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Keras datagenerator1/19/2024 ![]() join ( IMAGE_DIR, image_name ), image_cache ) X = datagen. zeros (( len ( image_names ), RESIZE_WIDTH, RESIZE_HEIGHT, 3 )) for i, image_name in enumerate ( image_names ): image = cached_imread ( os. imread ( image_path ) image = imresize ( image, ( RESIZE_WIDTH, RESIZE_HEIGHT )) image_cache = image return image_cache def preprocess_images ( image_names, seed, datagen, image_cache ): np. RESIZE_WIDTH = 300 RESIZE_HEIGHT = 300 def cached_imread ( image_path, image_cache ): if not image_cache. The generator is instantiated at each epoch, and the next() method is called to get the next batch of triples. We start with a basic generator that returns a batch of image triples per invocation. In this post, I will describe an image generator that I built for my Siamese network using the random_transform() method. It applies a random sequence of the transformations you have specified in the ImageDataGenerator constructor to your input image. So I went digging into the code and found a private (in the sense of undocumented) method called random_transform(). ![]() The first seems a bit wasteful, and the second seems incredibly complicated for my use case. However, all these seem to require that you either enumerate the LHS and RHS images in the pair as 4-dimensional tensors (using flow()) or store them in two parallel directories with identical names (using flow_from_directory()). In addition, the ImageDataGenerator documentation covers some cases where this can be done, using a pair of ImageDataGenerator instances that are instantiated with the same parameters. It seems to be something that others have been looking for as well, and there is some discussion in Keras Issue 3059. ![]() (This is not strictly necessary in my case, but can't hurt, and in any case I wanted to learn how to do this for another upcoming project). My problem this time was a litle different - my input is a pair of image names from a triple, and I wanted that the identical transformation be applied to both imaages. This is the default case the component is built to handle, so its actually very simple to use this. I have used the ImageDataGenerator previously to augment my dataset to train a simple classification CNN, where the input was an image and the output was a label. The Keras ImageDataGenerator is much more sophisticated, you instantiate it with the range of transformations you will allow on your dataset, and it returns you a generator containing transformations on your input images images from a directory. Before Keras, when I was working with Caffe, I would manually augment my input with a fixed number of standard transformations, such as rotation, flipping, zooming and affine transforms (these are all just matrix transforms). This gives us 2072 similar image pairs and 2072 different image pairs, ie, a total of 4144 image pairs for our training data.įearing that this might not be nearly enough to train my network adequately, I decided to use the Keras ImageDataGenerator to augment the dataset. Dissimilar image pairs are generated by pairing the left hand image of the similar pair with a random image from some other group. Similar image pairs are generated by considering all combination of image pairs within a group. The following code snippet loops through the image directory and uses the file naming convention to create all pairs of similar images and a corresponding pair of different images. For example, the code snippet displays three photos - the first two are from the same group and the last one is different. Similar image pairs are from the same vacation group. The input to my network consist of image pairs and the output is either 1 (similar) or 0 (different). For example, a photo named 100301.jpg is from vacation 1003 and is the first photo in that group. Each photo is numbered with six digits - the first 4 refer to the vacation and the last two is a unique sequence number within the vacation. The photos have a naming convenition from which the groups can be derived. My dataset is the INRIA Holidays Dataset, a set of 1491 photos from 500 different vacations. Keras provides an example of a Siamese network as part of the distribution. Siamese networks were first proposed in 1993 by Bromley, et al in their paper Signature Verification using a Siamese Time Delay Neural Network. During training, the parameters are updated identically across both subnetworks. Siamese networks are a type of Neural network that contain a pair of identical sub-networks that share the same parameters and weights. I have been looking at training a Siamese network to predict if two images are similar or different.
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