Prepare Test Data

In this section you will add the pre-trained model and copy the bitmap image data to the Android project.

Model

To add the model, create a folder named assets in the app/src/main folder.

Copy the pre-trained model you created in the previous steps, model.pth to the assets folder.

The model is also available in the GitHub repository if you need to copy it.

Image data

The data preparation script is shown below:

    

        
        
            from torchvision import datasets, transforms
from PIL import Image
import os

# Constants
NUM_DIGITS = 10  # Number of unique digits in MNIST (0-9)
EXAMPLES_PER_DIGIT = 2  # Number of examples per digit

# Define the transformation to convert the image to a tensor
transform = transforms.Compose([transforms.ToTensor()])

# Load the MNIST test dataset
test_data = datasets.MNIST(
    root="data",
    train=False,
    download=True,
    transform=transform
)

# Create a directory to save the bitmaps
os.makedirs("mnist_bitmaps", exist_ok=True)

# Dictionary to keep track of collected examples per digit
collected_examples = {digit: 0 for digit in range(NUM_DIGITS)}

# Loop through the dataset and collect the required number of images
for i, (image, label) in enumerate(test_data):
    if collected_examples[label] < EXAMPLES_PER_DIGIT:
        # Convert tensor to PIL image
        pil_image = transforms.ToPILImage()(image)
        # Create the filename with zero-padding
        filename = f"mnist_bitmaps/{label:02d}_{collected_examples[label]:02d}.png"
        # Save the image as PNG
        pil_image.save(filename)
        print(f"Saved: {filename}")

        # Update the count for the current label
        collected_examples[label] += 1

    # Break the loop if all required examples are collected
    if all(count == EXAMPLES_PER_DIGIT for count in collected_examples.values()):
        break
        
    

The above code processes the MNIST test dataset to generate and save bitmap images for digit classification.

It defines constants for the number of unique digits (0-9) and the number of examples to collect per digit. The dataset is loaded using torchvision.datasets with a transformation to convert images to tensors.

A directory named mnist_bitmaps is created to store the images. A dictionary tracks the number of collected examples for each digit. The code iterates through the dataset, converting each image tensor back to a PIL image, and saves two examples of each digit in the format digit_index_example_index.png.

The loop breaks once the specified number of examples per digit is saved, ensuring that exactly 20 images (2 per digit) are generated and stored in the specified directory.

For your convenience the data is included in the GitHub repository

Copy the mnist_bitmaps folder to the assets folder.

Once you have the model.pth and the mnist_bitmaps folder in the assets folder continue to the next step to run the Android application.