""" Extracting the name and extension of the image and the mask. def augment_data(images, masks, save_path, augment=True):įor x, y in tqdm(zip(images, masks), total=len(images)): Take a look at the complete function, now we are going to break this complete function to understand it more clearly. Now, we are going to write a function called augment_data, that will help us to augment our original dataset. The create_dir function takes a path and create an empty directory if it does not exist. Return images, masks Creating Empty Directory Masks = sorted(glob(os.path.join(path, "masks/"))) Images = sorted(glob(os.path.join(path, "images/ "))) The load_data function takes the path of the dataset and gives you a list of images and masks path. Next, from the albumentations library, we import five functions that helps us to transform our image and mask and create an augmented dataset.The glob function is used to find all the path matching a specified pattern.The tqdm function is used to display the progress bar in the for loop.The cv2 library is used to read the image, resize it and save the numpy array back into an image form.import osįrom albumentations import CenterCrop, RandomRotate90, GridDistortion, HorizontalFlip, VerticalFlip Here, we import all the required the libraries and the function required. In the following code, we are going to apply data augmentation to a semantic segmentation dataset. It would not be reasonable to either flip it horizontally or vertically, as it would change the meaning of the image. Let’s say we have an image of STOP sign from the Traffic Sign dataset. Of dogs flipped upside-down on their head. As in the real-world we would not be seeing many images The dog image horizontally, but it would not be reasonable to flip it Let us continue with the example of the dog. In some cases data augmentation techniques may not be appropriate. Introduce variance and make the model more robust. Orientation, location, scale, brightness etc. Now this model would not work very well in theĪugmentation and produce a variety of images with different Were actually dogs not it may not very well classify left-facing dogsĪs being actual dogs. Think that the model would believe that only these right-facing dogs If we train a model on these images, it is reasonable to
Keras data augmentation image mask full#
Assume that you have aĭataset full of images of dogs and most of the dogs were facing the Use data augmentation to create new data from our existing data.ĭata augmentation helps to reduce overfitting. Training dataset and it is difficult to get more data, then we can All these are some of the most common form of dataĪugmentation. We could rotate the image, crop it or even add some Training data by making some reasonable modifications orĮxample, we can augment an image by flipping it vertically or Through the process of applying data augmentation to any semanticĪugmentation is the process of that enables you to increase amount of Is not possible, as it takes good amount of efforts to collect theĭata and then labelling it requires the domain expertise. In someįields like medical imaging, the availability of huge amount of data (AI) is facilitated due to the availability large amount of datasetĪnd the computational hardware’s like GPU’s and TPU’s. Technological advancements in the field of Artificial Intelligence
0 kommentar(er)
