an OMR tool that automates data extraction from paper medical records
- Python 3
- Python Imaging Library (PIL) (
python3-pil
, distributed as part ofpython3-Pillow
) - Python lxml (
python3-lxml
) - NumPy (
python3-numpy
) - SciPy (
python3-scipy
) - scikit-image (
scikit-image
) - opencv (
opencv-contrib-python
) - onnxruntime (
onnxruntime
) - pathlib (
pathlib
) - flask (
flask
)
All of these can be installed used pip
or conda
. If you are planning to plug in
an external UVC-enabled USB camera to use the live alignment feature, make sure
to download opencv-contrib-python
, since the package hosted under
opencv-contrib
does not provide video device support via OpenCV.
Start the server using the following line:
python3 backend/server.py
To run a test suite, first navigate to backend/tests
and run the test_end_to_end.py
script
on the desired test set. For example, to run PIH Admission test set execute the following commands
from the repository root:
cd backend/tests/
python3 test_end_to_end.py --INPUT_FILE test_sets/pih/admission.csv
To generate new test cases from each scanned record run the server in debug mode, making sure to specify your desired camera and output directory. For example:
python3 backend/server.py --save-debug --camera_index 1 --upload_folder [upload_folder]
...where [upload_folder]
is the absolute path to the directory into which the server should
write all new test files.
Find out more about our project and vision at https://sdrp.github.io/digitize-mtc/.