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test.py
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test.py
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import tensorflow
from tensorflow.keras.layers import GlobalMaxPooling2D
import pickle
import numpy as np
from numpy.linalg import norm
from tensorflow.keras.preprocessing import image
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')))
filename = pickle.load(open('filenames.pkl','rb'))
model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
model.trainable = False
# Constructing the model using tf.keras.Sequential
model = tensorflow.keras.Sequential([
model,
GlobalMaxPooling2D()
])
img = image.load_img('samples/sneakers.jpeg',target_size=(224,224))
img_array = image.img_to_array(img)
expanded_img_array = np.expand_dims(img_array,axis=0)
preprocess_img = preprocess_input(expanded_img_array)
result = model.predict(preprocess_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]:
temp = cv2.imread((filename[file]))
cv2.imshow('output',cv2.resize(temp,(512,512)))
cv2.waitKey(0)