![]() In future posts we will explore how we can use OpenSearch (ElasticSearch) to store the vectors and do KNN search and also fine tune a pre-trained model to our domain.Index ImageMagick Examples Preface and Index Art-like Transformations There is still a lot to do to put this in a production setup. We use a pre-trained model to generate vectors out of the images so this will not necessarily work for images from all domains. In this post we saw how we can implement a simple image search. IntSlider ( min = 2, value = 5, max = 10, continuous_update = False ), ) IntSlider ( min = 2, value = 5, max = 10, continuous_update = False ), n_neighbors = w. IntSlider ( max = len ( images ) - 1, continuous_update = False ), n_inputs = w. interact ( show_similar_images, start_image_idx = w. savefig ( "02-image-search-grid.png" ) import ipywidgets as w w. imshow ( nbor_images ) # we get cosine distance, to convert to similarity we do 1 - cosine_distanceĪx. subplots ( 1, len ( nbors ) + 1, figsize = ( 10, 5 )) for i in range ( len ( nbor_images ) + 1 ): ax = axes ax. # so we take the first entry from those lists since we have only one imageĭistances, nbors = distances, nbors nbor_images = for i in nbors ] fig, axes = plt. kneighbors ( feature ) # output is a tuple of list of distances and list nbors of each image predict ( image ) distances, nbors = knn. expand_dims ( image, 0 ) # add a batch dimensionįeature = vectorizer. Image = images # take an existing image or create a numpy array from PIL image However, for the purposes of this demo, let’s load about 640 images into memory. Tensorflow Datasets is a powerful library with lot of features and can handle huge amount of datasets that do not fit in the memory. AUTOTUNE ) ) return train_ds BATCH_SIZE = 32 IMG_WIDTH = IMG_HEIGHT = 256 train_ds = get_image_batches ( batch_size = BATCH_SIZE, height = IMG_HEIGHT, width = IMG_WIDTH ) partial ( preprocess_image, height = height, width = width ) train_ds = ds train_ds = ( train_ds. ![]() float32 ) / 255.0 return image def get_image_batches ( batch_size = 128, height = 256, width = 256 ): partial_preprocess_image = functools. resize_with_crop_or_pad ( image, target_height = height, target_width = width ) image = tf. load ( "imagenette" ) def extract_image ( example ): image = example return image def preprocess_image ( image, height, width ): image = tf. All we’ve done is resize the image into desired size and normalize the pixel values to be between 0 and 1.ĭs = tfds. The code below loads “imagenette” using Tensorflow Datasets library. ![]() images of chest x-rays or images of clothing items, then the model will not produce meaningful vectors as it has never seen those kind of images. I chose this dataset because the pre-trained models that we find in the Internet is generally trained on ImageNet dataset and such models can extract meaningful feature vectors out of these images. It was prepared by Jeremy Howard and its homepage can be found here. It is a subset of 10 easily classified classes from Imagenet dataset. Import numpy as np import tensorflow as tf import matplotlib.pyplot as plt import tensorflow_datasets as tfds import tensorflow_hub as hub import functools print ( tf. The basic approach for any neural network based search application is as follows: Note: I’ve used Jupyer notebook to run the code in this post, so you might find some Jupyter notebook specific commands here and there. ![]() It can also be used to find visually similar images for recommendation engine or to find duplicates. For example an e-commerce website could allow users to upload a picture of a shirt their friends are wearing and using image search, it can find similar shirts from its catalog. There are several applications of image search. It is also possible to perform similar search but with images! In this post we will explore how we can implement an image search similar to Google’s reverse image search. We are all familiar with text search which returns document similar to our query.
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