diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..e63f03f834298ad52aa775b57b84a2e7c0d3cbad --- /dev/null +++ b/app.py @@ -0,0 +1,117 @@ +import gradio as gr + +import numpy as np +import h5py +import pandas as pd +import pickle + +from skimage.util import random_noise +import time + +import tensorflow as tf + +from model import VGGNet + +def query_normal(query_img: np.ndarray): + query_feature = model.extract_feature(query_img, verbose=0) + scores = np.dot(query_feature, features.T) + rank_ID = np.argsort(scores)[::-1] + rank_score = scores[rank_ID] + + imlist = [image_ids.astype(str)[index] for i, index in enumerate(rank_ID[0:10])] + + return imlist, rank_score[0:10].tolist(), len(scores) + +def query_kmeans(query_img: np.ndarray): + query_feature = model.extract_feature(query_img, verbose=0) + + cluster = kmeans.predict(query_feature.reshape(1, -1)) + cluster = cluster[0] + + df = pd.DataFrame({'image_id': image_ids.astype(str), 'cluster_id': kmeans.labels_}) + df = df[df['cluster_id'] == cluster] + + query = df[df['cluster_id'] == cluster].index + + query_feature = model.extract_feature(query_img) + scores = np.dot(query_feature, features[query].T) + rank_ID = np.argsort(scores)[::-1] + rank_score = scores[rank_ID] + + imlist = [image_ids.astype(str)[index] for i, index in enumerate(query[rank_ID[0:10]])] + + return imlist, rank_score[0:10].tolist(), len(scores), cluster + +def query(image, mode, noise, noise_seed, mean, var, amount, salt_vs_pepper): + if image == None or noise_seed == None: + return None, None, None, None, None + + query_img = tf.keras.utils.load_img(image, target_size=(model.input_shape[0], model.input_shape[1])) + query_img = tf.keras.utils.img_to_array(query_img).astype(int) + + if noise == 'none': + pass + elif noise == 'gaussian' or noise =='speckle': + query_img = random_noise(query_img / 255, mode=noise, rng=int(noise_seed), mean=mean, var=var, clip=True) + query_img = np.array(query_img * 255, dtype=np.uint8) + elif noise == 'localvar': + query_img = random_noise(query_img / 255, mode=noise, rng=int(noise_seed), clip=True) + query_img = np.array(query_img * 255, dtype=np.uint8) + elif noise == 'poisson': + query_img = random_noise(query_img / 255, mode=noise, rng=int(noise_seed), clip=True) + query_img = np.array(query_img * 255, dtype=np.uint8) + elif noise == 'salt' or noise == 'pepper': + query_img = random_noise(query_img / 255, mode=noise, rng=int(noise_seed), amount=amount, clip=True) + query_img = np.array(query_img * 255, dtype=np.uint8) + elif noise == 's&p': + query_img = random_noise(query_img / 255, mode=noise, rng=int(noise_seed), amount=amount, salt_vs_pepper=salt_vs_pepper, clip=True) + query_img = np.array(query_img * 255, dtype=np.uint8) + + start = time.time() + if mode == 'normal': + results, scores, length = query_normal(query_img) + elif mode == 'kmeans': + results, scores, length, cluster = query_kmeans(query_img) + end = time.time() + query_time = end - start + query_time = round(query_time * 1000, 2) + + return query_img, f'Query time: {query_time} ms', [(result, f'Score: {score}, file: {result}') for result, score in zip(results, scores)], length, f'{cluster}' if mode == 'kmeans' else 'None' + +if __name__ == '__main__': + model = VGGNet() + + # Load dataset + datasets = h5py.File('features.h5', 'r') + features = datasets['features'][:] + image_ids = datasets['image_ids'][:] + datasets.close() + + # Load kmeans model + with open('kmeans.pkl', 'rb') as f: + kmeans = pickle.load(f) + + # Run web app + iface = gr.Interface( + title='An image search engine based on VGG16', + description='dataset: https://www.kaggle.com/datasets/hcfighting/tinyimagenet200. Error may occur when result showing because i\'d deleted some images in the dataset, please try another image to search.', + fn=query, + inputs=[ + gr.Image(type='filepath'), + gr.Radio(['normal', 'kmeans'], value='normal', info='normal: search all images, kmeans: search images in the same cluster'), + gr.Radio(['none', 'gaussian', 'localvar', 'poisson', 'salt', 'pepper', 's&p', 'speckle'], value='none', info='noise type (default: none)'), + gr.Number(label='random_seed', value=0, info='random seed for noise (default: 0)'), + gr.Slider(label='mean', maximum=1, minimum=0, value=0, info='mean of the noise, works with gaussian and localvar (default: 0)'), + gr.Slider(label='var', maximum=1, minimum=0, value=0.01, info='variance of the noise, works with gaussian and localvar (default: 0.01)'), + gr.Slider(label='amount', maximum=1, minimum=0, value=0.05, info='proportion of image pixels to replace with noise, works with salt, pepper, and s&p (default: 0.05)'), + gr.Slider(label='salt_vs_pepper', maximum=1, minimum=0, value=0.5, info='proportion of s&p noise (default: 0.5)'), + ], + outputs=[ + gr.Image(label='query image', type='numpy'), + gr.Label(label='query time'), + gr.Gallery(label='Top 10 similar images').style(columns=[5], rows=[2]), + gr.Label(label='count of images for searching'), + gr.Label(label='cluster id') + ], + ) + iface.launch() \ No newline at end of file diff --git a/features.h5 b/features.h5 new file mode 100644 index 0000000000000000000000000000000000000000..2f4d18368842bf94f7bba43be35aa8f24a800f7f --- /dev/null +++ b/features.h5 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:213bc8f61b17123b4f6977843eeef32d479c176b03c65fb6f9d404e8a704ac4a +size 210702048 diff --git a/kmeans.pkl b/kmeans.pkl new file mode 100644 index 0000000000000000000000000000000000000000..17153ffa0a0b8a658afbc9b4ea8ccbee864e76f5 --- /dev/null +++ b/kmeans.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4634447f64ae7d12f0b0a04bf49a806a4bb2f3b13d308747deaa9942f136b153 +size 421136 diff --git a/model.py b/model.py new file mode 100644 index 0000000000000000000000000000000000000000..52716879f2d78bbc26e76bf33f3a06ff656920a1 --- /dev/null +++ b/model.py @@ -0,0 +1,34 @@ +import tensorflow as tf + +import numpy as np +from numpy import linalg as LA + +from keras.applications.vgg16 import VGG16 +from keras.applications.vgg16 import preprocess_input + +class VGGNet: + def __init__(self): + self.input_shape = (224, 224, 3) + self.weight = 'imagenet' + self.pooling = 'max' + self.model = VGG16(weights=self.weight, input_shape=(self.input_shape[0], self.input_shape[1], self.input_shape[2]), pooling=self.pooling, include_top=False) + self.model.predict(np.zeros((1, 224, 224, 3))) + + def extract_feature(self, img_path: any, verbose='auto'): + if type(img_path) == str or type(img_path) == np.str_: + img = tf.keras.utils.load_img(img_path, target_size=(self.input_shape[0], self.input_shape[1])) + img = tf.keras.utils.img_to_array(img) + img = np.expand_dims(img, axis=0) + img = preprocess_input(img) + feature = self.model.predict(img, verbose=verbose) + feature_normalized = feature[0] / LA.norm(feature[0]) + elif type(img_path) == np.ndarray: + img = img_path + img = np.expand_dims(img, axis=0) + img = preprocess_input(img) + feature = self.model.predict(img, verbose=verbose) + feature_normalized = feature[0] / LA.norm(feature[0]) + else: + raise TypeError('img_path must be str or np.ndarray') + + return feature_normalized \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8bddc6d69059e99398f9e2d7fab8a97baed79f04 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +gradio +h5py +scikit_learn +scikit-image +tensorflow diff --git a/tiny-imagenet-200/train/n01443537/images/n01443537_0.JPEG b/tiny-imagenet-200/train/n01443537/images/n01443537_0.JPEG new file mode 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