find_visually_similar_images (python)

find_visually_similar_images/resnet50_find_similar_images.py

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+from keras.applications.resnet50 import ResNet50
+from keras.applications.resnet50 import preprocess_input
+from sklearn.neighbors import NearestNeighbors
+from os import listdir
+import numpy as np
+from PIL import Image
+import pickle as pk
+import json
+from pathlib import Path
+
+def read_img_file(f):
+    img = Image.open(f)
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    return img
+
+def resize_img_to_array(img, img_shape):
+    img_array = np.array(
+        img.resize(
+            img_shape, 
+            Image.ANTIALIAS
+        )
+    )    
+    return img_array
+
+def get_features(f):
+    img_width, img_height = 224, 224
+    img = read_img_file(f)
+    
+    np_img = resize_img_to_array(img, img_shape=(img_width, img_height))
+    expanded_img_array = np.expand_dims(np_img, axis=0)
+    preprocessed_img = preprocess_input(expanded_img_array)
+    X_conv = model.predict(preprocessed_img)
+    return X_conv[0]
+
+model = ResNet50(weights='imagenet', include_top=False,input_shape=(224, 224, 3),pooling='max')
+features=pk.load( open("ResNet50.pkl", "rb"))
+path="../public/images"
+knn = NearestNeighbors(n_neighbors=20,algorithm='brute',metric='euclidean')
+knn.fit(features)
+file_names=listdir(path)
+ALL_SIMILAR_IMAGES={}
+for file_name in file_names:
+    id=Path(file_name).stem
+    print(id)
+    feature=get_features(path+"/"+file_name).reshape(1,-1)
+    indices = knn.kneighbors(feature, return_distance=False)
+    similar_images=[]
+    for i in range(indices[0].size):
+        similar_images.append(Path(file_names[indices[0][i]]).stem)
+    ALL_SIMILAR_IMAGES[id]=similar_images
+with open('data.txt', 'w') as outfile:
+    json.dump(ALL_SIMILAR_IMAGES, outfile)
+
+

find_visually_similar_images/resnet50_generate_vectors.py

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+from keras.applications.resnet50 import ResNet50
+from keras.applications.resnet50 import preprocess_input
+from os import listdir
+import numpy as np
+from PIL import Image
+import pickle as pk
+
+def read_img_file(f):
+    img = Image.open(f)
+    if img.mode != 'RGB':
+        img = img.convert('RGB')
+    return img
+
+def resize_img_to_array(img, img_shape):
+    img_array = np.array(
+        img.resize(
+            img_shape, 
+            Image.ANTIALIAS
+        )
+    )    
+    return img_array
+
+def get_features(f):
+    img_width, img_height = 224, 224
+    img = read_img_file(f)
+    np_img = resize_img_to_array(img, img_shape=(img_width, img_height))
+    expanded_img_array = np.expand_dims(np_img, axis=0)
+    preprocessed_img = preprocess_input(expanded_img_array)
+    X_conv = model.predict(preprocessed_img)
+    return X_conv[0]
+
+model = ResNet50(weights='imagenet', include_top=False,input_shape=(224, 224, 3),pooling='max')
+arr=[]
+
+path="../public/images"
+file_names=listdir(path)
+for f in file_names:
+    print(f)
+    features=get_features(path+"/"+f)
+    print(features)
+    arr.append(features)
+arr=np.array(arr)
+
+pk.dump(arr, open("ResNet50.pkl","wb"))
+