int main(int argc, char** argv)
{
int counter = 0;
ccv_dense_matrix_t* image = 0;
ccv_enable_default_cache();
ccv_read(argv[1], &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
if (image != 0)
{
ccv_array_t* words = ccv_swt_detect_words(image, ccv_swt_default_params);
if (words)
{
int i;
for (i = 0; i < words->rnum; i++)
{
char filename[256];
ccv_matrix_t* box = 0;
ccv_rect_t* rect = (ccv_rect_t*)ccv_array_get(words, i);
ccv_slice(image, &box, 0, rect->y, rect->x, rect->height, rect->width);
printf("%d %d %d %d\n", rect->x, rect->y, rect->width, rect->height);
snprintf(filename, 256, "out-%d.png", ++counter);
ccv_write(box, filename, NULL, CCV_IO_PNG_FILE, NULL);
}
}
ccv_array_free(words);
}
ccv_drain_cache();
return 0;
}
int main(int argc, char** argv)
{
ccv_enable_default_cache();
ccv_convnet_layer_param_t params[] = {
{
.type = CCV_CONVNET_CONVOLUTIONAL,
.bias = 0,
.sigma = 0.0001,
.input = {
.matrix = {
.rows = 31,
.cols = 31,
.channels = 3,
.partition = 1,
},
},
.output = {
.convolutional = {
.rows = 5,
.cols = 5,
.channels = 3,
.border = 2,
.strides = 1,
.count = 32,
.partition = 1,
},
},
},
int
main(int argc, char** argv)
{
printf("Edge Detection Benchmark ...\n");
ccv_enable_default_cache();
unsigned int elapsed_time;
ccv_dense_matrix_t* yuv = 0;
ccv_read(argv[1], &yuv, CCV_IO_GRAY | CCV_IO_ANY_FILE);
/* ORIGIN */
ccv_dense_matrix_t* canny = 0;
elapsed_time = get_current_time();
ccv_canny(yuv, &canny, 0, 3, 175, 320);
elapsed_time = get_current_time() - elapsed_time;
printf("origin: %ums\n", elapsed_time);
ccv_matrix_free(canny);
/* SLICE & DETECT */
int X_SLICE = atoi(argv[2]), Y_SLICE = atoi(argv[3]);
int i, count = X_SLICE * Y_SLICE;
int slice_rows = yuv->rows / Y_SLICE;
int slice_cols = yuv->cols / X_SLICE;
ccv_dense_matrix_t* canny_arr[count];
elapsed_time = get_current_time();
#pragma omp parallel for
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(yuv, (ccv_matrix_t**)&slice, 0, slice_rows * y, slice_cols * x, slice_rows, slice_cols);
#ifdef DEBUG
cos_ccv_slice_output(slice, y, x);
#endif
canny_arr[i] = 0;
ccv_canny(slice, &canny_arr[i], 0, 3, 175, 320);
}
elapsed_time = get_current_time() - elapsed_time;
printf("slice & detect: %ums\n", elapsed_time);
unsigned int slice_time = elapsed_time; /* save to compute total time */
/* MERGE */
ccv_dense_matrix_t* final_output = 0;
elapsed_time = get_current_time();
cos_ccv_merge(canny_arr, &final_output, yuv->rows, yuv->cols, X_SLICE, Y_SLICE);
elapsed_time = get_current_time() - elapsed_time;
printf("merge: %ums\n", elapsed_time);
ccv_matrix_free(final_output);
printf("parallel total: %ums\n", slice_time + elapsed_time);
ccv_matrix_free(yuv);
ccv_disable_cache();
return 0;
}
int main(int argc, char** argv)
{
assert(argc >= 3);
int i;
ccv_enable_default_cache();
ccv_dense_matrix_t* image = 0;
ccv_icf_classifier_cascade_t* cascade = ccv_icf_read_classifier_cascade(argv[2]);
ccv_read(argv[1], &image, CCV_IO_ANY_FILE | CCV_IO_RGB_COLOR);
if (image != 0)
{
unsigned int elapsed_time = get_current_time();
ccv_array_t* seq = ccv_icf_detect_objects(image, &cascade, 1, ccv_icf_default_params);
elapsed_time = get_current_time() - elapsed_time;
for (i = 0; i < seq->rnum; i++)
{
ccv_comp_t* comp = (ccv_comp_t*)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->confidence);
}
printf("total : %d in time %dms\n", seq->rnum, elapsed_time);
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_ANY_FILE | CCV_IO_RGB_COLOR);
assert(image != 0);
ccv_array_t* seq = ccv_icf_detect_objects(image, &cascade, 1, ccv_icf_default_params);
for (i = 0; i < seq->rnum; i++)
{
ccv_comp_t* comp = (ccv_comp_t*)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->confidence);
}
ccv_array_free(seq);
ccv_matrix_free(image);
}
free(file);
fclose(r);
}
}
ccv_icf_classifier_cascade_free(cascade);
ccv_disable_cache();
return 0;
}
int main(int argc, char** argv)
{
assert(argc == 3);
ccv_enable_default_cache();
ccv_dense_matrix_t* image = 0;
ccv_read(argv[1], &image, CCV_IO_RGB_COLOR | CCV_IO_ANY_FILE);
ccv_mser_param_t params = {
.min_area = 60,
.max_area = (int)(image->rows * image->cols * 0.3 + 0.5),
.min_diversity = 0.2,
.area_threshold = 1.01,
.min_margin = 0.003,
.max_evolution = 200,
.edge_blur_sigma = sqrt(3.0),
.delta = 5,
.max_variance = 0.25,
.direction = CCV_DARK_TO_BRIGHT,
};
if (image)
{
ccv_dense_matrix_t* yuv = 0;
// ccv_color_transform(image, &yuv, 0, CCV_RGB_TO_YUV);
ccv_read(argv[1], &yuv, CCV_IO_GRAY | CCV_IO_ANY_FILE);
unsigned int elapsed_time = get_current_time();
ccv_dense_matrix_t* canny = 0;
ccv_canny(yuv, &canny, 0, 3, 175, 320);
ccv_dense_matrix_t* outline = 0;
ccv_close_outline(canny, &outline, 0);
ccv_matrix_free(canny);
ccv_dense_matrix_t* mser = 0;
ccv_array_t* mser_keypoint = ccv_mser(yuv, outline, &mser, 0, params);
elapsed_time = get_current_time() - elapsed_time;
ccv_matrix_free(outline);
printf("total : %d in time %dms\n", mser_keypoint->rnum, elapsed_time);
ccv_array_free(mser_keypoint);
ccv_make_matrix_mutable(image);
int i, j;
for (i = 0; i < image->rows; i++)
for (j = 0; j < image->cols; j++)
{
if (mser->data.i32[i * mser->cols + j])
{
image->data.u8[i * image->step + j * 3] = colors[mser->data.i32[i * mser->cols + j] % 6][0];
image->data.u8[i * image->step + j * 3 + 1] = colors[mser->data.i32[i * mser->cols + j] % 6][1];
image->data.u8[i * image->step + j * 3 + 2] = colors[mser->data.i32[i * mser->cols + j] % 6][2];
}
}
ccv_write(image, argv[2], 0, CCV_IO_PNG_FILE, 0);
ccv_matrix_free(yuv);
ccv_matrix_free(image);
}
ccv_disable_cache();
return 0;
}
int main(int argc, char** argv)
{
ccv_enable_default_cache();
assert(argc == 2);
FILE *r = fopen(argv[1], "r");
char* file = (char*)malloc(1024);
ccv_array_t* categorizeds = ccv_array_new(sizeof(ccv_categorized_t), 64, 0);
size_t len = 1024;
ssize_t read;
while ((read = getline(&file, &len, r)) != -1)
{
while(read > 1 && isspace(file[read - 1]))
read--;
file[read] = 0;
ccv_file_info_t input;
input.filename = (char*)ccmalloc(1024);
strncpy(input.filename, file, 1024);
ccv_categorized_t categorized = ccv_categorized(0, 0, &input);
ccv_array_push(categorizeds, &categorized);
}
fclose(r);
free(file);
/* MattNet parameters */
ccv_convnet_layer_param_t params[13] = {
// first layer (convolutional => max pool => rnorm)
{
.type = CCV_CONVNET_CONVOLUTIONAL,
.bias = 0,
.glorot = sqrtf(2),
.input = {
.matrix = {
.rows = 225,
.cols = 225,
.channels = 3,
.partition = 1,
},
},
.output = {
.convolutional = {
.count = 96,
.strides = 2,
.border = 1,
.rows = 7,
.cols = 7,
.channels = 3,
.partition = 2,
},
},
},
{
.type = CCV_CONVNET_LOCAL_RESPONSE_NORM,
int main(int argc, char** argv)
{
if (argc != 5)
{
print_help();
return -1;
}
ccv_enable_default_cache();
int i, rt;
int posnum = atoi(argv[2]);
FILE* pf = fopen(argv[1], "r");
ccv_dense_matrix_t** posimg = (ccv_dense_matrix_t**)malloc(sizeof(posimg[0]) * posnum);
for (i = 0; i < posnum; i++)
{
char buf[1024];
rt = fscanf(pf, "%s", buf);
ccv_read(buf, &posimg[i], CCV_IO_GRAY | CCV_IO_ANY_FILE);
}
fclose(pf);
int negnum = atoi(argv[4]);
FILE* bgf = fopen(argv[3], "r");
int bgnum;
rt = fscanf(bgf, "%d", &bgnum);
char** bgfiles = (char**)malloc(sizeof(bgfiles[0]) * bgnum);
for (i = 0; i < bgnum; i++)
{
bgfiles[i] = (char*)malloc(1024);
rt = fscanf(bgf, "%s", bgfiles[i]);
}
fclose(bgf);
ccv_bbf_new_param_t params = {
.pos_crit = 0.9975,
.neg_crit = 0.50,
.balance_k = 1.0,
.layer = 24,
.feature_number = 100,
.optimizer = CCV_BBF_GENETIC_OPT | CCV_BBF_FLOAT_OPT,
};
ccv_bbf_classifier_cascade_new(posimg, posnum, bgfiles, bgnum, negnum, ccv_size(24, 24), "data", params);
for (i = 0; i < bgnum; i++)
free(bgfiles[i]);
for (i = 0; i < posnum; i++)
ccv_matrix_free(&posimg[i]);
free(posimg);
free(bgfiles);
ccv_disable_cache();
return 0;
}
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;
}
TDetector::TDetector(std::vector<ccv_swt_param_t> params) {
this->params = params;
ccv_enable_default_cache();
}
TDetector::TDetector() {
params.push_back(ccv_swt_default_params);
ccv_enable_default_cache();
}
int main(int argc, char** argv)
{
static struct option bbf_options[] = {
/* help */
{"help", 0, 0, 0},
/* required parameters */
{"positive-list", 1, 0, 0},
{"background-list", 1, 0, 0},
{"working-dir", 1, 0, 0},
{"negative-count", 1, 0, 0},
{"width", 1, 0, 0},
{"height", 1, 0, 0},
/* optional parameters */
{"base-dir", 1, 0, 0},
{"layer", 1, 0, 0},
{"positive-criteria", 1, 0, 0},
{"negative-criteria", 1, 0, 0},
{"balance", 1, 0, 0},
{"feature-number", 1, 0, 0},
{0, 0, 0, 0}
};
char* positive_list = 0;
char* background_list = 0;
char* working_dir = 0;
char* base_dir = 0;
int negnum = 0;
int width = 0, height = 0;
ccv_bbf_new_param_t params = {
.pos_crit = 0.9975,
.neg_crit = 0.50,
.balance_k = 1.0,
.layer = 24,
.feature_number = 100,
.optimizer = CCV_BBF_GENETIC_OPT | CCV_BBF_FLOAT_OPT,
};
int i, k;
while (getopt_long_only(argc, argv, "", bbf_options, &k) != -1)
{
switch (k)
{
case 0:
exit_with_help();
case 1:
positive_list = optarg;
break;
case 2:
background_list = optarg;
break;
case 3:
working_dir = optarg;
break;
case 4:
negnum = atoi(optarg);
break;
case 5:
width = atoi(optarg);
break;
case 6:
height = atoi(optarg);
break;
case 7:
base_dir = optarg;
break;
case 8:
params.layer = atoi(optarg);
break;
case 9:
params.pos_crit = atof(optarg);
break;
case 10:
params.neg_crit = atof(optarg);
break;
case 11:
params.balance_k = atof(optarg);
break;
case 12:
params.feature_number = atoi(optarg);
break;
}
}
assert(positive_list != 0);
assert(background_list != 0);
assert(working_dir != 0);
assert(negnum > 0);
assert(width > 0 && height > 0);
ccv_enable_default_cache();
FILE* r0 = fopen(positive_list, "r");
assert(r0 && "positive-list doesn't exists");
FILE* r1 = fopen(background_list, "r");
assert(r1 && "background-list doesn't exists");
char* file = (char*)malloc(1024);
int dirlen = (base_dir != 0) ? strlen(base_dir) + 1 : 0;
size_t len = 1024;
ssize_t read;
int capacity = 32, size = 0;
ccv_dense_matrix_t** posimg = (ccv_dense_matrix_t**)ccmalloc(sizeof(ccv_dense_matrix_t*) * capacity);
while ((read = getline(&file, &len, r0)) != -1)
{
while(read > 1 && isspace(file[read - 1]))
read--;
file[read] = 0;
char* posfile = (char*)ccmalloc(1024);
if (base_dir != 0)
{
strncpy(posfile, base_dir, 1024);
posfile[dirlen - 1] = '/';
}
strncpy(posfile + dirlen, file, 1024 - dirlen);
posimg[size] = 0;
ccv_read(posfile, &posimg[size], CCV_IO_GRAY | CCV_IO_ANY_FILE);
if (posimg != 0)
{
++size;
if (size >= capacity)
{
capacity *= 2;
posimg = (ccv_dense_matrix_t**)ccrealloc(posimg, sizeof(ccv_dense_matrix_t*) * capacity);
}
}
}
fclose(r0);
int posnum = size;
capacity = 32;
size = 0;
char** bgfiles = (char**)ccmalloc(sizeof(char*) * capacity);
while ((read = getline(&file, &len, r1)) != -1)
{
while(read > 1 && isspace(file[read - 1]))
read--;
file[read] = 0;
bgfiles[size] = (char*)ccmalloc(1024);
if (base_dir != 0)
{
strncpy(bgfiles[size], base_dir, 1024);
bgfiles[size][dirlen - 1] = '/';
}
strncpy(bgfiles[size] + dirlen, file, 1024 - dirlen);
++size;
if (size >= capacity)
{
capacity *= 2;
bgfiles = (char**)ccrealloc(bgfiles, sizeof(char*) * capacity);
}
}
fclose(r1);
int bgnum = size;
free(file);
ccv_bbf_classifier_cascade_new(posimg, posnum, bgfiles, bgnum, negnum, ccv_size(width, height), working_dir, params);
for (i = 0; i < bgnum; i++)
free(bgfiles[i]);
for (i = 0; i < posnum; i++)
ccv_matrix_free(&posimg[i]);
free(posimg);
free(bgfiles);
ccv_disable_cache();
return 0;
}
ofxCcv::ofxCcv() {
ccv_enable_default_cache();
}
int main(int argc, char** argv)
{
#ifdef HAVE_AVCODEC
#ifdef HAVE_AVFORMAT
#ifdef HAVE_SWSCALE
assert(argc == 6);
ccv_rect_t box = ccv_rect(atoi(argv[2]), atoi(argv[3]), atoi(argv[4]), atoi(argv[5]));
box.width = box.width - box.x + 1;
box.height = box.height - box.y + 1;
printf("%d,%d,%d,%d,%f\n", box.x, box.y, box.width + box.x - 1, box.height + box.y - 1, 1.0f);
// init av-related structs
AVFormatContext* ic = 0;
int video_stream = -1;
AVStream* video_st = 0;
AVFrame* picture = 0;
AVFrame rgb_picture;
memset(&rgb_picture, 0, sizeof(AVPicture));
AVPacket packet;
memset(&packet, 0, sizeof(AVPacket));
av_init_packet(&packet);
av_register_all();
avformat_network_init();
// load video and codec
avformat_open_input(&ic, argv[1], 0, 0);
avformat_find_stream_info(ic, 0);
int i;
for (i = 0; i < ic->nb_streams; i++)
{
AVCodecContext* enc = ic->streams[i]->codec;
enc->thread_count = 2;
if (AVMEDIA_TYPE_VIDEO == enc->codec_type && video_stream < 0)
{
AVCodec* codec = avcodec_find_decoder(enc->codec_id);
if (!codec || avcodec_open2(enc, codec, 0) < 0)
continue;
video_stream = i;
video_st = ic->streams[i];
picture = avcodec_alloc_frame();
rgb_picture.data[0] = (uint8_t*)ccmalloc(avpicture_get_size(PIX_FMT_RGB24, enc->width, enc->height));
avpicture_fill((AVPicture*)&rgb_picture, rgb_picture.data[0], PIX_FMT_RGB24, enc->width, enc->height);
break;
}
}
int got_picture = 0;
while (!got_picture)
{
int result = av_read_frame(ic, &packet);
if (result == AVERROR(EAGAIN))
continue;
avcodec_decode_video2(video_st->codec, picture, &got_picture, &packet);
}
ccv_enable_default_cache();
struct SwsContext* picture_ctx = sws_getCachedContext(0, video_st->codec->width, video_st->codec->height, video_st->codec->pix_fmt, video_st->codec->width, video_st->codec->height, PIX_FMT_RGB24, SWS_BICUBIC, 0, 0, 0);
sws_scale(picture_ctx, (const uint8_t* const*)picture->data, picture->linesize, 0, video_st->codec->height, rgb_picture.data, rgb_picture.linesize);
ccv_dense_matrix_t* x = 0;
ccv_read(rgb_picture.data[0], &x, CCV_IO_RGB_RAW | CCV_IO_GRAY, video_st->codec->height, video_st->codec->width, rgb_picture.linesize[0]);
ccv_tld_t* tld = ccv_tld_new(x, box, ccv_tld_default_params);
ccv_dense_matrix_t* y = 0;
for (;;)
{
got_picture = 0;
int result = av_read_frame(ic, &packet);
if (result == AVERROR(EAGAIN))
continue;
avcodec_decode_video2(video_st->codec, picture, &got_picture, &packet);
if (!got_picture)
break;
sws_scale(picture_ctx, (const uint8_t* const*)picture->data, picture->linesize, 0, video_st->codec->height, rgb_picture.data, rgb_picture.linesize);
ccv_read(rgb_picture.data[0], &y, CCV_IO_RGB_RAW | CCV_IO_GRAY, video_st->codec->height, video_st->codec->width, rgb_picture.linesize[0]);
ccv_tld_info_t info;
ccv_comp_t newbox = ccv_tld_track_object(tld, x, y, &info);
printf("%04d: performed learn: %d, performed track: %d, successfully track: %d; %d passed fern detector, %d passed nnc detector, %d merged, %d confident matches, %d close matches\n", tld->count, info.perform_learn, info.perform_track, info.track_success, info.ferns_detects, info.nnc_detects, info.clustered_detects, info.confident_matches, info.close_matches);
ccv_dense_matrix_t* image = 0;
ccv_read(rgb_picture.data[0], &image, CCV_IO_RGB_RAW | CCV_IO_RGB_COLOR, video_st->codec->height, video_st->codec->width, rgb_picture.linesize[0]);
// draw out
// for (i = 0; i < tld->top->rnum; i++)
if (tld->found)
{
ccv_comp_t* comp = &newbox; // (ccv_comp_t*)ccv_array_get(tld->top, i);
if (comp->rect.x >= 0 && comp->rect.x + comp->rect.width < image->cols &&
comp->rect.y >= 0 && comp->rect.y + comp->rect.height < image->rows)
{
int x, y;
for (x = comp->rect.x; x < comp->rect.x + comp->rect.width; x++)
{
image->data.u8[image->step * comp->rect.y + x * 3] =
image->data.u8[image->step * (comp->rect.y + comp->rect.height - 1) + x * 3] = 255;
image->data.u8[image->step * comp->rect.y + x * 3 + 1] =
image->data.u8[image->step * (comp->rect.y + comp->rect.height - 1) + x * 3 + 1] =
image->data.u8[image->step * comp->rect.y + x * 3 + 2] =
image->data.u8[image->step * (comp->rect.y + comp->rect.height - 1) + x * 3 + 2] = 0;
}
for (y = comp->rect.y; y < comp->rect.y + comp->rect.height; y++)
{
image->data.u8[image->step * y + comp->rect.x * 3] =
image->data.u8[image->step * y + (comp->rect.x + comp->rect.width - 1) * 3] = 255;
image->data.u8[image->step * y + comp->rect.x * 3 + 1] =
image->data.u8[image->step * y + (comp->rect.x + comp->rect.width - 1) * 3 + 1] =
image->data.u8[image->step * y + comp->rect.x * 3 + 2] =
image->data.u8[image->step * y + (comp->rect.x + comp->rect.width - 1) * 3 + 2] = 0;
}
}
}
char filename[1024];
sprintf(filename, "tld-out/output-%04d.png", tld->count);
ccv_write(image, filename, 0, CCV_IO_PNG_FILE, 0);
ccv_matrix_free(image);
if (tld->found)
printf("%d,%d,%d,%d,%f\n", newbox.rect.x, newbox.rect.y, newbox.rect.width + newbox.rect.x - 1, newbox.rect.height + newbox.rect.y - 1, newbox.confidence);
else
printf("NaN,NaN,NaN,NaN,NaN\n");
x = y;
y = 0;
}
ccv_matrix_free(x);
ccv_tld_free(tld);
ccfree(rgb_picture.data[0]);
ccv_disable_cache();
#endif
#endif
#endif
return 0;
}
int main(int argc, char** argv)
{
assert(argc >= 3);
int i, j;
ccv_enable_default_cache();
ccv_dense_matrix_t* image = 0;
ccv_read(argv[1], &image, CCV_IO_ANY_FILE);
ccv_dpm_mixture_model_t* model = ccv_dpm_read_mixture_model(argv[2]);
if (image != 0)
{
unsigned int elapsed_time = get_current_time();
ccv_array_t* seq = ccv_dpm_detect_objects(image, &model, 1, ccv_dpm_default_params);
elapsed_time = get_current_time() - elapsed_time;
if (seq)
{
for (i = 0; i < seq->rnum; i++)
{
ccv_root_comp_t* comp = (ccv_root_comp_t*)ccv_array_get(seq, i);
printf("%d %d %d %d %f %d\n", comp->rect.x, comp->rect.y, comp->rect.width, comp->rect.height, comp->classification.confidence, comp->pnum);
for (j = 0; j < comp->pnum; j++)
printf("| %d %d %d %d %f\n", comp->part[j].rect.x, comp->part[j].rect.y, comp->part[j].rect.width, comp->part[j].rect.height, comp->part[j].classification.confidence);
}
printf("total : %d in time %dms\n", seq->rnum, elapsed_time);
ccv_array_free(seq);
} else {
printf("elapsed time %dms\n", elapsed_time);
}
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);
ccv_array_t* seq = ccv_dpm_detect_objects(image, &model, 1, ccv_dpm_default_params);
if (seq != 0)
{
for (i = 0; i < seq->rnum; i++)
{
ccv_root_comp_t* comp = (ccv_root_comp_t*)ccv_array_get(seq, i);
printf("%s %d %d %d %d %f %d\n", file, comp->rect.x, comp->rect.y, comp->rect.width, comp->rect.height, comp->classification.confidence, comp->pnum);
for (j = 0; j < comp->pnum; j++)
printf("| %d %d %d %d %f\n", comp->part[j].rect.x, comp->part[j].rect.y, comp->part[j].rect.width, comp->part[j].rect.height, comp->part[j].classification.confidence);
}
ccv_array_free(seq);
}
ccv_matrix_free(image);
}
free(file);
fclose(r);
}
}
ccv_drain_cache();
ccv_dpm_mixture_model_free(model);
return 0;
}
int main(int argc, char** argv)
{
assert(argc == 3);
ccv_enable_default_cache();
ccv_dense_matrix_t* object = 0;
ccv_dense_matrix_t* image = 0;
ccv_read(argv[1], &object, CCV_IO_GRAY | CCV_IO_ANY_FILE);
ccv_read(argv[2], &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
unsigned int elapsed_time = get_current_time();
ccv_sift_param_t params = {
.noctaves = 3,
.nlevels = 6,
.up2x = 1,
.edge_threshold = 10,
.norm_threshold = 0,
.peak_threshold = 0,
};
ccv_array_t* obj_keypoints = 0;
ccv_dense_matrix_t* obj_desc = 0;
ccv_sift(object, &obj_keypoints, &obj_desc, 0, params);
ccv_array_t* image_keypoints = 0;
ccv_dense_matrix_t* image_desc = 0;
ccv_sift(image, &image_keypoints, &image_desc, 0, params);
elapsed_time = get_current_time() - elapsed_time;
int i, j, k;
int match = 0;
for (i = 0; i < obj_keypoints->rnum; i++)
{
float* odesc = obj_desc->data.f32 + i * 128;
int minj = -1;
double mind = 1e6, mind2 = 1e6;
for (j = 0; j < image_keypoints->rnum; j++)
{
float* idesc = image_desc->data.f32 + j * 128;
double d = 0;
for (k = 0; k < 128; k++)
{
d += (odesc[k] - idesc[k]) * (odesc[k] - idesc[k]);
if (d > mind2)
break;
}
if (d < mind)
{
mind2 = mind;
mind = d;
minj = j;
} else if (d < mind2) {
mind2 = d;
}
}
if (mind < mind2 * 0.36)
{
ccv_keypoint_t* op = (ccv_keypoint_t*)ccv_array_get(obj_keypoints, i);
ccv_keypoint_t* kp = (ccv_keypoint_t*)ccv_array_get(image_keypoints, minj);
printf("%f %f => %f %f\n", op->x, op->y, kp->x, kp->y);
match++;
}
}
printf("%dx%d on %dx%d\n", object->cols, object->rows, image->cols, image->rows);
printf("%d keypoints out of %d are matched\n", match, obj_keypoints->rnum);
printf("elpased time : %d\n", elapsed_time);
ccv_array_free(obj_keypoints);
ccv_array_free(image_keypoints);
ccv_matrix_free(obj_desc);
ccv_matrix_free(image_desc);
ccv_matrix_free(object);
ccv_matrix_free(image);
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;
}
int main(int argc, char** argv)
{
static struct option image_net_options[] = {
/* help */
{"help", 0, 0, 0},
/* required parameters */
{"train-list", 1, 0, 0},
{"test-list", 1, 0, 0},
{"working-dir", 1, 0, 0},
/* optional parameters */
{"base-dir", 1, 0, 0},
{"max-epoch", 1, 0, 0},
{"iterations", 1, 0, 0},
{0, 0, 0, 0}
};
char* train_list = 0;
char* test_list = 0;
char* working_dir = 0;
char* base_dir = 0;
ccv_convnet_train_param_t train_params = {
.max_epoch = 100,
.mini_batch = 64,
.sgd_frequency = 1, // do sgd every sgd_frequency batches (mini_batch * device_count * sgd_frequency)
.iterations = 50000,
.device_count = 4,
.peer_access = 1,
.symmetric = 1,
.image_manipulation = 0.2,
.color_gain = 0.001,
.input = {
.min_dim = 257,
.max_dim = 257,
},
};
int i, c;
while (getopt_long_only(argc, argv, "", image_net_options, &c) != -1)
{
switch (c)
{
case 0:
exit_with_help();
case 1:
train_list = optarg;
break;
case 2:
test_list = optarg;
break;
case 3:
working_dir = optarg;
break;
case 4:
base_dir = optarg;
break;
case 5:
train_params.max_epoch = atoi(optarg);
break;
case 6:
train_params.iterations = atoi(optarg);
break;
}
}
if (!train_list || !test_list || !working_dir)
exit_with_help();
ccv_enable_default_cache();
FILE *r0 = fopen(train_list, "r");
assert(r0 && "train-list doesn't exists");
FILE* r1 = fopen(test_list, "r");
assert(r1 && "test-list doesn't exists");
char* file = (char*)malloc(1024);
int dirlen = (base_dir != 0) ? strlen(base_dir) + 1 : 0;
ccv_array_t* categorizeds = ccv_array_new(sizeof(ccv_categorized_t), 64, 0);
while (fscanf(r0, "%d %s", &c, file) != EOF)
{
char* filename = (char*)ccmalloc(1024);
if (base_dir != 0)
{
strncpy(filename, base_dir, 1024);
filename[dirlen - 1] = '/';
}
strncpy(filename + dirlen, file, 1024 - dirlen);
ccv_file_info_t file_info = {
.filename = filename,
};
// imageNet's category class starts from 1, thus, minus 1 to get 0-index
ccv_categorized_t categorized = ccv_categorized(c - 1, 0, &file_info);
ccv_array_push(categorizeds, &categorized);
}
fclose(r0);
ccv_array_t* tests = ccv_array_new(sizeof(ccv_categorized_t), 64, 0);
while (fscanf(r1, "%d %s", &c, file) != EOF)
{
char* filename = (char*)ccmalloc(1024);
if (base_dir != 0)
{
strncpy(filename, base_dir, 1024);
filename[dirlen - 1] = '/';
}
strncpy(filename + dirlen, file, 1024 - dirlen);
ccv_file_info_t file_info = {
.filename = filename,
};
// imageNet's category class starts from 1, thus, minus 1 to get 0-index
ccv_categorized_t categorized = ccv_categorized(c - 1, 0, &file_info);
ccv_array_push(tests, &categorized);
}
fclose(r1);
free(file);
// #define model_params vgg_d_params
#define model_params matt_c_params
int depth = sizeof(model_params) / sizeof(ccv_convnet_layer_param_t);
ccv_convnet_t* convnet = ccv_convnet_new(1, ccv_size(257, 257), model_params, depth);
if (ccv_convnet_verify(convnet, 1000) == 0)
{
ccv_convnet_layer_train_param_t layer_params[depth];
memset(layer_params, 0, sizeof(layer_params));
for (i = 0; i < depth; i++)
{
layer_params[i].w.decay = 0.0005;
layer_params[i].w.learn_rate = 0.01;
layer_params[i].w.momentum = 0.9;
layer_params[i].bias.decay = 0;
layer_params[i].bias.learn_rate = 0.01;
layer_params[i].bias.momentum = 0.9;
}
// set the two full connect layers to last with dropout rate at 0.5
for (i = depth - 3; i < depth - 1; i++)
layer_params[i].dor = 0.5;
train_params.layer_params = layer_params;
ccv_set_cli_output_levels(ccv_cli_output_level_and_above(CCV_CLI_INFO));
ccv_convnet_supervised_train(convnet, categorizeds, tests, working_dir, train_params);
} else {
PRINT(CCV_CLI_ERROR, "Invalid convnet configuration\n");
}
ccv_convnet_free(convnet);
ccv_disable_cache();
return 0;
}
int main(int argc, char** argv)
{
// process arguments
char* image_file = "";
int scale_invariant = 0;
int min_height = 0;
int min_area = 0;
char ch;
while ((ch = getopt(argc, argv, "a:h:i")) != EOF)
switch(ch) {
case 'i':
scale_invariant = 1;
printf("Scale invariant\n");
break;
case 'h':
min_height = atoi(optarg);
printf("Min height %d\n", min_height);
continue;
case 'a':
min_area = atoi(optarg);
printf("Min area %d\n", min_area);
continue;
}
argc -= optind;
argv += optind;
image_file = argv[0];
ccv_enable_default_cache();
ccv_dense_matrix_t* image = 0;
ccv_read(image_file, &image, CCV_IO_GRAY | CCV_IO_ANY_FILE);
if (image==0) {
fprintf(stderr, "ERROR: image could not be read\n");
return 1;
}
unsigned int elapsed_time = get_current_time();
ccv_swt_param_t params = ccv_swt_default_params;
if (scale_invariant)
params.scale_invariant = 1;
if (min_height)
params.min_height = min_height;
if (min_area)
params.min_area = min_area;
ccv_array_t* letters = ccv_swt_detect_chars_contour(image, params);
elapsed_time = get_current_time() - elapsed_time;
if (letters)
{
printf("total : %d in time %dms\n", letters->rnum, elapsed_time);
int i;
for (i = 0; i < letters->rnum; i++)
{
// for each letter
ccv_contour_t* cont = *(ccv_contour_t**)ccv_array_get(letters, i);
printf("Contour %d (size=%d):\n", i, cont->size);
for (int j = 0; j < cont->size; j++) {
// for each point in contour
ccv_point_t* point = (ccv_point_t*)ccv_array_get(cont->set, j);
printf("%d %d\n", point->x, point->y);
}
printf("End contour %d\n", i);
}
ccv_array_free(letters);
}
ccv_matrix_free(image);
ccv_drain_cache();
return 0;
}