Delete test.py

test.py

@@ -1,43 +0,0 @@
-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)