Metadata-Version: 2.1
Name: intelligent-scissors
Version: 2.0
Summary: Intelligent Scissors tool
Home-page: https://github.com/stanislaushimovolos/Intelligent-Scissors
Author: Stanislav Shimovolos
Author-email: shimovolos.sa@phystech.edu
License: MIT
Download-URL: https://github.com/stanislaushimovolos/Intelligent-Scissors/v2.0.tar.gz
Description: # Intelligent-Scissors
        The Intelligent Scissors can be used to select an image region defined by strong color-changes at the edges.
        
        You can learn more about it in papers:
        - [Intelligent Scissors for Image Composition](https://courses.cs.washington.edu/courses/cse455/02wi/readings/mort-sigg95.pdf)
        - [Interactive Segmentation with Intelligent Scissors](https://pdf.sciencedirectassets.com/272316/1-s2.0-S1077316900X00068/1-s2.0-S1077316998904804/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBgaCXVzLWVhc3QtMSJHMEUCIAWCjFZL7PQFDvUmwMnc5US2%2FVONJ%2F35D8YSbksXWzmRAiEAjcUTMNaKtHM9Fyl7mnXrZyI%2F3cN%2BZ9dnjh9wLft7vzkqtAMIQRADGgwwNTkwMDM1NDY4NjUiDK3za45FaD20672xiCqRAzmaa28z8IzGVOPRTwsJ%2F8hbivchKVoxzXmSqzyEGgUFrLoAimHRtHnIPIuUQvTEl%2FE%2Boh%2FFmFWEUDxTsUu7ttIUX1VFrwQ03Jhcydpj20g5o%2FEsuYpo%2FCIyWq0FmJQMruOn%2BvLeQesm6ODeV%2Bq5jgZ3hPGal3t7QqfaAc8EQtgm2pj2NHtLFBBazJIulPgmKegrwjhTilWK%2Bq8zfEfM8DI8fQb%2BRexIJ3gr6EpIFtDA40SRx2jj7MX%2B3cAErSRHnn%2B2R3F%2FklE%2FwfCs3IJT3Ff%2BOgGMPNc2Smx5OdwdIPCWJnuPDD5AtqViVDK8JxwPLsUv6EQPkecuDz0f5rGl3KuRL2orR6LI6LC7uoNMT9rP1si8joGts9C0zU9vjTT1po9%2ByIxQjMU1An5QZD8VhTpZJ8qUaFfzF6yc%2F9hhybKdjC6ZCEmNBFSV48KSO14y0e4rOeEYWokbFy3759ULPb53xkoJa1NBzY7hJb4PotAtWUUhTyE%2BWuShRpEsCr7EJlgkd%2BBQMlstNeFwMy7OVo1IMKHk%2F%2FQFOusBLkihikZdSCk%2FpXCngTV%2BurqIPdtnBExW4Y%2FpVrUVk89D11TzMy368ugka1A2aLEJr02F1kEsyKNarhC3%2FuDcaJU%2BJLblbbOElrjs8IpOiGT976Iq90oXw6VM%2BNvJZsE53sTBcRIrV%2FSsQXcJzLqUr1KpJtSySTW8D9GmeHN7KDxjMwLDshJuE8J7EK5FTFahnOhhaCA34YWaC8PE3uZu%2BqJj0GRwaOOCNMudg9xAPcPklrmnfPTs6jNdeHyM0mcAefvVO%2BpWmoQRYJHGcGvm0CqYpWRgbTDMvDMXt8XR5JDZtKHAV%2FG9OpC6Zw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20200422T074335Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY7OHFSYOL%2F20200422%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=0f94e5c520d1726100ee62b9d40dc924c5fabc87c0cd1d88853e9a0e7a8e0267&hash=39309b5cca6bffdbc7dee114145cacc84838a91f8349d98390c5452a8e69a347&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1077316998904804&tid=spdf-bf7d7bea-c627-4dce-bec0-272fa3d00d20&sid=ee51f81c2575484c068ab8b9d830a36c9d73gxrqb&type=client)
        
        <p align="center">
          <img width="690" height="413" src="https://i.ibb.co/6XxjNSp/kakdu.png">
        </p>
        
        ## Installation
        `pip install intelligent-scissors`
        
        
        ## Usage
        
        To use in your program
        ```python
        
        from scissors.feature_extraction import Scissors
        
        image = ...
        scissors = Scissors(image)
        
        seed_x, seed_y = ...
        free_x, free_y = ...
        path = scissors.find_path(seed_x, seed_y, free_x, free_y)
        ```
        
        Also you can run a simple demo
        
        ```python
        from scissors.gui import run_demo
        
        file_name = 'image.png'
        run_demo(file_name)
        ```
        
        ## Details
        
        The current implementation includes 
        
        * Static features
        * Dynamic features
        * On-the-fly Training
        * Unrestricted graph search
        
        On-the-fly Training allows you to select a “good” initial-boundary segment. 
        However, this results in poor performance for new segments with different intensity/gradient magnitudes.
        To overcome this, first try selecting a small region of the new segment to create correct dynamic features. 
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.5
Description-Content-Type: text/markdown
