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807 lines
30 KiB
C
807 lines
30 KiB
C
/*
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* This file is part of the OpenMV project.
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*
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* Copyright (c) 2013-2019 Ibrahim Abdelkader <iabdalkader@openmv.io>
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* Copyright (c) 2013-2019 Kwabena W. Agyeman <kwagyeman@openmv.io>
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*
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* This work is licensed under the MIT license, see the file LICENSE for details.
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*
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* ORB keypoints descriptor based on OpenCV code.
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*/
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#include <stdio.h>
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#include <string.h>
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#include <stdint.h>
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#include <stdbool.h>
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#include "fmath.h"
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#include "arm_math.h"
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#include "ff.h"
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#include "imlib.h"
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#include "xalloc.h"
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#include "fb_alloc.h"
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#define PATCH_SIZE (31) // 31x31 pixels
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#define KDESC_SIZE (32) // 32 bytes
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#define MAX_KP_DIST (KDESC_SIZE*8)
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typedef struct {
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int x;
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int y;
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} sample_point_t;
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const static int u_max[] = {
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15, 15, 15, 15, 14, 14, 14, 13,
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13, 12, 11, 10, 9, 8, 6, 3, 0
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};
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const static int sample_pattern[256*4] = {
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8,-3, 9,5/*mean (0), correlation (0)*/,
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4,2, 7,-12/*mean (1.12461e-05), correlation (0.0437584)*/,
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-11,9, -8,2/*mean (3.37382e-05), correlation (0.0617409)*/,
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7,-12, 12,-13/*mean (5.62303e-05), correlation (0.0636977)*/,
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2,-13, 2,12/*mean (0.000134953), correlation (0.085099)*/,
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1,-7, 1,6/*mean (0.000528565), correlation (0.0857175)*/,
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-2,-10, -2,-4/*mean (0.0188821), correlation (0.0985774)*/,
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-13,-13, -11,-8/*mean (0.0363135), correlation (0.0899616)*/,
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-13,-3, -12,-9/*mean (0.121806), correlation (0.099849)*/,
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10,4, 11,9/*mean (0.122065), correlation (0.093285)*/,
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-13,-8, -8,-9/*mean (0.162787), correlation (0.0942748)*/,
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-11,7, -9,12/*mean (0.21561), correlation (0.0974438)*/,
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7,7, 12,6/*mean (0.160583), correlation (0.130064)*/,
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-4,-5, -3,0/*mean (0.228171), correlation (0.132998)*/,
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-13,2, -12,-3/*mean (0.00997526), correlation (0.145926)*/,
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-9,0, -7,5/*mean (0.198234), correlation (0.143636)*/,
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12,-6, 12,-1/*mean (0.0676226), correlation (0.16689)*/,
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-3,6, -2,12/*mean (0.166847), correlation (0.171682)*/,
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-6,-13, -4,-8/*mean (0.101215), correlation (0.179716)*/,
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11,-13, 12,-8/*mean (0.200641), correlation (0.192279)*/,
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4,7, 5,1/*mean (0.205106), correlation (0.186848)*/,
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5,-3, 10,-3/*mean (0.234908), correlation (0.192319)*/,
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3,-7, 6,12/*mean (0.0709964), correlation (0.210872)*/,
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-8,-7, -6,-2/*mean (0.0939834), correlation (0.212589)*/,
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-2,11, -1,-10/*mean (0.127778), correlation (0.20866)*/,
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-13,12, -8,10/*mean (0.14783), correlation (0.206356)*/,
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-7,3, -5,-3/*mean (0.182141), correlation (0.198942)*/,
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-4,2, -3,7/*mean (0.188237), correlation (0.21384)*/,
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-10,-12, -6,11/*mean (0.14865), correlation (0.23571)*/,
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5,-12, 6,-7/*mean (0.222312), correlation (0.23324)*/,
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5,-6, 7,-1/*mean (0.229082), correlation (0.23389)*/,
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1,0, 4,-5/*mean (0.241577), correlation (0.215286)*/,
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9,11, 11,-13/*mean (0.00338507), correlation (0.251373)*/,
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4,7, 4,12/*mean (0.131005), correlation (0.257622)*/,
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2,-1, 4,4/*mean (0.152755), correlation (0.255205)*/,
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-4,-12, -2,7/*mean (0.182771), correlation (0.244867)*/,
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-8,-5, -7,-10/*mean (0.186898), correlation (0.23901)*/,
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4,11, 9,12/*mean (0.226226), correlation (0.258255)*/,
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0,-8, 1,-13/*mean (0.0897886), correlation (0.274827)*/,
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-13,-2, -8,2/*mean (0.148774), correlation (0.28065)*/,
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-3,-2, -2,3/*mean (0.153048), correlation (0.283063)*/,
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-6,9, -4,-9/*mean (0.169523), correlation (0.278248)*/,
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8,12, 10,7/*mean (0.225337), correlation (0.282851)*/,
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0,9, 1,3/*mean (0.226687), correlation (0.278734)*/,
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7,-5, 11,-10/*mean (0.00693882), correlation (0.305161)*/,
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-13,-6, -11,0/*mean (0.0227283), correlation (0.300181)*/,
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10,7, 12,1/*mean (0.125517), correlation (0.31089)*/,
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-6,-3, -6,12/*mean (0.131748), correlation (0.312779)*/,
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10,-9, 12,-4/*mean (0.144827), correlation (0.292797)*/,
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-13,8, -8,-12/*mean (0.149202), correlation (0.308918)*/,
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-13,0, -8,-4/*mean (0.160909), correlation (0.310013)*/,
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3,3, 7,8/*mean (0.177755), correlation (0.309394)*/,
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5,7, 10,-7/*mean (0.212337), correlation (0.310315)*/,
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-1,7, 1,-12/*mean (0.214429), correlation (0.311933)*/,
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3,-10, 5,6/*mean (0.235807), correlation (0.313104)*/,
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2,-4, 3,-10/*mean (0.00494827), correlation (0.344948)*/,
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-13,0, -13,5/*mean (0.0549145), correlation (0.344675)*/,
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-13,-7, -12,12/*mean (0.103385), correlation (0.342715)*/,
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-13,3, -11,8/*mean (0.134222), correlation (0.322922)*/,
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-7,12, -4,7/*mean (0.153284), correlation (0.337061)*/,
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6,-10, 12,8/*mean (0.154881), correlation (0.329257)*/,
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-9,-1, -7,-6/*mean (0.200967), correlation (0.33312)*/,
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-2,-5, 0,12/*mean (0.201518), correlation (0.340635)*/,
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-12,5, -7,5/*mean (0.207805), correlation (0.335631)*/,
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3,-10, 8,-13/*mean (0.224438), correlation (0.34504)*/,
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-7,-7, -4,5/*mean (0.239361), correlation (0.338053)*/,
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-3,-2, -1,-7/*mean (0.240744), correlation (0.344322)*/,
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2,9, 5,-11/*mean (0.242949), correlation (0.34145)*/,
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-11,-13, -5,-13/*mean (0.244028), correlation (0.336861)*/,
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-1,6, 0,-1/*mean (0.247571), correlation (0.343684)*/,
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5,-3, 5,2/*mean (0.000697256), correlation (0.357265)*/,
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-4,-13, -4,12/*mean (0.00213675), correlation (0.373827)*/,
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-9,-6, -9,6/*mean (0.0126856), correlation (0.373938)*/,
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-12,-10, -8,-4/*mean (0.0152497), correlation (0.364237)*/,
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10,2, 12,-3/*mean (0.0299933), correlation (0.345292)*/,
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7,12, 12,12/*mean (0.0307242), correlation (0.366299)*/,
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-7,-13, -6,5/*mean (0.0534975), correlation (0.368357)*/,
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-4,9, -3,4/*mean (0.099865), correlation (0.372276)*/,
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7,-1, 12,2/*mean (0.117083), correlation (0.364529)*/,
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-7,6, -5,1/*mean (0.126125), correlation (0.369606)*/,
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-13,11, -12,5/*mean (0.130364), correlation (0.358502)*/,
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-3,7, -2,-6/*mean (0.131691), correlation (0.375531)*/,
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7,-8, 12,-7/*mean (0.160166), correlation (0.379508)*/,
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-13,-7, -11,-12/*mean (0.167848), correlation (0.353343)*/,
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1,-3, 12,12/*mean (0.183378), correlation (0.371916)*/,
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2,-6, 3,0/*mean (0.228711), correlation (0.371761)*/,
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-4,3, -2,-13/*mean (0.247211), correlation (0.364063)*/,
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-1,-13, 1,9/*mean (0.249325), correlation (0.378139)*/,
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7,1, 8,-6/*mean (0.000652272), correlation (0.411682)*/,
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1,-1, 3,12/*mean (0.00248538), correlation (0.392988)*/,
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9,1, 12,6/*mean (0.0206815), correlation (0.386106)*/,
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-1,-9, -1,3/*mean (0.0364485), correlation (0.410752)*/,
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-13,-13, -10,5/*mean (0.0376068), correlation (0.398374)*/,
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7,7, 10,12/*mean (0.0424202), correlation (0.405663)*/,
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12,-5, 12,9/*mean (0.0942645), correlation (0.410422)*/,
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6,3, 7,11/*mean (0.1074), correlation (0.413224)*/,
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5,-13, 6,10/*mean (0.109256), correlation (0.408646)*/,
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2,-12, 2,3/*mean (0.131691), correlation (0.416076)*/,
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3,8, 4,-6/*mean (0.165081), correlation (0.417569)*/,
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2,6, 12,-13/*mean (0.171874), correlation (0.408471)*/,
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9,-12, 10,3/*mean (0.175146), correlation (0.41296)*/,
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-8,4, -7,9/*mean (0.183682), correlation (0.402956)*/,
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-11,12, -4,-6/*mean (0.184672), correlation (0.416125)*/,
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1,12, 2,-8/*mean (0.191487), correlation (0.386696)*/,
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6,-9, 7,-4/*mean (0.192668), correlation (0.394771)*/,
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2,3, 3,-2/*mean (0.200157), correlation (0.408303)*/,
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6,3, 11,0/*mean (0.204588), correlation (0.411762)*/,
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3,-3, 8,-8/*mean (0.205904), correlation (0.416294)*/,
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7,8, 9,3/*mean (0.213237), correlation (0.409306)*/,
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-11,-5, -6,-4/*mean (0.243444), correlation (0.395069)*/,
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-10,11, -5,10/*mean (0.247672), correlation (0.413392)*/,
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-5,-8, -3,12/*mean (0.24774), correlation (0.411416)*/,
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-10,5, -9,0/*mean (0.00213675), correlation (0.454003)*/,
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8,-1, 12,-6/*mean (0.0293635), correlation (0.455368)*/,
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4,-6, 6,-11/*mean (0.0404971), correlation (0.457393)*/,
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-10,12, -8,7/*mean (0.0481107), correlation (0.448364)*/,
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4,-2, 6,7/*mean (0.050641), correlation (0.455019)*/,
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-2,0, -2,12/*mean (0.0525978), correlation (0.44338)*/,
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-5,-8, -5,2/*mean (0.0629667), correlation (0.457096)*/,
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7,-6, 10,12/*mean (0.0653846), correlation (0.445623)*/,
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-9,-13, -8,-8/*mean (0.0858749), correlation (0.449789)*/,
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-5,-13, -5,-2/*mean (0.122402), correlation (0.450201)*/,
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8,-8, 9,-13/*mean (0.125416), correlation (0.453224)*/,
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-9,-11, -9,0/*mean (0.130128), correlation (0.458724)*/,
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1,-8, 1,-2/*mean (0.132467), correlation (0.440133)*/,
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7,-4, 9,1/*mean (0.132692), correlation (0.454)*/,
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-2,1, -1,-4/*mean (0.135695), correlation (0.455739)*/,
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11,-6, 12,-11/*mean (0.142904), correlation (0.446114)*/,
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-12,-9, -6,4/*mean (0.146165), correlation (0.451473)*/,
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3,7, 7,12/*mean (0.147627), correlation (0.456643)*/,
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5,5, 10,8/*mean (0.152901), correlation (0.455036)*/,
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0,-4, 2,8/*mean (0.167083), correlation (0.459315)*/,
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-9,12, -5,-13/*mean (0.173234), correlation (0.454706)*/,
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0,7, 2,12/*mean (0.18312), correlation (0.433855)*/,
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-1,2, 1,7/*mean (0.185504), correlation (0.443838)*/,
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5,11, 7,-9/*mean (0.185706), correlation (0.451123)*/,
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3,5, 6,-8/*mean (0.188968), correlation (0.455808)*/,
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-13,-4, -8,9/*mean (0.191667), correlation (0.459128)*/,
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-5,9, -3,-3/*mean (0.193196), correlation (0.458364)*/,
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-4,-7, -3,-12/*mean (0.196536), correlation (0.455782)*/,
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6,5, 8,0/*mean (0.1972), correlation (0.450481)*/,
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-7,6, -6,12/*mean (0.199438), correlation (0.458156)*/,
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-13,6, -5,-2/*mean (0.211224), correlation (0.449548)*/,
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1,-10, 3,10/*mean (0.211718), correlation (0.440606)*/,
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4,1, 8,-4/*mean (0.213034), correlation (0.443177)*/,
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-2,-2, 2,-13/*mean (0.234334), correlation (0.455304)*/,
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2,-12, 12,12/*mean (0.235684), correlation (0.443436)*/,
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-2,-13, 0,-6/*mean (0.237674), correlation (0.452525)*/,
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4,1, 9,3/*mean (0.23962), correlation (0.444824)*/,
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-6,-10, -3,-5/*mean (0.248459), correlation (0.439621)*/,
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-3,-13, -1,1/*mean (0.249505), correlation (0.456666)*/,
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7,5, 12,-11/*mean (0.00119208), correlation (0.495466)*/,
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4,-2, 5,-7/*mean (0.00372245), correlation (0.484214)*/,
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-13,9, -9,-5/*mean (0.00741116), correlation (0.499854)*/,
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7,1, 8,6/*mean (0.0208952), correlation (0.499773)*/,
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7,-8, 7,6/*mean (0.0220085), correlation (0.501609)*/,
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-7,-4, -7,1/*mean (0.0233806), correlation (0.496568)*/,
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-8,11, -7,-8/*mean (0.0236505), correlation (0.489719)*/,
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-13,6, -12,-8/*mean (0.0268781), correlation (0.503487)*/,
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2,4, 3,9/*mean (0.0323324), correlation (0.501938)*/,
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10,-5, 12,3/*mean (0.0399235), correlation (0.494029)*/,
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-6,-5, -6,7/*mean (0.0420153), correlation (0.486579)*/,
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8,-3, 9,-8/*mean (0.0548021), correlation (0.484237)*/,
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2,-12, 2,8/*mean (0.0616622), correlation (0.496642)*/,
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-11,-2, -10,3/*mean (0.0627755), correlation (0.498563)*/,
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-12,-13, -7,-9/*mean (0.0829622), correlation (0.495491)*/,
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-11,0, -10,-5/*mean (0.0843342), correlation (0.487146)*/,
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5,-3, 11,8/*mean (0.0929937), correlation (0.502315)*/,
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-2,-13, -1,12/*mean (0.113327), correlation (0.48941)*/,
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-1,-8, 0,9/*mean (0.132119), correlation (0.467268)*/,
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-13,-11, -12,-5/*mean (0.136269), correlation (0.498771)*/,
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-10,-2, -10,11/*mean (0.142173), correlation (0.498714)*/,
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-3,9, -2,-13/*mean (0.144141), correlation (0.491973)*/,
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2,-3, 3,2/*mean (0.14892), correlation (0.500782)*/,
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-9,-13, -4,0/*mean (0.150371), correlation (0.498211)*/,
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-4,6, -3,-10/*mean (0.152159), correlation (0.495547)*/,
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-4,12, -2,-7/*mean (0.156152), correlation (0.496925)*/,
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-6,-11, -4,9/*mean (0.15749), correlation (0.499222)*/,
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6,-3, 6,11/*mean (0.159211), correlation (0.503821)*/,
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-13,11, -5,5/*mean (0.162427), correlation (0.501907)*/,
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11,11, 12,6/*mean (0.16652), correlation (0.497632)*/,
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7,-5, 12,-2/*mean (0.169141), correlation (0.484474)*/,
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-1,12, 0,7/*mean (0.169456), correlation (0.495339)*/,
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-4,-8, -3,-2/*mean (0.171457), correlation (0.487251)*/,
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-7,1, -6,7/*mean (0.175), correlation (0.500024)*/,
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-13,-12, -8,-13/*mean (0.175866), correlation (0.497523)*/,
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-7,-2, -6,-8/*mean (0.178273), correlation (0.501854)*/,
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-8,5, -6,-9/*mean (0.181107), correlation (0.494888)*/,
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-5,-1, -4,5/*mean (0.190227), correlation (0.482557)*/,
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-13,7, -8,10/*mean (0.196739), correlation (0.496503)*/,
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1,5, 5,-13/*mean (0.19973), correlation (0.499759)*/,
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1,0, 10,-13/*mean (0.204465), correlation (0.49873)*/,
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9,12, 10,-1/*mean (0.209334), correlation (0.49063)*/,
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5,-8, 10,-9/*mean (0.211134), correlation (0.503011)*/,
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-1,11, 1,-13/*mean (0.212), correlation (0.499414)*/,
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-9,-3, -6,2/*mean (0.212168), correlation (0.480739)*/,
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-1,-10, 1,12/*mean (0.212731), correlation (0.502523)*/,
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-13,1, -8,-10/*mean (0.21327), correlation (0.489786)*/,
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8,-11, 10,-6/*mean (0.214159), correlation (0.488246)*/,
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2,-13, 3,-6/*mean (0.216993), correlation (0.50287)*/,
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7,-13, 12,-9/*mean (0.223639), correlation (0.470502)*/,
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-10,-10, -5,-7/*mean (0.224089), correlation (0.500852)*/,
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-10,-8, -8,-13/*mean (0.228666), correlation (0.502629)*/,
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4,-6, 8,5/*mean (0.22906), correlation (0.498305)*/,
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3,12, 8,-13/*mean (0.233378), correlation (0.503825)*/,
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-4,2, -3,-3/*mean (0.234323), correlation (0.476692)*/,
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5,-13, 10,-12/*mean (0.236392), correlation (0.475462)*/,
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4,-13, 5,-1/*mean (0.236842), correlation (0.504132)*/,
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-9,9, -4,3/*mean (0.236977), correlation (0.497739)*/,
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0,3, 3,-9/*mean (0.24314), correlation (0.499398)*/,
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-12,1, -6,1/*mean (0.243297), correlation (0.489447)*/,
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3,2, 4,-8/*mean (0.00155196), correlation (0.553496)*/,
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-10,-10, -10,9/*mean (0.00239541), correlation (0.54297)*/,
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8,-13, 12,12/*mean (0.0034413), correlation (0.544361)*/,
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-8,-12, -6,-5/*mean (0.003565), correlation (0.551225)*/,
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2,2, 3,7/*mean (0.00835583), correlation (0.55285)*/,
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10,6, 11,-8/*mean (0.00885065), correlation (0.540913)*/,
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6,8, 8,-12/*mean (0.0101552), correlation (0.551085)*/,
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-7,10, -6,5/*mean (0.0102227), correlation (0.533635)*/,
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-3,-9, -3,9/*mean (0.0110211), correlation (0.543121)*/,
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-1,-13, -1,5/*mean (0.0113473), correlation (0.550173)*/,
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-3,-7, -3,4/*mean (0.0140913), correlation (0.554774)*/,
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-8,-2, -8,3/*mean (0.017049), correlation (0.55461)*/,
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4,2, 12,12/*mean (0.01778), correlation (0.546921)*/,
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2,-5, 3,11/*mean (0.0224022), correlation (0.549667)*/,
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6,-9, 11,-13/*mean (0.029161), correlation (0.546295)*/,
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3,-1, 7,12/*mean (0.0303081), correlation (0.548599)*/,
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11,-1, 12,4/*mean (0.0355151), correlation (0.523943)*/,
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|
-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, 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);
|
|
// 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; 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 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; 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));
|
|
|
|
// 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;
|
|
}
|
|
|
|
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; i<kpts_size; i++) {
|
|
kp_t *kp = array_at(kpts, i);
|
|
|
|
// Write X
|
|
res = f_write(fp, &kp->x, 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; i<kpts_size; i++) {
|
|
kp_t *kp = xalloc(sizeof(*kp));
|
|
kp->matched = 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;
|
|
}
|
|
|
|
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);
|
|
|
|
}
|