Image-CCV
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ccv-src/lib/ccv_bbf.c view on Meta::CPAN
for (i = 0; i < classifier->count; i++)
{
stat |= fscanf(r, "%d", &classifier->feature[i].size);
for (j = 0; j < classifier->feature[i].size; j++)
{
stat |= fscanf(r, "%d %d %d", &classifier->feature[i].px[j], &classifier->feature[i].py[j], &classifier->feature[i].pz[j]);
stat |= fscanf(r, "%d %d %d", &classifier->feature[i].nx[j], &classifier->feature[i].ny[j], &classifier->feature[i].nz[j]);
}
union { float fl; int i; } flia, flib;
stat |= fscanf(r, "%d %d", &flia.i, &flib.i);
classifier->alpha[i * 2] = flia.fl;
classifier->alpha[i * 2 + 1] = flib.fl;
}
fclose(r);
return 0;
}
#ifdef HAVE_GSL
static unsigned int _ccv_bbf_time_measure()
{
struct timeval tv;
gettimeofday(&tv, 0);
return tv.tv_sec * 1000000 + tv.tv_usec;
}
#define less_than(a, b, aux) ((a) < (b))
CCV_IMPLEMENT_QSORT(_ccv_sort_32f, float, less_than)
#undef less_than
static void _ccv_bbf_eval_data(ccv_bbf_stage_classifier_t* classifier, unsigned char** posdata, int posnum, unsigned char** negdata, int negnum, ccv_size_t size, float* peval, float* neval)
{
int i, j;
int steps[] = { _ccv_width_padding(size.width),
_ccv_width_padding(size.width >> 1),
_ccv_width_padding(size.width >> 2) };
int isizs0 = steps[0] * size.height;
int isizs01 = isizs0 + steps[1] * (size.height >> 1);
for (i = 0; i < posnum; i++)
{
unsigned char* u8[] = { posdata[i], posdata[i] + isizs0, posdata[i] + isizs01 };
float sum = 0;
float* alpha = classifier->alpha;
ccv_bbf_feature_t* feature = classifier->feature;
for (j = 0; j < classifier->count; ++j, alpha += 2, ++feature)
sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
peval[i] = sum;
}
for (i = 0; i < negnum; i++)
{
unsigned char* u8[] = { negdata[i], negdata[i] + isizs0, negdata[i] + isizs01 };
float sum = 0;
float* alpha = classifier->alpha;
ccv_bbf_feature_t* feature = classifier->feature;
for (j = 0; j < classifier->count; ++j, alpha += 2, ++feature)
sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
neval[i] = sum;
}
}
static int _ccv_prune_positive_data(ccv_bbf_classifier_cascade_t* cascade, unsigned char** posdata, int posnum, ccv_size_t size)
{
float* peval = (float*)ccmalloc(posnum * sizeof(float));
int i, j, k, rpos = posnum;
for (i = 0; i < cascade->count; i++)
{
_ccv_bbf_eval_data(cascade->stage_classifier + i, posdata, rpos, 0, 0, size, peval, 0);
k = 0;
for (j = 0; j < rpos; j++)
if (peval[j] >= cascade->stage_classifier[i].threshold)
{
posdata[k] = posdata[j];
++k;
} else {
ccfree(posdata[j]);
}
rpos = k;
}
ccfree(peval);
return rpos;
}
static int _ccv_prepare_background_data(ccv_bbf_classifier_cascade_t* cascade, char** bgfiles, int bgnum, unsigned char** negdata, int negnum)
{
int t, i, j, k, q;
int negperbg;
int negtotal = 0;
int steps[] = { _ccv_width_padding(cascade->size.width),
_ccv_width_padding(cascade->size.width >> 1),
_ccv_width_padding(cascade->size.width >> 2) };
int isizs0 = steps[0] * cascade->size.height;
int isizs1 = steps[1] * (cascade->size.height >> 1);
int isizs2 = steps[2] * (cascade->size.height >> 2);
int* idcheck = (int*)ccmalloc(negnum * sizeof(int));
gsl_rng_env_setup();
gsl_rng* rng = gsl_rng_alloc(gsl_rng_default);
gsl_rng_set(rng, (unsigned long int)idcheck);
ccv_size_t imgsz = cascade->size;
int rneg = negtotal;
for (t = 0; negtotal < negnum; t++)
{
PRINT(CCV_CLI_INFO, "preparing negative data ... 0%%");
for (i = 0; i < bgnum; i++)
{
negperbg = (t < 2) ? (negnum - negtotal) / (bgnum - i) + 1 : negnum - negtotal;
ccv_dense_matrix_t* image = 0;
ccv_read(bgfiles[i], &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
assert((image->type & CCV_C1) && (image->type & CCV_8U));
if (image == 0)
{
PRINT(CCV_CLI_ERROR, "\n%s file corrupted\n", bgfiles[i]);
continue;
}
if (t % 2 != 0)
ccv_flip(image, 0, 0, CCV_FLIP_X);
if (t % 4 >= 2)
ccv_flip(image, 0, 0, CCV_FLIP_Y);
ccv_bbf_param_t params = { .interval = 3, .min_neighbors = 0, .accurate = 1, .flags = 0, .size = cascade->size };
ccv_array_t* detected = ccv_bbf_detect_objects(image, &cascade, 1, params);
memset(idcheck, 0, ccv_min(detected->rnum, negperbg) * sizeof(int));
for (j = 0; j < ccv_min(detected->rnum, negperbg); j++)
{
int r = gsl_rng_uniform_int(rng, detected->rnum);
int flag = 1;
ccv_rect_t* rect = (ccv_rect_t*)ccv_array_get(detected, r);
while (flag) {
flag = 0;
for (k = 0; k < j; k++)
if (r == idcheck[k])
{
flag = 1;
r = gsl_rng_uniform_int(rng, detected->rnum);
break;
}
rect = (ccv_rect_t*)ccv_array_get(detected, r);
if ((rect->x < 0) || (rect->y < 0) || (rect->width + rect->x > image->cols) || (rect->height + rect->y > image->rows))
{
flag = 1;
r = gsl_rng_uniform_int(rng, detected->rnum);
}
}
idcheck[j] = r;
ccv_dense_matrix_t* temp = 0;
ccv_dense_matrix_t* imgs0 = 0;
ccv_dense_matrix_t* imgs1 = 0;
ccv_dense_matrix_t* imgs2 = 0;
ccv_slice(image, (ccv_matrix_t**)&temp, 0, rect->y, rect->x, rect->height, rect->width);
ccv_resample(temp, &imgs0, 0, imgsz.height, imgsz.width, CCV_INTER_AREA);
assert(imgs0->step == steps[0]);
ccv_matrix_free(temp);
ccv_sample_down(imgs0, &imgs1, 0, 0, 0);
assert(imgs1->step == steps[1]);
ccv_sample_down(imgs1, &imgs2, 0, 0, 0);
assert(imgs2->step == steps[2]);
negdata[negtotal] = (unsigned char*)ccmalloc(isizs0 + isizs1 + isizs2);
unsigned char* u8s0 = negdata[negtotal];
unsigned char* u8s1 = negdata[negtotal] + isizs0;
unsigned char* u8s2 = negdata[negtotal] + isizs0 + isizs1;
unsigned char* u8[] = { u8s0, u8s1, u8s2 };
memcpy(u8s0, imgs0->data.u8, imgs0->rows * imgs0->step);
ccv_matrix_free(imgs0);
memcpy(u8s1, imgs1->data.u8, imgs1->rows * imgs1->step);
ccv_matrix_free(imgs1);
memcpy(u8s2, imgs2->data.u8, imgs2->rows * imgs2->step);
ccv_matrix_free(imgs2);
flag = 1;
ccv_bbf_stage_classifier_t* classifier = cascade->stage_classifier;
for (k = 0; k < cascade->count; ++k, ++classifier)
{
float sum = 0;
float* alpha = classifier->alpha;
ccv_bbf_feature_t* feature = classifier->feature;
for (q = 0; q < classifier->count; ++q, alpha += 2, ++feature)
sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
if (sum < classifier->threshold)
{
flag = 0;
break;
}
}
if (!flag)
ccfree(negdata[negtotal]);
else {
++negtotal;
if (negtotal >= negnum)
break;
}
}
ccv_array_free(detected);
ccv_matrix_free(image);
ccv_drain_cache();
PRINT(CCV_CLI_INFO, "\rpreparing negative data ... %2d%%", 100 * negtotal / negnum);
fflush(0);
if (negtotal >= negnum)
break;
}
if (rneg == negtotal)
break;
rneg = negtotal;
PRINT(CCV_CLI_INFO, "\nentering additional round %d\n", t + 1);
}
gsl_rng_free(rng);
ccfree(idcheck);
ccv_drain_cache();
PRINT(CCV_CLI_INFO, "\n");
return negtotal;
}
static void _ccv_prepare_positive_data(ccv_dense_matrix_t** posimg, unsigned char** posdata, ccv_size_t size, int posnum)
{
PRINT(CCV_CLI_INFO, "preparing positive data ... 0%%");
int i;
for (i = 0; i < posnum; i++)
{
ccv_dense_matrix_t* imgs0 = posimg[i];
ccv_dense_matrix_t* imgs1 = 0;
ccv_dense_matrix_t* imgs2 = 0;
assert((imgs0->type & CCV_C1) && (imgs0->type & CCV_8U) && imgs0->rows == size.height && imgs0->cols == size.width);
ccv_sample_down(imgs0, &imgs1, 0, 0, 0);
ccv_sample_down(imgs1, &imgs2, 0, 0, 0);
int isizs0 = imgs0->rows * imgs0->step;
int isizs1 = imgs1->rows * imgs1->step;
int isizs2 = imgs2->rows * imgs2->step;
posdata[i] = (unsigned char*)ccmalloc(isizs0 + isizs1 + isizs2);
memcpy(posdata[i], imgs0->data.u8, isizs0);
memcpy(posdata[i] + isizs0, imgs1->data.u8, isizs1);
memcpy(posdata[i] + isizs0 + isizs1, imgs2->data.u8, isizs2);
ccv-src/lib/ccv_bbf.c view on Meta::CPAN
static int _ccv_write_bbf_stage_classifier(const char* file, ccv_bbf_stage_classifier_t* classifier)
{
FILE* w = fopen(file, "wb");
if (w == 0) return -1;
fprintf(w, "%d\n", classifier->count);
union { float fl; int i; } fli;
fli.fl = classifier->threshold;
fprintf(w, "%d\n", fli.i);
int i, j;
for (i = 0; i < classifier->count; i++)
{
fprintf(w, "%d\n", classifier->feature[i].size);
for (j = 0; j < classifier->feature[i].size; j++)
{
fprintf(w, "%d %d %d\n", classifier->feature[i].px[j], classifier->feature[i].py[j], classifier->feature[i].pz[j]);
fprintf(w, "%d %d %d\n", classifier->feature[i].nx[j], classifier->feature[i].ny[j], classifier->feature[i].nz[j]);
}
union { float fl; int i; } flia, flib;
flia.fl = classifier->alpha[i * 2];
flib.fl = classifier->alpha[i * 2 + 1];
fprintf(w, "%d %d\n", flia.i, flib.i);
}
fclose(w);
return 0;
}
static int _ccv_read_background_data(const char* file, unsigned char** negdata, int* negnum, ccv_size_t size)
{
int stat = 0;
FILE* r = fopen(file, "rb");
if (r == 0) return -1;
stat |= fread(negnum, sizeof(int), 1, r);
int i;
int isizs012 = _ccv_width_padding(size.width) * size.height +
_ccv_width_padding(size.width >> 1) * (size.height >> 1) +
_ccv_width_padding(size.width >> 2) * (size.height >> 2);
for (i = 0; i < *negnum; i++)
{
negdata[i] = (unsigned char*)ccmalloc(isizs012);
stat |= fread(negdata[i], 1, isizs012, r);
}
fclose(r);
return 0;
}
static int _ccv_write_background_data(const char* file, unsigned char** negdata, int negnum, ccv_size_t size)
{
FILE* w = fopen(file, "w");
if (w == 0) return -1;
fwrite(&negnum, sizeof(int), 1, w);
int i;
int isizs012 = _ccv_width_padding(size.width) * size.height +
_ccv_width_padding(size.width >> 1) * (size.height >> 1) +
_ccv_width_padding(size.width >> 2) * (size.height >> 2);
for (i = 0; i < negnum; i++)
fwrite(negdata[i], 1, isizs012, w);
fclose(w);
return 0;
}
static int _ccv_resume_bbf_cascade_training_state(const char* file, int* i, int* k, int* bg, double* pw, double* nw, int posnum, int negnum)
{
int stat = 0;
FILE* r = fopen(file, "r");
if (r == 0) return -1;
stat |= fscanf(r, "%d %d %d", i, k, bg);
int j;
union { double db; int i[2]; } dbi;
for (j = 0; j < posnum; j++)
{
stat |= fscanf(r, "%d %d", &dbi.i[0], &dbi.i[1]);
pw[j] = dbi.db;
}
for (j = 0; j < negnum; j++)
{
stat |= fscanf(r, "%d %d", &dbi.i[0], &dbi.i[1]);
nw[j] = dbi.db;
}
fclose(r);
return 0;
}
static int _ccv_save_bbf_cacade_training_state(const char* file, int i, int k, int bg, double* pw, double* nw, int posnum, int negnum)
{
FILE* w = fopen(file, "w");
if (w == 0) return -1;
fprintf(w, "%d %d %d\n", i, k, bg);
int j;
union { double db; int i[2]; } dbi;
for (j = 0; j < posnum; ++j)
{
dbi.db = pw[j];
fprintf(w, "%d %d ", dbi.i[0], dbi.i[1]);
}
fprintf(w, "\n");
for (j = 0; j < negnum; ++j)
{
dbi.db = nw[j];
fprintf(w, "%d %d ", dbi.i[0], dbi.i[1]);
}
fprintf(w, "\n");
fclose(w);
return 0;
}
void ccv_bbf_classifier_cascade_new(ccv_dense_matrix_t** posimg, int posnum, char** bgfiles, int bgnum, int negnum, ccv_size_t size, const char* dir, ccv_bbf_new_param_t params)
{
int i, j, k;
/* allocate memory for usage */
ccv_bbf_classifier_cascade_t* cascade = (ccv_bbf_classifier_cascade_t*)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
cascade->count = 0;
cascade->size = size;
cascade->stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(sizeof(ccv_bbf_stage_classifier_t));
unsigned char** posdata = (unsigned char**)ccmalloc(posnum * sizeof(unsigned char*));
unsigned char** negdata = (unsigned char**)ccmalloc(negnum * sizeof(unsigned char*));
double* pw = (double*)ccmalloc(posnum * sizeof(double));
double* nw = (double*)ccmalloc(negnum * sizeof(double));
float* peval = (float*)ccmalloc(posnum * sizeof(float));
float* neval = (float*)ccmalloc(negnum * sizeof(float));
double inv_balance_k = 1. / params.balance_k;
/* balance factor k, and weighted with 0.01 */
params.balance_k *= 0.01;
inv_balance_k *= 0.01;
int steps[] = { _ccv_width_padding(cascade->size.width),
_ccv_width_padding(cascade->size.width >> 1),
_ccv_width_padding(cascade->size.width >> 2) };
int isizs0 = steps[0] * cascade->size.height;
int isizs01 = isizs0 + steps[1] * (cascade->size.height >> 1);
i = 0;
k = 0;
int bg = 0;
int cacheK = 10;
/* state resume code */
char buf[1024];
sprintf(buf, "%s/stat.txt", dir);
_ccv_resume_bbf_cascade_training_state(buf, &i, &k, &bg, pw, nw, posnum, negnum);
if (i > 0)
{
cascade->count = i;
ccfree(cascade->stage_classifier);
cascade->stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(i * sizeof(ccv_bbf_stage_classifier_t));
for (j = 0; j < i; j++)
{
sprintf(buf, "%s/stage-%d.txt", dir, j);
_ccv_read_bbf_stage_classifier(buf, &cascade->stage_classifier[j]);
}
}
if (k > 0)
cacheK = k;
int rpos, rneg = 0;
if (bg)
{
sprintf(buf, "%s/negs.txt", dir);
_ccv_read_background_data(buf, negdata, &rneg, cascade->size);
}
for (; i < params.layer; i++)
{
if (!bg)
{
rneg = _ccv_prepare_background_data(cascade, bgfiles, bgnum, negdata, negnum);
/* save state of background data */
sprintf(buf, "%s/negs.txt", dir);
_ccv_write_background_data(buf, negdata, rneg, cascade->size);
bg = 1;
}
double totalw;
/* save state of cascade : level, weight etc. */
sprintf(buf, "%s/stat.txt", dir);
_ccv_save_bbf_cacade_training_state(buf, i, k, bg, pw, nw, posnum, negnum);
ccv_bbf_stage_classifier_t classifier;
if (k > 0)
{
/* resume state of classifier */
sprintf( buf, "%s/stage-%d.txt", dir, i );
_ccv_read_bbf_stage_classifier(buf, &classifier);
} else {
/* initialize classifier */
for (j = 0; j < posnum; j++)
pw[j] = params.balance_k;
for (j = 0; j < rneg; j++)
nw[j] = inv_balance_k;
classifier.count = k;
classifier.threshold = 0;
classifier.feature = (ccv_bbf_feature_t*)ccmalloc(cacheK * sizeof(ccv_bbf_feature_t));
classifier.alpha = (float*)ccmalloc(cacheK * 2 * sizeof(float));
}
_ccv_prepare_positive_data(posimg, posdata, cascade->size, posnum);
rpos = _ccv_prune_positive_data(cascade, posdata, posnum, cascade->size);
PRINT(CCV_CLI_INFO, "%d postivie data and %d negative data in training\n", rpos, rneg);
/* reweight to 1.00 */
totalw = 0;
for (j = 0; j < rpos; j++)
totalw += pw[j];
for (j = 0; j < rneg; j++)
totalw += nw[j];
for (j = 0; j < rpos; j++)
pw[j] = pw[j] / totalw;
for (j = 0; j < rneg; j++)
nw[j] = nw[j] / totalw;
for (; ; k++)
{
/* get overall true-positive, false-positive rate and threshold */
double tp = 0, fp = 0, etp = 0, efp = 0;
_ccv_bbf_eval_data(&classifier, posdata, rpos, negdata, rneg, cascade->size, peval, neval);
_ccv_sort_32f(peval, rpos, 0);
classifier.threshold = peval[(int)((1. - params.pos_crit) * rpos)] - 1e-6;
for (j = 0; j < rpos; j++)
{
if (peval[j] >= 0)
++tp;
if (peval[j] >= classifier.threshold)
++etp;
}
tp /= rpos; etp /= rpos;
for (j = 0; j < rneg; j++)
{
if (neval[j] >= 0)
++fp;
if (neval[j] >= classifier.threshold)
++efp;
}
fp /= rneg; efp /= rneg;
PRINT(CCV_CLI_INFO, "stage classifier real TP rate : %f, FP rate : %f\n", tp, fp);
PRINT(CCV_CLI_INFO, "stage classifier TP rate : %f, FP rate : %f at threshold : %f\n", etp, efp, classifier.threshold);
if (k > 0)
{
/* save classifier state */
sprintf(buf, "%s/stage-%d.txt", dir, i);
_ccv_write_bbf_stage_classifier(buf, &classifier);
sprintf(buf, "%s/stat.txt", dir);
_ccv_save_bbf_cacade_training_state(buf, i, k, bg, pw, nw, posnum, negnum);
}
if (etp > params.pos_crit && efp < params.neg_crit)
break;
/* TODO: more post-process is needed in here */
/* select the best feature in current distribution through genetic algorithm optimization */
ccv_bbf_feature_t best;
if (params.optimizer == CCV_BBF_GENETIC_OPT)
{
best = _ccv_bbf_genetic_optimize(posdata, rpos, negdata, rneg, params.feature_number, cascade->size, pw, nw);
} else if (params.optimizer == CCV_BBF_FLOAT_OPT) {
best = _ccv_bbf_convex_optimize(posdata, rpos, negdata, rneg, 0, cascade->size, pw, nw);
} else {
best = _ccv_bbf_genetic_optimize(posdata, rpos, negdata, rneg, params.feature_number, cascade->size, pw, nw);
best = _ccv_bbf_convex_optimize(posdata, rpos, negdata, rneg, &best, cascade->size, pw, nw);
}
double err = _ccv_bbf_error_rate(&best, posdata, rpos, negdata, rneg, cascade->size, pw, nw);
double rw = (1 - err) / err;
totalw = 0;
/* reweight */
for (j = 0; j < rpos; j++)
{
unsigned char* u8[] = { posdata[j], posdata[j] + isizs0, posdata[j] + isizs01 };
if (!_ccv_run_bbf_feature(&best, steps, u8))
pw[j] *= rw;
pw[j] *= params.balance_k;
totalw += pw[j];
}
for (j = 0; j < rneg; j++)
{
unsigned char* u8[] = { negdata[j], negdata[j] + isizs0, negdata[j] + isizs01 };
if (_ccv_run_bbf_feature(&best, steps, u8))
nw[j] *= rw;
nw[j] *= inv_balance_k;
totalw += nw[j];
}
for (j = 0; j < rpos; j++)
pw[j] = pw[j] / totalw;
for (j = 0; j < rneg; j++)
nw[j] = nw[j] / totalw;
double c = log(rw);
PRINT(CCV_CLI_INFO, "coefficient of feature %d: %f\n", k + 1, c);
classifier.count = k + 1;
/* resizing classifier */
if (k >= cacheK)
{
ccv_bbf_feature_t* feature = (ccv_bbf_feature_t*)ccmalloc(cacheK * 2 * sizeof(ccv_bbf_feature_t));
memcpy(feature, classifier.feature, cacheK * sizeof(ccv_bbf_feature_t));
ccfree(classifier.feature);
float* alpha = (float*)ccmalloc(cacheK * 4 * sizeof(float));
memcpy(alpha, classifier.alpha, cacheK * 2 * sizeof(float));
ccfree(classifier.alpha);
classifier.feature = feature;
classifier.alpha = alpha;
cacheK *= 2;
}
/* setup new feature */
classifier.feature[k] = best;
classifier.alpha[k * 2] = -c;
classifier.alpha[k * 2 + 1] = c;
}
cascade->count = i + 1;
ccv_bbf_stage_classifier_t* stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
memcpy(stage_classifier, cascade->stage_classifier, i * sizeof(ccv_bbf_stage_classifier_t));
ccfree(cascade->stage_classifier);
stage_classifier[i] = classifier;
cascade->stage_classifier = stage_classifier;
k = 0;
bg = 0;
for (j = 0; j < rpos; j++)
ccfree(posdata[j]);
for (j = 0; j < rneg; j++)
ccfree(negdata[j]);
}
ccfree(neval);
ccfree(peval);
ccfree(nw);
ccfree(pw);
ccfree(negdata);
ccfree(posdata);
ccfree(cascade);
}
#else
void ccv_bbf_classifier_cascade_new(ccv_dense_matrix_t** posimg, int posnum, char** bgfiles, int bgnum, int negnum, ccv_size_t size, const char* dir, ccv_bbf_new_param_t params)
{
fprintf(stderr, " ccv_bbf_classifier_cascade_new requires libgsl support, please compile ccv with libgsl.\n");
}
#endif
static int _ccv_is_equal(const void* _r1, const void* _r2, void* data)
{
const ccv_comp_t* r1 = (const ccv_comp_t*)_r1;
const ccv_comp_t* r2 = (const ccv_comp_t*)_r2;
int distance = (int)(r1->rect.width * 0.25 + 0.5);
return r2->rect.x <= r1->rect.x + distance &&
r2->rect.x >= r1->rect.x - distance &&
r2->rect.y <= r1->rect.y + distance &&
r2->rect.y >= r1->rect.y - distance &&
r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) &&
(int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width;
}
static int _ccv_is_equal_same_class(const void* _r1, const void* _r2, void* data)
{
const ccv_comp_t* r1 = (const ccv_comp_t*)_r1;
const ccv_comp_t* r2 = (const ccv_comp_t*)_r2;
int distance = (int)(r1->rect.width * 0.25 + 0.5);
return r2->classification.id == r1->classification.id &&
r2->rect.x <= r1->rect.x + distance &&
r2->rect.x >= r1->rect.x - distance &&
r2->rect.y <= r1->rect.y + distance &&
r2->rect.y >= r1->rect.y - distance &&
r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) &&
(int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width;
}
ccv_array_t* ccv_bbf_detect_objects(ccv_dense_matrix_t* a, ccv_bbf_classifier_cascade_t** _cascade, int count, ccv_bbf_param_t params)
{
int hr = a->rows / params.size.height;
int wr = a->cols / params.size.width;
double scale = pow(2., 1. / (params.interval + 1.));
int next = params.interval + 1;
int scale_upto = (int)(log((double)ccv_min(hr, wr)) / log(scale));
ccv_dense_matrix_t** pyr = (ccv_dense_matrix_t**)alloca((scale_upto + next * 2) * 4 * sizeof(ccv_dense_matrix_t*));
memset(pyr, 0, (scale_upto + next * 2) * 4 * sizeof(ccv_dense_matrix_t*));
if (params.size.height != _cascade[0]->size.height || params.size.width != _cascade[0]->size.width)
ccv_resample(a, &pyr[0], 0, a->rows * _cascade[0]->size.height / params.size.height, a->cols * _cascade[0]->size.width / params.size.width, CCV_INTER_AREA);
else
pyr[0] = a;
int i, j, k, t, x, y, q;
for (i = 1; i < ccv_min(params.interval + 1, scale_upto + next * 2); i++)
ccv_resample(pyr[0], &pyr[i * 4], 0, (int)(pyr[0]->rows / pow(scale, i)), (int)(pyr[0]->cols / pow(scale, i)), CCV_INTER_AREA);
for (i = next; i < scale_upto + next * 2; i++)
ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4], 0, 0, 0);
if (params.accurate)
for (i = next * 2; i < scale_upto + next * 2; i++)
{
ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 1], 0, 1, 0);
ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 2], 0, 0, 1);
ccv_sample_down(pyr[i * 4 - next * 4], &pyr[i * 4 + 3], 0, 1, 1);
}
ccv_array_t* idx_seq;
ccv_array_t* seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
ccv_array_t* seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
ccv_array_t* result_seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
/* detect in multi scale */
for (t = 0; t < count; t++)
{
ccv_bbf_classifier_cascade_t* cascade = _cascade[t];
float scale_x = (float) params.size.width / (float) cascade->size.width;
float scale_y = (float) params.size.height / (float) cascade->size.height;
ccv_array_clear(seq);
for (i = 0; i < scale_upto; i++)
{
int dx[] = {0, 1, 0, 1};
int dy[] = {0, 0, 1, 1};
int i_rows = pyr[i * 4 + next * 8]->rows - (cascade->size.height >> 2);
int steps[] = { pyr[i * 4]->step, pyr[i * 4 + next * 4]->step, pyr[i * 4 + next * 8]->step };
int i_cols = pyr[i * 4 + next * 8]->cols - (cascade->size.width >> 2);
int paddings[] = { pyr[i * 4]->step * 4 - i_cols * 4,
pyr[i * 4 + next * 4]->step * 2 - i_cols * 2,
pyr[i * 4 + next * 8]->step - i_cols };
for (q = 0; q < (params.accurate ? 4 : 1); q++)
{
unsigned char* u8[] = { pyr[i * 4]->data.u8 + dx[q] * 2 + dy[q] * pyr[i * 4]->step * 2, pyr[i * 4 + next * 4]->data.u8 + dx[q] + dy[q] * pyr[i * 4 + next * 4]->step, pyr[i * 4 + next * 8 + q]->data.u8 };
for (y = 0; y < i_rows; y++)
{
for (x = 0; x < i_cols; x++)
{
float sum;
int flag = 1;
ccv_bbf_stage_classifier_t* classifier = cascade->stage_classifier;
for (j = 0; j < cascade->count; ++j, ++classifier)
{
sum = 0;
float* alpha = classifier->alpha;
ccv_bbf_feature_t* feature = classifier->feature;
for (k = 0; k < classifier->count; ++k, alpha += 2, ++feature)
sum += alpha[_ccv_run_bbf_feature(feature, steps, u8)];
if (sum < classifier->threshold)
{
flag = 0;
break;
}
}
if (flag)
{
ccv_comp_t comp;
comp.rect = ccv_rect((int)((x * 4 + dx[q] * 2) * scale_x + 0.5), (int)((y * 4 + dy[q] * 2) * scale_y + 0.5), (int)(cascade->size.width * scale_x + 0.5), (int)(cascade->size.height * scale_y + 0.5));
comp.neighbors = 1;
comp.classification.id = t;
comp.classification.confidence = sum;
ccv_array_push(seq, &comp);
}
u8[0] += 4;
u8[1] += 2;
u8[2] += 1;
}
u8[0] += paddings[0];
u8[1] += paddings[1];
u8[2] += paddings[2];
}
}
scale_x *= scale;
scale_y *= scale;
}
/* the following code from OpenCV's haar feature implementation */
if(params.min_neighbors == 0)
{
for (i = 0; i < seq->rnum; i++)
{
ccv_comp_t* comp = (ccv_comp_t*)ccv_array_get(seq, i);
ccv_array_push(result_seq, comp);
}
} else {
idx_seq = 0;
ccv_array_clear(seq2);
// group retrieved rectangles in order to filter out noise
int ncomp = ccv_array_group(seq, &idx_seq, _ccv_is_equal_same_class, 0);
ccv_comp_t* comps = (ccv_comp_t*)ccmalloc((ncomp + 1) * sizeof(ccv_comp_t));
memset(comps, 0, (ncomp + 1) * sizeof(ccv_comp_t));
// count number of neighbors
for(i = 0; i < seq->rnum; i++)
{
ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(seq, i);
int idx = *(int*)ccv_array_get(idx_seq, i);
if (comps[idx].neighbors == 0)
comps[idx].classification.confidence = r1.classification.confidence;
++comps[idx].neighbors;
comps[idx].rect.x += r1.rect.x;
comps[idx].rect.y += r1.rect.y;
comps[idx].rect.width += r1.rect.width;
comps[idx].rect.height += r1.rect.height;
comps[idx].classification.id = r1.classification.id;
comps[idx].classification.confidence = ccv_max(comps[idx].classification.confidence, r1.classification.confidence);
}
// calculate average bounding box
for(i = 0; i < ncomp; i++)
{
int n = comps[i].neighbors;
if(n >= params.min_neighbors)
{
ccv_comp_t comp;
ccv-src/lib/ccv_bbf.c view on Meta::CPAN
}
}
if(flag)
ccv_array_push(result_seq, &r1);
}
ccv_array_free(idx_seq);
ccfree(comps);
}
}
ccv_array_free(seq);
ccv_array_free(seq2);
ccv_array_t* result_seq2;
/* the following code from OpenCV's haar feature implementation */
if (params.flags & CCV_BBF_NO_NESTED)
{
result_seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0);
idx_seq = 0;
// group retrieved rectangles in order to filter out noise
int ncomp = ccv_array_group(result_seq, &idx_seq, _ccv_is_equal, 0);
ccv_comp_t* comps = (ccv_comp_t*)ccmalloc((ncomp + 1) * sizeof(ccv_comp_t));
memset(comps, 0, (ncomp + 1) * sizeof(ccv_comp_t));
// count number of neighbors
for(i = 0; i < result_seq->rnum; i++)
{
ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(result_seq, i);
int idx = *(int*)ccv_array_get(idx_seq, i);
if (comps[idx].neighbors == 0 || comps[idx].classification.confidence < r1.classification.confidence)
{
comps[idx].classification.confidence = r1.classification.confidence;
comps[idx].neighbors = 1;
comps[idx].rect = r1.rect;
comps[idx].classification.id = r1.classification.id;
}
}
// calculate average bounding box
for(i = 0; i < ncomp; i++)
if(comps[i].neighbors)
ccv_array_push(result_seq2, &comps[i]);
ccv_array_free(result_seq);
ccfree(comps);
} else {
result_seq2 = result_seq;
}
for (i = 1; i < scale_upto + next * 2; i++)
ccv_matrix_free(pyr[i * 4]);
if (params.accurate)
for (i = next * 2; i < scale_upto + next * 2; i++)
{
ccv_matrix_free(pyr[i * 4 + 1]);
ccv_matrix_free(pyr[i * 4 + 2]);
ccv_matrix_free(pyr[i * 4 + 3]);
}
if (params.size.height != _cascade[0]->size.height || params.size.width != _cascade[0]->size.width)
ccv_matrix_free(pyr[0]);
return result_seq2;
}
ccv_bbf_classifier_cascade_t* ccv_bbf_read_classifier_cascade(const char* directory)
{
char buf[1024];
sprintf(buf, "%s/cascade.txt", directory);
int s, i;
FILE* r = fopen(buf, "r");
if (r == 0)
return 0;
ccv_bbf_classifier_cascade_t* cascade = (ccv_bbf_classifier_cascade_t*)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
s = fscanf(r, "%d %d %d", &cascade->count, &cascade->size.width, &cascade->size.height);
assert(s > 0);
cascade->stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
for (i = 0; i < cascade->count; i++)
{
sprintf(buf, "%s/stage-%d.txt", directory, i);
if (_ccv_read_bbf_stage_classifier(buf, &cascade->stage_classifier[i]) < 0)
{
cascade->count = i;
break;
}
}
fclose(r);
return cascade;
}
ccv_bbf_classifier_cascade_t* ccv_bbf_classifier_cascade_read_binary(char* s)
{
int i;
ccv_bbf_classifier_cascade_t* cascade = (ccv_bbf_classifier_cascade_t*)ccmalloc(sizeof(ccv_bbf_classifier_cascade_t));
memcpy(&cascade->count, s, sizeof(cascade->count)); s += sizeof(cascade->count);
memcpy(&cascade->size.width, s, sizeof(cascade->size.width)); s += sizeof(cascade->size.width);
memcpy(&cascade->size.height, s, sizeof(cascade->size.height)); s += sizeof(cascade->size.height);
ccv_bbf_stage_classifier_t* classifier = cascade->stage_classifier = (ccv_bbf_stage_classifier_t*)ccmalloc(cascade->count * sizeof(ccv_bbf_stage_classifier_t));
for (i = 0; i < cascade->count; i++, classifier++)
{
memcpy(&classifier->count, s, sizeof(classifier->count)); s += sizeof(classifier->count);
memcpy(&classifier->threshold, s, sizeof(classifier->threshold)); s += sizeof(classifier->threshold);
classifier->feature = (ccv_bbf_feature_t*)ccmalloc(classifier->count * sizeof(ccv_bbf_feature_t));
classifier->alpha = (float*)ccmalloc(classifier->count * 2 * sizeof(float));
memcpy(classifier->feature, s, classifier->count * sizeof(ccv_bbf_feature_t)); s += classifier->count * sizeof(ccv_bbf_feature_t);
memcpy(classifier->alpha, s, classifier->count * 2 * sizeof(float)); s += classifier->count * 2 * sizeof(float);
}
return cascade;
}
int ccv_bbf_classifier_cascade_write_binary(ccv_bbf_classifier_cascade_t* cascade, char* s, int slen)
{
int i;
int len = sizeof(cascade->count) + sizeof(cascade->size.width) + sizeof(cascade->size.height);
ccv_bbf_stage_classifier_t* classifier = cascade->stage_classifier;
for (i = 0; i < cascade->count; i++, classifier++)
len += sizeof(classifier->count) + sizeof(classifier->threshold) + classifier->count * sizeof(ccv_bbf_feature_t) + classifier->count * 2 * sizeof(float);
if (slen >= len)
{
memcpy(s, &cascade->count, sizeof(cascade->count)); s += sizeof(cascade->count);
memcpy(s, &cascade->size.width, sizeof(cascade->size.width)); s += sizeof(cascade->size.width);
memcpy(s, &cascade->size.height, sizeof(cascade->size.height)); s += sizeof(cascade->size.height);
classifier = cascade->stage_classifier;
for (i = 0; i < cascade->count; i++, classifier++)
{
memcpy(s, &classifier->count, sizeof(classifier->count)); s += sizeof(classifier->count);
memcpy(s, &classifier->threshold, sizeof(classifier->threshold)); s += sizeof(classifier->threshold);
memcpy(s, classifier->feature, classifier->count * sizeof(ccv_bbf_feature_t)); s += classifier->count * sizeof(ccv_bbf_feature_t);
memcpy(s, classifier->alpha, classifier->count * 2 * sizeof(float)); s += classifier->count * 2 * sizeof(float);
}
}
return len;
}
void ccv_bbf_classifier_cascade_free(ccv_bbf_classifier_cascade_t* cascade)
{
int i;
for (i = 0; i < cascade->count; ++i)
{
ccfree(cascade->stage_classifier[i].feature);
ccfree(cascade->stage_classifier[i].alpha);
}
ccfree(cascade->stage_classifier);
ccfree(cascade);
}
( run in 0.783 second using v1.01-cache-2.11-cpan-5837b0d9d2c )