utils.py

import collections

import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
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")
    n_colors = len(df.instrument.unique())
    colorscale = px.colors.sample_colorscale("viridis", [n/(n_colors -1) for n in range(n_colors)])
    colordict = {f:colorscale[i] for i, f 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