rgb-to-segmentation 0.1.3

Tools for processing and cleaning segmentation images using palette mapping and neural networks

RGB to Segmentation

A Python package for processing and cleaning segmentation images. This package provides tools to convert RGB images to segmentation masks using palette-based color mapping and neural network-based refinement.

Features

• Palette-based Cleaning: Clean noisy segmentation images by mapping pixels to the nearest colors in a predefined palette, with optional morphological operations to refine boundaries.
• Strict Palette Mapping: Directly map RGB values to class indices with strict validation - throws an error if any pixel value is not in the colour map.
• Neural Network Refinement: Use trained neural network models to refine segmentation masks using PyTorch Lightning:
  • Pixelwise Classifier: MLP-based pixel-by-pixel classification
  • CNN Decoder: Convolutional encoder-decoder architecture for spatial context
• Flexible Input Types: Accept both NumPy arrays and PyTorch tensors, with output type matching input type.
• Command-Line Interface: Unified CLI for cleaning with method selection, plus separate training command.
• Programmatic API: Direct access to cleaning and training functions for integration into other workflows.

Installation

Install from PyPI:

pip install rgb-to-segmentation

Or install from source:

git clone https://github.com/alexsenden/rgb-to-segmentation.git
cd rgb-to-segmentation
pip install .

Usage

Cleaning Noisy Segmentation Images

Use the segment-clean command to clean segmentation images using various methods:

Palette-based cleaning:

segment-clean --method palette --input_dir /path/to/input --output_dir /path/to/output --colour_map "0,0,0;255,0,0;0,255,0" --output_type rgb

Neural network-based cleaning:

# Works with both pixelwise and CNN decoder models
segment-clean --method nn --input_dir /path/to/input --output_dir /path/to/output --model_path /path/to/model.ckpt --colour_map "0,0,0;255,0,0;0,255,0" --output_type index

Strict palette mapping:

segment-clean --method strict_palette --input_dir /path/to/input --output_dir /path/to/output --colour_map "0,0,0;255,0,0;0,255,0" --output_type index

You can also provide colours via file with --colour_map_file /path/to/colours.txt (one r,g,b per line). The CLI parses colours and constructs the palette/colour map internally, mirroring the Python API which accepts parsed structures (NumPy array for palette, dictionary for colour map).

Options:

• --method: Cleaning method ('palette', 'nn', or 'strict_palette')
• --input_dir: Path to input directory containing images
• --output_dir: Directory where cleaned images will be written
• --inplace: Overwrite input images in place
• --exts: Comma-separated list of allowed image extensions
• --name_filter: Only process files whose name contains this substring
• --output_type: Output format ('rgb' or 'index')
• --colour_map: Semicolon-separated list of RGB triples
• --colour_map_file: Path to a file listing RGB triples

For palette method:

• --morph_kernel_size: Size of morphological kernel for boundary cleaning

For nn method (both pixelwise and CNN decoder models):

• --model_path: Path to trained model file

The strict_palette method requires no additional options beyond the common ones.

Training the Neural Network Model

Train a neural network model to refine segmentation masks. Choose between pixelwise MLP or CNN decoder:

# Train pixelwise classifier (default)
segment-train --image_dir /path/to/noisy_images --label_dir /path/to/labels --output_dir /path/to/model_output --colour_map "0,0,0;255,0,0;0,255,0"

# Train CNN decoder
segment-train --image_dir /path/to/noisy_images --label_dir /path/to/labels --output_dir /path/to/model_output --model_type cnn_decoder --colour_map "0,0,0;255,0,0;0,255,0"

Options:

• --image_dir: Path to directory containing noisy images
• --label_dir: Path to directory containing target RGB labels
• --output_dir: Directory where model weights will be saved
• --colour_map: Semicolon-separated list of RGB triples
• --colour_map_file: Path to a file listing RGB triples
• --model_type: The type of model to train ('pixel_decoder' or 'cnn_decoder', default: pixel_decoder)

Note that one label image may have multiple corresponding noisy masks. Labels are matched to noisy masks whose filenames contain the label file basename (pre-extension name, i.e. my_image.png -> my_image).

API

You can also use the package programmatically:

import numpy as np
from rgb_to_segmentation import clean, nn, train, utils, clean_image

# Palette cleaning
colours = utils.parse_colours_from_string("0,0,0;255,0,0;0,255,0")
palette = np.asarray(colours, dtype=np.uint8)
clean.clean_segmentation(input_dir="/path/to/input", output_dir="/path/to/output", palette=palette, output_type="index")

# NN inference (works with both pixelwise and CNN decoder models)
colours = utils.parse_colours_from_string("0,0,0;255,0,0;0,255,0")
colour_map = {i: rgb for i, rgb in enumerate(colours)}
nn.run_inference(input_dir="/path/to/input", output_dir="/path/to/output", model_path="/path/to/model.ckpt", colour_map=colour_map, output_type="rgb")

# Train pixelwise model
colours = utils.parse_colours_from_string("0,0,0;255,0,0;0,255,0")
colour_map = {i: rgb for i, rgb in enumerate(colours)}
train.train_model(image_dir="/path/to/images", label_dir="/path/to/labels", output_dir="/path/to/output", colour_map=colour_map)

# Train CNN decoder model
train.train_model(image_dir="/path/to/images", label_dir="/path/to/labels", output_dir="/path/to/output", colour_map=colour_map, model_type="cnn_decoder")

# Single-image cleaning (programmatic-only API)
# Accepts both numpy arrays and torch tensors; output type matches input type

# Palette method (returns RGB)
import numpy as np
rgb_out = clean_image(
	image_array=np.zeros((512, 512, 3), dtype=np.uint8),
	method="palette",
	colour_map=colour_map,
	morph_kernel_size=3,
	output_type="rgb",
)

# Strict palette method (returns index mask, validates all pixels are in colour_map)
index_out = clean_image(
	image_array=np.zeros((512, 512, 3), dtype=np.uint8),
	method="strict_palette",
	colour_map=colour_map,
	output_type="index",
)

# Pixel decoder method (returns index mask, works with pixelwise or CNN models)
index_out = clean_image(
	image_array=np.zeros((512, 512, 3), dtype=np.uint8),
	method="pixel_decoder",
	model=None,  # Provide a loaded model instance
	colour_map=colour_map,
	output_type="index",
)

# CNN decoder method (returns index mask)
index_out = clean_image(
	image_array=np.zeros((512, 512, 3), dtype=np.uint8),
	method="cnn_decoder",
	model=None,  # Provide a loaded CNN decoder model instance
	colour_map=colour_map,
	output_type="index",
)

# Using PyTorch tensors
import torch
tensor_input = torch.zeros((512, 512, 3), dtype=torch.uint8)
tensor_out = clean_image(
	image_array=tensor_input,
	method="strict_palette",
	colour_map=colour_map,
	output_type="rgb",
)
# tensor_out will be a torch.Tensor with same dtype and device as tensor_input

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.