Metadata-Version: 2.1
Name: SegSRGAN
Version: 1.1.2
Summary: Segmentation and super resolution GAN network
Home-page: https://github.com/koopa31/SegSRGAN/tree/develop
Author: Clément Cazorla
Author-email: clement.cazorla@univ-reims.fr
License: UNKNOWN
Description: # SegSRGAN
        
        ## Installation
        
        `pip install SegSRGAN`
        
        ## Perform a segmentation
        
        `from SegSRGAN.SegSRGAN.Function_for_application_test_python3 import segmentation`
        
        `segmentation(input_file_path, step, NewResolution, path_output_cortex, path_output_HR, weights_path, patch=None,
                         spline_order=3, by_batch=False, is_conditional=False)`
                         
        Where:
        > * **input_file_path** is the path of the image to be super resolved and segmented 
        > * **step** is the shifting step for the patches
        > * **NewResolution** is the new z-resolution we want for the output image 
        > * **path_output_cortex** output path of the segmented cortex
        > * **path_output_HR** output path of the super resolution output image
        > * **weights_path** is the path of the file which contains the pre-trained weights for the neural network
        > * **patch** is the size of the patches
        > * **spline_order** for the interpolation
        > * **by_batch** is to enable the by-batch processing
        > * **is_conditional** to perform a conditional GAN on the LR image resolution
        
        
        ## Segmentation of a set of images with several step and patch values
        
        In order to facilitate the segmentation of several images, you can run SegSRGAN/SegSRGAN/job_model.py:
        
        `python job_model.py --path
        --patch --step --result_folder_name --weights_relative_path --is_conditional`
        
        The list of the paths of the images to be processed must be stored in a csv file.
        
        Where:
        
        > * **path** Path of the csv file
        > * **patch** list of Patch sizes (example: 64 128)
        > * **step** list of steps (example: 32 64,64 128 in this example we run steps 32 and 64 for 
            patch 64 and steps 64 and 128 for patch 128)
        > * **result_folder_name** Name of the folder containing the results
        > * **is_conditional** Boolean to perform a conditional neural network with a condition on z-resolution
        
Platform: UNKNOWN
Description-Content-Type: text/plain
