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fer_ser_fusion.py
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fer_ser_fusion.py
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from __future__ import division
from AudioExtraction.audio_extraction import extract_video_audio
from FrameExtraction.image_frame_extraction import extract_video_frames
from FER_SER_fusion.speech_emotion_recognition import SpeechEmotionRecognition
from FER_SER_fusion.face_emotion_recognition import FaceEmotionRecognition
from keras.models import model_from_json
import os
import numpy as np
import cv2
import operator
import time
def analyse(video_path, output_file):
for _file in os.listdir('extracted-frames'):
os.remove('extracted-frames/' + _file)
for _file in os.listdir('extracted-audios'):
os.remove('extracted-audios/' + _file)
step = 1
sample_rate = 16000
filename = video_path.split('/')[-1]
filename_class = filename.split('.')[0]
# Extrair o áudio do vídeo enviado:
audio_path = extract_video_audio(video_path, 'extracted-audios/')
# Extrair os frames do vídeo enviado:
frames_path = extract_video_frames(video_path, 'extracted-frames')
# Analise do áudio extraído:
SER = SpeechEmotionRecognition('models/ser/best_model.hdf5', 'models/ser/model-weights.h5')
emotions, timestamp = SER.predict_emotion_from_file(audio_path, predict_proba=True, chunk_step=step * sample_rate)
emotion = np.argmax(emotions)
predictions_from_audio_float = {
'angry': emotions[0][0],
'disgust': emotions[0][1],
'fear': emotions[0][2],
'happy': emotions[0][3],
'neutral': emotions[0][4],
'sad': emotions[0][5],
'surprise': emotions[0][6]
}
# Análise dos frames extraídos
with open('models/model1-fer.json', 'r') as file:
json = file.read()
model = model_from_json(json)
# model = create_model1()
model.load_weights('models/model1-fer-weights.h5')
# Arrays pra salvar as probabilidades de cada emoção por frame analisado
angry, disgust, fear, happy, sad, surprise, neutral = [], [], [], [], [], [], []
for frame in os.listdir(frames_path):
if frame not in ['.gitkeep']:
FER = FaceEmotionRecognition()
image_to_predict = frames_path + '/' + frame
face = cv2.imread(image_to_predict)
predictions_frame = []
for face in FER.extract_features_from_face(FER.face_detector(face)):
to_predict = np.reshape(face.flatten(), (1, 48, 48, 1))
emotions = model.predict(to_predict)
angry.append(float('{:f}'.format(emotions[0][0])))
disgust.append(float('{:f}'.format(emotions[0][1])))
fear.append(float('{:f}'.format(emotions[0][2])))
happy.append(float('{:f}'.format(emotions[0][3])))
sad.append(float('{:f}'.format(emotions[0][4])))
surprise.append(float('{:f}'.format(emotions[0][5])))
neutral.append(float('{:f}'.format(emotions[0][6])))
prediction = np.argmax(emotions)
predictions_frame.append(prediction)
predictions_from_frames_float = {
'angry': sum(angry) / len(angry),
'disgust': sum(disgust) / len(disgust),
'fear': sum(fear) / len(fear),
'happy': sum(happy) / len(happy),
'neutral': sum(neutral) / len(neutral),
'sad': sum(sad) / len(sad),
'surprise': sum(surprise) / len(surprise)
}
predictions_with_sum = {
'angry': predictions_from_frames_float['angry'] + predictions_from_audio_float['angry'],
'disgust': predictions_from_frames_float['disgust'] + predictions_from_audio_float['disgust'],
'fear': predictions_from_frames_float['fear'] + predictions_from_audio_float['fear'],
'happy': predictions_from_frames_float['happy'] + predictions_from_audio_float['happy'],
'neutral': predictions_from_frames_float['neutral'] + predictions_from_audio_float['neutral'],
'sad': predictions_from_frames_float['sad'] + predictions_from_audio_float['sad'],
'surprise': predictions_from_frames_float['surprise'] + predictions_from_audio_float['surprise'],
}
predictions_with_product = {
'angry': predictions_from_frames_float['angry'] * predictions_from_audio_float['angry'],
'disgust': predictions_from_frames_float['disgust'] * predictions_from_audio_float['disgust'],
'fear': predictions_from_frames_float['fear'] * predictions_from_audio_float['fear'],
'happy': predictions_from_frames_float['happy'] * predictions_from_audio_float['happy'],
'neutral': predictions_from_frames_float['neutral'] * predictions_from_audio_float['neutral'],
'sad': predictions_from_frames_float['sad'] * predictions_from_audio_float['sad'],
'surprise': predictions_from_frames_float['surprise'] * predictions_from_audio_float['surprise'],
}
class_sum = max(predictions_with_sum.items(), key=operator.itemgetter(1))[0]
class_product = max(predictions_with_product.items(), key=operator.itemgetter(1))[0]
# filename, s_angry, s_disgust, s_fear, s_happy, s_neutral, s_sad, s_surprise, p_angry, p_disgust, p_fear, p_happy, p_neutral, p_sad, p_surprise, class_sum, class_product
append_to_file = "{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}"\
.format(
filename_class,
predictions_with_sum['angry'],
predictions_with_sum['disgust'],
predictions_with_sum['fear'],
predictions_with_sum['happy'],
predictions_with_sum['neutral'],
predictions_with_sum['sad'],
predictions_with_sum['surprise'],
predictions_with_product['angry'],
predictions_with_product['disgust'],
predictions_with_product['fear'],
predictions_with_product['happy'],
predictions_with_product['neutral'],
predictions_with_product['sad'],
predictions_with_product['surprise'],
class_sum,
class_product,
)
output_file.write(append_to_file)
output_file.write("\n")
if __name__ == '__main__':
print("Classification started")
start = time.time()
output_folder = "FER_SER_fusion"
total_actors = len(os.listdir('/home/mateus/Downloads/Video_Speech'))
current_actor = 0
for actor in os.listdir('/home/mateus/Downloads/Video_Speech'):
if actor.startswith('Actor'):
current_actor += 1
total_videos_per_actor = len(os.listdir('/home/mateus/Downloads/Video_Speech/' + actor))
done_videos = 0
for video in os.listdir('/home/mateus/Downloads/Video_Speech/' + actor):
done_videos += 1
with open(output_folder + '/results-correct.csv', mode='a+') as file:
analyse('/home/mateus/Downloads/Video_Speech/'+actor+'/'+video, file)
percentage = round(((done_videos / total_videos_per_actor) * 100), 2)
print("Done {} of {} actors. Video {} of {}. ({}%)".format(current_actor, total_actors, done_videos, total_videos_per_actor, percentage))
end = time.time()
print("Classification finished. Total time: {}".format(end-start))