108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
import os
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import sys
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import numpy as np
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from tensorflow.keras.applications import vgg19
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from tensorflow.keras.preprocessing import image
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from tensorflow.keras.applications.vgg19 import preprocess_input
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import faiss
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import cv2
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def query_yes_no(question, default="yes"):
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"""Ask a yes/no question via raw_input() and return their answer.
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"question" is a string that is presented to the user.
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"default" is the presumed answer if the user just hits <Enter>.
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It must be "yes" (the default), "no" or None (meaning
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an answer is required of the user).
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The "answer" return value is True for "yes" or False for "no".
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"""
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valid = {"yes": True, "y": True, "ye": True,
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"no": False, "n": False}
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if default is None:
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prompt = " [y/n] "
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elif default == "yes":
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prompt = " [Y/n] "
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elif default == "no":
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prompt = " [y/N] "
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else:
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raise ValueError("invalid default answer: '%s'" % default)
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while True:
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sys.stdout.write(question + prompt)
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choice = input().lower()
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if default is not None and choice == '':
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return valid[default]
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elif choice in valid:
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return valid[choice]
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else:
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sys.stdout.write("Please respond with 'yes' or 'no' "
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"(or 'y' or 'n').\n")
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model = vgg19.VGG19(weights="imagenet", include_top=False, pooling="avg")
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def extract_features(img_path, model):
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img = image.load_img(img_path, target_size=(224, 224))
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img = image.img_to_array(img)
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img = np.expand_dims(img, axis=0)
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img = preprocess_input(img)
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feature = model.predict(img)
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return feature.flatten()
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image_dir = "images/"
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image_paths = [
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os.path.join(image_dir, f) for f in os.listdir(image_dir) if f.endswith(".jpg")
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]
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features = []
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if os.path.exists("image_index.bin"):
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if query_yes_no("Load the index?", default="yes"):
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index = faiss.read_index("image_index.bin")
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else:
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for image_path in image_paths:
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img_feature = extract_features(image_path, model)
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features.append(img_feature)
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features = np.array(features)
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d = features.shape[1]
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index = faiss.IndexFlatL2(d)
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index.add(features)
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if query_yes_no("Save the index?", default="yes"):
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faiss.write_index(index, "image_index.bin")
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else:
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for image_path in image_paths:
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img_feature = extract_features(image_path, model)
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features.append(img_feature)
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features = np.array(features)
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d = features.shape[1]
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index = faiss.IndexFlatL2(d)
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index.add(features)
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if query_yes_no("Save the index?", default="yes"):
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faiss.write_index(index, "image_index.bin")
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def find_similar_images(query_image_path, index, k=6):
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query_feature = extract_features(query_image_path, model).reshape(1, -1)
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_distance, indices = index.search(query_feature, k)
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return indices.flatten()
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query_image_path = "query_image.jpg"
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similar_image_indices = find_similar_images(query_image_path, index)
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# display the results
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for idx in similar_image_indices:
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similar_image_path = image_paths[idx]
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img = cv2.imread(similar_image_path)
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cv2.imshow("Similar Image", img)
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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