ADD file via upload

pydata-huang/情感识别/huang-emotion-recognition-master/testEmotion.py

@@ -0,0 +1,55 @@
+import os
+from random import shuffle
+from train import getFeature
+from drawRadar import draw
+import joblib
+import numpy as np
+import pyaudio
+import wave
+
+path = r'wave'
+
+wav_paths = []
+
+person_dirs = os.listdir(path)
+for person in person_dirs:
+    if person.endswith('txt'):
+        continue
+    emotion_dir_path = os.path.join(path, person)
+    emotion_dirs = os.listdir(emotion_dir_path)
+    for emotion_dir in emotion_dirs:
+        if emotion_dir.endswith('.ini'):
+            continue
+        emotion_file_path = os.path.join(emotion_dir_path, emotion_dir)
+        emotion_files = os.listdir(emotion_file_path)
+        for file in emotion_files:
+            if not file.endswith('wav'):
+                continue
+            wav_path = os.path.join(emotion_file_path, file)
+            wav_paths.append(wav_path)
+
+# 将语音文件随机排列
+#shuffle(wav_paths)
+
+model = joblib.load("classfier.m")
+
+p = pyaudio.PyAudio()
+for wav_path in wav_paths:
+    f = wave.open(wav_path, 'rb')
+    stream = p.open(
+        format=p.get_format_from_width(f.getsampwidth()),
+        channels=f.getnchannels(),
+        rate=f.getframerate(),
+        output=True)
+    data = f.readframes(f.getparams()[3])
+    stream.write(data)
+    stream.stop_stream()
+    stream.close()
+    f.close()
+    data_feature = getFeature(wav_path, 48)
+    print(model.predict([data_feature]))
+    print(model.predict_proba([data_feature]))
+    labels = np.array(['angry', 'Delate', 'disgust', 'fear', 'happy','neutral','sad','surprised','TS'])
+    draw(model.predict_proba([data_feature])[0], labels, 6)
+
+p.terminate()