Image rotation
Image rotation is a technique where an image is rotated by a certain angle. This technique is used to increase the size of the training dataset and improve the model’s ability to recognize objects from different angles. The Python code for image rotation is as follows:
from PIL import Image
import numpy as np
def rotate_image(image_path, degrees):
img = Image.open(image_path)
rotated_image = img.rotate(degrees)
return rotated_image
image_path = “path/to/image.jpg”
rotated_image = rotate_image(image_path, 45)
rotated_image.show()
In the preceding code, we load the image from the image path and rotate it by a given number of degrees. This creates a new dataset for the same image from different angles and improves model training.
Image translation
Image translation is a technique where an image is shifted horizontally or vertically by a certain amount of pixels. This technique is used to increase the size of the training dataset and improve the model’s ability to recognize objects in different positions. The Python code for image translation is as follows:
from PIL import Image
import numpy as np
def translate_image(image_path, x_offset, y_offset):
img = Image.open(image_path)
translated_image = img.transform(img.size, \
Image.AFFINE, (1, 0, x_offset, 0, 1, y_offset))
return translated_image
image_path = “path/to/image.jpg”
translated_image = translate_image(image_path, 50, 50)
translated_image.show()
In the preceding code, we define a Python function that shifts the image by a certain amount of pixels.
Image scaling
Image scaling is an augmentation technique where an image is scaled up or down by a certain factor. This technique is used to increase the size of the training dataset and improve the model’s ability to recognize objects at different scales. The Python code for image scaling is as follows:
from PIL import Image
import numpy as np
def scale_image(image_path, scale_factor):
img = Image.open(image_path)
scaled_image = img.resize((int(img.size[0]*scale_factor),\
int(img.size[1]*scale_factor)))
return scaled_image
image_path = “path/to/image.jpg”
scaled_image = scale_image(image_path, 0.5)
scaled_image.show()
In the preceding code, we change the image size by multiplying the image by a scale factor in a Python function. Next, let’s see how to implement an SVM with data augmentation using the CIFAR-10 dataset.
Implementing an SVM with data augmentation in Python
In this section, we will provide a step-by-step guide to implement an SVM with data augmentation in Python using the CIFAR-10 dataset. We will start by introducing the CIFAR-10 dataset and then move on to loading the dataset in Python. We will then preprocess the data for SVM training and implement an SVM with the default hyperparameters and dataset. Next, we train and evaluate the performance of the SVM with an augmented dataset, showing that the performance of the SVM improves on the augmented dataset.