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MLOgraphy++

This project compares grain boundary detection using MLOgraphy and the enhanced model, MLOgraphy++, on the TBM dataset. MLOgraphy trains a U-Net on 128x128 cropped sub-images with complete annotations but limited context. In contrast, MLOgraphy++ uses 256x256 partial annotations (combining four adjacent 128x128 labels) over the full image, capturing broader context for edge segmentation. We assessed performance using a variation of the Heyn intercept method, analyzing 256x256 image crops (with and without 50% overlap) via Guo-Hall thinning. The results show MLOgraphy++ closely aligns with the GT, similar to MLOgraphy, but more efficiently and without post-processing.

Sample Image

Key Steps

  1. Unify human-tagged 128x128 GT crops into 256x256 images, create additional 256x256 crops with 50% overlap.
  2. Crop non-overlapping 256x256 crops from MLOgraphy and MLOgraphy++ predictions, with some sections having 50% overlap.
  3. Use the crops from all models to compare their grain sizes(Ground Truth, MLOgraphy, and MLOgraphy++) using a variation of the Heyn intercept method.

Scripts

  • unify_crops_GT.py: Unifing 128x128 GT crops into 256x256 images .
  • non_overlapping_crops.py: Cropping non-overlapping 256x256 crops from MLOgraphy and MLOgraphy++ predictions.
  • overlapping_crops_GT.py: Cropping overlapping 256x256 GT crops having 50% overlap.
  • grain_size.py: Functions for calculating grain size from images using a variation of the Heyn intercept method. It processes images, detects grain boundaries, calculates grain sizes, and optionally saves the processed images.

Usage Instructions

  1. Unify the GT crops: Run the script unify_crops_GT.py in the following way:

     python unify_crops_GT.py --gt_path <path to GT_128_LABELS> --gt_output_path <path to GT_256_CROPS>

  2. Cropping non-overlapping 256x256 crops: Run the script non_overlapping_crops.py in the following way:

    python non_overlapping_crops.py --zone_size 256 256 --gt_image_dir <path to GT crops(128x128)> --image_dir1 <path to MLOgraphy++ full predictions> --output_dir1 <path to MLOgraphy++ non-overlapping 
crops(256x256) with GT> --image_dir2 <path to MLOgraphy full predictions> --output_dir2 <path to MLOgraphy non-overlapping crops(256x256) with GT>

  3. Cropping overlapping 256x256 GT crops having 50% overlap: Run the script overlapping_crops_GT.py in the following way:

    python overlapping_crops_GT.py --gt_directory <path to GT crops(256x256)> --image_directory <path to GT annotations_overlayed_on_full_images> --output_directory <path to output overlapping crops of GT (256x256)>

  4. Calculating grain sizes: Run the script grain_size.py in the following way:

    python grain_size.py --gt_path <PATH_TO_256X256_GT_CROPS> --mlography_path <PATH_TO_256X256_MLOGRAPHY_CROPS> --mlography_plus_plus_path <PATH_TO_256X256_MLOGRAPHY_PLUS_PLUS_CROPS>

Data

The data that was used in the paper is from the TBM Dataset. The specific data used for the evaluation can be found in the /Datasets/ directory.

Results

The results, including the grain sizes measured for all models (Ground Truth, MLOgraphy, and MLOgraphy++), are saved in the Results_grain_sizes.csv file.

AutoSAM

This repository includes the fine-tuning of AutoSAM on the TBM dataset to improve the segmentation of complex grain boundaries, similar to the MLOgraphy method.

This code is forked and highly based on AutoSAM repository by Tal Shaharabany.

SAM Checkpoints

SAM Base | SAM Large | SAM Huge

Usage Instructions

  1. Set up the Environment for Running AutoSAM:

    Use the conda configuration file created by SAM environment setup, located at AutoSAM/sam.yaml. Set up the environment by running the following command:

    conda env create --name sam -f AutoSAM/sam.yaml

    Note: This enviorment will be used when running the training and inference scripts.

  2. Activate the sam environment:

    conda activate sam

  3. Run the training script to train the model on the TBM dataset:

    python train.py --learning_rate 0.0003 --Batch_size 2 --epoches 100 --task tbm --train_data_root AutoSAM/TBM_dataset/TrainDataset --test_data_root AutoSAM/TBM_dataset/TestDataset --sam_checkpoint /path/to/sam_checkpoint.pth --model_type vit_h

  4. Run the Inference Script to perform inference using the fine-tuned model on the TBM dataset:

    python inference.py --task tbm --folder <folder_name>  --train_data_root AutoSAM/TBM_dataset/TrainDataset --test_data_root AutoSAM/TBM_dataset/TestDataset --sam_checkpoint /path/to/sam_checkpoint.pth --model_type vit_h

    Note: The results comparing the Heyn intercept method applied on MLOgraphy++ and AutoSAM can be found in the research paper.