494888)*/, -5, -1, -4, 5 /*mean (0.190227), correlation (0.482557)*/, -13, 7, -8, 10 /*mean (0.196739), correlation (0.496503)*/, 1, 5, 5, -13 /*mean (0.19973), correlation (0.499759)*/, 1, 0, 10, -13 /*mean (0.204465), correlation (0.49873)*/, 9, 12, 10, -1 /*mean (0.209334), correlation (0.49063)*/, 5, -8, 10, -9 /*mean (0.211134), correlation (0.503011)*/, -1, 11, 1, -13 /*mean (0.212), correlation (0.499414)*/, -9, -3, -6, 2 /*mean (0.212168), correlation (0.480739)*/, -1, -10, 1, 12 /*mean (0.212731), correlation (0.502523)*/, -13, 1, -8, -10 /*mean (0.21327), correlation (0.489786)*/, 8, -11, 10, -6 /*mean (0.214159), correlation (0.488246)*/, 2, -13, 3, -6 /*mean (0.216993), correlation (0.50287)*/, 7, -13, 12, -9 /*mean (0.223639), correlation (0.470502)*/, -10, -10, -5, -7 /*mean (0.224089), correlation (0.500852)*/, -10, -8, -8, -13 /*mean (0.228666), correlation (0.502629)*/, 4, -6, 8, 5 /*mean (0.22906), correlation (0.498305)*/, 3, 12, 8, -13 /*mean (0.233378), correlation (0.503825)*/, -4, 2, -3, -3 /*mean (0.234323), correlation (0.476692)*/, 5, -13, 10, -12 /*mean (0.236392), correlation (0.475462)*/, 4, -13, 5, -1 /*mean (0.236842), correlation (0.504132)*/, -9, 9, -4, 3 /*mean (0.236977), correlation (0.497739)*/, 0, 3, 3, -9 /*mean (0.24314), correlation (0.499398)*/, -12, 1, -6, 1 /*mean (0.243297), correlation (0.489447)*/, 3, 2, 4, -8 /*mean (0.00155196), correlation (0.553496)*/, -10, -10, -10, 9 /*mean (0.00239541), correlation (0.54297)*/, 8, -13, 12, 12 /*mean (0.0034413), correlation (0.544361)*/, -8, -12, -6, -5 /*mean (0.003565), correlation (0.551225)*/, 2, 2, 3, 7 /*mean (0.00835583), correlation (0.55285)*/, 10, 6, 11, -8 /*mean (0.00885065), correlation (0.540913)*/, 6, 8, 8, -12 /*mean (0.0101552), correlation (0.551085)*/, -7, 10, -6, 5 /*mean (0.0102227), correlation (0.533635)*/, -3, -9, -3, 9 /*mean (0.0110211), correlation (0.543121)*/, -1, -13, -1, 5 /*mean (0.0113473), correlation (0.550173)*/, -3, -7, -3, 4 /*mean (0.0140913), correlation (0.554774)*/, -8, -2, -8, 3 /*mean (0.017049), correlation (0.55461)*/, 4, 2, 12, 12 /*mean (0.01778), correlation (0.546921)*/, 2, -5, 3, 11 /*mean (0.0224022), correlation (0.549667)*/, 6, -9, 11, -13 /*mean (0.029161), correlation (0.546295)*/, 3, -1, 7, 12 /*mean (0.0303081), correlation (0.548599)*/, 11, -1, 12, 4 /*mean (0.0355151), correlation (0.523943)*/, -3, 0, -3, 6 /*mean (0.0417904), correlation (0.543395)*/, 4, -11, 4, 12 /*mean (0.0487292), correlation (0.542818)*/, 2, -4, 2, 1 /*mean (0.0575124), correlation (0.554888)*/, -10, -6, -8, 1 /*mean (0.0594242), correlation (0.544026)*/, -13, 7, -11, 1 /*mean (0.0597391), correlation (0.550524)*/, -13, 12, -11, -13 /*mean (0.0608974), correlation (0.55383)*/, 6, 0, 11, -13 /*mean (0.065126), correlation (0.552006)*/, 0, -1, 1, 4 /*mean (0.074224), correlation (0.546372)*/, -13, 3, -9, -2 /*mean (0.0808592), correlation (0.554875)*/, -9, 8, -6, -3 /*mean (0.0883378), correlation (0.551178)*/, -13, -6, -8, -2 /*mean (0.0901035), correlation (0.548446)*/, 5, -9, 8, 10 /*mean (0.0949843), correlation (0.554694)*/, 2, 7, 3, -9 /*mean (0.0994152), correlation (0.550979)*/, -1, -6, -1, -1 /*mean (0.10045), correlation (0.552714)*/, 9, 5, 11, -2 /*mean (0.100686), correlation (0.552594)*/, 11, -3, 12, -8 /*mean (0.101091), correlation (0.532394)*/, 3, 0, 3, 5 /*mean (0.101147), correlation (0.525576)*/, -1, 4, 0, 10 /*mean (0.105263), correlation (0.531498)*/, 3, -6, 4, 5 /*mean (0.110785), correlation (0.540491)*/, -13, 0, -10, 5 /*mean (0.112798), correlation (0.536582)*/, 5, 8, 12, 11 /*mean (0.114181), correlation (0.555793)*/, 8, 9, 9, -6 /*mean (0.117431), correlation (0.553763)*/, 7, -4, 8, -12 /*mean (0.118522), correlation (0.553452)*/, -10, 4, -10, 9 /*mean (0.12094), correlation (0.554785)*/, 7, 3, 12, 4 /*mean (0.122582), correlation (0.555825)*/, 9, -7, 10, -2 /*mean (0.124978), correlation (0.549846)*/, 7, 0, 12, -2 /*mean (0.127002), correlation (0.537452)*/, -1, -6, 0, -11/*mean (0.127148), correlation (0.547401)*/ }; static int kpt_comp(const kp_t *kp1, const kp_t *kp2) { // Descending order return kp2->score - kp1->score; } static int comp_angle(image_t *img, kp_t *kp, float *a, float *b) { int step = img->w; int half_k = 31 / 2; int m_01 = 0, m_10 = 0; uint8_t *center = img->pixels + (kp->y * img->w + kp->x); // Treat the center line differently, v=0 for (int u = -half_k; u <= half_k; ++u) { m_10 += u * center[u]; } // Go line by line in the circular patch for (int v = 1; v <= half_k; ++v) { // Proceed over the two lines int v_sum = 0; int d = u_max[v]; for (int u = -d; u <= d; ++u) { int val_plus = center[u + v * step], val_minus = center[u - v * step]; v_sum += (val_plus - val_minus); m_10 += u * (val_plus + val_minus); } m_01 += v * v_sum; } int angle = (int) (atan2f((float) m_01, (float) m_10) * (180.0f / M_PI)); if (angle < 0) { angle += 360; } // Quantize angle to 15 degrees angle = angle - (angle % 15); *a = cos_table[angle]; *b = sin_table[angle]; return angle; } static void image_scale(image_t *src, image_t *dst) { int x_ratio = (int) ((src->w << 16) / dst->w) + 1; int y_ratio = (int) ((src->h << 16) / dst->h) + 1; for (int y = 0; y < dst->h; y++) { int sy = (y * y_ratio) >> 16; for (int x = 0; x < dst->w; x++) { int sx = (x * x_ratio) >> 16; dst->pixels[y * dst->w + x] = IM_TO_GS_PIXEL(src, sx, sy); } } } array_t *orb_find_keypoints(image_t *img, bool normalized, int threshold, float scale_factor, int max_keypoints, corner_detector_t corner_detector, rectangle_t *roi) { array_t *kpts; array_alloc(&kpts, m_free); int octave = 1; int kpts_index = 0; rectangle_t roi_scaled; for (float scale = 1.0f; ; scale *= scale_factor, octave++) { image_t img_scaled = { .w = (int) roundf(img->w / scale), .h = (int) roundf(img->h / scale), .pixfmt = PIXFORMAT_GRAYSCALE, .pixels = NULL }; // Add patch size to ROI roi_scaled.x = (int) roundf(roi->x / scale) + (PATCH_SIZE); roi_scaled.y = (int) roundf(roi->y / scale) + (PATCH_SIZE); roi_scaled.w = (int) roundf(roi->w / scale) - (PATCH_SIZE * 2); roi_scaled.h = (int) roundf(roi->h / scale) - (PATCH_SIZE * 2); if (roi_scaled.w <= (PATCH_SIZE * 2) || roi_scaled.h <= (PATCH_SIZE * 2)) { break; } img_scaled.pixels = fb_alloc(img_scaled.w * img_scaled.h, FB_ALLOC_NO_HINT); // Down scale image image_scale(img, &img_scaled); // Gaussian smooth the image before extracting keypoints imlib_sepconv3(&img_scaled, kernel_gauss_3, 1.0f / 16.0f, 0.0f); // Find kpts #ifdef IMLIB_ENABLE_FAST if (corner_detector == CORNER_FAST) { fast_detect(&img_scaled, kpts, threshold, &roi_scaled); } #endif #ifdef IMLIB_ENABLE_AGAST if (corner_detector == CORNER_AGAST) { agast_detect(&img_scaled, kpts, threshold, &roi_scaled); } #endif for (int k = kpts_index; k < array_length(kpts); k++, kpts_index++) { // Set keypoint octave/scale kp_t *kpt = array_at(kpts, k); kpt->octave = octave; int x, y; float a, b; sample_point_t *pattern = (sample_point_t *) sample_pattern; kpt->angle = comp_angle(&img_scaled, kpt, &a, &b); #if 1 #define GET_VALUE(idx) \ (x = (int) roundf(pattern[idx].x * a - pattern[idx].y * b), \ y = (int) roundf(pattern[idx].x * b + pattern[idx].y * a), \ img_scaled.pixels[((kpt->y + y) * img_scaled.w) + (kpt->x + x)]) #else #define GET_VALUE(idx) \ (img_scaled.pixels[((kpt->y + pattern[idx].y) * img_scaled.w) + (kpt->x + pattern[idx].x)]) #endif for (int i = 0; i < KDESC_SIZE; ++i, pattern += 16) { int t0, t1, t2, t3, u, v, k, val; t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2); t3 = GET_VALUE(3); u = 0, v = 2; if (t1 > t0) { t0 = t1, u = 1; } if (t3 > t2) { t2 = t3, v = 3; } k = t0 > t2 ? u : v; val = k; t0 = GET_VALUE(4); t1 = GET_VALUE(5); t2 = GET_VALUE(6); t3 = GET_VALUE(7); u = 0, v = 2; if (t1 > t0) { t0 = t1, u = 1; } if (t3 > t2) { t2 = t3, v = 3; } k = t0 > t2 ? u : v; val |= k << 2; t0 = GET_VALUE(8); t1 = GET_VALUE(9); t2 = GET_VALUE(10); t3 = GET_VALUE(11); u = 0, v = 2; if (t1 > t0) { t0 = t1, u = 1; } if (t3 > t2) { t2 = t3, v = 3; } k = t0 > t2 ? u : v; val |= k << 4; t0 = GET_VALUE(12); t1 = GET_VALUE(13); t2 = GET_VALUE(14); t3 = GET_VALUE(15); u = 0, v = 2; if (t1 > t0) { t0 = t1, u = 1; } if (t3 > t2) { t2 = t3, v = 3; } k = t0 > t2 ? u : v; val |= k << 6; kpt->desc[i] = (uint8_t) val; } kpt->x = (int) floorf(kpt->x * scale); kpt->y = (int) floorf(kpt->y * scale); } // Free current scale fb_free(); if (normalized) { break; } } // Sort keypoints by score and return top n keypoints array_sort(kpts, (array_comp_t) kpt_comp); if (array_length(kpts) > max_keypoints) { array_resize(kpts, max_keypoints); } return kpts; } // This is a modified popcount that counts every 2 different bits as 1. // This is what should actually be used with wta_k == 3 or 4. static inline uint32_t popcount(uint32_t i) { i = i - ((i >> 1) & 0x55555555); i = ((i & 0xAAAAAAAA) >> 1) | (i & 0x55555555); i = (i & 0x33333333) + ((i >> 2) & 0x33333333); return (((i + (i >> 4)) & 0x0F0F0F0F) * 0x01010101) >> 24; } static kp_t *find_best_match(kp_t *kp1, array_t *kpts, int *dist_out1, int *dist_out2, int *index) { kp_t *min_kp = NULL; int min_dist1 = MAX_KP_DIST; int min_dist2 = MAX_KP_DIST; int kpts_size = array_length(kpts); for (int i = 0; i < kpts_size; i++) { int dist = 0; kp_t *kp2 = array_at(kpts, i); if (kp2->matched == 0) { for (int m = 0; m < (KDESC_SIZE / 4); m++) { dist += popcount(((uint32_t *) (kp1->desc))[m] ^ ((uint32_t *) (kp2->desc))[m]); } if (dist < min_dist1) { *index = i; min_kp = kp2; min_dist2 = min_dist1; min_dist1 = dist; } } } *dist_out1 = min_dist1; *dist_out2 = min_dist2; return min_kp; } int orb_match_keypoints(array_t *kpts1, array_t *kpts2, int *match, int threshold, rectangle_t *r, point_t *c, int *angle) { int matches = 0; int cx = 0, cy = 0; uint16_t angles[360] = {0}; int kpts1_size = array_length(kpts1); r->w = r->h = 0; r->x = r->y = 20000; // Match keypoints and find "good matches" This runs 2/3 tests found in the RobustMatcher from the OpenCV programming cookbook. // The first test is based on the distance ratio between the two best matches for a feature, to remove ambiguous matches. // Second test is the symmetry test (corss-matching) both points in a match must be the best matching feature of each other. for (int i = 0; i < kpts1_size; i++) { int kp_index1 = 0; int kp_index2 = 0; int min_dist1 = 0; int min_dist2 = 0; kp_t *min_kp = NULL; kp_t *kp1 = array_at(kpts1, i); // Find the best match in second set min_kp = find_best_match(kp1, kpts2, &min_dist1, &min_dist2, &kp_index2); // Test the distance ratio between the best two matches if ((min_dist1 * 100 / min_dist2) > threshold) { continue; } // Cross-match the keypoint in the first set kp_t *kp2 = find_best_match(min_kp, kpts1, &min_dist1, &min_dist2, &kp_index1); // Test the distance ratio between the best two matches if ((min_dist1 * 100 / min_dist2) > threshold) { continue; } // Cross-match test if (kp1 == kp2) { int x, y; matches++; min_kp->matched = 1; cx += x = min_kp->x; cy += y = min_kp->y; rectangle_expand(r, x, y); int angle = (int) abs(min_kp->angle - kp1->angle); if (angle >= 0 && angle < 360) { angles[angle]++; } *match++ = kp_index1; *match++ = kp_index2; } } if (matches == 0) { r->x = r->y = 0; return 0; } // Fix centroid x/y c->x = cx / matches; c->y = cy / matches; // Fix rectangle w/h r->w = r->w - r->x; r->h = r->h - r->y; int max_angle = 0; for (int i = 0; i < 360; i++) { if (angles[i] > max_angle) { max_angle = angles[i]; *angle = i; } } return matches; } int orb_filter_keypoints(array_t *kpts, rectangle_t *r, point_t *c) { int matches = 0; int cx = 0, cy = 0; int kpts_size = array_length(kpts); r->w = r->h = 0; r->x = r->y = 20000; float *kpts_dist = fb_alloc(kpts_size * sizeof(float), FB_ALLOC_NO_HINT); // Find centroid for (int i = 0; i < kpts_size; i++) { kp_t *kp = array_at(kpts, i); if (kp->matched) { matches++; cx += kp->x; cy += kp->y; } } // Centroid cx /= matches; cy /= matches; // Find mean distance from centroid float mdist = 0.0f; for (int i = 0; i < kpts_size; i++) { kp_t *kp = array_at(kpts, i); if (kp->matched) { kpts_dist[i] = orb_cluster_dist(cx, cy, kp); mdist += kpts_dist[i]; } } // Mean distance from centroid mdist /= matches; // Find variance float var = 0.0f; for (int i = 0; i < kpts_size; i++) { kp_t *kp = array_at(kpts, i); if (kp->matched) { float dist = kpts_dist[i]; var += (mdist - dist) * (mdist - dist); } } // Find standard deviation float stdist = fast_sqrtf(var / matches); // Reset centroid matches = 0; cx = cy = 0; // Remove outliers and get new centroid for (int i = 0; i < kpts_size; i++) { kp_t *kp = array_at(kpts, i); if (kp->matched) { float dist = fabs(mdist - kpts_dist[i]); if (dist > stdist) { kp->matched = 0; } else { int x, y; matches++; cx += x = kp->x; cy += y = kp->y; rectangle_expand(r, x, y); } } } // Fix centroid x/y c->x = cx / matches; c->y = cy / matches; // Fix rectangle w/h r->w = r->w - r->x; r->h = r->h - r->y; // Free distance array fb_free(); return matches; } #if defined(IMLIB_ENABLE_IMAGE_FILE_IO) int orb_save_descriptor(file_t *fp, array_t *kpts) { int kpts_size = array_length(kpts); // Write the number of keypoints file_write(fp, &kpts_size, sizeof(kpts_size)); // Write keypoints for (int i = 0; i < kpts_size; i++) { kp_t *kp = array_at(kpts, i); file_write(fp, &kp->x, sizeof(kp->x)); file_write(fp, &kp->y, sizeof(kp->y)); file_write(fp, &kp->score, sizeof(kp->score)); file_write(fp, &kp->octave, sizeof(kp->octave)); file_write(fp, &kp->angle, sizeof(kp->angle)); file_write(fp, kp->desc, KDESC_SIZE); } return 0; // Success } int orb_load_descriptor(file_t *fp, array_t *kpts) { int kpts_size = 0; // Read number of keypoints file_read(fp, &kpts_size, sizeof(kpts_size)); // Read keypoints for (int i = 0; i < kpts_size; i++) { kp_t *kp = m_malloc(sizeof(*kp)); kp->matched = 0; file_read(fp, &kp->x, sizeof(kp->x)); file_read(fp, &kp->y, sizeof(kp->y)); file_read(fp, &kp->score, sizeof(kp->score)); file_read(fp, &kp->octave, sizeof(kp->octave)); file_read(fp, &kp->angle, sizeof(kp->angle)); file_read(fp, kp->desc, KDESC_SIZE); // Add keypoint to array array_push_back(kpts, kp); } return 0; // Success } #endif //IMLIB_ENABLE_IMAGE_FILE_IO float orb_cluster_dist(int cx, int cy, void *kp_in) { float sum = 0.0f; kp_t *kp = kp_in; sum += (cx - kp->x) * (cx - kp->x); sum += (cy - kp->y) * (cy - kp->y); return fast_sqrtf(sum); } #endif // IMLIB_ENABLE_FIND_KEYPOINTS