romerogroup/ForensicFit

ForensicFit

ForensicFit is created to preprocess scanned images from different tears and generate a database that will be used in different machine-learning approaches. This package will prepare the data in four different approaches, which will be shown in the tutorial sections. For more detailed documentation please visit https://romerogroup.github.io/ForensicFit/. ForensicFit can be installed from PyPI https://pypi.org/project/forensicfit/.

Machine Learning Model

To access the trined models please follow the following steps:

1. Create a virtual envronment python -m venv venv

   Activate the environment

   For ubunutu

   source venv/bin/activate

   For windows

   venv\Scripts\activate.bat

2. Clone repo git clone git@github.com:romerogroup/ForensicFit.git

3. Go into repo cd ForensicFit

4. After cloning the repo. Install the ML Requirements pip install -r requirements_ml.txt pip install .

5. Download the processed data from into ForensicFit/data/processed.

6. Download the the pretrained models: (This must be executed from the ForensicFit directory)

from forensicfit.machine_learning.download_models import download_models
download_models()

6. If training from scratch. Call the following function (This must be executed from the ForensicFit directory)

from forensicfit.machine_learning.trainer_tensorflow import train
train()

7. Evaluate the dataset. (This must be executed from the ForensicFit directory)

from forensicfit.machine_learning.evaluate import evaluate

evaluate()

8. If you want to predict on a single image do the following: (This must be executed from the ForensicFit directory)

import os
from forensicfit.machine_learning.predict import predict
processed_dir=os.path.join("data", "processed", "normal_split", "match_nonmatch_ratio_0.3")
processed_back_dir = os.path.join(processed_dir,'back','test','match')
processed_front_dir = os.path.join(processed_dir,'front','test','match')
image_front_path=os.path.join(processed_front_dir,'HQC_001_R-HQC_003_L.jpg')
image_back_path=os.path.join(processed_back_dir,'HQC_002_L-HQC_004_R.jpg')
prediction=ml.predict(image_front_path=image_front_path,image_back_path=image_back_path)
print(prediction)