| 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('images'): | |
| filenames.append(os.path.join('images',file)) | |
| feature_list = [] | |
| for i in tqdm(filenames): | |
| feature_list.append(extract_features(i,model1)) | |
| print(np.array(feature_list).shape) | |
| pickle.dump(feature_list,open('embeddings.pkl','wb')) | |
| pickle.dump(filenames,open('filenames.pkl','wb')) |