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import numpy as np | |
import tensorflow | |
from tensorflow.keras.preprocessing import image | |
from tensorflow.keras.layers import GlobalMaxPooling2D | |
from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input | |
from numpy.linalg import norm | |
import os | |
from tqdm import tqdm | |
import pickle | |
model = ResNet50(weights="imagenet", include_top=False,input_shape=(224,224,3)) | |
model.trainable=False | |
model1 = tensorflow.keras.Sequential([ | |
model, | |
GlobalMaxPooling2D() | |
]) | |
def extract_features(img_path,model): | |
img=image.load_img(img_path,target_size = (224,224)) | |
image_array = image.img_to_array(img) | |
expanded_image_array = np.expand_dims(image_array,axis=0) | |
processed_image = preprocess_input(expanded_image_array) | |
result = model.predict(processed_image).flatten() | |
normalized_result=result/norm(result) | |
return normalized_result | |
filenames =[] | |
for file in os.listdir('set0'): | |
filenames.append(os.path.join('set0',file)) | |
for file in os.listdir('set1'): | |
filenames.append(os.path.join('set1',file)) | |
for file in os.listdir('set2'): | |
filenames.append(os.path.join('set2',file)) | |
for file in os.listdir('set3'): | |
filenames.append(os.path.join('set3',file)) | |
for file in os.listdir('set4'): | |
filenames.append(os.path.join('set4',file)) | |
feature_list = [] | |
for i in tqdm(filenames): | |
feature_list.append(extract_features(i,model1)) | |
print(np.array(feature_list).shape) | |
import pickle | |
pickle.dump(feature_list,open('embeddings2.pkl','wb')) | |
pickle.dump(filenames,open('filenames2.pkl','wb')) |