2016-10-20 69 views
2

我想通过使用arm霓虹库实现一篇论文,在ARM cortex a8上发现了大约5ms的ORB特性计算。但我已经在使用FAST特征检测功能挣扎。 因此,我尝试实施的论文可以找到here。所以首先我不确定明亮和黑暗的限制。因此,根据我的理解,如果中心像素周围有9个较暗或9个较亮的像素,则必须检查FAST。所以我检查了两者。但是现在我遇到这样的问题,即我的实现平均需要3倍的时间才能完成,如果不是最终的移位操作,那么计算它是否是拐角,然后是整个进度的opencv的平均计算。所以这里是我的代码到目前为止,也许有人可以指点我可以做的一些优化。针对ARM的优化FAST计算

 //detect with opncv 
     Clock::time_point t0 = Clock::now(); 
     detectors[y]->detect(img, ocv_kps); 
     Clock::time_point t1 = Clock::now(); 

     vector<Point2f> my_kps; 
     //threshhold for FAST 
     const uchar th = 8; 

     int b_cnt = 0; 
     int d_cnt = 0; 
     //array with four possible corners to be processed in parallel 
     uint32_t id_arr[4]; 
     uint32_t ib_arr[4]; 

     Clock::time_point t01 = Clock::now(); 
     for (int i = 3; i < img.rows - 3; i++) { 
      //get pointer to seven Image rows three above and three below center and center itself 
      const uchar* Mt3 = img.ptr<uchar>(i - 3); 
      const uchar* Mt2 = img.ptr<uchar>(i - 2); 
      const uchar* Mt1 = img.ptr<uchar>(i - 1); 
      const uchar* Mc = img.ptr<uchar>(i); 
      const uchar* Mb1 = img.ptr<uchar>(i + 1); 
      const uchar* Mb2 = img.ptr<uchar>(i + 2); 
      const uchar* Mb3 = img.ptr<uchar>(i + 3); 
      for (int j = 3; j < img.cols - 3; j++) { 
       const uchar j3 = j + 3; 
       const uchar j2 = j + 2; 
       const uchar j1 = j + 1; 
       const uchar jn3 = j - 3; 
       const uchar jn2 = j - 2; 
       const uchar jn1 = j - 1; 

       //image values for center left right top and bottom intensity of pixel 
       const uchar c = Mc[j]; 
       const uchar l = Mc[jn3]; 
       const uchar r = Mc[j3]; 
       const uchar t = Mt3[j]; 
       const uchar b = Mb3[j]; 

       //threshold for bright FAST constraint 
       const uchar thb = c + th; 

       //bools for bright constraint 
       const bool cbt = t > thb; 
       const bool cbb = b > thb; 
       const bool cbl = l > thb; 
       const bool cbr = r > thb; 

       uchar mt3; 
       uchar mt3n; 
       uchar mt2; 
       uchar mt2n; 
       uchar mt1; 
       uchar mt1n; 
       uchar mb3; 
       uchar mb3n; 
       uchar mb2; 
       uchar mb2n; 
       uchar mb1; 
       uchar mb1n; 
       bool bc = false; 
       //pre test do we have at least two points which fulfill bright constraint 
       if ((cbl && cbt) || (cbt && cbr) || (cbr && cbb) 
         || (cbb && cbl)) { 
        bc = true; 
        //get rest of image intensity values of circle 
        mt3 = Mt3[j1]; 
        mt3n = Mt3[jn1]; 
        mt2 = Mt2[j2]; 
        mt2n = Mt2[jn2]; 
        mt1 = Mt1[j3]; 
        mt1n = Mt1[jn3]; 
        mb3 = Mb3[j1]; 
        mb3n = Mb3[jn1]; 
        mb2 = Mb2[j2]; 
        mb2n = Mb2[jn2]; 
        mb1 = Mb1[j3]; 
        mb1n = Mb1[jn3]; 

        //values for bright constrain 
        ib_arr[b_cnt] = cbt | ((mt3) > thb) << 1 
          | ((mt2) > thb) << 2 | ((mt1) > thb) << 3 
          | (cbr << 4) | ((mb1) > thb) << 5 
          | ((mb2) > thb) << 6 | ((mb3) > thb) << 7 
          | cbb << 8 | ((mb3n) > thb) << 9 
          | ((mb2n) > thb) << 10 | ((mb1n) > thb) << 11 
          | (cbl) << 12 | ((mt1n) > thb) << 13 
          | ((mt2n) > thb) << 14 | ((mt3n) > thb) << 15 
          | (cbt) << 16 | ((mt3) > thb) << 17 
          | ((mt2) > thb) << 18 | ((mt1) > thb) << 19 
          | (cbr) << 20 | ((mb1) > thb) << 21 
          | ((mb2) > thb) << 22 | ((mb3) > thb) << 23; 
        b_cnt++; 
        //if we have four possible corners in array check if they are corners 
        if (b_cnt == 4) { 
         uint32x2x4_t IB = vld4_u32(ib_arr); 
         /* 
         * here the actual shift operation would take place 
         */ 
         b_cnt = 0; 
        } 
       } 

       //threshold for dark constraint 
       const uchar thd = c - th; 
       //bools for dark constraint 
       const bool cdl = l < thd; 
       const bool cdr = r < thd; 
       const bool cdt = t < thd; 
       const bool cdb = b < thd; 
       //pre test do we have at least two points which fulfill dark constraint 
       if ((cdl && cdt) || (cdt && cdr) || (cdr && cdb) 
         || (cdb && cdl)) { 
        //if bright pre test failed intensity values are not initialised 
        if (!bc) { 
         //get rest of image intensity values of circle 
         mt3 = Mt3[j1]; 
         mt3n = Mt3[jn1]; 
         mt2 = Mt2[j2]; 
         mt2n = Mt2[jn2]; 
         mt1 = Mt1[j3]; 
         mt1n = Mt1[jn3]; 
         mb3 = Mb3[j1]; 
         mb3n = Mb3[jn1]; 
         mb2 = Mb2[j2]; 
         mb2n = Mb2[jn2]; 
         mb1 = Mb1[j3]; 
         mb1n = Mb1[jn3]; 
        } 
        //bool values for dark constrain 
        id_arr[d_cnt] = cdt | ((mt3) < thd) << 1 
          | ((mt2) < thd) << 2 | ((mt1) < thd) << 3 
          | (cdr) << 4 | ((mb1) < thd) << 5 
          | ((mb2) < thd) << 6 | ((mb3) < thd) << 7 
          | (cdb) << 8 | ((mb3n) < thd) << 9 
          | ((mb2n) < thd) << 10 | ((mb1n) < thd) << 11 
          | (cdl) << 12 | ((mt1n) < thd) << 13 
          | ((mt2n) < thd) << 14 | ((mt3n) < thd) << 15 
          | (cdt) << 16 | ((mt3) < thd) << 17 
          | ((mt2) < thd) << 18 | ((mt1) < thd) << 19 
          | (cdr) << 20 | ((mb1) < thd) << 21 
          | ((mb2) < thd) << 22 | ((mb3) < thd) << 23; 
        d_cnt++; 
        //if we have four possible corners in array check if they are corners 
        if (d_cnt == 4) { 
         uint32x2x4_t IA = vld4_u32(id_arr); 
         /* 
         * here the actual shift operation would take place 
         */ 
         d_cnt = 0; 
        } 
        int h = cdt; 

       } 
      } 
     } 
     Clock::time_point t11 = Clock::now(); 
     cout << "my algorithm found " << my_kps.size() 
       << " and ocv found " << ocv_kps.size() << endl; 

     microseconds ms1 = std::chrono::duration_cast < microseconds 
       > (t1 - t0); 
     microseconds ms2 = std::chrono::duration_cast < microseconds 
       > (t11 - t01); 

     rs.Push((double) ms2.count()); 
     cout << "my algorithm duration " << ms2.count() 
       << " and ocv duration is " << ms1.count() << endl; 

回答

0

所以在Arm Assembler中挖了一点之后。我想出了一个代码,在Arm上运行的速度至少快了2倍,然后在Fast9的OpenCv实现中内置了代码。您可以查看GitHub上的代码。对于优化它的任何重新调整,我感到非常高兴。 在我的树莓派3花轮: 1000毫秒为我的算法的320×240灰度图像 2000毫秒为OpenCV的

1

我有一个ORB提取器,在覆盆子pi上以30fps运行。

https://github.com/0xfaded/pislam

优化真的是一个黑色的艺术,而更糟糕的是ARM从未发布了A53的优化指南。我们最好的是a57,它可能有一个类似的NEON单元。

我不能在这里提供完整的答案,但我会分享一些关于我的过程。

我的FAST提取器的第一部分加载测试像素环,并将它们转换为16位向量,就像您的代码一样。我没有直接写asm,而是使用了gcc intrinsics。不过,虽然,我确信,GCC:

  1. 没有将任何寄存器来发出指令的最小数量为每个比较堆栈

你会发现,第一次比较呢不要用掩码来隔离它的位,这将是0x80。这释放了一个本来会保持不变的寄存器,并且它给gcc足够的摆动空间以防止寄存器溢出。

您还会注意到一些相当可怕的内在用法:

d0 = vbslq_u8(vdupq_n_u8(0x40u), vcgeq_u8(test, dark), d0); 
    l0 = vbslq_u8(vdupq_n_u8(0x40u), vcleq_u8(test, light), l0); 

这相当于

d0 |= test >= dark & 0x40; 
    l0 |= test >= light & 0x40; 

GCC将愉快地编译后者,但放出1.5倍尽可能多的指令。

第二部分是对16位向量进行FAST-9测试。下面编译成16条指令,但花了我近一个月的思考和想法。

uint8x16_t t0 = vtstq_u8(d0, d1); 
    uint8x16_t t1 = vtstq_u8(d0, d1); 

    t0 = vbslq_u8(t0, l0, d0); 
    t1 = vbslq_u8(t1, l1, d1); 

    uint8x16_t cntLo = vclzq_u8(t0); 
    uint8x16_t testLo = t1 << (cntLo - 1); 
    asm("vceq.u8 %q0, %q0, #0" : [val] "+w" (testLo)); 

    uint8x16_t cntHi = vclzq_u8(t1); 
    uint8x16_t testHi = t0 << (cntHi - 1); 
    asm("vceq.u8 %q0, %q0, #0" : [val] "+w" (testHi)); 

    uint8x16_t result = (cntLo & testLo) | (cntHi & testHi); 
    result = vtstq_u8(result, result); 

烦人,GCC不会编译testLo == 0vceq.u8 %q0, %q0, #0,这是比较恒定的零特殊的指令。我结束了这些手动插入这削减了另一些夫妇的指示。

希望能提供一些见解。 Fast.h