Exemple #1
0
// combining several vocab trees into one
int combineVocab(vector<string> &allTreesIn, char *tree_out)
{
	int num_trees = (int)allTreesIn.size();

	VocabTree tree;
	tree.Read(allTreesIn[0].c_str());

	//Start with the second tree, as we just read the first one
	for (int i = 1; i < num_trees; i++)
	{
		printf("[VocabCombine] Adding tree %d [%s]...\n", i, allTreesIn[i].c_str());
		fflush(stdout);

		VocabTree tree_add;
		tree_add.Read(allTreesIn[i].c_str());
		tree.Combine(tree_add);
		tree_add.Clear();
	}

	//Now do the reweighting:
	int total_num_db_images = tree.GetMaxDatabaseImageIndex() + 1;
	printf("Total num_db_images: %d\n", total_num_db_images);
	tree.ComputeTFIDFWeights(total_num_db_images);
	tree.NormalizeDatabase(0, total_num_db_images);
	tree.Write(tree_out);

	//Write vectors to a file 
	tree.WriteDatabaseVectors("vectors_all.txt", 0, total_num_db_images);

	return 0;
}
Exemple #2
0
int main(int argc, char **argv) 
{
    if (argc < 3) {
        printf("Usage: %s <tree1.in> <tree2.in> ... <tree.out>\n",
               argv[0]);

        return 1;
    }

    int num_trees = argc - 2;
    
    char *tree_out = argv[argc-1];

    VocabTree tree;
    tree.Read(argv[1]);

    /* Start with the second tree, as we just read the first one */
    for (int i = 1; i < num_trees; i++) {
        printf("[VocabCombine] Adding tree %d [%s]...\n", i, argv[i+1]);
        fflush(stdout);

        VocabTree tree_add;
        tree_add.Read(argv[i+1]);
        tree.Combine(tree_add);
        tree_add.Clear();
    }

    /* Now do the reweighting */
    // if (use_tfidf)
    int total_num_db_images = tree.GetMaxDatabaseImageIndex() + 1;
    printf("Total num_db_images: %d\n", total_num_db_images);
    tree.ComputeTFIDFWeights(total_num_db_images);
    tree.NormalizeDatabase(0, total_num_db_images);
    tree.Write(tree_out);

    /* Write vectors to a file */
    tree.WriteDatabaseVectors("vectors_all.txt", 0, total_num_db_images);

    return 0;
}
int main(int argc, char **argv) 
{
    if (argc < 4 || argc > 8) {
        printf("Usage: %s <list.in> <tree.in> <tree.out> [use_tfidf:1] "
               "[normalize:1] [start_id:0] [distance_type:1]\n", 
               argv[0]);

        return 1;
    }

    double min_feature_scale = 1.4;
    bool use_tfidf = true;
    bool normalize = true;
    
    char *list_in = argv[1];
    char *tree_in = argv[2];
    char *tree_out = argv[3];
    DistanceType distance_type = DistanceMin;
    int start_id = 0;

    if (argc >= 5)
        use_tfidf = atoi(argv[4]);

    if (argc >= 6)
        normalize = atoi(argv[5]);

    if (argc >= 7)
        start_id = atoi(argv[6]);

    if (argc >= 8)
        distance_type = (DistanceType) atoi(argv[7]);

    switch (distance_type) {
    case DistanceDot:
        printf("[VocabMatch] Using distance Dot\n");
        break;        
    case DistanceMin:
        printf("[VocabMatch] Using distance Min\n");
        break;
    default:
        printf("[VocabMatch] Using no known distance!\n");
        break;
    }

    FILE *f = fopen(list_in, "r");
    
    if (f == NULL) {
        printf("Error opening file %s for reading\n", list_in);
        return 1;
    }

    std::vector<std::string> key_files;
    char buf[256];
    while (fgets(buf, 256, f)) {
        /* Remove trailing newline */
        if (buf[strlen(buf) - 1] == '\n')
            buf[strlen(buf) - 1] = 0;

        key_files.push_back(std::string(buf));
    }

    printf("[VocabBuildDB] Reading tree %s...\n", tree_in);
    fflush(stdout);

    VocabTree tree;
    tree.Read(tree_in);

#if 1
    tree.Flatten();
#endif

    tree.m_distance_type = distance_type;
    tree.SetInteriorNodeWeight(0.0);

    /* Initialize leaf weights to 1.0 */
    tree.SetConstantLeafWeights();

    const int dim = 128;
    int num_db_images = (int) key_files.size();
    unsigned long count = 0;

    tree.ClearDatabase();

    for (int i = 0; i < num_db_images; i++) {
        int num_keys = 0;
        unsigned char *keys = ReadAndFilterKeys(key_files[i].c_str(), 
                                                dim, min_feature_scale,
                                                0, num_keys);

        printf("[VocabBuildDB] Adding vector %d (%d keys)\n", 
               start_id + i, num_keys);
        tree.AddImageToDatabase(start_id + i, num_keys, keys);

        if (num_keys > 0) 
            delete [] keys;
    }

    printf("[VocabBuildDB] Pushed %lu features\n", count);
    fflush(stdout);

    if (use_tfidf)
        tree.ComputeTFIDFWeights(num_db_images);

    if (normalize) 
        tree.NormalizeDatabase(start_id, num_db_images);

    printf("[VocabBuildDB] Writing tree...\n");
    tree.Write(tree_out);

    // char filename[256];
    // sprintf(filename, "vectors_%03d.txt", start_id);
    // tree.WriteDatabaseVectors(filename, start_id, num_db_images);

    return 0;
}
Exemple #4
0
// compare images feature using db
int VocabCompare(int argc, char **argv)
{
	if (argc != 5 && argc != 6)
	{
		printf("Usage: %s <tree.in> <image1.key> <image2.key> <matches.out> [distance_type]\n", argv[0]);
		return 1;
	}

	char *tree_in = argv[1];
	char *image1_in = argv[2];
	char *image2_in = argv[3];
	char *matches_out = argv[4];
	DistanceType distance_type = DistanceMin;

	if (argc >= 6)
		distance_type = (DistanceType)atoi(argv[5]);

	switch (distance_type) {
	case DistanceDot:
		printf("[VocabMatch] Using distance Dot\n");
		break;
	case DistanceMin:
		printf("[VocabMatch] Using distance Min\n");
		break;
	default:
		printf("[VocabMatch] Using no known distance!\n");
		break;
	}

	printf("[VocabBuildDB] Reading tree %s...\n", tree_in); fflush(stdout);
	VocabTree tree;
	tree.Read(tree_in);

	printf("[VocabCompare] Flattening tree...\n");
	tree.Flatten();
	tree.m_distance_type = distance_type;
	tree.SetInteriorNodeWeight(0.0);

	/* Initialize leaf weights to 1.0 */
	tree.SetConstantLeafWeights();

	const int dim = 128;

	tree.ClearDatabase();

	int num_keys_1 = 0, num_keys_2 = 0;
	unsigned char *keys1 = ReadKeys(image1_in, dim, num_keys_1);
	unsigned char *keys2 = ReadKeys(image2_in, dim, num_keys_2);

	unsigned long *ids1 = new unsigned long[num_keys_1];
	unsigned long *ids2 = new unsigned long[num_keys_2];

	printf("[VocabCompare] Adding image 0 (%d keys)\n", num_keys_1);
	tree.AddImageToDatabase(0, num_keys_1, keys1, ids1);
	if (num_keys_1 > 0)
		delete[] keys1;

	printf("[VocabCompare] Adding image 1 (%d keys)\n", num_keys_2);
	tree.AddImageToDatabase(1, num_keys_2, keys2, ids2);
	if (num_keys_2 > 0)
		delete[] keys2;

	// tree.ComputeTFIDFWeights();
	tree.NormalizeDatabase(0, 2);

	//Find collisions among visual word IDs 
	std::multimap<unsigned long, unsigned int> word_map;
	for (unsigned int i = 0; i < (unsigned int)num_keys_1; i++)
	{
		printf("0 %d -> %lu\n", i, ids1[i]);
		std::pair<unsigned long, unsigned int> elem(ids1[i], i);
		word_map.insert(elem);
	}

	//Count number of matches 
	int num_matches = 0;
	for (unsigned int i = 0; i < (unsigned int)num_keys_2; i++)
	{
		unsigned long id = ids2[i];
		printf("1 %d -> %lu\n", i, ids2[i]);

		std::pair<std::multimap<unsigned long, unsigned int>::iterator, std::multimap<unsigned long, unsigned int>::iterator> ret;
		ret = word_map.equal_range(id);

		unsigned int size = 0;
		std::multimap<unsigned long, unsigned int>::iterator iter;
		for (iter = ret.first; iter != ret.second; iter++)
			size++, num_matches++;

		if (size > 0)
			printf("size[%lu] = %d\n", id, size);
	}
	printf("number of matches: %d\n", num_matches); fflush(stdout);

	FILE *f = fopen(matches_out, "w");
	if (f == NULL)
	{
		printf("Error opening file %s for writing\n", matches_out);
		return 1;
	}
	fprintf(f, "%d\n", num_matches);

	/* Write matches */
	for (unsigned int i = 0; i < (unsigned int)num_keys_2; i++)
	{
		unsigned long id = ids2[i];

		std::pair<std::multimap<unsigned long, unsigned int>::iterator, std::multimap<unsigned long, unsigned int>::iterator> ret;
		ret = word_map.equal_range(id);

		std::multimap<unsigned long, unsigned int>::iterator iter;
		for (iter = ret.first; iter != ret.second; iter++)
			fprintf(f, "%d %d\n", iter->second, i);
	}
	fclose(f);

	// printf("[VocabBuildDB] Writing tree...\n");
	// tree.Write(tree_out);

	return 0;
}
Exemple #5
0
// Building a database with a vocabulary tree
int VocabDB(char *list_in, char *tree_in, char *db_out, int use_tfidf = 1, int normalize = 1, int start_id = 0, DistanceType distance_type = DistanceMin)
{
	double min_feature_scale = 1.4;

	switch (distance_type) {
	case DistanceDot:
		printf("[VocabMatch] Using distance Dot\n");
		break;
	case DistanceMin:
		printf("[VocabMatch] Using distance Min\n");
		break;
	default:
		printf("[VocabMatch] Using no known distance!\n");
		break;
	}

	FILE *f = fopen(list_in, "r");
	if (f == NULL) {
		printf("Error opening file %s for reading\n", list_in);
		return 1;
	}

	std::vector<std::string> key_files;
	char buf[256];
	while (fgets(buf, 256, f))
	{
		/* Remove trailing newline */
		if (buf[strlen(buf) - 1] == '\n')
			buf[strlen(buf) - 1] = 0;

		key_files.push_back(std::string(buf));
	}

	printf("[VocabBuildDB] Reading tree %s...\n", tree_in);
	fflush(stdout);

	VocabTree tree;
	tree.Read(tree_in);
	tree.Flatten();
	tree.m_distance_type = distance_type;
	tree.SetInteriorNodeWeight(0.0);

	//Initialize leaf weights to 1.0 
	tree.SetConstantLeafWeights();

	const int dim = 128;
	int num_db_images = (int)key_files.size();
	unsigned long count = 0;

	tree.ClearDatabase();

	for (int i = 0; i < num_db_images; i++)
	{
		int num_keys = 0;
		unsigned char *keys = ReadAndFilterKeys(key_files[i].c_str(), dim, min_feature_scale, 0, num_keys);

		printf("[VocabBuildDB] Adding vector %d (%d keys)\n", start_id + i, num_keys);
		tree.AddImageToDatabase(start_id + i, num_keys, keys);

		if (num_keys > 0)
			delete[] keys;
	}

	printf("[VocabBuildDB] Pushed %lu features\n", count);
	fflush(stdout);

	if (use_tfidf == 1)
		tree.ComputeTFIDFWeights(num_db_images);

	if (normalize == 1)
		tree.NormalizeDatabase(start_id, num_db_images);

	printf("[VocabBuildDB] Writing database ...\n");
	tree.Write(db_out);

	// char filename[256];
	// sprintf(filename, "vectors_%03d.txt", start_id);
	// tree.WriteDatabaseVectors(filename, start_id, num_db_images);

	return 0;
}