Skip to content
Navigation Menu
{{ message }}
forked from joaofaro/KCFcpp
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathfhog.cpp
More file actions
511 lines (455 loc) · 16.7 KB
/
Copy pathfhog.cpp
File metadata and controls
511 lines (455 loc) · 16.7 KB
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
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2013, University of Nizhny Novgorod, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// 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.
//
//M*/
//Modified from latentsvm module's "lsvmc_featurepyramid.cpp".
//#include "precomp.hpp"
//#include "_lsvmc_latentsvm.h"
//#include "_lsvmc_resizeimg.h"
#include "fhog.hpp"
#ifdef HAVE_TBB
#include <tbb/tbb.h>
#include "tbb/parallel_for.h"
#include "tbb/blocked_range.h"
#endif
#ifndef max
#define max(a,b) (((a) > (b)) ? (a) : (b))
#endif
#ifndef min
#define min(a,b) (((a) < (b)) ? (a) : (b))
#endif
/*
// Getting feature map for the selected subimage
//
// API
// int getFeatureMaps(const IplImage * image, const int k, featureMap **map);
// INPUT
// image - selected subimage
// k - size of cells
// OUTPUT
// map - feature map
// RESULT
// Error status
*/
int getFeatureMaps(const IplImage* image, const int k, CvLSVMFeatureMapCaskade **map)
{
int sizeX, sizeY;
int p, px, stringSize;
int height, width, numChannels;
int i, j, kk, c, ii, jj, d;
float * datadx, * datady;
int ch;
float magnitude, x, y, tx, ty;
IplImage * dx, * dy;
int *nearest;
float *w, a_x, b_x;
float kernel[3] = {-1.f, 0.f, 1.f};
CvMat kernel_dx = cvMat(1, 3, CV_32F, kernel);
CvMat kernel_dy = cvMat(3, 1, CV_32F, kernel);
float * r;
int * alfa;
float boundary_x[NUM_SECTOR + 1];
float boundary_y[NUM_SECTOR + 1];
float max, dotProd;
int maxi;
height = image->height;
width = image->width ;
numChannels = image->nChannels;
dx = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
dy = cvCreateImage(cvSize(image->width, image->height),
IPL_DEPTH_32F, 3);
sizeX = width / k;
sizeY = height / k;
px = 3 * NUM_SECTOR;
p = px;
stringSize = sizeX * p;
allocFeatureMapObject(map, sizeX, sizeY, p);
cvFilter2D(image, dx, &kernel_dx, cvPoint(-1, 0));
cvFilter2D(image, dy, &kernel_dy, cvPoint(0, -1));
float arg_vector;
for(i = 0; i <= NUM_SECTOR; i++)
{
arg_vector = ( (float) i ) * ( (float)(PI) / (float)(NUM_SECTOR) );
boundary_x[i] = cosf(arg_vector);
boundary_y[i] = sinf(arg_vector);
}/*for(i = 0; i <= NUM_SECTOR; i++) */
r = (float *)malloc( sizeof(float) * (width * height));
alfa = (int *)malloc( sizeof(int ) * (width * height * 2));
for(j = 1; j < height - 1; j++)
{
datadx = (float*)(dx->imageData + dx->widthStep * j);
datady = (float*)(dy->imageData + dy->widthStep * j);
for(i = 1; i < width - 1; i++)
{
c = 0;
x = (datadx[i * numChannels + c]);
y = (datady[i * numChannels + c]);
r[j * width + i] =sqrtf(x * x + y * y);
for(ch = 1; ch < numChannels; ch++)
{
tx = (datadx[i * numChannels + ch]);
ty = (datady[i * numChannels + ch]);
magnitude = sqrtf(tx * tx + ty * ty);
if(magnitude > r[j * width + i])
{
r[j * width + i] = magnitude;
c = ch;
x = tx;
y = ty;
}
}/*for(ch = 1; ch < numChannels; ch++)*/
max = boundary_x[0] * x + boundary_y[0] * y;
maxi = 0;
for (kk = 0; kk < NUM_SECTOR; kk++)
{
dotProd = boundary_x[kk] * x + boundary_y[kk] * y;
if (dotProd > max)
{
max = dotProd;
maxi = kk;
}
else
{
if (-dotProd > max)
{
max = -dotProd;
maxi = kk + NUM_SECTOR;
}
}
}
alfa[j * width * 2 + i * 2 ] = maxi % NUM_SECTOR;
alfa[j * width * 2 + i * 2 + 1] = maxi;
}/*for(i = 0; i < width; i++)*/
}/*for(j = 0; j < height; j++)*/
nearest = (int *)malloc(sizeof(int ) * k);
w = (float*)malloc(sizeof(float) * (k * 2));
for(i = 0; i < k / 2; i++)
{
nearest[i] = -1;
}/*for(i = 0; i < k / 2; i++)*/
for(i = k / 2; i < k; i++)
{
nearest[i] = 1;
}/*for(i = k / 2; i < k; i++)*/
for(j = 0; j < k / 2; j++)
{
b_x = k / 2 + j + 0.5f;
a_x = k / 2 - j - 0.5f;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = 0; j < k / 2; j++)*/
for(j = k / 2; j < k; j++)
{
a_x = j - k / 2 + 0.5f;
b_x =-j + k / 2 - 0.5f + k;
w[j * 2 ] = 1.0f/a_x * ((a_x * b_x) / ( a_x + b_x));
w[j * 2 + 1] = 1.0f/b_x * ((a_x * b_x) / ( a_x + b_x));
}/*for(j = k / 2; j < k; j++)*/
for(i = 0; i < sizeY; i++)
{
for(j = 0; j < sizeX; j++)
{
for(ii = 0; ii < k; ii++)
{
for(jj = 0; jj < k; jj++)
{
if ((i * k + ii > 0) &&
(i * k + ii < height - 1) &&
(j * k + jj > 0) &&
(j * k + jj < width - 1))
{
d = (k * i + ii) * width + (j * k + jj);
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 ]] +=
r[d] * w[ii * 2] * w[jj * 2];
(*map)->map[ i * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2] * w[jj * 2];
if ((i + nearest[ii] >= 0) &&
(i + nearest[ii] <= sizeY - 1))
{
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
(*map)->map[(i + nearest[ii]) * stringSize + j * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 ];
}
if ((j + nearest[jj] >= 0) &&
(j + nearest[jj] <= sizeX - 1))
{
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2] * w[jj * 2 + 1];
(*map)->map[i * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2] * w[jj * 2 + 1];
}
if ((i + nearest[ii] >= 0) &&
(i + nearest[ii] <= sizeY - 1) &&
(j + nearest[jj] >= 0) &&
(j + nearest[jj] <= sizeX - 1))
{
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 ] ] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
(*map)->map[(i + nearest[ii]) * stringSize + (j + nearest[jj]) * (*map)->numFeatures + alfa[d * 2 + 1] + NUM_SECTOR] +=
r[d] * w[ii * 2 + 1] * w[jj * 2 + 1];
}
}
}/*for(jj = 0; jj < k; jj++)*/
}/*for(ii = 0; ii < k; ii++)*/
}/*for(j = 1; j < sizeX - 1; j++)*/
}/*for(i = 1; i < sizeY - 1; i++)*/
cvReleaseImage(&dx);
cvReleaseImage(&dy);
free(w);
free(nearest);
free(r);
free(alfa);
return LATENT_SVM_OK;
}
/*
// Feature map Normalization and Truncation
//
// API
// int normalizeAndTruncate(featureMap *map, const float alfa);
// INPUT
// map - feature map
// alfa - truncation threshold
// OUTPUT
// map - truncated and normalized feature map
// RESULT
// Error status
*/
int normalizeAndTruncate(CvLSVMFeatureMapCaskade *map, const float alfa)
{
int i,j, ii;
int sizeX, sizeY, p, pos, pp, xp, pos1, pos2;
float * partOfNorm; // norm of C(i, j)
float * newData;
float valOfNorm;
sizeX = map->sizeX;
sizeY = map->sizeY;
partOfNorm = (float *)malloc (sizeof(float) * (sizeX * sizeY));
p = NUM_SECTOR;
xp = NUM_SECTOR * 3;
pp = NUM_SECTOR * 12;
for(i = 0; i < sizeX * sizeY; i++)
{
valOfNorm = 0.0f;
pos = i * map->numFeatures;
for(j = 0; j < p; j++)
{
valOfNorm += map->map[pos + j] * map->map[pos + j];
}/*for(j = 0; j < p; j++)*/
partOfNorm[i] = valOfNorm;
}/*for(i = 0; i < sizeX * sizeY; i++)*/
sizeX -= 2;
sizeY -= 2;
newData = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
//normalization
for(i = 1; i <= sizeY; i++)
{
for(j = 1; j <= sizeX; j++)
{
valOfNorm = sqrtf(
partOfNorm[(i )*(sizeX + 2) + (j )] +
partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
partOfNorm[(i + 1)*(sizeX + 2) + (j + 1)]) + FLT_EPSILON;
pos1 = (i ) * (sizeX + 2) * xp + (j ) * xp;
pos2 = (i-1) * (sizeX ) * pp + (j-1) * pp;
for(ii = 0; ii < p; ii++)
{
newData[pos2 + ii ] = map->map[pos1 + ii ] / valOfNorm;
}/*for(ii = 0; ii < p; ii++)*/
for(ii = 0; ii < 2 * p; ii++)
{
newData[pos2 + ii + p * 4] = map->map[pos1 + ii + p] / valOfNorm;
}/*for(ii = 0; ii < 2 * p; ii++)*/
valOfNorm = sqrtf(
partOfNorm[(i )*(sizeX + 2) + (j )] +
partOfNorm[(i )*(sizeX + 2) + (j + 1)] +
partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
partOfNorm[(i - 1)*(sizeX + 2) + (j + 1)]) + FLT_EPSILON;
for(ii = 0; ii < p; ii++)
{
newData[pos2 + ii + p ] = map->map[pos1 + ii ] / valOfNorm;
}/*for(ii = 0; ii < p; ii++)*/
for(ii = 0; ii < 2 * p; ii++)
{
newData[pos2 + ii + p * 6] = map->map[pos1 + ii + p] / valOfNorm;
}/*for(ii = 0; ii < 2 * p; ii++)*/
valOfNorm = sqrtf(
partOfNorm[(i )*(sizeX + 2) + (j )] +
partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
partOfNorm[(i + 1)*(sizeX + 2) + (j )] +
partOfNorm[(i + 1)*(sizeX + 2) + (j - 1)]) + FLT_EPSILON;
for(ii = 0; ii < p; ii++)
{
newData[pos2 + ii + p * 2] = map->map[pos1 + ii ] / valOfNorm;
}/*for(ii = 0; ii < p; ii++)*/
for(ii = 0; ii < 2 * p; ii++)
{
newData[pos2 + ii + p * 8] = map->map[pos1 + ii + p] / valOfNorm;
}/*for(ii = 0; ii < 2 * p; ii++)*/
valOfNorm = sqrtf(
partOfNorm[(i )*(sizeX + 2) + (j )] +
partOfNorm[(i )*(sizeX + 2) + (j - 1)] +
partOfNorm[(i - 1)*(sizeX + 2) + (j )] +
partOfNorm[(i - 1)*(sizeX + 2) + (j - 1)]) + FLT_EPSILON;
for(ii = 0; ii < p; ii++)
{
newData[pos2 + ii + p * 3 ] = map->map[pos1 + ii ] / valOfNorm;
}/*for(ii = 0; ii < p; ii++)*/
for(ii = 0; ii < 2 * p; ii++)
{
newData[pos2 + ii + p * 10] = map->map[pos1 + ii + p] / valOfNorm;
}/*for(ii = 0; ii < 2 * p; ii++)*/
}/*for(j = 1; j <= sizeX; j++)*/
}/*for(i = 1; i <= sizeY; i++)*/
//truncation
for(i = 0; i < sizeX * sizeY * pp; i++)
{
if(newData [i] > alfa) newData [i] = alfa;
}/*for(i = 0; i < sizeX * sizeY * pp; i++)*/
//swop data
map->numFeatures = pp;
map->sizeX = sizeX;
map->sizeY = sizeY;
free (map->map);
free (partOfNorm);
map->map = newData;
return LATENT_SVM_OK;
}
/*
// Feature map reduction
// In each cell we reduce dimension of the feature vector
// according to original paper special procedure
//
// API
// int PCAFeatureMaps(featureMap *map)
// INPUT
// map - feature map
// OUTPUT
// map - feature map
// RESULT
// Error status
*/
int PCAFeatureMaps(CvLSVMFeatureMapCaskade *map)
{
int i,j, ii, jj, k;
int sizeX, sizeY, p, pp, xp, yp, pos1, pos2;
float * newData;
float val;
float nx, ny;
sizeX = map->sizeX;
sizeY = map->sizeY;
p = map->numFeatures;
pp = NUM_SECTOR * 3 + 4;
yp = 4;
xp = NUM_SECTOR;
nx = 1.0f / sqrtf((float)(xp * 2));
ny = 1.0f / sqrtf((float)(yp ));
newData = (float *)malloc (sizeof(float) * (sizeX * sizeY * pp));
for(i = 0; i < sizeY; i++)
{
for(j = 0; j < sizeX; j++)
{
pos1 = ((i)*sizeX + j)*p;
pos2 = ((i)*sizeX + j)*pp;
k = 0;
for(jj = 0; jj < xp * 2; jj++)
{
val = 0;
for(ii = 0; ii < yp; ii++)
{
val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
}/*for(ii = 0; ii < yp; ii++)*/
newData[pos2 + k] = val * ny;
k++;
}/*for(jj = 0; jj < xp * 2; jj++)*/
for(jj = 0; jj < xp; jj++)
{
val = 0;
for(ii = 0; ii < yp; ii++)
{
val += map->map[pos1 + ii * xp + jj];
}/*for(ii = 0; ii < yp; ii++)*/
newData[pos2 + k] = val * ny;
k++;
}/*for(jj = 0; jj < xp; jj++)*/
for(ii = 0; ii < yp; ii++)
{
val = 0;
for(jj = 0; jj < 2 * xp; jj++)
{
val += map->map[pos1 + yp * xp + ii * xp * 2 + jj];
}/*for(jj = 0; jj < xp; jj++)*/
newData[pos2 + k] = val * nx;
k++;
} /*for(ii = 0; ii < yp; ii++)*/
}/*for(j = 0; j < sizeX; j++)*/
}/*for(i = 0; i < sizeY; i++)*/
//swop data
map->numFeatures = pp;
free (map->map);
map->map = newData;
return LATENT_SVM_OK;
}
//modified from "lsvmc_routine.cpp"
int allocFeatureMapObject(CvLSVMFeatureMapCaskade **obj, const int sizeX,
const int sizeY, const int numFeatures)
{
int i;
(*obj) = (CvLSVMFeatureMapCaskade *)malloc(sizeof(CvLSVMFeatureMapCaskade));
(*obj)->sizeX = sizeX;
(*obj)->sizeY = sizeY;
(*obj)->numFeatures = numFeatures;
(*obj)->map = (float *) malloc(sizeof (float) *
(sizeX * sizeY * numFeatures));
for(i = 0; i < sizeX * sizeY * numFeatures; i++)
{
(*obj)->map[i] = 0.0f;
}
return LATENT_SVM_OK;
}
int freeFeatureMapObject (CvLSVMFeatureMapCaskade **obj)
{
if(*obj == NULL) return LATENT_SVM_MEM_NULL;
free((*obj)->map);
free(*obj);
(*obj) = NULL;
return LATENT_SVM_OK;
}
You can’t perform that action at this time.