Esempio n. 1
0
def vlad_vector_batch(input_path, output_path, vocabulary):
    vladL = []
    file_names = list_all_files(input_path)
    for f in file_names:
        vec = vlad_vector(vector_path=input_path + f, vocabulary=vocabulary)
        vladL.append(vec)
    output_path = output_path + '_vlad.txt'
    savetxt(output_path, asarray(vladL), delimiter=",", fmt='%.4f')
Esempio n. 2
0
def vlad_vector_batch(input_path, output_path, vocabulary):
    vladL = []
    file_names = list_all_files(input_path)
    for f in file_names:
        vec = vlad_vector(vector_path=input_path + f, vocabulary=vocabulary)
        vladL.append(vec)
    output_path = output_path + '_vlad.txt'
    savetxt(output_path, asarray(vladL), delimiter=",", fmt='%.4f')
Esempio n. 3
0
def sift_image_batch(input_path, output_path, params=None):
    # Process all images in the input_path by SIFT, e.g. windows processing
    # Return those images whose SIFT vector is invalid, i.e. empty
    image_names = list_all_files(input_path, onlyImage=True)
    res_path_L = []
    for img in image_names:
        res_path = sift_image(input_path=input_path, image_name=img, output_path=output_path, params=params)
        res_path_L.append(res_path)
    return res_path_L
Esempio n. 4
0
def batch_all_images(input_image_path, annotation_path, output_parent_path,
            unit_ratio_list, overlap_ratio, target, target_pos_path, vladVector, target_count=20, pca=True,
            k=30, max_iter=30, preVLAD=False, voca_path=None, dataset_mode=False, overlap_threshold=0.5):
    global_sift_path = '%sglobal_sift.txt' % (output_parent_path)
    image_name_list = get_target_pos_names(input_path=target_pos_path, target=target, target_count=target_count)
    sift_path_L = []
    if preVLAD:
        delete_file(file_path=global_sift_path)
        for image_name in image_name_list:
            metadata_path = '%s%s.xml' % (annotation_path, image_name)
            win_sift_path = batch_one_image_pre_VLAD(input_image_path, image_name, metadata_path, output_parent_path,
                unit_ratio_list, overlap_ratio)
            sift_path_L.append(win_sift_path)
            append_file(dest_file=global_sift_path, input_path=win_sift_path)
        print "----------pre-VLAD Done"
    else:
        all_dir = list_all_files(input_path=output_parent_path + 'windows/', onlyDir=True)
        for d in all_dir:
            sift_path_L.append("%s/windows/%s/temp_sift/" % (output_parent_path, d))
        print "----------pre-VLAD is enabled"
    if voca_path is None or not isfile(voca_path):
        print "~~~~~~~Learning vocabulary by the sift vectors of all windows of all images"
        vector_matrix = None
        if pca:
            vector_matrix = pca_dataset(input_path=global_sift_path)
        vocabulary = learn_vocabulary(input_path=global_sift_path,
                    k=k, max_iter=max_iter, single_file=True, vector_matrix=vector_matrix)
        save_matrix(v=vocabulary, output_path=voca_path)
        print "~~~~~~~Learning vocabulary done"
    elif vladVector:
        print "~~~~~~~Loading existing vocabulary"
        vocabulary = load_matrix(input_path=voca_path)
    if vladVector:
        for i in xrange(len(image_name_list)):
            image_name = image_name_list[i]
            output_path = "%swindows/%s/%s" % (output_parent_path, image_name, image_name)
            print "\t======Creating VLAD vectors"
            vlad_vector_batch(input_path=sift_path_L[i],
                    output_path=output_path, vocabulary=vocabulary)
            print "\t======VLAD Done for", image_name
    else:
        print "##########No VLAD vector generated...."
    if dataset_mode:
        print "^^^^^^^^^^Generate data set for global windows and VLAD"
        global_X_path = output_parent_path + "global_X.txt"
        global_Y_path = output_parent_path + "global_Y.txt"
        delete_file(global_X_path)
        delete_file(global_Y_path)
        for img_name in image_name_list:
            img_window_path = "%s/windows/%s/%s_windows.txt" % (output_parent_path, img_name , img_name)
            img_vlad_path = "%s/windows/%s/%s_vlad.txt" % (output_parent_path, img_name , img_name)
            metadata_path = '%s%s.xml' % (annotation_path, img_name)
            batch_one_image_dataset(global_X_path, global_Y_path, img_window_path,
                        img_vlad_path, metadata_path, target, overlap_threshold=overlap_threshold)
            print "\tData set done for", img_name
    print "....................All done"
Esempio n. 5
0
def sift_image_batch(input_path, output_path, params=None):
    # Process all images in the input_path by SIFT, e.g. windows processing
    # Return those images whose SIFT vector is invalid, i.e. empty
    image_names = list_all_files(input_path, onlyImage=True)
    res_path_L = []
    for img in image_names:
        res_path = sift_image(input_path=input_path,
                              image_name=img,
                              output_path=output_path,
                              params=params)
        res_path_L.append(res_path)
    return res_path_L
Esempio n. 6
0
def learn_vocabulary(k, max_iter, single_file, vector_matrix=None, input_path=None):
    if vector_matrix is None:
        if single_file:
            vector_matrix = read_feature_vector(input_path)
        else:
            file_names = list_all_files(input_path)
            vector_matrix = read_feature_vector(input_path + file_names[0])
            for f in xrange(1, len(file_names)):
                vector = read_feature_vector(input_path + file_names[f])
                if vector is not None:
                    vector_matrix = append(vector_matrix, vector, axis=0)
    return mykmeans_plus(data=vector_matrix,k=k, max_iter=max_iter)[0]
Esempio n. 7
0
def learn_vocabulary(k,
                     max_iter,
                     single_file,
                     vector_matrix=None,
                     input_path=None):
    if vector_matrix is None:
        if single_file:
            vector_matrix = read_feature_vector(input_path)
        else:
            file_names = list_all_files(input_path)
            vector_matrix = read_feature_vector(input_path + file_names[0])
            for f in xrange(1, len(file_names)):
                vector = read_feature_vector(input_path + file_names[f])
                if vector is not None:
                    vector_matrix = append(vector_matrix, vector, axis=0)
    return mykmeans_plus(data=vector_matrix, k=k, max_iter=max_iter)[0]
Esempio n. 8
0
def batch_all_images(input_image_path,
                     annotation_path,
                     output_parent_path,
                     unit_ratio_list,
                     overlap_ratio,
                     target,
                     target_pos_path,
                     vladVector,
                     target_count=20,
                     pca=True,
                     k=30,
                     max_iter=30,
                     preVLAD=False,
                     voca_path=None,
                     dataset_mode=False,
                     overlap_threshold=0.5):
    global_sift_path = '%sglobal_sift.txt' % (output_parent_path)
    image_name_list = get_target_pos_names(input_path=target_pos_path,
                                           target=target,
                                           target_count=target_count)
    sift_path_L = []
    if preVLAD:
        delete_file(file_path=global_sift_path)
        for image_name in image_name_list:
            metadata_path = '%s%s.xml' % (annotation_path, image_name)
            win_sift_path = batch_one_image_pre_VLAD(input_image_path,
                                                     image_name, metadata_path,
                                                     output_parent_path,
                                                     unit_ratio_list,
                                                     overlap_ratio)
            sift_path_L.append(win_sift_path)
            append_file(dest_file=global_sift_path, input_path=win_sift_path)
        print "----------pre-VLAD Done"
    else:
        all_dir = list_all_files(input_path=output_parent_path + 'windows/',
                                 onlyDir=True)
        for d in all_dir:
            sift_path_L.append("%s/windows/%s/temp_sift/" %
                               (output_parent_path, d))
        print "----------pre-VLAD is enabled"
    if voca_path is None or not isfile(voca_path):
        print "~~~~~~~Learning vocabulary by the sift vectors of all windows of all images"
        vector_matrix = None
        if pca:
            vector_matrix = pca_dataset(input_path=global_sift_path)
        vocabulary = learn_vocabulary(input_path=global_sift_path,
                                      k=k,
                                      max_iter=max_iter,
                                      single_file=True,
                                      vector_matrix=vector_matrix)
        save_matrix(v=vocabulary, output_path=voca_path)
        print "~~~~~~~Learning vocabulary done"
    elif vladVector:
        print "~~~~~~~Loading existing vocabulary"
        vocabulary = load_matrix(input_path=voca_path)
    if vladVector:
        for i in xrange(len(image_name_list)):
            image_name = image_name_list[i]
            output_path = "%swindows/%s/%s" % (output_parent_path, image_name,
                                               image_name)
            print "\t======Creating VLAD vectors"
            vlad_vector_batch(input_path=sift_path_L[i],
                              output_path=output_path,
                              vocabulary=vocabulary)
            print "\t======VLAD Done for", image_name
    else:
        print "##########No VLAD vector generated...."
    if dataset_mode:
        print "^^^^^^^^^^Generate data set for global windows and VLAD"
        global_X_path = output_parent_path + "global_X.txt"
        global_Y_path = output_parent_path + "global_Y.txt"
        delete_file(global_X_path)
        delete_file(global_Y_path)
        for img_name in image_name_list:
            img_window_path = "%s/windows/%s/%s_windows.txt" % (
                output_parent_path, img_name, img_name)
            img_vlad_path = "%s/windows/%s/%s_vlad.txt" % (output_parent_path,
                                                           img_name, img_name)
            metadata_path = '%s%s.xml' % (annotation_path, img_name)
            batch_one_image_dataset(global_X_path,
                                    global_Y_path,
                                    img_window_path,
                                    img_vlad_path,
                                    metadata_path,
                                    target,
                                    overlap_threshold=overlap_threshold)
            print "\tData set done for", img_name
    print "....................All done"