/* * This file is part of the OpenMV project. * * Copyright (c) 2013-2021 Ibrahim Abdelkader * Copyright (c) 2013-2021 Kwabena W. Agyeman * * ORB keypoints descriptor based on OpenCV ORB detector. * Software License Agreement (BSD License) * * Copyright (c) 2009, Willow Garage, Inc. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above * copyright notice, this list of conditions and the following * disclaimer in the documentation and/or other materials provided * with the distribution. * * 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. */ #include #include #include #include #include "fmath.h" #include "arm_math.h" #include "ff.h" #include "imlib.h" #include "xalloc.h" #include "fb_alloc.h" #ifdef IMLIB_ENABLE_FIND_KEYPOINTS #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; yh; y++) { int sy = (y*y_ratio)>>16; for (int x=0; xw; 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, xfree); int octave = 1; int kpts_index = 0; rectangle_t roi_scaled; for(float scale=1.0f; ; scale*=scale_factor, octave++) { image_t img_scaled = { .bpp = 1, .w = (int) roundf(img->w/scale), .h = (int) roundf(img->h/scale), .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); } else #endif { agast_detect(&img_scaled, kpts, threshold, &roi_scaled); } for (int k=kpts_index; koctave = 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 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 modifed 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; imatched == 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 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; imatched) { 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; imatched) { 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; imatched) { 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; imatched) { 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(FIL *fp, array_t *kpts) { UINT bytes; FRESULT res; int kpts_size = array_length(kpts); // Write the number of keypoints res = f_write(fp, &kpts_size, sizeof(kpts_size), &bytes); if (res != FR_OK || bytes != sizeof(kpts_size)) { goto error; } // Write keypoints for (int i=0; ix, sizeof(kp->x), &bytes); if (res != FR_OK || bytes != sizeof(kp->x)) { goto error; } // Write Y res = f_write(fp, &kp->y, sizeof(kp->y), &bytes); if (res != FR_OK || bytes != sizeof(kp->y)) { goto error; } // Write Score res = f_write(fp, &kp->score, sizeof(kp->score), &bytes); if (res != FR_OK || bytes != sizeof(kp->score)) { goto error; } // Write Octave res = f_write(fp, &kp->octave, sizeof(kp->octave), &bytes); if (res != FR_OK || bytes != sizeof(kp->octave)) { goto error; } // Write Angle res = f_write(fp, &kp->angle, sizeof(kp->angle), &bytes); if (res != FR_OK || bytes != sizeof(kp->angle)) { goto error; } // Write descriptor res = f_write(fp, kp->desc, KDESC_SIZE, &bytes); if (res != FR_OK || bytes != KDESC_SIZE) { goto error; } } error: return res; } int orb_load_descriptor(FIL *fp, array_t *kpts) { UINT bytes; FRESULT res=FR_OK; int kpts_size=0; // Read number of keypoints res = f_read(fp, &kpts_size, sizeof(kpts_size), &bytes); if (res != FR_OK || bytes != sizeof(kpts_size)) { goto error; } // Read keypoints for (int i=0; imatched = 0; // Read X res = f_read(fp, &kp->x, sizeof(kp->x), &bytes); if (res != FR_OK || bytes != sizeof(kp->x)) { goto error; } // Read Y res = f_read(fp, &kp->y, sizeof(kp->y), &bytes); if (res != FR_OK || bytes != sizeof(kp->y)) { goto error; } // Read Score res = f_read(fp, &kp->score, sizeof(kp->score), &bytes); if (res != FR_OK || bytes != sizeof(kp->score)) { goto error; } // Read Octave res = f_read(fp, &kp->octave, sizeof(kp->octave), &bytes); if (res != FR_OK || bytes != sizeof(kp->octave)) { goto error; } // Read Angle res = f_read(fp, &kp->angle, sizeof(kp->angle), &bytes); if (res != FR_OK || bytes != sizeof(kp->angle)) { goto error; } // Read descriptor res = f_read(fp, kp->desc, KDESC_SIZE, &bytes); if (res != FR_OK || bytes != KDESC_SIZE) { goto error; } // Add keypoint to array array_push_back(kpts, kp); } error: return res; } #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