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Project to validate portrait image based on icao guidelines

Project description

# ICAO Validation Engine Project leverages on OpenCV technology to achieve portrait validation in accordance to ICAO specifications

## Stack i. Python v3 ii. OpenCV

## External Libraries - NumPy

## Papers and Reference - [Measuring Perceptual Contrast in Digital Images](https://www.ansatt.hig.no/mariusp/publications/Simone2011_JVCIR.pdf) - [Analysis of Image Quality Assessment Algorithm to Detect the presence of Unnatural contrast enhancement](https://pdfs.semanticscholar.org/af13/71068c1950ab841b5e51ccf934c4e454343e.pdf)

##Using the Applications

Attached to the project is a icaovalidator-0.1.16-test-poc.tar.gz which is the python distribution of the project

Using a python environment is the best way to run the package. If Anaconda is installed you can create a python virtual environment from the Anaconda prompt: conda create -n testIcao python=3.6

Switch to the newly created python environment conda activate testIcao

Else if anaconda is not installed kindly follow the link below for a guide to install Anaconda https://docs.anaconda.com/anaconda/install/windows/

Once this is done, refer back to creating a virtual environment and continue

Once the environment is created install the following packages into the new python environment pip install CMake pip install cython pip install imutils

To install the package pip install path/to/icaovalidator-0.1.16-test-poc.tar.gz

To test the installed module

Create a threshold_config.ini file - I have attached a sample. The threshold_config.ini file allows you to change the thresholds as well as select the classifications you intend to work with.

You can run the code directly from the Anaconda prompt by initiating a python shell with the command python in the activated testIcao environment. If using jupyter notebook, you can launch jupyter notebook directly from Anaconda Prompt with the command jupyter notebook then run the following python codes.

`python from icaoengine.resources import load_classifier_config, load_threshold_config classifierConfig, classifications = load_classifier_config('path/to/threshold_config.ini') from icaoengine.core import CoreValidation validation = CoreValidation(classifierConfig=classifierConfig, classifications=classifications) validation.icao_validate("../../path/to/image.jpeg") `

Project details


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