279 lines
8.1 KiB
Python
279 lines
8.1 KiB
Python
"""
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可视化工具模块，用于可视化训练历史、模型预测结果等
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import librosa
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import librosa.display
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import seaborn as sns
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from sklearn.metrics import confusion_matrix
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import pandas as pd
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import matplotlib.font_manager as fm
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import os
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import platform
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# 为中文显示设置字体
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def set_chinese_font():
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"""
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设置中文字体，支持Windows、MacOS和Linux
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"""
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system = platform.system()
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if system == 'Windows':
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# Windows系统
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font_paths = [
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'C:/Windows/Fonts/simhei.ttf', # 黑体
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'C:/Windows/Fonts/simsun.ttc', # 宋体
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'C:/Windows/Fonts/simkai.ttf', # 楷体
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'C:/Windows/Fonts/msyh.ttc' # 微软雅黑
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]
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elif system == 'Darwin':
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# MacOS系统
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font_paths = [
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'/System/Library/Fonts/PingFang.ttc',
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'/Library/Fonts/Arial Unicode.ttf'
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]
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else:
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# Linux系统
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font_paths = [
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'/usr/share/fonts/truetype/wqy/wqy-microhei.ttc',
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'/usr/share/fonts/wqy-microhei/wqy-microhei.ttc'
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]
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# 尝试加载字体
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font_path = None
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for path in font_paths:
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if os.path.exists(path):
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font_path = path
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break
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if font_path:
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plt.rcParams['font.sans-serif'] = [fm.FontProperties(fname=font_path).get_name()]
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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else:
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print("警告：未找到中文字体，可能无法正确显示中文")
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def plot_training_history(history, save_path=None):
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"""
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绘制训练历史（准确率和损失曲线）
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Args:
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history: 训练历史对象
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save_path: 保存路径，如果为None则显示图形
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"""
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set_chinese_font()
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
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# 绘制准确率
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ax1.plot(history.history['accuracy'], label='训练准确率')
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ax1.plot(history.history['val_accuracy'], label='验证准确率')
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ax1.set_title('模型准确率')
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ax1.set_ylabel('准确率')
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ax1.set_xlabel('轮次')
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ax1.legend(loc='lower right')
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ax1.grid(True)
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# 绘制损失
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ax2.plot(history.history['loss'], label='训练损失')
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ax2.plot(history.history['val_loss'], label='验证损失')
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ax2.set_title('模型损失')
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ax2.set_ylabel('损失')
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ax2.set_xlabel('轮次')
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ax2.legend(loc='upper right')
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ax2.grid(True)
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path)
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plt.close()
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else:
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plt.show()
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def plot_confusion_matrix(y_true, y_pred, classes, save_path=None, normalize=False, title='混淆矩阵'):
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"""
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绘制混淆矩阵
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Args:
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y_true: 真实标签
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y_pred: 预测标签
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classes: 类别名称列表
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save_path: 保存路径，如果为None则显示图形
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normalize: 是否归一化
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title: 图表标题
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"""
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set_chinese_font()
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# 计算混淆矩阵
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cm = confusion_matrix(y_true, y_pred)
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if normalize:
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cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
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fig, ax = plt.subplots(figsize=(10, 8))
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# 使用seaborn绘制热图
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sns.heatmap(cm, annot=True, fmt='.2f' if normalize else 'd', cmap='Blues',
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xticklabels=classes, yticklabels=classes)
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ax.set_ylabel('真实标签')
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ax.set_xlabel('预测标签')
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ax.set_title(title)
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path)
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plt.close()
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else:
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plt.show()
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def plot_audio_features(audio, sr, features=None, feature_names=None, save_path=None):
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"""
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可视化音频波形和特征
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Args:
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audio: 音频数据
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sr: 采样率
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features: 特征字典
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feature_names: 要显示的特征名称列表
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save_path: 保存路径，如果为None则显示图形
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"""
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set_chinese_font()
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fig = plt.figure(figsize=(15, 10))
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# 绘制波形
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ax1 = plt.subplot(3, 1, 1)
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librosa.display.waveshow(audio, sr=sr)
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ax1.set_title('音频波形')
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ax1.set_ylabel('振幅')
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# 绘制声谱图
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ax2 = plt.subplot(3, 1, 2)
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D = librosa.amplitude_to_db(np.abs(librosa.stft(audio)), ref=np.max)
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librosa.display.specshow(D, y_axis='log', x_axis='time', sr=sr)
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ax2.set_title('声谱图')
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plt.colorbar(format='%+2.0f dB')
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# 绘制梅尔频谱图
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ax3 = plt.subplot(3, 1, 3)
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S = librosa.feature.melspectrogram(y=audio, sr=sr, n_mels=128)
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S_dB = librosa.power_to_db(S, ref=np.max)
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librosa.display.specshow(S_dB, y_axis='mel', x_axis='time', sr=sr)
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ax3.set_title('梅尔频谱图')
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plt.colorbar(format='%+2.0f dB')
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path)
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plt.close()
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else:
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plt.show()
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# 如果提供了特征，绘制特征条形图
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if features and feature_names:
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selected_features = {k: features[k] for k in feature_names if k in features}
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plt.figure(figsize=(12, 6))
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plt.bar(selected_features.keys(), selected_features.values())
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plt.xticks(rotation=90)
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plt.title('选定特征值')
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plt.tight_layout()
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if save_path:
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# 修改文件名，避免覆盖前一个图
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feature_save_path = save_path.replace('.', '_features.')
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plt.savefig(feature_save_path)
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plt.close()
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else:
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plt.show()
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def plot_feature_importance(feature_names, importances, top_n=20, save_path=None):
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"""
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绘制特征重要性
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Args:
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feature_names: 特征名称列表
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importances: 特征重要性列表
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top_n: 显示前N个最重要的特征
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save_path: 保存路径，如果为None则显示图形
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"""
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set_chinese_font()
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# 创建特征重要性数据框
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feature_importance_df = pd.DataFrame({
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'特征': feature_names,
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'重要性': importances
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})
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# 排序并选择前N个特征
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feature_importance_df = feature_importance_df.sort_values('重要性', ascending=False).head(top_n)
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plt.figure(figsize=(12, 8))
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sns.barplot(x='重要性', y='特征', data=feature_importance_df)
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plt.title(f'前{top_n}个最重要的特征')
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path)
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plt.close()
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else:
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plt.show()
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def plot_prediction_result(audio_path, emotion, predicted_emotion, probabilities, emotion_labels, save_path=None):
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"""
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可视化预测结果
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Args:
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audio_path: 音频文件路径
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emotion: 真实情感标签
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predicted_emotion: 预测的情感标签
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probabilities: 预测概率
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emotion_labels: 情感标签列表
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save_path: 保存路径，如果为None则显示图形
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"""
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set_chinese_font()
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# 加载音频
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audio, sr = librosa.load(audio_path, sr=22050)
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# 创建图形
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fig = plt.figure(figsize=(15, 10))
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# 绘制波形
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ax1 = plt.subplot(2, 1, 1)
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librosa.display.waveshow(audio, sr=sr)
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ax1.set_title(f'音频波形 - 文件: {os.path.basename(audio_path)}')
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ax1.set_ylabel('振幅')
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# 绘制预测结果条形图
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ax2 = plt.subplot(2, 1, 2)
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sns.barplot(x=emotion_labels, y=probabilities)
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ax2.set_title(f'情感预测结果 (真实: {emotion}, 预测: {predicted_emotion})')
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ax2.set_ylabel('预测概率')
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ax2.set_ylim([0, 1])
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plt.xticks(rotation=45)
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# 添加颜色高亮显示真实和预测标签
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for i, label in enumerate(emotion_labels):
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if label == emotion and label == predicted_emotion:
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# 真实标签和预测标签相同（正确预测）
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ax2.patches[i].set_facecolor('green')
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elif label == emotion:
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# 只是真实标签
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ax2.patches[i].set_facecolor('blue')
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elif label == predicted_emotion:
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# 只是预测标签（错误预测）
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ax2.patches[i].set_facecolor('red')
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plt.tight_layout()
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if save_path:
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plt.savefig(save_path)
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plt.close()
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else:
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plt.show() |