void uri_bbf_detect_objects_destroy(void* context)
{
bbf_context_t* bbf_context = (bbf_context_t*)context;
ccv_bbf_classifier_cascade_free(bbf_context->face);
free(bbf_context->desc.data);
free(bbf_context);
}
int
main(int argc, char** argv)
{
printf("Face Detection Benchmark ...\n");
ccv_enable_default_cache();
ccv_dense_matrix_t* image = 0;
ccv_bbf_classifier_cascade_t* cascade = ccv_bbf_read_classifier_cascade(argv[4]);
ccv_read(argv[1], &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
unsigned int elapsed_time;
ccv_array_t* seq;
elapsed_time = get_current_time();
seq = ccv_bbf_detect_objects(image, &cascade, 1, ccv_bbf_default_params);
elapsed_time = get_current_time() - elapsed_time;
printf("origin: %d in %dms\n", seq->rnum, elapsed_time);
ccv_array_free(seq);
int X_SLICE = atoi(argv[2]), Y_SLICE = atoi(argv[3]);
int sliced_total = 0;
int slice_rows = image->rows / Y_SLICE;
int slice_cols = image->cols / X_SLICE;
int i, count = X_SLICE * Y_SLICE;
elapsed_time = get_current_time();
#pragma omp parallel for shared(sliced_total)
for (i = 0; i < count; i++) {
int y = i / X_SLICE;
int x = i - X_SLICE * y;
ccv_dense_matrix_t* slice = 0;
ccv_slice(image, (ccv_matrix_t**)&slice, 0, slice_rows * y, slice_cols * x, slice_rows, slice_cols);
ccv_array_t* sseq = ccv_bbf_detect_objects(slice, &cascade, 1, ccv_bbf_default_params);
sliced_total += sseq->rnum;
#ifdef DEBUG
cos_ccv_slice_output(slice, y, x);
#endif
}
elapsed_time = get_current_time() - elapsed_time;
printf("slice & detect: %d in %dms\n", sliced_total, elapsed_time);
ccv_matrix_free(image);
ccv_bbf_classifier_cascade_free(cascade);
ccv_disable_cache();
return 0;
}
int main(int argc, char** argv)
{
FILE* file;
char *output_file = "my_output.txt";
int i, ret_val;
ccv_dense_matrix_t* image = 0;
ccv_array_t* seq;
accept_roi_begin();
assert(argc >= 3);
ccv_enable_default_cache();
ccv_bbf_classifier_cascade_t* cascade = ccv_bbf_read_classifier_cascade(argv[2]);
ccv_read(argv[1], &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
if (image != 0)
{
unsigned int elapsed_time = get_current_time();
seq = ccv_bbf_detect_objects(image, &cascade, 1, ccv_bbf_default_params);
elapsed_time = get_current_time() - elapsed_time;
for (i = 0; ENDORSE(i < seq->rnum); i++)
{
ccv_comp_t* comp = (ccv_comp_t*)ENDORSE(ccv_array_get(seq, i));
printf("%d %d %d %d %f\n", comp->rect.x, comp->rect.y, comp->rect.width, comp->rect.height, comp->classification.confidence);
}
printf("total : %d in time %dms\n", seq->rnum, elapsed_time);
ccv_bbf_classifier_cascade_free(cascade);
ccv_disable_cache();
accept_roi_end();
file = fopen(output_file, "w");
if (file == NULL) {
perror("fopen for write failed");
return EXIT_FAILURE;
}
// latest changes
struct coordinates {
APPROX int x;
APPROX int y;
};
for (i = 0; ENDORSE(i < seq->rnum); i++) {
ccv_comp_t* comp = (ccv_comp_t*) ENDORSE(ccv_array_get(seq, i));
struct coordinates upperleft, upperright, lowerleft, lowerright;
upperleft.x = comp->rect.x;
upperleft.y = comp->rect.y;
upperright.x = comp->rect.x + comp->rect.width;
upperright.y = upperleft.y;
lowerleft.x = upperleft.x;
lowerleft.y = upperleft.y + comp->rect.height;
lowerright.x = upperright.x;
lowerright.y = lowerleft.y;
ret_val = fprintf(file, "%d %d\n%d %d\n%d %d\n%d %d\n", upperleft.x, upperleft.y, upperright.x, upperright.y,
lowerright.x, lowerright.y, lowerleft.x, lowerleft.y);
// latest changes
if (ret_val < 0) {
perror("fprintf of coordinates failed");
fclose(file);
return EXIT_FAILURE;
}
}
ret_val = fclose(file);
if (ret_val != 0) {
perror("fclose failed");
return EXIT_FAILURE;
}
ccv_array_free(seq);
ccv_matrix_free(image);
} else {
FILE* r = fopen(argv[1], "rt");
if (argc == 4)
chdir(argv[3]);
if(r)
{
size_t len = 1024;
char* file = (char*)malloc(len);
ssize_t read;
while((read = getline(&file, &len, r)) != -1)
{
while(read > 1 && isspace(file[read - 1]))
read--;
file[read] = 0;
image = 0;
ccv_read(file, &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
assert(image != 0);
seq = ccv_bbf_detect_objects(image, &cascade, 1, ccv_bbf_default_params); // seq already declared above
for (i = 0; ENDORSE(i < seq->rnum); i++)
{
ccv_comp_t* comp = (ccv_comp_t*) ENDORSE(ccv_array_get(seq, i));
printf("%s %d %d %d %d %f\n", file, comp->rect.x, comp->rect.y, comp->rect.width, comp->rect.height, comp->classification.confidence);
}
}
free(file);
fclose(r);
}
ccv_bbf_classifier_cascade_free(cascade);
ccv_disable_cache();
accept_roi_end();
file = fopen(output_file, "w");
if (file == NULL) {
perror("fopen for write failed");
return EXIT_FAILURE;
}
for (i = 0; ENDORSE(i < seq->rnum); i++) {
ccv_comp_t* comp = (ccv_comp_t*) ENDORSE(ccv_array_get(seq, i));
ret_val = fprintf(file, "%d\n%d\n%d\n%d\n%f\n", comp->rect.x, comp->rect.y, comp->rect.width,
comp->rect.height, comp->classification.confidence);
if (ret_val < 0) {
perror("fprintf of coordinates and confidence failed");
fclose(file);
return EXIT_FAILURE;
}
}
ret_val = fclose(file);
if (ret_val != 0) {
perror("fclose failed");
return EXIT_FAILURE;
}
ccv_array_free(seq);
ccv_matrix_free(image);
}
return 0;
}
