| import pickle | |
| import tensorflow | |
| import numpy as np | |
| from numpy.linalg import norm | |
| from tensorflow.keras.preprocessing import image | |
| from tensorflow.keras.layers import GlobalMaxPooling2D | |
| from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input | |
| from sklearn.neighbors import NearestNeighbors | |
| import cv2 | |
| feature_list = np.array(pickle.load(open('embeddings.pkl','rb'))) | |
| filenames = pickle.load(open('filenames.pkl','rb')) | |
| model = ResNet50(weights='imagenet',include_top=False,input_shape=(224,224,3)) | |
| model.trainable = False | |
| model = tensorflow.keras.Sequential([ | |
| model, | |
| GlobalMaxPooling2D() | |
| ]) | |
| img = image.load_img('sample/i4.jpg',target_size=(224,224)) | |
| img_array = image.img_to_array(img) | |
| expanded_img_array = np.expand_dims(img_array, axis=0) | |
| preprocessed_img = preprocess_input(expanded_img_array) | |
| result = model.predict(preprocessed_img).flatten() | |
| normalized_result = result / norm(result) | |
| neighbors = NearestNeighbors(n_neighbors=5,algorithm='brute',metric='euclidean') | |
| neighbors.fit(feature_list) | |
| distances,indices = neighbors.kneighbors([normalized_result]) | |
| print(indices) | |
| for file in indices[0][0:5]: | |
| temp_img = cv2.imread(filenames[file]) | |
| cv2.imshow('output',cv2.resize(temp_img,(512,512))) | |
| cv2.waitKey(0) | |
| distances,indices = neighbors.kneighbors([normalized_result]) | |
| print(indices) |