from src.sample_collector import SampleCollector

# 初始化样本采集器
collector = SampleCollector()

# 添加猫叫声样本
import os
# sounds_dir, species = "./data/cat_sounds_2", "cat"
sounds_dir, species = "./data/extras/dataset", "cat"
# sounds_dir, species = "./data/dog_sounds", "dog"
for file in os.listdir(sounds_dir):
    if file.endswith(".wav") or file.endswith(".WAV"):
        collector.add_sounds(os.path.join(sounds_dir, file),species)

# 添加非物种叫声样本
non_sounds_dir = "./data/non_sounds"
for file in os.listdir(non_sounds_dir):
    if file.endswith(".wav") or file.endswith(".WAV"):
        collector.add_non_sounds(os.path.join(non_sounds_dir, file))

# 查看样本数量
print(collector.get_sample_counts())

# from src.audio_input import AudioInput
# from src.audio_processor import AudioProcessor
# from src.feature_extractor import FeatureExtractor
# from src.cat_intent_classifier_v2 import CatIntentClassifier
# import os
# import numpy as np
#
# # 初始化组件
# audio_input = AudioInput()
# audio_processor = AudioProcessor()
# feature_extractor = FeatureExtractor()
#
# # 提取情感类别特征
# emotions_dir = "./cat_intents/emotions"
# emotion_embeddings = []
# emotion_labels = []
#
# for emotion in os.listdir(emotions_dir):
#     emotion_path = os.path.join(emotions_dir, emotion)
#     if os.path.isdir(emotion_path):
#         for file in os.listdir(emotion_path):
#             if file.endswith(".wav") or file.endswith(".WAV"):
#                 file_path = os.path.join(emotion_path, file)
#                 print(f"处理情感样本: {file_path}")
#
#                 # 加载音频
#                 audio_data, sample_rate = audio_input.load_from_file(file_path)
#
#                 # 预处理音频
#                 processed_audio = audio_processor.preprocess(audio_data)
#
#                 # 准备YAMNet输入
#                 yamnet_input = audio_processor.prepare_yamnet_input(processed_audio)
#
#                 # 提取特征
#                 features = feature_extractor.process_audio(yamnet_input)
#
#                 # 使用平均嵌入向量
#                 embedding_mean = np.mean(features["embeddings"], axis=0)
#
#                 # 添加到训练数据
#                 emotion_embeddings.append(embedding_mean)
#                 emotion_labels.append(emotion)
#
# # 训练情感分类器
# print(f"训练情感分类器，样本数: {len(emotion_embeddings)}")
# emotion_classifier = CatIntentClassifier()
# emotion_history = emotion_classifier.train(
#     np.array(emotion_embeddings),
#     emotion_labels,
#     epochs=100,
#     batch_size=16
# )
#
# # 保存情感分类器
# os.makedirs("./models", exist_ok=True)
# emotion_paths = emotion_classifier.save_model("./models", "emotions")
# # phrases_paths = emotion_classifier.save_model("./models", "phrases")
# print(f"情感分类器已保存: {emotion_paths}")

# 类似地，训练短语分类器
# ...（重复上述过程，但使用phrases目录）

# aa = "F_BAC01_MC_MN_SIM01_101.wav, F_BAC01_MC_MN_SIM01_102.wav, F_BAC01_MC_MN_SIM01_103.wav, F_BAC01_MC_MN_SIM01_104.wav, F_BAC01_MC_MN_SIM01_105.wav, F_BAC01_MC_MN_SIM01_201.wav, F_BAC01_MC_MN_SIM01_202.wav, F_BAC01_MC_MN_SIM01_203.wav, F_BAC01_MC_MN_SIM01_301.wav, F_BAC01_MC_MN_SIM01_302.wav, F_BAC01_MC_MN_SIM01_303.wav, F_BAC01_MC_MN_SIM01_304.wav, F_BLE01_EU_FN_DEL01_101.wav, F_BLE01_EU_FN_DEL01_102.wav, F_BLE01_EU_FN_DEL01_103.wav, F_BRA01_MC_MN_SIM01_301.wav, F_BRA01_MC_MN_SIM01_302.wav, F_BRI01_MC_FI_SIM01_101.wav, F_BRI01_MC_FI_SIM01_102.wav, F_BRI01_MC_FI_SIM01_103.wav, F_BRI01_MC_FI_SIM01_104.wav, F_BRI01_MC_FI_SIM01_105.wav, F_BRI01_MC_FI_SIM01_106.wav, F_BRI01_MC_FI_SIM01_201.wav, F_BRI01_MC_FI_SIM01_202.wav, F_CAN01_EU_FN_GIA01_201.wav, F_CAN01_EU_FN_GIA01_202.wav, F_DAK01_MC_FN_SIM01_301.wav, F_DAK01_MC_FN_SIM01_302.wav, F_DAK01_MC_FN_SIM01_303.wav, F_DAK01_MC_FN_SIM01_304.wav, F_IND01_EU_FN_ELI01_101.wav, F_IND01_EU_FN_ELI01_102.wav, F_IND01_EU_FN_ELI01_103.wav, F_IND01_EU_FN_ELI01_104.wav, F_IND01_EU_FN_ELI01_201.wav, F_IND01_EU_FN_ELI01_202.wav, F_IND01_EU_FN_ELI01_203.wav, F_IND01_EU_FN_ELI01_301.wav, F_IND01_EU_FN_ELI01_302.wav, F_IND01_EU_FN_ELI01_304.wav, F_LEO01_EU_MI_RIT01_101.wav, F_LEO01_EU_MI_RIT01_102.wav, F_LEO01_EU_MI_RIT01_103.wav, F_LEO01_EU_MI_RIT01_104.wav, F_LEO01_EU_MI_RIT01_105.wav, F_MAG01_EU_FN_FED01_101.wav, F_MAG01_EU_FN_FED01_102.wav, F_MAG01_EU_FN_FED01_103.wav, F_MAG01_EU_FN_FED01_104.wav, F_MAG01_EU_FN_FED01_105.wav, F_MAG01_EU_FN_FED01_106.wav, F_MAG01_EU_FN_FED01_201.wav, F_MAG01_EU_FN_FED01_202.wav, F_MAG01_EU_FN_FED01_203.wav, F_MAG01_EU_FN_FED01_301.wav, F_MAG01_EU_FN_FED01_302.wav, F_MAG01_EU_FN_FED01_303.wav, F_MAG01_EU_FN_FED01_304.wav, F_MAG01_EU_FN_FED01_305.wav, F_MAT01_EU_FN_RIT01_101.wav, F_MAT01_EU_FN_RIT01_102.wav, F_MAT01_EU_FN_RIT01_103.wav, F_MAT01_EU_FN_RIT01_301.wav, F_MAT01_EU_FN_RIT01_302.wav, F_MAT01_EU_FN_RIT01_303.wav, F_MEG01_MC_FI_SIM01_301.wav, F_MEG01_MC_FI_SIM01_302.wav, F_MEG01_MC_FI_SIM01_303.wav, F_MEG01_MC_FI_SIM01_304.wav, F_MIN01_EU_FN_BEN01_101.wav, F_MIN01_EU_FN_BEN01_102.wav, F_MIN01_EU_FN_BEN01_103.wav, F_MIN01_EU_FN_BEN01_104.wav, F_REG01_EU_FN_GIO01_201.wav, F_SPI01_EU_MN_NAI01_101.wav, F_SPI01_EU_MN_NAI01_102.wav, F_SPI01_EU_MN_NAI01_103.wav, F_SPI01_EU_MN_NAI01_104.wav, F_SPI01_EU_MN_NAI01_201.wav, F_SPI01_EU_MN_NAI01_202.wav, F_SPI01_EU_MN_NAI01_203.wav, F_SPI01_EU_MN_NAI01_301.wav, F_WHO01_MC_FI_SIM01_101.wav, F_WHO01_MC_FI_SIM01_102.wav, F_WHO01_MC_FI_SIM01_103.wav, F_WHO01_MC_FI_SIM01_301.wav, F_WHO01_MC_FI_SIM01_302.wav, F_WHO01_MC_FI_SIM01_303.wav, F_WHO01_MC_FI_SIM01_304.wav, F_WHO01_MC_FI_SIM01_306.wav, F_WHO01_MC_FI_SIM01_307.wav"
#
#
#
# print(
#     [{
#         "path": f"./data/is_cat_sound_true/{dd}", "intent": "等待喂食"
#     }  for dd in aa.split(", ")]
# )