Ejemplo n.º 1
0
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;
}
Ejemplo n.º 2
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,
				},
			},
		},
Ejemplo n.º 3
0
Archivo: canny.c Proyecto: patriot7/ccv
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;
}
Ejemplo n.º 4
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;
}
Ejemplo n.º 5
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,
Ejemplo n.º 7
0
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;
}
Ejemplo n.º 8
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;
}
Ejemplo n.º 9
0
TDetector::TDetector(std::vector<ccv_swt_param_t> params) {
	this->params = params;
	ccv_enable_default_cache();
}
Ejemplo n.º 10
0
TDetector::TDetector() {
	params.push_back(ccv_swt_default_params);
	ccv_enable_default_cache();
}
Ejemplo n.º 11
0
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;
}
Ejemplo n.º 12
0
ofxCcv::ofxCcv() {
    ccv_enable_default_cache();
}
Ejemplo n.º 13
0
Archivo: tld.c Proyecto: gigfork/ccv
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;
}
Ejemplo n.º 14
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;
}
Ejemplo n.º 15
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;
}
Ejemplo n.º 16
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;
}
Ejemplo n.º 17
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;
}
Ejemplo n.º 18
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;
}