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import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.ticker import MultipleLocator
def spec_to_figure(spec, vmin=None, vmax=None, title=None):
if isinstance(spec, torch.Tensor):
spec = spec.cpu().numpy()
fig = plt.figure(figsize=(12, 9))
plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
if title is not None:
plt.title(title, fontsize=15)
plt.tight_layout()
return fig
def dur_to_figure(dur_gt, dur_pred, txt, title=None):
if isinstance(dur_gt, torch.Tensor):
dur_gt = dur_gt.cpu().numpy()
if isinstance(dur_pred, torch.Tensor):
dur_pred = dur_pred.cpu().numpy()
dur_gt = dur_gt.astype(np.int64)
dur_pred = dur_pred.astype(np.int64)
dur_gt = np.cumsum(dur_gt)
dur_pred = np.cumsum(dur_pred)
width = max(12, min(48, len(txt) // 2))
fig = plt.figure(figsize=(width, 8))
plt.vlines(dur_pred, 12, 22, colors='r', label='pred')
plt.vlines(dur_gt, 0, 10, colors='b', label='gt')
for i in range(len(txt)):
shift = (i % 8) + 1
plt.text((dur_pred[i-1] + dur_pred[i]) / 2 if i > 0 else dur_pred[i] / 2, 12 + shift, txt[i],
size=16, horizontalalignment='center')
plt.text((dur_gt[i-1] + dur_gt[i]) / 2 if i > 0 else dur_gt[i] / 2, shift, txt[i],
size=16, horizontalalignment='center')
plt.plot([dur_pred[i], dur_gt[i]], [12, 10], color='black', linewidth=2, linestyle=':')
plt.yticks([])
plt.xlim(0, max(dur_pred[-1], dur_gt[-1]))
plt.legend()
if title is not None:
plt.title(title, fontsize=15)
plt.tight_layout()
return fig
def pitch_note_to_figure(pitch_gt, pitch_pred=None, note_midi=None, note_dur=None, note_rest=None, title=None):
if isinstance(pitch_gt, torch.Tensor):
pitch_gt = pitch_gt.cpu().numpy()
if isinstance(pitch_pred, torch.Tensor):
pitch_pred = pitch_pred.cpu().numpy()
if isinstance(note_midi, torch.Tensor):
note_midi = note_midi.cpu().numpy()
if isinstance(note_dur, torch.Tensor):
note_dur = note_dur.cpu().numpy()
if isinstance(note_rest, torch.Tensor):
note_rest = note_rest.cpu().numpy()
fig = plt.figure()
if note_midi is not None and note_dur is not None:
note_dur_acc = np.cumsum(note_dur)
if note_rest is None:
note_rest = np.zeros_like(note_midi, dtype=np.bool_)
for i in range(len(note_midi)):
# if note_rest[i]:
# continue
plt.gca().add_patch(
plt.Rectangle(
xy=(note_dur_acc[i-1] if i > 0 else 0, note_midi[i] - 0.5),
width=note_dur[i], height=1,
edgecolor='grey', fill=False,
linewidth=1.5, linestyle='--' if note_rest[i] else '-'
)
)
plt.plot(pitch_gt, color='b', label='gt')
if pitch_pred is not None:
plt.plot(pitch_pred, color='r', label='pred')
plt.gca().yaxis.set_major_locator(MultipleLocator(1))
plt.grid(axis='y')
plt.legend()
if title is not None:
plt.title(title, fontsize=15)
plt.tight_layout()
return fig
def curve_to_figure(curve_gt, curve_pred=None, curve_base=None, grid=None, title=None):
if isinstance(curve_gt, torch.Tensor):
curve_gt = curve_gt.cpu().numpy()
if isinstance(curve_pred, torch.Tensor):
curve_pred = curve_pred.cpu().numpy()
if isinstance(curve_base, torch.Tensor):
curve_base = curve_base.cpu().numpy()
fig = plt.figure()
if curve_base is not None:
plt.plot(curve_base, color='g', label='base')
plt.plot(curve_gt, color='b', label='gt')
if curve_pred is not None:
plt.plot(curve_pred, color='r', label='pred')
if grid is not None:
plt.gca().yaxis.set_major_locator(MultipleLocator(grid))
plt.grid(axis='y')
plt.legend()
if title is not None:
plt.title(title, fontsize=15)
plt.tight_layout()
return fig
def distribution_to_figure(title, x_label, y_label, items: list, values: list, zoom=0.8):
fig = plt.figure(figsize=(int(len(items) * zoom), 10))
plt.bar(x=items, height=values)
plt.tick_params(labelsize=15)
plt.xlim(-1, len(items))
for a, b in zip(items, values):
plt.text(a, b, b, ha='center', va='bottom', fontsize=15)
plt.grid()
plt.title(title, fontsize=30)
plt.xlabel(x_label, fontsize=20)
plt.ylabel(y_label, fontsize=20)
return fig
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