emotion_filter.py
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import os
import cv2
import numpy as np
from keras.models import Model
from keras.models import load_model
from sklearn.externals import joblib
from tensorflow.keras.preprocessing.image import img_to_array
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
class FeatureExtractor(object):
def __init__(self, input_size=224, out_put_layer='avg_pool', model_path='FerPlus3.h5'):
self.model = load_model(model_path)
self.input_size = input_size
self.model_inter = Model(inputs=self.model.input, outputs=self.model.get_layer(out_put_layer).output)
def inference(self, image):
image = cv2.resize(image, (self.input_size, self.input_size))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = image.astype("float") / 255.0
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
feature = self.model_inter.predict(image)[0]
return feature
def features2feature(pics_features):
pics_features = np.array(pics_features)
fea_mean = pics_features.mean(axis=0)
fea_max = np.amax(pics_features, axis=0)
fea_min = np.amin(pics_features, axis=0)
fea_std = pics_features.std(axis=0)
return np.concatenate((fea_mean, fea_max, fea_min, fea_std), axis=1).reshape(1, -1)
def start_filter(config):
cls_emotion_path = config['MODEL']['CLS_EMOTION']
face_feature_dir = config['VIDEO']['FACE_FEATURE_DIR']
frame_list_dir = config['VIDEO']['FRAME_LIST_DIR']
result_file_name = config['EMOTION']['RESULT_FILE']
svm_clf = joblib.load(cls_emotion_path)
result_file_path = os.path.join(frame_list_dir, result_file_name)
result_file = open(result_file_path, 'w')
face_feature_names = os.listdir(face_feature_dir)
for face_feature in face_feature_names:
face_feature_path = os.path.join(face_feature_dir, face_feature)
features_np = np.load(face_feature_path, allow_pickle=True)
feature = features2feature(features_np)
res = svm_clf.predict_proba(feature)
proba = np.squeeze(res)
# class_pre = svm_clf.predict(feature)
result_file.write(face_feature[:-4] + ' ')
result_file.write(str(proba[0]) + ',' + str(proba[1]) + ',' + str(proba[2]) + '\n')
result_file.close()