openmv/src/omv/img/orb.c
2019-09-05 22:17:50 +02:00

807 lines
30 KiB
C

/*
* This file is part of the OpenMV project.
*
* Copyright (c) 2013-2019 Ibrahim Abdelkader <iabdalkader@openmv.io>
* Copyright (c) 2013-2019 Kwabena W. Agyeman <kwagyeman@openmv.io>
*
* This work is licensed under the MIT license, see the file LICENSE for details.
*
* ORB keypoints descriptor based on OpenCV code.
*/
#include <stdio.h>
#include <string.h>
#include <stdint.h>
#include <stdbool.h>
#include "fmath.h"
#include "arm_math.h"
#include "ff.h"
#include "imlib.h"
#include "xalloc.h"
#include "fb_alloc.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, 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);
}