Example #1
0
def visualize_mat_proba(pr, path, file_name_no_extension, time_indices=None):
    if time_indices:
        start_time, end_time = time_indices
        pr = pr[start_time:end_time, :]
    temp_csv = path + '/' + file_name_no_extension + '.csv'
    np.savetxt(temp_csv, pr, delimiter=',')
    dump_to_csv(temp_csv, temp_csv)
    write_numpy_array_html(path + '/' + file_name_no_extension + ".html", file_name_no_extension, 'rainbow', (0, 1), d3js_source_path='/Users/leo/Recherche/GitHub_Aciditeam/acidano/acidano/visualization/d3.v3.min.js')
Example #2
0
def visualize_dict(pr, path, file_name_no_extension, time_indices=None):
    AAA = sum_along_instru_dim(pr)
    if time_indices:
        start_time, end_time = time_indices
        AAA = AAA[start_time:end_time, :]
    temp_csv = path + '/' + file_name_no_extension + '.csv'
    np.savetxt(temp_csv, AAA, delimiter=',')
    dump_to_csv(temp_csv, temp_csv)
    write_numpy_array_html(path + '/' + file_name_no_extension + ".html", file_name_no_extension, d3js_source_path='/Users/leo/Recherche/GitHub_Aciditeam/acidano/acidano/visualization/d3.v3.min.js')
Example #3
0
def aux(var, name, csv_path, html_path):
    np.savetxt(csv_path, var, delimiter=',')
    dump_to_csv(csv_path, csv_path)
    write_numpy_array_html(html_path, name)
    return
Example #4
0
    T = matrix.shape[0]
    N = 128

    for instrument_name, ranges in mapping.iteritems():
        index_min = ranges['index_min']
        index_max = ranges['index_max']
        pitch_min = ranges['pitch_min']
        pitch_max = ranges['pitch_max']

        this_pr = np.zeros((T,N), dtype=np.int16)
        this_pr[:,pitch_min:pitch_max] = matrix[:,index_min:index_max]
        this_pr = this_pr * max_velocity
        pr_instru[instrument_name] = this_pr

    return pr_instru


if __name__ == '__main__':
    import cPickle as pickle
    metadata = pickle.load(open('../Data/metadata.pkl', 'rb'))
    instru_mapping = metadata['instru_mapping']
    pr = np.tile(np.arange(1,590,1), (50,1))
    pr_instru = reconstruct_pr(pr, instru_mapping, False)

    # Visualisation
    AAA = np.concatenate(pr_instru.values(), axis=1)
    temp_csv = 'temp.csv'
    np.savetxt(temp_csv, AAA, delimiter=',')
    dump_to_csv(temp_csv, temp_csv)
    write_numpy_array_html("temp.html", "temp")
def check_orchestration_alignment(path_db, subfolder_names, quantization, gapopen, gapextend):

    output_dir = 'DEBUG/' + str(quantization) +\
                 '_' + str(gapopen) +\
                 '_' + str(gapextend)

    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
    else:
        # Avoid re-running the algo on already tested parameters
        return

    counter = 0
    sum_score = 0
    nbFrame = 0
    nbId = 0
    nbDiffs = 0

    # num_track_browsed = 30
    for sub_db in subfolder_names:
        print '#' * 30
        print sub_db
        sub_db_path = path_db + '/' + sub_db
        if not os.path.isdir(sub_db_path):
            continue

        # list_tracks_dir = os.listdir(sub_db_path)
        # ind_folder = np.random.permutation(len(list_tracks_dir))
        # for ind in ind_folder[:num_track_browsed]:
        # for ind in list_tracks_dir:

            # folder_name = list_tracks_dir[ind]

        for folder_name in os.listdir(sub_db_path):

            print '#' * 20
            print '#' + folder_name + '\n'
            folder_path = sub_db_path + '/' + folder_name
            if not os.path.isdir(folder_path):
                continue

            # Get instrus and prs from a folder name name
            pr0, instru0, T0, path_0, pr1, instru1, T1, path_1 = build_data_aux.get_instru_and_pr_from_folder_path(folder_path, quantization=quantization, clip=True)
            # name_0 = re.split('/', path_0)[-1]
            # name_1 = re.split('/', path_1)[-1]

            ################################################
            ################################################
            # def auxiaux(pr, limit):
            #     pr_bis = pr
            #     pr = {}
            #     for k,v in pr_bis.iteritems():
            #         pr[k] = v[:limit,:]
            #     return pr
            # pr0 = auxiaux(pr0, 26)
            # pr1 = auxiaux(pr1, 48)
            ################################################
            ################################################

            # Get trace from needleman_wunsch algorithm
            # Traces are binary lists, 0 meaning a gap is inserted
            trace_0, trace_1, this_sum_score, this_nbId, this_nbDiffs = needleman_chord_wrapper(sum_along_instru_dim(pr0), sum_along_instru_dim(pr1))

            # Wrap dictionnaries according to the traces
            assert(len(trace_0) == len(trace_1)), "size mismatch"
            pr0_warp = warp_dictionnary_trace(pr0, trace_0)
            pr1_warp = warp_dictionnary_trace(pr1, trace_1)

            # In fact we just discard 0 in traces for both pr
            trace_prod = [e1 * e2 for (e1,e2) in zip(trace_0, trace_1)]
            if sum(trace_prod) == 0:
                # It's definitely not a match...
                # Check for the files : are they really an piano score and its orchestration ??
                with(open('log.txt', 'a')) as f:
                    f.write(folder_path + '\n')
                continue
            pr0_aligned = remove_zero_in_trace(pr0_warp, trace_prod)
            pr1_aligned = remove_zero_in_trace(pr1_warp, trace_prod)

            # Sum all instrument
            AAA_warp = sum_along_instru_dim(pr0_warp)
            BBB_warp = sum_along_instru_dim(pr1_warp)
            OOO_warp = np.zeros((BBB_warp.shape[0], 30), dtype=np.int16)
            CCC_warp = np.concatenate((AAA_warp, OOO_warp, BBB_warp), axis=1)
            AAA_aligned = sum_along_instru_dim(pr0_aligned)
            BBB_aligned = sum_along_instru_dim(pr1_aligned)
            OOO_aligned = np.zeros((BBB_aligned.shape[0], 30), dtype=np.int16)
            CCC_aligned = np.concatenate((AAA_aligned, OOO_aligned, BBB_aligned), axis=1)

            # Update statistics
            nbFrame += len(trace_0)
            sum_score += this_sum_score
            nbId += this_nbId
            nbDiffs += this_nbDiffs

            counter = counter + 1

            # Save every 100 example
            if not counter % 10 == 0:
                continue

            save_folder_name = output_dir +\
                '/' + sub_db + '_' + folder_name

            if not os.path.exists(save_folder_name):
                os.makedirs(save_folder_name)
            temp_csv = save_folder_name + '/warp.csv'
            np.savetxt(temp_csv, CCC_warp, delimiter=',')
            dump_to_csv(temp_csv, temp_csv)
            write_numpy_array_html(save_folder_name + "/pr_warp.html", "warp")

            temp_csv = save_folder_name + '/aligned.csv'
            np.savetxt(temp_csv, CCC_aligned, delimiter=',')
            dump_to_csv(temp_csv, temp_csv)
            write_numpy_array_html(save_folder_name + "/pr_aligned.html", "aligned")

            write_midi(pr={'piano1': sum_along_instru_dim(pr0)}, quantization=quantization, write_path=save_folder_name + '/0.mid', tempo=80)
            write_midi(pr={'piano1': sum_along_instru_dim(pr1)}, quantization=quantization, write_path=save_folder_name + '/1.mid', tempo=80)
            write_midi(pr={'piano1': AAA_warp, 'piano2': BBB_warp}, quantization=quantization, write_path=save_folder_name + '/both__warp.mid', tempo=80)
            write_midi(pr={'piano1': AAA_aligned, 'piano2': BBB_aligned}, quantization=quantization, write_path=save_folder_name + '/both__aligned.mid', tempo=80)

    # Write statistics
    mean_score = float(sum_score) / nbFrame
    nbId_norm = nbId / quantization
    nbDiffs_norm = nbDiffs / quantization

    with open(output_dir + '/log.txt', 'wb') as f:
        f.write("##########################\n" +
                "quantization = %d\n" % quantization +
                "Gapopen = %d\n" % gapopen +
                "Gapextend = %d\n" % gapextend +
                "Number frame = %d\n" % nbFrame +
                "\n\n\n" +
                "Sum score = %d\n" % sum_score+
                "Mean score = %f\n" % mean_score+
                "Number id = %d\n" % nbId +
                "Number id / quantization = %d\n" % nbId_norm+
                "Number diffs = %d\n" % nbDiffs+
                "Number diffs / quantization = %d\n" % nbDiffs_norm)