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feature_extractor.py
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feature_extractor.py
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# from fileinput import filename
# import os
# import pickle
# actors = os.listdir('data')
# filename = []
# for actor in actors:
# for file in os.listdir(os.path.join('data',actor)):
# filename.append(os.path.join('data',actor,file))
# pickle.dump(filename,open('filenames.pkl','wb'))
from fileinput import filename
import imp
import pickle
from statistics import mode
from tensorflow.keras.preprocessing import image
import numpy as np
from keras_vggface.utils import preprocess_input
from keras_vggface.vggface import VGGFace
from sklearn.feature_extraction import img_to_graph
import tensorflow
filenames = pickle.load(open('filenames.pkl','rb'))
model = VGGFace(model='resnet50',include_top=False,input_shape=(224,224,3),pooling='avg')
def feature_extractor(img_path,model):
img = image.load_img(img_path,target_size=(224,224))
img_array = image.img_to_array(img)
expanded_img = np.expand_dims(img_array,axis=0)
preprocess_img = preprocess_input(expanded_img)
result = model.predict(preprocess_img).flatten()
return result
# features = []
# for file in filenames:
# result = feature_extractor(file,model)
# features.append(result)
# pickle.dump(features,open('embedding.pkl','wb'))