Metadata-Version: 2.1
Name: photo4d
Version: 0.2.2
Summary: Open source project to perform time-lapse photogrammetry
Home-page: https://github.com/ArcticSnow/photo4D
Author: S. Filhol, A. Perret, G. Sutter, and L. Girod
Author-email: simon.filhol@geo.uio.no
License: MIT
Description: 
        # Photo4D: open-source time-lapse photogrammetry 
        
        Contributors by alphabetical orders:
        - Simon Filhol (simon.filhol@geo.uio.no)
        - Luc Girod (luc.girod@geo.uio.no)
        - Alexis Perret (aperret2010@hotmail.fr)
        - Guillaume Sutter (sutterguigui@gmail.com)
        
        ## Description
        
        This project consists of an automated program to generate point cloud from time-lapse set of images from independent cameras. The software: 
        1. sorts images by timestamps, 
        2. assess the image quality based on lumincace and bluriness, 
        3. identify automatically GCPs through the stacks of images, 
        4. run Micmac to compute point clouds, and 
        5. convert point cloud to rasters. (not implemented)
        
        The project should be based on open-source libraries, for public release. 
        
        ## Installation
        1. install the latest version of [micmac](https://micmac.ensg.eu/index.php/Install)
        
        2. install python 3.6, and with anaconda, create a virtual environment with the following packages: 
             - opencv 
             - pandas 
             - matplotlib
             - lxml
             - pillow
             - [pyxif](https://github.com/zenwerk/Pyxif) (that needs to be downloaded from https://github.com/zenwerk/Pyxif)
             ```sh
             wget https://github.com/zenwerk/Pyxif/archive/master.zip
             unzip Pyxif-master.zip
             cd Pyxif-master
             python setup.py install
             ```
             - [PDAL](https://pdal.io/)
             - json
        
         3. The package is available via Pypi
        
             ```python
             pip install photo4d
             ```
        
        ## Usage
        
        ### 1. prepare your environment: 
              - create a Python >= 3.6 virtual environment in which you install the required libraries (see above)
              - create a folder for the project with inside the project folder a folder called Images containing itself one folder per
              - Organize your photo with one folder per camera. For instance folder /cam1 constains all the images from Camera 1.
               camera
               
        ```bash
        ├── Project
            └── Images
                 ├── Cam1
                 ├── Cam2
                 ├── Cam3
                 └── Cam...
        ```
        
        
        ### 2. Use the Photo4d class to process the images through MicMac
        
        Set the path correctly in the file MicmacApp/Class_photo4D.py, and follow these steps
        
        ```python
        
        ############################################################
        ## Part 1
        
        import photo4d as p4d
        
        # Create a new photo4d object by indicating the Project path
        myproj = p4d.Photo4d(project_path="point to project folder /Project")
        
        # Algorithm to sort images in triplets, and create the reference table with sets :date, valid set, image names
        myproj.sort_picture()
        
        # Algorithm to check picture quality (exposure and blurriness)
        myproj.check_picture_quality()
        
        ############################################################
        ## Part 2: Estimate camera orientation
        
        # Compute camera orientation using the timeSIFT method:
        myproj.timeSIFT_orientation()
        
        # Convert a text file containing the GCP coordinates to the proper format (.xml) for Micmac
        myproj.prepare_gcp_files(path_to_GCP_file, file_format="N_X_Y_Z")
        
        # Select a set to input GCPs
        myproj.set_selected_set("DSC02728.JPG")
        
        # Input GCPs in 3 steps
        # first select 3 to five GCPs to pre-orient the images
        myproj.pick_initial_gcps()
        
        # Apply transformation based on the few GCPs previously picked
        myproj.compute_transform()
        
        # Pick additionnal GCPs, that are now pre-estimated
        myproj.pick_all_gcps()
        
        ############################################################
        ## Part2, optional: pick GCPs on extre image set
        ## If you need to pick GCPs on another set of images, change selected set (this can be repeated n times):
        #myproj.compute_transform()
        #myproj.set_selected_set("DSC02871.JPG")
        #myproj.pick_all_gcps()
        
        # Compute final transform using all picked GCPs
        myproj.compute_transform(doCampari=True)
        
        ## FUNCTION TO CHANGE FOR TIMESIFT
        # myproj.create_mask() #To be finished
        
        ############################################################
        ## Part3: Compute point clouds
        
        # Compute point cloud, correlation matrix, and depth matrix for each set of image
        myproj.process_all_timesteps()
        
        # Clean (remove) the temporary working direction
        myproj.clean_up_tmp()
        
        ```
        
        ### 3. Process the point clouds with [PDAL](https://pdal.io/)
        
        **Currently Under Development**
        
        [PDAL](https://pdal.io/) is a python library to process point cloud. It has an extensive library of algorithms available, and here we wrapped a general method to filter and extract Digital Elevation Models (DEMs) from the point clouds derived in the previous step.
        
        Micmac produces point clouds in the format `.ply`. The functions in the python class `pcl_process()` can convert, filter and crop the `.ply` point clouds and save them as `.las` files. Then the function `convert_all_pcl2dem()` will convert all point clouds stored in `my_pcl.las_pcl_flist` to DEMs. 
        
        With the function `my_pcl.custom_pipeline()`, it is possible to build custom processing pipeline following the PDAL JSON syntax. This pipeline can then be executed by running the function `my_pcl.apply_custom_pipeline()`.
        
        See the source file [Class_pcl_processing.py](./photo4d/Class_pcl_processing.py) for more details.
        
        ```python
        
        # Create a pcl_class object, indication the path to the photo4d project
        my_pcl = p4d.pcl_process(project_path="path_to_project_folder")
        
        my_pcl.resolution = 1  # set the resolution of the final DEMs
        
        # Set the bounding box the Region of Interest (ROI)
        my_pcl.crop_xmin = 416100
        my_pcl.crop_xmax = 416900
        my_pcl.crop_ymin = 6715900
        my_pcl.crop_ymax = 6716700
        my_pcl.nodata = -9999
        
        # add path og the .ply point cloud files to the python class
        my_pcl.add_ply_pcl()
        
        # filter the point clouds with pdal routine, and save resulting point clouds as .las file
        my_pcl.filter_all_pcl()
        
        # add path of the .las files
        my_pcl.add_las_pcl()
        
        # conver the .las point clouds to DEMs (geotiff)
        my_pcl.convert_all_pcl2dem()
        
        # Extract Value orthophoto from RGB 
        my_pcl.extract_all_ortho_value()
        
        ```
        
        After this section you have clean point clouds, as well as DEMs in GeoTiff ready!
        
        
        ## Ressources
        
        - Micmac: http://micmac.ensg.eu/index.php/Accueil
        - Image processing images: skimage, openCV, Pillow
        - python package to read exif data: https://pip.pypa.io/en/latest/user_guide/
        
        ## Development
        
        Message us to be added as a contributor, then if you can also modify the code to your own convenience with the following steps:
        
        To work on a development version and keep using the latest change install it with the following
        
        ```shell
        git clone git@github.com:ArcticSnow/photo4D.git
        pip install -e [path2folder/photo4D]
        ```
        
        and to upload latest change to Pypi.org, simply:
        
        1. change the version number in the file ```photo4d/__version__.py```
        2.  run from a terminal from the photo4D folder, given your $HOME/.pyc is correctly set:
        
        ```shell
        python setup.py upload
        ```
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: Required to use the class pcl_process()
