Tutorial
Welcome to the IFTG (Image From Text Generator) package tutorial. This guide will walk you through the key modules and functionalities provided by the package, allowing you to create robust datasets for OCR (Optical Character Recognition) models. Whether you're augmenting images with noise, generating text-based images, or creating batches of data, this tutorial will guide you step-by-step.
Adders
ImageNoiseAdder Module
ImageNoiseAdder is a class used to apply various noise effects to an image and save the resulting noisy image to a specified directory. It extends the NoiseAdder class, leveraging its functionality.
How to use ImageNoiseAdder
iftg.adders import ImageNoiseAdder
from iftg.noises import PixelDropoutNoise, RandomGaussianNoise
# Initialize ImageNoiseAdder with the specified parameters
result = ImageNoiseAdder(img_path='path/to/image.tif', # Path to the input image file.
output_path='', # Directory where the noisy image will be saved. Default is an empty string.
noises=[PixelDropoutNoise(), RandomGaussianNoise()], # List of noise objects to be applied to the image.
identifier='noisy' # Identifier for the noisy image file.
)
# Apply the specified noises and save the transformed images
result.transform_image()
DirectoryNoiseAdder Module
The DirectoryNoiseAdder class is an extension of the abstract base class NoiseAdder. It provides functionality for applying noise to multiple images located in a directory and saving the resulting noisy images to a specified output path. This class is ideal for batch image processing tasks where noise needs to be added to a collection of images.
How to use DirectoryNoiseAdder
from iftg.adders import DirectoryNoiseAdder
from iftg.noises import (GaussianNoise, PixelDropoutNoise, RotationNoise, ShadowNoise)
# Initialize DirectoryNoiseAdder with the specified parameters
results = DirectoryNoiseAdder(dir_path='path/to/directory', # The path to the directory containing images to be processed.
output_path='path/to/output/directory', # The path where the processed images will be saved.
noises=[RotationNoise(), # A list of noise objects to be applied to the images.
GaussianNoise(),
PixelDropoutNoise(),
ShadowNoise()
],
identifier='noisy', # A unique identifier to append to the filenames of the processed images.
img_formats=['jpg', 'png'], # A list of image formats to be considered for processing.
)
# Apply the specified noises and save the transformed images
results.transform_images()
Creators
ImageCreator Module
The ImageCreator class extends the Creator
base class to provide functionality for generating images with customizable text, noise, and visual effects. This class is particularly useful for creating synthetic data by augmenting text-based images with noise or other transformations.
How to use ImageCreator
from PIL import Image
from iftg.creators import ImageCreator
# Define the text
text = 'Hello, World!'
# Create an image with specified parameters
image = ImageCreator.create_image(text=text, # The text to be drawn on the image.
font_path='path/to/the/font', # The file path to the font.
noises=[], # A list of noise objects to apply to the image.
font_size=50, # The size of the font.
font_color='black', # The color of the text. It can be text or hexadecimal.
font_opacity=1.0, # The opacity of the text where 1.0 is fully opaque and 0.0 is fully transparent.
background_color='white', # The background color of the image. It can be text or hexadecimal.
margins=(5, 5, 5, 5), # Margins for text placement on the image (left, top, right, bottom).
dpi=(300, 300), # The resolution of the image (dots per inch).
background_img=Image.open('path/to/background/image'), # An optional background image to be used as a base.
clear_font=True, # Whether to clear the font cache after creating the image.
)
# Save the created image to a file
image.save('image.tif', **image.info)
Generators
ImagesGenerator Module
The ImagesGenerator class extends the Generator class to create a sequence of images with varying text and noise effects. It handles the generation of images based on the provided configuration and includes functionality to save both images and their corresponding text labels.
How to use ImagesGenerator
from iftg.generators import ImagesGenerator
from iftg.noises import (BlurNoise, BrightnessNoise, DilateNoise)
texts = ['Hello, World!']
results = ImagesGenerator(texts=texts, # A list of texts to be used for image creation.
font_path= "path/to/the/font", # The file path to the font used in the images.
noises=[BlurNoise(), # A list of noise objects to be applied to the images.
BrightnessNoise(),
DilateNoise()
],
font_size=40, # The size of the font used in the images.
font_color='black', # The color of the text in the images.
font_opacity=1.0, # The opacity of the text in the images.
background_color='white', # The background color of the images.
margins=(5, 5, 5, 5), # Margins for text placement on the images.
dpi=(300, 300), # The DPI (dots per inch) settings for the images.
img_name='img', # The base name for the output image files.
img_format='.tif', # The file format for the output images.
img_output_path='output', # The directory where the generated images will be saved.
txt_name='text', # The base name for the output text files containing the image labels.
txt_format='.txt', # The file format for the output text files.
txt_output_path='output', # The directory where the generated text files will be saved.
auto_remove_font=True, # A flag indicating whether to automatically remove the font from the cache after image generation.
background_image_path='', # The file path to the background image, if any.
)
# Generate and save the images without labels
results.generate_images()
# OR
# Generate and save images with labels
results.generate_images_with_text()
# OR
# You can use for-loop to further modify your images or do something else
for img, lbl, i in results:
img.save(f'img_{i}.tif', **img.info)
BatchesImagesGenerator Module
BatchesImagesGenerator is a class designed to generate images in batches, where each batch can have its own unique settings such as font, noise, background color, and margins. It inherits from the Generator class and provides functionality to create and iterate over different batches of images.
How to use BatchesImagesGenerator
from iftg.generators import BatchesImagesGenerator
from iftg.noises import (ElasticNoise, ErodeNoise, FlipNoise)
# Define the texts for each batch
texts = [['Hello, World!'], ['Hello, World!']]
# Initialize the BatchesImagesGenerator with the specified parameters
results = BatchesImagesGenerator(texts=texts, # A list of lists of texts, where each inner list contains texts for one batch of images.
font_paths=["path/to/the/font"], # A list of font file paths, where each font corresponds to a batch of images.
noises=[ # A list of lists of noise objects, where each inner list contains noises to be applied to one batch of images.
[ElasticNoise(), FlipNoise()],
[ErodeNoise(), FlipNoise()],
],
font_sizes=[40], # A list of font sizes, where each size corresponds to a batch of images.
font_colors=['black'], # A list of font colors, where each color corresponds to a batch of images.
font_opacities=[1.0], # A list of font opacities, where each opacity corresponds to a batch of images.
background_colors=['white'], # A list of background colors, where each color corresponds to a batch of images.
margins=[(5, 5, 5, 5)], # A list of margin tuples (left, top, right, bottom) for text placement, where each margin corresponds to a batch of images.
dpi=[(300, 300)], # A list of DPI (dots per inch) settings, where each DPI value corresponds to a batch of images.
img_names=['img'], # A list of base names for the output image files, where each name corresponds to a batch of images.
img_formats=['.tif'], # A list of file formats for the output images, where each format corresponds to a batch of images.
img_output_paths=[''], # A list of directories where the generated images will be saved, where each directory corresponds to a batch of images.
txt_names=['text'], # A list of base names for the output text files containing image labels, where each name corresponds to a batch of images.
txt_formats=['.txt'], # A list of file formats for the output text files, where each format corresponds to a batch of images.
txt_output_paths=[''], # A list of directories where the generated text files will be saved, where each directory corresponds to a batch of images.
background_image_paths=[''] # A list of file paths to the background image, if any.
)
# Generate and save the images without labels
results.generate_batches(is_with_label=True) # Set to False to generate images without labels