-01-生成ORB离散查找表【特征点检测】
来源:互联网 发布:简易个人博客php源码 编辑:程序博客网 时间:2024/06/14 21:40
ORB算法计算描述子时需要使用到一个提前计算好的查找表,具体可以看OpenCV的orb.cpp文件中的数组:
static int bit_pattern_31_[256*4]
这个查找表对应的是0度,当特征点不是0度时,需要将查找表中的坐标进行旋转,这部分代码在OpenCV中有。再看论文中,作者将查找表以12°步进的方式生成了30个离散的查找表,这样计算时就能够快速选定坐标位置。
作者选的30等分角度应该是评估过误差的,我就先不去考虑了,而且我一时找不到包含这30个查找表的源码,所以只能手动生成了。
Python
一如既往用Python来做。
脚本还有相关的文件都在后面,粘贴保存后直接可用。
我把一对坐标信息,四个数值:x0,y0,x1,y1直接保存到了一个uint32类型当中去,并对各个数值进行了一个偏移(坐标旋转后最大边界会到达±18,所以偏移选了18,坐标所在的窗口为37*37)。
这样方便我后面在fpga对这些数据进行查表操作,嘻嘻··
要是大家有需求的话,把这个脚本稍微改一改就能得到自己想用到的格式了。
Python脚本文件:
bit_pattern_12degrees_inc.py
import mathdef char2int(n): if n&0x80: ret = -((~n&0xff)+1) else: ret = n return retfile = open('bit_pattern_31_.txt', 'r')lines_int_array = []while 1: lines = file.readlines(1000) if not lines: break for line in lines: line_str = line[:line.find('/*')] line_str_array = line_str.split(',') tmp = map(int, line_str_array) lines_int_array.append(tmp) pass#print lines_int_arrayprint '--------------'#disperse_incDINC = 12KERNEL = 31kernel_half = KERNEL/2#------------------------------------------max_num = 0;min_num = 0;max_rot = 0;for line_int_array in lines_int_array: max_temp = max(line_int_array) min_temp = min(line_int_array) if(max_num < max_temp): max_num = max_temp if(min_num > min_temp): min_num = min_tempif(abs(min_num) > abs(max_num)): max_rot = abs(min_num)else: max_rot = abs(min_num)max_offset = int(round(max_rot*math.cos(math.radians(45))+max_rot*math.sin(math.radians(45))))print 'max num = %d'%max_numprint 'min num = %d'%min_numprint 'max rot = %d'%max_rotprint 'max offset = %d'%max_offset#------------------------------------------file_dst = open('bit_pattern_12degrees_inc.c', 'w')file_dst.write('// Attention:\n')file_dst.write('// The look up table max offset is : ' + str(max_offset) + '\n')file_dst.write('// The image window is : ' + str(max_offset*2+1) + '^2\n')file_dst.write('\n\n ')for j in range(360/DINC): lut_num_int_s = j*DINC-6 if(lut_num_int_s < 0): lut_num_int_s += 360 lut_num_int_e = (j+1)*DINC-1-6 lut_num_str_s = str(lut_num_int_s) lut_num_str_e = str(lut_num_int_e) lut_num_str = lut_num_str_s + '_' + lut_num_str_e file_dst.write('//lut from ' + lut_num_str_s + ' degree to ' + lut_num_str_e + ' degree.\n') file_dst.write('unsigned int bit_pattern_' + str(max_offset*2+1) + '_unt32_' + str(j+1) + '_of' + str(360/DINC) + '[256] =\n{\n') for i in range(len(lines_int_array)): x0 = lines_int_array[i][0] y0 = lines_int_array[i][1] x1 = lines_int_array[i][2] y1 = lines_int_array[i][3] r_cos = math.cos(math.radians(DINC*j)) r_sin = math.sin(math.radians(DINC*j)) r_x0 = int(round(x0*r_cos - y0*r_sin)) r_y0 = int(round(x0*r_sin + y0*r_cos)) r_x1 = int(round(x1*r_cos - y1*r_sin)) r_y1 = int(round(x1*r_sin + y1*r_cos)) line_int32_offset = ((r_x0+max_offset)&0xff)<<0 | \ ((r_y0+max_offset)&0xff)<<8 | \ ((r_x1+max_offset)&0xff)<<16 | \ ((r_y1+max_offset)&0xff)<<24 file_dst.write('\t') file_dst.write('0x%08x'%line_int32_offset) if(i == len(lines_int_array)-1): file_dst.write(' ') else: file_dst.write(',') file_dst.write(' //%3d,'%r_x0 + '%3d,'%r_y0 + '%3d,'%r_x1 + '%3d,'%r_y1 + ' ---%3d'%i) file_dst.write('\n') file_dst.write('};\n') file_dst.write('\n') file_dst.write('\n')file_dst.close()file.close()
数组文件:
bit_pattern_31_.txt
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)*/
阅读全文
0 0
- -01-生成ORB离散查找表【特征点检测】
- ORB特征点检测
- ORB特征点检测
- ORB特征点检测
- ORB特征点检测
- ORB特征点检测
- ORB特征点检测
- ORB特征点检测
- ORB特征点检测
- ORB特征点检测
- ORB特征点检测
- OpenCV特征点检测------ORB特征
- OpenCV特征点检测------ORB特征
- OpenCV特征点检测------ORB特征
- OpenCV特征点检测------ORB特征
- ORB特征点检测与描述详解
- ORB特征点检测与匹配
- ORB特征点检测和匹配
- ubuntu14.04+caffe2
- MyBatis中mapper接口方法多参数传入
- uC/OS-III的任务管理
- Android Studio 配置使用 Kotlin
- 降维技术-理解PCA
- -01-生成ORB离散查找表【特征点检测】
- 1:HTML 中 onclick 触发函数 xxx(param) 要传递对象参数的解决方法 2:LocalStorage存储JSON对象的问题 3:ajax请求传送参数为对象问题
- Flag:《道连·格雷的画像》
- ubuntu14.04、CPU的py-faster-rcnn安装步骤
- 陶陶摘苹果(升级版)
- 还原
- (转)FOF、MOM投资模式与金融科技应用展望
- CI 框架利用hooks 做登录/权限验证
- mysql在window下的安装