Get Started

Here, in this example, we’ll create a simple preprocessing and augmentation model.

Dataset for the model

For the dataset, just collect some images from the internet and put it in one folder.

Install the library

Check the install page for installation instructions.

Importing dependencies from Hocrox

Let’s import the dependencies from Hocrox.

Here we make a simple model with some basic preprocessing and augmentation layers.

from hocrox.model import Model

from hocrox.layer.preprocessing.transformation import Grayscale, Resize
from hocrox.layer.augmentation.flip import RandomFlip
from hocrox.layer.augmentation.transformation import RandomRotate
from hocrox.layer import Read, Save

Making the model

The model class provides an easy .add() method to add layers. We will use the .add() method here to add some layers.

# Initializing the model
model = Model()

# Adding model layers
model.add(Read(path="./img"))
model.add(Resize((224, 224)))
model.add(Grayscale())
model.add(RandomFlip(probability=1.0, number_of_outputs=2))
model.add(RandomRotate(probability=1.0, start_angle=-10, end_angle=10, number_of_outputs=5))
model.add(Save("./processed_images"))

Summary of the model

Once we defined the model, it is a good idea to print the model summary to make sure the pipeline is correct.

To print summary, we have a simple .summary() method. We will use it here.

# Printing the summary of the model
print(model.summary())

Transforming the images

Once we are done with the model pipeline, we can use the .transform() method transform the images based on the model pipeline.

The transform method transform the images and saves it into the given path.

# Apply transform to the images
model.transform()