Metadata-Version: 2.1
Name: G6-iris-recognition
Version: 0.0.6
Summary: A Python package to iris recognition.
Home-page: UNKNOWN
Author: Gate6
Author-email: rajendra.khabiya@gate6.com
License: MIT
Project-URL: Source, https://github.com/gate6/g6irishapp/tree/G6-iris-recognition_Example
Description: # Gate6 Iris Recognition Package
           G6_iris_recognition is a module for iris recognition.   Using the image processing libraries and high-level mathematical functions, we’ll be providing fast and secure iris recognition solution. 
        
        ## Installation 
        
        #### Installations required before installing the package module
          ```
             python 
             numpy
             opencv-python
             matplotlib
             opencv-contrib-python
             requests
             scikit-image
             scipy
             imutils==0.5.2
          ```  
          
        #### - Install Python
        
        [Windows](http://timmyreilly.azurewebsites.net/python-flask-windows-development-environment-setup/), [Mac](http://docs.python-guide.org/en/latest/starting/install/osx/), [Linux](https://docs.aws.amazon.com/cli/latest/userguide/awscli-install-linux-python.html)
        
        
        #### - Install package module using ``pip``:
          ```
            $ pip install G6-iris-recognition
          ```  
          
        ## Project Structure
          
          - On your project folder, create an encodingModel directory & in that directory create a file named irisEncodings.pickle(encodingModel/irisEncodings.pickle).
          - Create a directory named Input_database & under that directory put an individual's eye iris images, in the directories made on their individual names.
        
        
        ```shell
        
            Project/
            ├── encodingModel/
            │   ├── irisEncodings.pickle/                               # train model
            | 
            ├── Input_database/ 
            │   ├── person1 name/                                       # person1 directory
            |   │   ├── eye iris images of person1 /                    # images of person eye iris
            │   ├── person2 name/                                       # person2 directory
            |   │   ├── eye iris images of person2 /                    # images of person eye iris
            │   ├── person3 name/                                       # person3 directory
            |   │   ├── eye iris images of person3 /                    # images of person eye iris                   
         
        ```
        
         
        
        ## How to use
        
        Once all the settings of project are configured, you are ready to run the project. Import G6_iris_recognition module in your project to start.
        
        
        ```shell
           import G6_iris_recognition
        ```
        
        Once the import is completed, user need to train existing images which are saved in the Input_database Folder.
        
        ```shell
           Input_database/
        ```
        
        After that, create and train encoding module using Input_database Folder images, as per the instructions given below:
        
        
        ```shell
           G6_iris_recognition.iris_model_train(train_database_path,train_encoding_model_path)
           train_database_path        ===>  Input_database/
           train_encoding_model_path  ===>  encodingModel/irisEncodings.pickle
        ```
        
        Once the model is trained, it’s ready to test with real-time images. Follow the process that is mentioned to test real time iris image:
        
        ```shell
           iris_name = G6_iris_recognition.iris_model_test(test_encoding_model_path,real_time_image_path) 
           test_encoding_model_path   ===>  encodingModel/irisEncodings.pickle
           real_time_image_path       ===>  real-time_image_path
           iris_name                  ===>  In response you’ll get the registered person’s name if image matches with the person’s image in the trained image model & if the image doesn’t match then the name returns as unmatched.
        ```
        
        
        ## Requirements :
        
          * Need clearer images from the scanner.
          * Images shouldn't be captured in direct sunlight.
          * Person shouldn't wear glasses or lenses while scanning.
          * All the scanned images should be of same size/resolution (eg - 320x240).
          * The parameters of filters need to be changed as per the size and quality/noise of the images.
          * 90% of the eye iris needs to be captured.
          * Minimum 5 clear images are required to train the model.
          * Once everything is done accordingly, set threshold of Hamming Distance for easier recognition.
        
        
        
        ## Support
        
        If you face any difficulty in configuration or usage of Gate6 Iris Recognition Package as per the instructions documented above, please feel free to contact our development team.
        
        ## License
        
        [MIT](LICENSE)
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
