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test.py

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+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)