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| import collections | |
| import random | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import matplotlib.colors as mcolors | |
| from matplotlib.patches import Rectangle | |
| from PIL import Image | |
| import note_seq | |
| class AudioIOReadError(BaseException): # pylint:disable=g-bad-exception-name | |
| pass | |
| def upload_audio(audio, sample_rate): | |
| return note_seq.audio_io.wav_data_to_samples_librosa(audio, sample_rate=sample_rate) | |
| # Generate piano_roll | |
| def sequence_to_pandas_dataframe(sequence): | |
| pd_dict = collections.defaultdict(list) | |
| for note in sequence.notes: | |
| pd_dict["start_time"].append(note.start_time) | |
| pd_dict["end_time"].append(note.end_time) | |
| pd_dict["duration"].append(note.end_time - note.start_time) | |
| pd_dict["pitch"].append(note.pitch) | |
| pd_dict["instrument"].append(note.instrument) | |
| return pd.DataFrame(pd_dict) | |
| def dataframe_to_pianoroll_img(df): | |
| fig = plt.figure(figsize=(8, 5)) | |
| ax = fig.add_subplot(111) | |
| ax.scatter(df.start_time, df.pitch, c="white") | |
| colors = [mcolors.CSS4_COLORS[name] for i, name in enumerate(list(mcolors.CSS4_COLORS))] | |
| random.shuffle(colors) | |
| colordict = {inst: colors[i] for i, inst in enumerate(df["instrument"].unique())} | |
| for _, row in df.iterrows(): | |
| ax.add_patch(Rectangle((row["start_time"], | |
| row["pitch"]-0.4), | |
| row["duration"], | |
| 0.4, | |
| color=colordict[row["instrument"]])) | |
| plt.xlabel('time (sec.)', fontsize=18) | |
| plt.ylabel('pitch (MIDI)', fontsize=16) | |
| return fig | |
| def fig2img(fig): | |
| """Convert a Matplotlib figure to a PIL Image and return it""" | |
| import io | |
| buf = io.BytesIO() | |
| fig.savefig(buf, format="png") | |
| buf.seek(0) | |
| img = Image.open(buf) | |
| return img | |
| def create_image_from_note_sequence(sequence): | |
| df_sequence = sequence_to_pandas_dataframe(sequence) | |
| fig = dataframe_to_pianoroll_img(df_sequence) | |
| img = fig2img(fig) | |
| return img |