Metadata-Version: 2.1
Name: building-footprint-segmentation
Version: 0.2.1
Summary: Building footprint segmentation from satellite and aerial imagery
Home-page: https://github.com/fuzailpalnak/building-footprint-segmentation
Author: Fuzail Palnak
Author-email: fuzailpalnak@gmail.com
License: UNKNOWN
Description: # Building Footprint Segmentation
        
        #### Library to train building footprint on satellite and aerial imagery.
        
        ![Python](https://img.shields.io/badge/python-v3.6+-blue.svg)
        ![Contributions welcome](https://img.shields.io/badge/contributions-welcome-orange.svg)
        ![Licence](https://img.shields.io/github/license/fuzailpalnak/building-footprint-segmentation)
        
        ![merge1](https://user-images.githubusercontent.com/24665570/97859410-91fa6100-1d26-11eb-8a47-e41982c748d7.jpg)
        
        
        
        ## Installation
            
            
            pip install building-footprint-segmentation
            
        
        ## Dataset 
        
        - [Massachusetts Buildings Dataset](https://www.cs.toronto.edu/~vmnih/data/)
        - [Inria Aerial Image Labeling Dataset](https://project.inria.fr/aerialimagelabeling/)
        
        ## Training
        
        - [Train With Config](https://github.com/fuzailpalnak/building-footprint-segmentation/blob/main/examples/Run%20with%20config.ipynb)
            , Use [config template](https://codebeautify.org/yaml-validator/cbc60637) for generating training config
        
        - [Train With Arguments](https://github.com/fuzailpalnak/building-footprint-segmentation/blob/main/examples/Run%20with%20defined%20arguments.ipynb)
        
        ## Visualize Training
        
        ##### Test images at end of every epoch
        
        - Follow [Example](https://github.com/fuzailpalnak/building-footprint-segmentation/blob/main/examples/TestCallBack.ipynb)
         
        ##### Visualizing on Tensorboard
        
        ```python
        from building_footprint_segmentation.helpers.callbacks import CallbackList, TensorBoardCallback
        where_to_log_the_callback = r"path_to_log_callback"   
        callbacks = CallbackList()
        
        # Ouptut from all the callbacks caller will be stored at the path specified in log_dir
        callbacks.append(TensorBoardCallback(where_to_log_the_callback))
        
        ```
        
        To view Tensorboard dash board
        
            tensorboard --logdir="path_to_log_callback"
        
        ## Defining Custom Callback
        ```python
        from building_footprint_segmentation.helpers.callbacks import CallbackList, Callback
        
        class CustomCallback(Callback):
            def __init__(self, log_dir):
                super().__init__(log_dir)
        
        
        where_to_log_the_callback = r"path_to_log_callback"   
        callbacks = CallbackList()
        
        # Ouptut from all the callbacks caller will be stored at the path specified in log_dir
        callbacks.append(CustomCallback(where_to_log_the_callback))
        ```
        
        ## Segmentation for building footprint
        
        - [x] binary
        - [ ] building with boundary (multi class segmentation)
        
        ## Weight File
        
        - [RefineNet](https://github.com/fuzailpalnak/building-footprint-segmentation/releases/download/alpha/refine.zip)
Keywords: Deep Learning,CNN,Semantic Segmentation,Building Footprint Extraction
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Requires-Python: ~=3.3
Description-Content-Type: text/markdown
