Training_gen = img_datagen.flow_from_directory(PATH, target_size=(224,224),Ĭolor_mode='rgb',batch_size=32, shuffle=True) Preprocessing_function = preprocessing_fun) img_datagen = ImageDataGenerator(rescale=1./255, Here, we use the function defined in the previous section in our training generator. Part 2 – Extending the preprocessing function Now that we have implemented our algorithm, let’s use it in the ImageDataGenerator class. OpenCV also provides other algorithms that work on images with a single-channel. We are using the fastN1MeansDenoisingColored algorithm from OpenCV because this algorithm works on colored images. import cv2ĭst = cv2.fastN1MeansDenoisingColored(img, None, 10, 10, 7, 21) Let us prepare a function that takes an image as an input, applies the inbuilt denoising algorithm, and returns the processed image. Let’s get started right away! Part 1 – Implementing the denoising algorithm This tutorial would be broadly divided into 2 parts. ![]() In this article, I would show how to define our own preprocessing function, pass it to the training generator, and feed the images directly into the model thus eliminating the need to save them. Denoising is fairly straightforward using OpenCV which provides several in-built algorithms to do so. flow_from_directory() method implemented in Keras. In order to load the images for training, I am using the. Another method is to perform this transformation on the fly using the preprocessing_function attribute. However, this costs us both time and space. One trivial way to do this is to apply the denoising function to all the images in the dataset and save the processed images in another directory. In our particular example, we will apply a denoising algorithm as a pre-processing transformation to our dataset. However, it becomes difficult to apply custom transformations that are not available in Keras. The ImageDataGenerator class in Keras provides a variety of transformations such as flipping, normalizing, etc. Many times while working on computer vision problems, we encounter situations where we need to apply some form of transformation to our entire dataset. This article was published as a part of the Data Science Blogathon.
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