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Detection and recognition of texts in cartographic images
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README.md

README.md

Detection and recognition of texts in cartographic images

This repository contains my work for the UMass Amherst research project whose project page can be found here.

Setup

  1. Clone
  2. Install submodules: git submodule init && git submodule update

This repository uses ssd_detectors and cascaded-faster-rcnn.

Repository Structure

  • lib: custom libs as well as the submodules
  • notebooks: any jupyter notebooks for experimenting
  • python_scripts: all scripts for training, prediction, and evaluation of TextBoxes++ on the map imagery
  • sbatch_scripts: corresponding sbatch scripts for running on Gypsum compute cluster

Script Details

  • convery_txt_preds_npy

    • args
    • --results file with prediction results
    • --dir_output output directory for .npy files
    • --images_dir directory with 31 maps
  • custom_multithreaded_scorer

    • notes

    Slightly modified version of original script from here.

    • args
    • --train_dir directory with all annotations .npy files
    • --test_dir directory with predictions .npy files
  • draw_gt_annots

    • args
    • path_to_annotations directory with all annotations .npy files (not cmd arg)
    • path_to_maps directory with 31 maps (not cmd arg)
  • draw_preds

    • args
    • --txt read annots from txt file
    • --annots_pathpath to either annots folder or txt file
    • --map_images_dir dir where map images are
    • --output_dir dir to output images
    • --test_only whether or not to only evaluate test images")
    • --test_split file from torch_phoc with test split
  • generate_tbpp_preds

    • args
    • --output_dir output dir
    • --weights_file file with model weights
    • --images_dir map images directory
    • --preprocesswhether or not to preform same preprocess as done in original implementations (background removal, etc...)
    • --test_only whether or not to only evaluate test images
    • --test_split file from torch_phoc with test split
    • --confidence confidence threshold for predictions
    • --rotate whether or not to rotate image
  • train_tbpp

    • notes

    Reading annotations directly from the .npy files is currently not working. The code seg faults while creating the crops for the input in the function tbpp_raw_generate_data located at line 72 of lib/tbpp_custom_utils.py. This could be fixed by either better controlling memory allocation for the arrays or modifying DataGenerator.py to output arbitrary quadrilaterals instead of ones that are horizontally aligned.

    • args
    • --use_gen_annots use generated annotations
    • --vgg use vgg backend (default: densenet)
    • --annots_path path to either annots folder or txt file
    • --map_images_dir dir where map images are
    • --output_dir dir to output checkpoints and logs
    • --train_split_file file from torch_phoc with train split
    • --val_split_file file from torch_phoc with val split
    • --weights_path weights for transfer learning
    • --batch_size batch size for training
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