haarobjectdetect.cl 25.2 KB
Newer Older
wester committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
//    Niko Li, newlife20080214@gmail.com
//    Wang Weiyan, wangweiyanster@gmail.com
//    Jia Haipeng, jiahaipeng95@gmail.com
//    Nathan, liujun@multicorewareinc.com
//    Peng Xiao, pengxiao@outlook.com
//    Erping Pang, erping@multicorewareinc.com
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's in binary form must reproduce the above copyright notice,
//     this list of conditions and the following disclaimer in the documentation
//     and/or other materials provided with the distribution.
//
//   * The name of the copyright holders may not be used to endorse or promote products
//     derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors as is and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//

#define CV_HAAR_FEATURE_MAX           3

#define calc_sum(rect,offset)        (sum[(rect).p0+offset] - sum[(rect).p1+offset] - sum[(rect).p2+offset] + sum[(rect).p3+offset])
#define calc_sum1(rect,offset,i)     (sum[(rect).p0[i]+offset] - sum[(rect).p1[i]+offset] - sum[(rect).p2[i]+offset] + sum[(rect).p3[i]+offset])

typedef int   sumtype;
typedef float sqsumtype;

#ifndef STUMP_BASED
#define STUMP_BASED 1
#endif

typedef struct __attribute__((aligned (128) )) GpuHidHaarTreeNode
{
    int p[CV_HAAR_FEATURE_MAX][4] __attribute__((aligned (64)));
    float weight[CV_HAAR_FEATURE_MAX];
    float threshold;
    float alpha[3] __attribute__((aligned (16)));
    int left __attribute__((aligned (4)));
    int right __attribute__((aligned (4)));
}
GpuHidHaarTreeNode;


typedef struct __attribute__((aligned (32))) GpuHidHaarClassifier
{
    int count __attribute__((aligned (4)));
    GpuHidHaarTreeNode* node __attribute__((aligned (8)));
    float* alpha __attribute__((aligned (8)));
}
GpuHidHaarClassifier;


typedef struct __attribute__((aligned (64))) GpuHidHaarStageClassifier
{
    int  count __attribute__((aligned (4)));
    float threshold __attribute__((aligned (4)));
    int two_rects __attribute__((aligned (4)));
    int reserved0 __attribute__((aligned (8)));
    int reserved1 __attribute__((aligned (8)));
    int reserved2 __attribute__((aligned (8)));
    int reserved3 __attribute__((aligned (8)));
}
GpuHidHaarStageClassifier;


typedef struct __attribute__((aligned (64))) GpuHidHaarClassifierCascade
{
    int  count __attribute__((aligned (4)));
    int  is_stump_based __attribute__((aligned (4)));
    int  has_tilted_features __attribute__((aligned (4)));
    int  is_tree __attribute__((aligned (4)));
    int pq0 __attribute__((aligned (4)));
    int pq1 __attribute__((aligned (4)));
    int pq2 __attribute__((aligned (4)));
    int pq3 __attribute__((aligned (4)));
    int p0 __attribute__((aligned (4)));
    int p1 __attribute__((aligned (4)));
    int p2 __attribute__((aligned (4)));
    int p3 __attribute__((aligned (4)));
    float inv_window_area __attribute__((aligned (4)));
} GpuHidHaarClassifierCascade;


#ifdef PACKED_CLASSIFIER
// this code is scalar, one pixel -> one workitem
__kernel void gpuRunHaarClassifierCascadePacked(
    global const GpuHidHaarStageClassifier * stagecascadeptr,
    global const int4 * info,
    global const GpuHidHaarTreeNode * nodeptr,
    global const int * restrict sum,
    global const float * restrict sqsum,
    volatile global int4 * candidate,
    const int pixelstep,
    const int loopcount,
    const int start_stage,
    const int split_stage,
    const int end_stage,
    const int startnode,
    const int splitnode,
    const int4 p,
    const int4 pq,
    const float correction,
    global const int* pNodesPK,
    global const int4* pWGInfo
    )

{
    int     gid = (int)get_group_id(0);
    int     lid_x = (int)get_local_id(0);
    int     lid_y = (int)get_local_id(1);
    int     lid = lid_y*LSx+lid_x;
    int4    WGInfo = pWGInfo[WGSTART+gid];
    int     GroupX = (WGInfo.y >> 16)&0xFFFF;
    int     GroupY = (WGInfo.y >> 0 )& 0xFFFF;
    int     Width  = (WGInfo.x >> 16)&0xFFFF;
    int     Height = (WGInfo.x >> 0 )& 0xFFFF;
    int     ImgOffset = WGInfo.z;
    float   ScaleFactor = as_float(WGInfo.w);

#define DATA_SIZE_X (PIXEL_STEP*LSx+WND_SIZE_X)
#define DATA_SIZE_Y (PIXEL_STEP*LSy+WND_SIZE_Y)
#define DATA_SIZE (DATA_SIZE_X*DATA_SIZE_Y)

    local int SumL[DATA_SIZE];

    // read input data window into local mem
    for(int i = 0; i<DATA_SIZE; i+=(LSx*LSy))
    {
        int     index = i+lid; // index in shared local memory
        if(index<DATA_SIZE)
        {// calc global x,y coordinat and read data from there
            int     x = min(GroupX + (index % (DATA_SIZE_X)),Width-1+WND_SIZE_X);
            int     y = min(GroupY + (index / (DATA_SIZE_X)),Height-1+WND_SIZE_Y);
            SumL[index] = sum[ImgOffset+y*pixelstep+x];
        }
    }
    barrier(CLK_LOCAL_MEM_FENCE);

    // calc variance_norm_factor for all stages
    float   variance_norm_factor;
    int     nodecounter= startnode;
    int4    info1 = p;
    int4    info2 = pq;

    // calc processed ROI coordinate in local mem
    int     xl = lid_x*PIXEL_STEP;
    int     yl = lid_y*PIXEL_STEP;

    {// calc variance_norm_factor for all stages
        int     OffsetLocal =          yl * DATA_SIZE_X +         xl;
        int     OffsetGlobal = (GroupY+yl)* pixelstep   + (GroupX+xl);

        // add shift to get position on scaled image
        OffsetGlobal += ImgOffset;

        float   mean =
            SumL[info1.y*DATA_SIZE_X+info1.x+OffsetLocal] -
            SumL[info1.y*DATA_SIZE_X+info1.z+OffsetLocal] -
            SumL[info1.w*DATA_SIZE_X+info1.x+OffsetLocal] +
            SumL[info1.w*DATA_SIZE_X+info1.z+OffsetLocal];
        float sq =
            sqsum[info2.y*pixelstep+info2.x+OffsetGlobal] -
            sqsum[info2.y*pixelstep+info2.z+OffsetGlobal] -
            sqsum[info2.w*pixelstep+info2.x+OffsetGlobal] +
            sqsum[info2.w*pixelstep+info2.z+OffsetGlobal];

        mean *= correction;
        sq *= correction;

        variance_norm_factor = sq - mean * mean;
        variance_norm_factor = (variance_norm_factor >=0.f) ? sqrt(variance_norm_factor) : 1.f;
    }// end calc variance_norm_factor for all stages

    int result = (1.0f>0.0f);
    for(int stageloop = start_stage; (stageloop < end_stage) && result; stageloop++ )
    {// iterate until candidate is valid
        float   stage_sum = 0.0f;
        int2    stageinfo = *(global int2*)(stagecascadeptr+stageloop);
        float   stagethreshold = as_float(stageinfo.y);
        int     lcl_off = (lid_y*DATA_SIZE_X)+(lid_x);
        for(int nodeloop = 0; nodeloop < stageinfo.x; nodecounter++,nodeloop++ )
        {
        // simple macro to extract shorts from int
#define M0(_t) ((_t)&0xFFFF)
#define M1(_t) (((_t)>>16)&0xFFFF)
            // load packed node data from global memory (L3) into registers
            global const int4* pN = (__global int4*)(pNodesPK+nodecounter*NODE_SIZE);
            int4    n0 = pN[0];
            int4    n1 = pN[1];
            int4    n2 = pN[2];
            float   nodethreshold  = as_float(n2.y) * variance_norm_factor;
            // calc sum of intensity pixels according to classifier node information
            float classsum =
                (SumL[M0(n0.x)+lcl_off] - SumL[M1(n0.x)+lcl_off] - SumL[M0(n0.y)+lcl_off] + SumL[M1(n0.y)+lcl_off]) * as_float(n1.z) +
                (SumL[M0(n0.z)+lcl_off] - SumL[M1(n0.z)+lcl_off] - SumL[M0(n0.w)+lcl_off] + SumL[M1(n0.w)+lcl_off]) * as_float(n1.w) +
                (SumL[M0(n1.x)+lcl_off] - SumL[M1(n1.x)+lcl_off] - SumL[M0(n1.y)+lcl_off] + SumL[M1(n1.y)+lcl_off]) * as_float(n2.x);
            //accumulate stage response
            stage_sum += (classsum >= nodethreshold) ? as_float(n2.w) : as_float(n2.z);
        }
        result = (stage_sum >= stagethreshold);
    }// next stage if needed

    if(result)
    {// all stages will be passed and there is a detected face on the tested position
        int index = 1+atomic_inc((volatile global int*)candidate); //get index to write global data with face info
        if(index<OUTPUTSZ)
        {
            int     x = GroupX+xl;
            int     y = GroupY+yl;
            int4 candidate_result;
            candidate_result.x = convert_int_rtn(x*ScaleFactor);
            candidate_result.y = convert_int_rtn(y*ScaleFactor);
            candidate_result.z = convert_int_rtn(ScaleFactor*WND_SIZE_X);
            candidate_result.w = convert_int_rtn(ScaleFactor*WND_SIZE_Y);
            candidate[index] = candidate_result;
        }
    }
}//end gpuRunHaarClassifierCascade
#else

__kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCascade(
    global GpuHidHaarStageClassifier * stagecascadeptr,
    global int4 * info,
    global GpuHidHaarTreeNode * nodeptr,
    global const int * restrict sum1,
    global const float * restrict sqsum1,
    global int4 * candidate,
    const int pixelstep,
    const int loopcount,
    const int start_stage,
    const int split_stage,
    const int end_stage,
    const int startnode,
    const int splitnode,
    const int4 p,
    const int4 pq,
    const float correction)
{
    int grpszx = get_local_size(0);
    int grpszy = get_local_size(1);
    int grpnumx = get_num_groups(0);
    int grpidx = get_group_id(0);
    int lclidx = get_local_id(0);
    int lclidy = get_local_id(1);

    int lcl_sz = mul24(grpszx,grpszy);
    int lcl_id = mad24(lclidy,grpszx,lclidx);

    __local int lclshare[1024];
    __local int* lcldata = lclshare;//for save win data
    __local int* glboutindex = lcldata + 28*28;//for save global out index
    __local int* lclcount = glboutindex + 1;//for save the numuber of temp pass pixel
    __local int* lcloutindex = lclcount + 1;//for save info of temp pass pixel
    __local float* partialsum = (__local float*)(lcloutindex + (lcl_sz<<1));
    glboutindex[0]=0;
    int outputoff = mul24(grpidx,256);

    //assume window size is 20X20
#define WINDOWSIZE 20+1
    //make sure readwidth is the multiple of 4
    //ystep =1, from host code
    int readwidth = ((grpszx-1 + WINDOWSIZE+3)>>2)<<2;
    int readheight = grpszy-1+WINDOWSIZE;
    int read_horiz_cnt = readwidth >> 2;//each read int4
    int total_read = mul24(read_horiz_cnt,readheight);
    int read_loop = (total_read + lcl_sz - 1) >> 6;
    candidate[outputoff+(lcl_id<<2)] = (int4)0;
    candidate[outputoff+(lcl_id<<2)+1] = (int4)0;
    candidate[outputoff+(lcl_id<<2)+2] = (int4)0;
    candidate[outputoff+(lcl_id<<2)+3] = (int4)0;
    for(int scalei = 0; scalei <loopcount; scalei++)
    {
        int4 scaleinfo1= info[scalei];
        int height = scaleinfo1.x & 0xffff;
        int grpnumperline =(scaleinfo1.y & 0xffff0000) >> 16;
        int totalgrp = scaleinfo1.y & 0xffff;
        int imgoff = scaleinfo1.z;
        float factor = as_float(scaleinfo1.w);

        __global const int * sum = sum1 + imgoff;
        __global const float * sqsum = sqsum1 + imgoff;
        for(int grploop=grpidx; grploop<totalgrp; grploop+=grpnumx)
        {
            int grpidy = grploop / grpnumperline;
            int grpidx = grploop - mul24(grpidy, grpnumperline);
            int x = mad24(grpidx,grpszx,lclidx);
            int y = mad24(grpidy,grpszy,lclidy);
            int grpoffx = x-lclidx;
            int grpoffy = y-lclidy;

            for(int i=0; i<read_loop; i++)
            {
                int pos_id = mad24(i,lcl_sz,lcl_id);
                pos_id = pos_id < total_read ? pos_id : 0;

                int lcl_y = pos_id / read_horiz_cnt;
                int lcl_x = pos_id - mul24(lcl_y, read_horiz_cnt);

                int glb_x = grpoffx + (lcl_x<<2);
                int glb_y = grpoffy + lcl_y;

                int glb_off = mad24(min(glb_y, height + WINDOWSIZE - 1),pixelstep,glb_x);
                int4 data = *(__global int4*)&sum[glb_off];
                int lcl_off = mad24(lcl_y, readwidth, lcl_x<<2);

                vstore4(data, 0, &lcldata[lcl_off]);
            }

            lcloutindex[lcl_id] = 0;
            lclcount[0] = 0;
            int result = 1;
            int nodecounter= startnode;
            float mean, variance_norm_factor;
            barrier(CLK_LOCAL_MEM_FENCE);

            int lcl_off = mad24(lclidy,readwidth,lclidx);
            int4 cascadeinfo1, cascadeinfo2;
            cascadeinfo1 = p;
            cascadeinfo2 = pq;

            cascadeinfo1.x +=lcl_off;
            cascadeinfo1.z +=lcl_off;
            mean = (lcldata[mad24(cascadeinfo1.y,readwidth,cascadeinfo1.x)] - lcldata[mad24(cascadeinfo1.y,readwidth,cascadeinfo1.z)] -
                    lcldata[mad24(cascadeinfo1.w,readwidth,cascadeinfo1.x)] + lcldata[mad24(cascadeinfo1.w,readwidth,cascadeinfo1.z)])
                    *correction;

            int p_offset = mad24(y, pixelstep, x);

            cascadeinfo2.x +=p_offset;
            cascadeinfo2.z +=p_offset;
            variance_norm_factor =sqsum[mad24(cascadeinfo2.y, pixelstep, cascadeinfo2.x)] - sqsum[mad24(cascadeinfo2.y, pixelstep, cascadeinfo2.z)] -
                                    sqsum[mad24(cascadeinfo2.w, pixelstep, cascadeinfo2.x)] + sqsum[mad24(cascadeinfo2.w, pixelstep, cascadeinfo2.z)];

            variance_norm_factor = variance_norm_factor * correction - mean * mean;
            variance_norm_factor = variance_norm_factor >=0.f ? sqrt(variance_norm_factor) : 1.f;

            for(int stageloop = start_stage; (stageloop < split_stage)  && result; stageloop++ )
            {
                float stage_sum = 0.f;
                int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
                float stagethreshold = as_float(stageinfo.y);
                for(int nodeloop = 0; nodeloop < stageinfo.x; )
                {
                    __global GpuHidHaarTreeNode* currentnodeptr = (nodeptr + nodecounter);

                    int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
                    int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
                    int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0]));
                    float4 w = *(__global float4*)(&(currentnodeptr->weight[0]));
                    float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0]));

                    float nodethreshold  = w.w * variance_norm_factor;

                    info1.x +=lcl_off;
                    info1.z +=lcl_off;
                    info2.x +=lcl_off;
                    info2.z +=lcl_off;

                    float classsum = (lcldata[mad24(info1.y,readwidth,info1.x)] - lcldata[mad24(info1.y,readwidth,info1.z)] -
                                        lcldata[mad24(info1.w,readwidth,info1.x)] + lcldata[mad24(info1.w,readwidth,info1.z)]) * w.x;

                    classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] -
                                    lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y;

                    info3.x +=lcl_off;
                    info3.z +=lcl_off;
                    classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] -
                                    lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z;

                    bool passThres = classsum >= nodethreshold;
#if STUMP_BASED
                    stage_sum += passThres ? alpha3.y : alpha3.x;
                    nodecounter++;
                    nodeloop++;
#else
                    bool isRootNode = (nodecounter & 1) == 0;
                    if(isRootNode)
                    {
                        if( (passThres && currentnodeptr->right) ||
                            (!passThres && currentnodeptr->left))
                        {
                            nodecounter ++;
                        }
                        else
                        {
                            stage_sum += alpha3.x;
                            nodecounter += 2;
                            nodeloop ++;
                        }
                    }
                    else
                    {
                        stage_sum += passThres ? alpha3.z : alpha3.y;
                        nodecounter ++;
                        nodeloop ++;
                    }
#endif
                }

                result = (stage_sum >= stagethreshold);
            }
            if(factor < 2)
            {
                if(result && lclidx %2 ==0 && lclidy %2 ==0 )
                {
                    int queueindex = atomic_inc(lclcount);
                    lcloutindex[queueindex<<1] = (lclidy << 16) | lclidx;
                    lcloutindex[(queueindex<<1)+1] = as_int((float)variance_norm_factor);
                }
            }
            else
            {
                if(result)
                {
                    int queueindex = atomic_inc(lclcount);
                    lcloutindex[queueindex<<1] = (lclidy << 16) | lclidx;
                    lcloutindex[(queueindex<<1)+1] = as_int((float)variance_norm_factor);
                }
            }
            barrier(CLK_LOCAL_MEM_FENCE);
            int queuecount  = lclcount[0];
            barrier(CLK_LOCAL_MEM_FENCE);
            nodecounter = splitnode;
            for(int stageloop = split_stage; stageloop< end_stage && queuecount>0; stageloop++)
            {
                lclcount[0]=0;
                barrier(CLK_LOCAL_MEM_FENCE);

                int2 stageinfo = *(global int2*)(stagecascadeptr+stageloop);
                float stagethreshold = as_float(stageinfo.y);

                int perfscale = queuecount > 4 ? 3 : 2;
                int queuecount_loop = (queuecount + (1<<perfscale)-1) >> perfscale;
                int lcl_compute_win = lcl_sz >> perfscale;
                int lcl_compute_win_id = (lcl_id >>(6-perfscale));
                int lcl_loops = (stageinfo.x + lcl_compute_win -1) >> (6-perfscale);
                int lcl_compute_id = lcl_id - (lcl_compute_win_id << (6-perfscale));
                for(int queueloop=0; queueloop<queuecount_loop; queueloop++)
                {
                    float stage_sum = 0.f;
                    int temp_coord = lcloutindex[lcl_compute_win_id<<1];
                    float variance_norm_factor = as_float(lcloutindex[(lcl_compute_win_id<<1)+1]);
                    int queue_pixel = mad24(((temp_coord  & (int)0xffff0000)>>16),readwidth,temp_coord & 0xffff);

                    if(lcl_compute_win_id < queuecount)
                    {
                        int tempnodecounter = lcl_compute_id;
                        float part_sum = 0.f;
                        const int stump_factor = STUMP_BASED ? 1 : 2;
                        int root_offset = 0;
                        for(int lcl_loop=0; lcl_loop<lcl_loops && tempnodecounter<stageinfo.x;)
                        {
                            __global GpuHidHaarTreeNode* currentnodeptr =
                                nodeptr + (nodecounter + tempnodecounter) * stump_factor + root_offset;

                            int4 info1 = *(__global int4*)(&(currentnodeptr->p[0][0]));
                            int4 info2 = *(__global int4*)(&(currentnodeptr->p[1][0]));
                            int4 info3 = *(__global int4*)(&(currentnodeptr->p[2][0]));
                            float4 w = *(__global float4*)(&(currentnodeptr->weight[0]));
                            float3 alpha3 = *(__global float3*)(&(currentnodeptr->alpha[0]));
                            float nodethreshold  = w.w * variance_norm_factor;

                            info1.x +=queue_pixel;
                            info1.z +=queue_pixel;
                            info2.x +=queue_pixel;
                            info2.z +=queue_pixel;

                            float classsum = (lcldata[mad24(info1.y,readwidth,info1.x)] - lcldata[mad24(info1.y,readwidth,info1.z)] -
                                                lcldata[mad24(info1.w,readwidth,info1.x)] + lcldata[mad24(info1.w,readwidth,info1.z)]) * w.x;


                            classsum += (lcldata[mad24(info2.y,readwidth,info2.x)] - lcldata[mad24(info2.y,readwidth,info2.z)] -
                                            lcldata[mad24(info2.w,readwidth,info2.x)] + lcldata[mad24(info2.w,readwidth,info2.z)]) * w.y;

                            info3.x +=queue_pixel;
                            info3.z +=queue_pixel;
                            classsum += (lcldata[mad24(info3.y,readwidth,info3.x)] - lcldata[mad24(info3.y,readwidth,info3.z)] -
                                            lcldata[mad24(info3.w,readwidth,info3.x)] + lcldata[mad24(info3.w,readwidth,info3.z)]) * w.z;

                            bool passThres = classsum >= nodethreshold;
#if STUMP_BASED
                            part_sum += passThres ? alpha3.y : alpha3.x;
                            tempnodecounter += lcl_compute_win;
                            lcl_loop++;
#else
                            if(root_offset == 0)
                            {
                                if( (passThres && currentnodeptr->right) ||
                                    (!passThres && currentnodeptr->left))
                                {
                                    root_offset = 1;
                                }
                                else
                                {
                                    part_sum += alpha3.x;
                                    tempnodecounter += lcl_compute_win;
                                    lcl_loop++;
                                }
                            }
                            else
                            {
                                part_sum += passThres ? alpha3.z : alpha3.y;
                                tempnodecounter += lcl_compute_win;
                                lcl_loop++;
                                root_offset = 0;
                            }
#endif
                        }//end for(int lcl_loop=0;lcl_loop<lcl_loops;lcl_loop++)
                        partialsum[lcl_id]=part_sum;
                    }
                    barrier(CLK_LOCAL_MEM_FENCE);
                    if(lcl_compute_win_id < queuecount)
                    {
                        for(int i=0; i<lcl_compute_win && (lcl_compute_id==0); i++)
                        {
                            stage_sum += partialsum[lcl_id+i];
                        }
                        if(stage_sum >= stagethreshold && (lcl_compute_id==0))
                        {
                            int queueindex = atomic_inc(lclcount);
                            lcloutindex[queueindex<<1] = temp_coord;
                            lcloutindex[(queueindex<<1)+1] = as_int(variance_norm_factor);
                        }
                        lcl_compute_win_id +=(1<<perfscale);
                    }
                    barrier(CLK_LOCAL_MEM_FENCE);
                }//end for(int queueloop=0;queueloop<queuecount_loop;queueloop++)

                queuecount = lclcount[0];
                barrier(CLK_LOCAL_MEM_FENCE);
                nodecounter += stageinfo.x;
            }//end for(int stageloop = splitstage; stageloop< endstage && queuecount>0;stageloop++)

            if(lcl_id<queuecount)
            {
                int temp = lcloutindex[lcl_id<<1];
                int x = mad24(grpidx,grpszx,temp & 0xffff);
                int y = mad24(grpidy,grpszy,((temp & (int)0xffff0000) >> 16));
                temp = glboutindex[0];
                int4 candidate_result;
                candidate_result.zw = (int2)convert_int_rte(factor*20.f);
                candidate_result.x = convert_int_rte(x*factor);
                candidate_result.y = convert_int_rte(y*factor);
                atomic_inc(glboutindex);

                int i = outputoff+temp+lcl_id;
                if(candidate[i].z == 0)
                {
                    candidate[i] = candidate_result;
                }
                else
                {
                    for(i=i+1;;i++)
                    {
                        if(candidate[i].z == 0)
                        {
                            candidate[i] = candidate_result;
                            break;
                        }
                    }
                }
            }
            barrier(CLK_LOCAL_MEM_FENCE);
        }//end for(int grploop=grpidx;grploop<totalgrp;grploop+=grpnumx)
    }//end for(int scalei = 0; scalei <loopcount; scalei++)
}
#endif