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"""MIDI_Images_Solo_Piano_Dataset_Maker.ipynb |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/15E6o3Y1xPific5RtIZ-1CneHQhts3eEr |
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# MIDI Images Solo Piano Dataset Maker (ver. 1.0) |
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*** |
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Powered by tegridy-tools: https://github.com/asigalov61/tegridy-tools |
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*** |
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#### Project Los Angeles |
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#### Tegridy Code 2024 |
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*** |
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# (SETUP ENVIRONMENT) |
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""" |
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!git clone --depth 1 https://github.com/asigalov61/tegridy-tools |
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print('=' * 70) |
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print('Loading core modules...') |
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print('Please wait...') |
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print('=' * 70) |
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import os |
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import copy |
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import math |
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import statistics |
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import random |
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import pickle |
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import shutil |
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from itertools import groupby |
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from collections import Counter |
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from sklearn.metrics import pairwise_distances |
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from sklearn import metrics |
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from joblib import Parallel, delayed, parallel_config |
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import numpy as np |
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from tqdm import tqdm |
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from PIL import Image |
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import matplotlib.pyplot as plt |
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print('Done!') |
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print('=' * 70) |
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print('Creating I/O dirs...') |
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if not os.path.exists('/content/Dataset'): |
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os.makedirs('/content/Dataset') |
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print('Done!') |
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print('=' * 70) |
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print('Loading tegridy-tools modules...') |
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print('=' * 70) |
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import TMIDIX |
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import TMELODIES |
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import TPLOTS |
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import HaystackSearch |
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print('=' * 70) |
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print('Done!') |
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print('=' * 70) |
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"""# (DOWNLOAD SAMPLE MIDI DATASET)""" |
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!git clone --depth 1 https://github.com/music-x-lab/POP909-Dataset |
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print('=' * 70) |
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print('Loading MIDI files...') |
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print('This may take a while on a large dataset in particular...') |
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dataset_addr = '/content/Dataset/' |
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filez = list() |
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for (dirpath, dirnames, filenames) in os.walk(dataset_addr): |
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filez += [os.path.join(dirpath, file) for file in filenames if file.endswith('.mid') or file.endswith('.midi') or file.endswith('.kar')] |
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print('=' * 70) |
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if filez == []: |
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print('Could not find any MIDI files. Please check Dataset dir...') |
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print('=' * 70) |
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print('Randomizing file list...') |
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random.shuffle(filez) |
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print('Done!') |
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print('=' * 70) |
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print('Total found MIDI files:', len(filez)) |
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print('=' * 70) |
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TMIDIX.Tegridy_Any_Pickle_File_Writer(filez, 'filez') |
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print('=' * 70) |
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"""# (LOAD TMIDIX MIDI PROCESSOR)""" |
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print('=' * 70) |
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print('TMIDIX MIDI Processor') |
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print('=' * 70) |
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print('Loading...') |
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def TMIDIX_MIDI_Processor(midi_file): |
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fn = os.path.basename(midi_file) |
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fn1 = fn.split('.mid')[0] |
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try: |
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raw_score = TMIDIX.midi2single_track_ms_score(midi_file) |
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escore_notes = TMIDIX.advanced_score_processor(raw_score, return_enhanced_score_notes=True)[0] |
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escore_notes = TMIDIX.augment_enhanced_score_notes(escore_notes, timings_divider=256) |
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sp_escore_notes = TMIDIX.recalculate_score_timings(TMIDIX.solo_piano_escore_notes(escore_notes, keep_drums=False)) |
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if sp_escore_notes: |
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bmatrix = TMIDIX.escore_notes_to_binary_matrix(sp_escore_notes) |
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return [fn1, bmatrix] |
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else: |
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return [fn1, []] |
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except Exception as ex: |
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print('WARNING !!!') |
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print('=' * 70) |
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print('Bad MIDI:', midi_file) |
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print('Error detected:', ex) |
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print('=' * 70) |
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return None |
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print('Done!') |
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print('=' * 70) |
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"""# (PROCESS MIDIs)""" |
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output_folder = "/content/MIDI-Images/" |
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NUMBER_OF_PARALLEL_JOBS = 4 |
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NUMBER_OF_FILES_PER_ITERATION = 4 |
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SAVE_EVERY_NUMBER_OF_ITERATIONS = 128 |
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print('=' * 70) |
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print('TMIDIX MIDI Processor') |
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print('=' * 70) |
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print('Starting up...') |
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print('=' * 70) |
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melody_chords_f = [] |
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files_count = 0 |
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print('Processing MIDI files...') |
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print('Please wait...') |
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print('=' * 70) |
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for i in tqdm(range(0, len(filez), NUMBER_OF_FILES_PER_ITERATION)): |
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with parallel_config(backend='threading', n_jobs=NUMBER_OF_PARALLEL_JOBS, verbose = 0): |
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output = Parallel(n_jobs=NUMBER_OF_PARALLEL_JOBS, verbose=0)(delayed(TMIDIX_MIDI_Processor)(f) for f in filez[i:i+NUMBER_OF_FILES_PER_ITERATION]) |
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for o in output: |
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if o is not None: |
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melody_chords_f.append(o) |
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if i % (NUMBER_OF_FILES_PER_ITERATION * SAVE_EVERY_NUMBER_OF_ITERATIONS) == 0 and i != 0: |
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print('SAVING !!!') |
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print('=' * 70) |
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print('Saving processed files...') |
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files_count += len(melody_chords_f) |
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print('=' * 70) |
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print('Processed so far:', files_count, 'out of', len(filez), '===', files_count / len(filez), 'good files ratio') |
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print('=' * 70) |
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print('Writing images...') |
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print('Please wait...') |
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for mat in melody_chords_f: |
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if mat[1]: |
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TPLOTS.binary_matrix_to_images(mat[1], |
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128, |
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32, |
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output_folder=output_folder+str(mat[0])+'/', |
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output_img_prefix=str(mat[0]), |
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output_img_ext='.png', |
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verbose=False |
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) |
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print('Done!') |
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print('=' * 70) |
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melody_chords_f = [] |
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print('SAVING !!!') |
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print('=' * 70) |
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print('Saving processed files...') |
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files_count += len(melody_chords_f) |
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print('=' * 70) |
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print('Processed so far:', files_count, 'out of', len(filez), '===', files_count / len(filez), 'good files ratio') |
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print('=' * 70) |
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print('Writing images...') |
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print('Please wait...') |
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for mat in melody_chords_f: |
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if mat[1]: |
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TPLOTS.binary_matrix_to_images(mat[1], |
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128, |
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32, |
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output_folder=output_folder+str(mat[0])+'/', |
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output_img_prefix=str(mat[0]), |
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output_img_ext='.png', |
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verbose=False |
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) |
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print('Done!') |
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print('=' * 70) |
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"""# (LOAD IMAGES)""" |
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full_path_to_metadata_pickle_files = "/content/MIDI-Images" |
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print('=' * 70) |
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print('MIDI Images Reader') |
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print('=' * 70) |
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print('Searching for images...') |
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filez = list() |
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for (dirpath, dirnames, filenames) in os.walk(full_path_to_metadata_pickle_files): |
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filez += [os.path.join(dirpath, file) for file in filenames if file.endswith('.png')] |
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print('=' * 70) |
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filez.sort() |
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print('Found', len(filez), 'images!') |
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print('=' * 70) |
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print('Reading images...') |
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print('Please wait...') |
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print('=' * 70) |
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fidx = 0 |
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all_read_images = [] |
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for img in tqdm(filez): |
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img = Image.open(img) |
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img_arr = np.array(img).tolist() |
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all_read_images.append(img_arr) |
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fidx += 1 |
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print('Done!') |
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print('=' * 70) |
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print('Loaded', fidx, 'images!') |
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print('=' * 70) |
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print('Done!') |
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print('=' * 70) |
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"""# (TEST IMAGES)""" |
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print('=' * 70) |
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image = random.choice(all_read_images) |
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escore = TMIDIX.binary_matrix_to_original_escore_notes(image) |
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output_score, patches, overflow_patches = TMIDIX.patch_enhanced_score_notes(escore) |
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detailed_stats = TMIDIX.Tegridy_ms_SONG_to_MIDI_Converter(output_score, |
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output_signature = 'MIDI Images', |
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output_file_name = '/content/MIDI-Images-Composition', |
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track_name='Project Los Angeles', |
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list_of_MIDI_patches=patches, |
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timings_multiplier=256 |
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) |
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print('=' * 70) |
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"""# (ZIP IMAGES)""" |
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!zip -9 -r POP909_MIDI_Images_128_128_32_BW.zip MIDI-Images/ > /dev/null |
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"""# Congrats! You did it! :)""" |