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import os
import cv2
import speech_recognition as sr
import dlib
import sys
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
from functions.eye_track import Facetrack
from functions.fer import Model
from functions.valence_arousal import load_models
from tensorflow.keras.models import load_model # type: ignore
def load_values(file_path):
values_dict = {}
with open(file_path, 'r') as file:
for line in file:
word, value = line.strip().split('\t')
values_dict[word.lower()] = float(value)
return values_dict
models_folder='models'
fer_model_path=os.path.join(models_folder,'22.6_AffectNet_10K_part2.pt')
arousal_dict_path=os.path.join(models_folder,'arousal-NRC-VAD-Lexicon.txt')
dominance_dict_path=os.path.join(models_folder,'dominance-NRC-VAD-Lexicon.txt')
valence_arousal_model=os.path.join(models_folder,'emotion_model.pt')
val_ar_feat_path=os.path.join(models_folder,'resnet_features.pt')
speech_model=os.path.join(models_folder,'speech.keras')
valence_dict_path=os.path.join(models_folder,'valence-NRC-VAD-Lexicon.txt')
print("Loading models ")
#Face detection models
dnn_net = cv2.dnn.readNetFromCaffe(os.path.join(models_folder,"deploy.prototxt"), os.path.join(models_folder,"res10_300x300_ssd_iter_140000.caffemodel"))
predictor = dlib.shape_predictor(os.path.join(models_folder,"shape_predictor_68_face_landmarks.dat"))
# print("face models loaded ")
#FER model
fer_model=Model(fps=30,fer_model=fer_model_path)
# print("fer model loaded ")
#Speech model
model_s = load_model(speech_model)
# print("speech model loaded ")
#Load valence,arousal,dominance_dicts
valence_dict = load_values(valence_dict_path)
arousal_dict = load_values(arousal_dict_path)
dominance_dict = load_values(dominance_dict_path)
#Loading valence_arousal_models
resnet,emotion_model=load_models(valence_arousal_model,val_ar_feat_path)
models_dict={
'face':(dnn_net,predictor),
'speech':model_s,
'fer':fer_model,
'vad':(valence_dict,arousal_dict,dominance_dict),
"valence_fer":(resnet,emotion_model)
}
print("models loaded") |