Metadata-Version: 2.1
Name: django-large-image
Version: 0.2.2
Summary: UNKNOWN
Home-page: UNKNOWN
Author: Kitware, Inc.
Author-email: kitware@kitware.com
License: Apache 2.0
Description: # django-large-image
        
        [![PyPI](https://img.shields.io/pypi/v/django-large-image.svg?logo=python&logoColor=white)](https://pypi.org/project/django-large-image/)
        [![codecov](https://codecov.io/gh/ResonantGeoData/django-large-image/branch/main/graph/badge.svg?token=VBK1F6JWNY)](https://codecov.io/gh/ResonantGeoData/django-large-image)
        [![Tests](https://github.com/ResonantGeoData/django-large-image/actions/workflows/ci.yml/badge.svg)](https://github.com/ResonantGeoData/django-large-image/actions/workflows/ci.yml)
        
        *Created by Kitware, Inc.*
        
        `django-large-image` is an abstraction of [`large-image`](https://github.com/girder/large_image)
        for use with `django-rest-framework` providing view mixins for endpoints to
        work with large images in Django -- specifically geared towards geospatial and
        medical image tile serving.
        
        *DISCLAIMER:* this is a work in progress and is currently in an experimental phase.
        
        | RGB Raster | Colormapped Raster Band |
        |---|---|
        | ![raster](https://raw.githubusercontent.com/ResonantGeoData/django-large-image/main/doc/raster.png) | ![raster-colormap](https://raw.githubusercontent.com/ResonantGeoData/django-large-image/main/doc/raster_colormap.png) |
        
        | Swagger Documentation | Tiles Endpoint |
        |---|---|
        |![swagger-spec](https://raw.githubusercontent.com/ResonantGeoData/django-large-image/main/doc/swagger.png) | ![tiles-spec](https://raw.githubusercontent.com/ResonantGeoData/django-large-image/main/doc/tiles_endpoint.png)|
        
        
        ## Overview
        
        This package ports Kitware's [large-image](https://github.com/girder/large_image)
        to Django by providing a set of abstract, mixin API view classes that will
        handle tile serving, fetching metadata from images, and extracting regions of
        interest.
        
        `django-large-image` is an optionally installable Django app with
        a few classes that can be mixed into a Django project (or application)'s
        drf-based views to provide tile serving endpoints out of the box. Notably,
        `django-large-image` is designed to work specifically with `FileFeild`
        interfaces with development being tailored to Kitware's
        [`S3FileField`](https://github.com/girder/django-s3-file-field). We are working
        to also support GeoDjango's [`GDALRaster`](https://docs.djangoproject.com/en/4.0/ref/contrib/gis/gdal/#django.contrib.gis.gdal.GDALRaster)
        in the future
        
        This package ships with pre-made HTML templates for rendering geospatial image
        tiles with CesiumJS and non-geospatial image tiles with [GeoJS](https://github.com/OpenGeoscience/geojs).
        
        ### Features
        
        Rich set of RESTful endpoints to extract information from large image formats:
        - Image metadata (`/metadata`, `/internal_metadata`)
        - Tile serving (`/tiles/{z}/{x}/{y}.png?projection=EPSG:3857`)
        - Region extraction (`/region.tif?left=v&right=v&top=v&bottom=v`)
        - Image thumbnails (`/thumbnail`)
        - Individual pixels (`/pixel?left=v&top=v`)
        - Band histograms (`/histogram`)
        
        Support for general FileFeild's or File URLs
        - Supports django's FileFeild
        - Supports [`S3FileField`](https://github.com/girder/django-s3-file-field)
        - Supports GDAL's [Virtual File System](https://gdal.org/user/virtual_file_systems.html) for `s3://`, `ftp://`, etc. URLs
        
        Miscellaneous:
        - caching - tile sources are cached for rapid file re-opening
          - tiles and thumbnails are cached to prevent recreating these data on multiple requests
        - Easily extensible SSR templates for tile viewing with CesiumJS and GeoJS
        - OpenAPI documentation in swagger
        
        ## Installation
        
        Out of the box, `django-large-image` only depends of the core `large-image`
        module, but you will need a `large-image-source-*` module in order for this
        to work. Most of our users probably want to work with geospatial images so we
        will focus on the `large-image-source-gdal` case, but it is worth noting that
        `large-image` has source modules for a wide variety of image formats
        (e.g., medical image formats for microscopy).
        
        See [`large-image`](https://github.com/girder/large_image#installation)'s
        installation instructions for more details.
        
        **Tip:* installing GDAL is notoriously difficult, so at Kitware we provide
        pre-built Python wheels with the GDAL binary bundled for easily installation in
        production environments. To install our GDAL wheel, use: `pip install --find-links https://girder.github.io/large_image_wheels GDAL`*
        
        
        ```bash
        pip install \
          --find-links https://girder.github.io/large_image_wheels \
          django-large-image \
          large-image-source-gdal
        ```
        
        
        ## Usage
        
        Simply import and mixin the `LargeImageView` class to your existing
        `django-rest-framework` viewsets and specify the `FILE_FIELD_NAME` as the
        string name of the `FileField` in which your image data are saved.
        
        ```py
        from django_large_image.rest import LargeImageView
        
        class MyModelViewset(viewsets.GenericViewSet, LargeImageView):
          ...  # configuration for your model's viewset
          FILE_FIELD_NAME = 'field_name'
        ```
        
        And that's it!
        
        ### Example Code
        
        To use the mixin classes provided here, create a model, serializer, and view in
        your Django project like so:
        
        ```py
        models.py
        ---
        from django.db import models
        from rest_framework import serializers
        
        
        class ImageFile(models.Model):
            name = models.TextField()
            file = models.FileField()
        
        
        class ImageFileSerializer(serializers.ModelSerializer):
            class Meta:
                model = ImageFile
                fields = '__all__'
        ```
        
        ```py
        admin.py
        ---
        from django.contrib import admin
        from example.core.models import ImageFile
        
        
        @admin.register(ImageFile)
        class ImageFileAdmin(admin.ModelAdmin):
            list_display = ('pk', 'name')
        ```
        
        Then create the viewset, mixing in the `django-large-image` view class:
        ```py
        viewsets.py
        ---
        from example.core import models
        from rest_framework import mixins, viewsets
        
        from django_large_image.rest import LargeImageView
        
        
        class ImageFileDetailView(
            mixins.ListModelMixin,
            viewsets.GenericViewSet,
            LargeImageView,
        ):
            queryset = models.ImageFile.objects.all()
            serializer_class = models.ImageFileSerializer
        
            # for `django-large-image`: the name of the image FileField on your model
            FILE_FIELD_NAME = 'file'
        ```
        
        Then register the URLs:
        
        ```py
        urls.py
        ---
        from django.urls import path
        from example.core.viewsets import ImageFileDetailView
        from rest_framework.routers import SimpleRouter
        
        router = SimpleRouter(trailing_slash=False)
        router.register(r'api/large-image', ImageFileDetailView, basename='image-file')
        
        urlpatterns = [
          path('', include('django_large_image.urls')),  # Some additional diagnostic URLs from django-large-image
        ] + router.urls
        
        ```
        
        Please note the example Django project in the `project/` directory of this
        repository that shows how to use `django-large-image`.
        
        ## Work Plan
        
        Our primary goal is to get through phases 1 and 2, focusing on tile serving of
        large geospatial images specifically in Cloud Optimized GeoTiff (COG) format.
        
        ### Phase 1
        
        - [x] Abstract API View classes that can be mixed-in downstream to expose all available endpoints
          - [x] endpoints for metadata (/tiles, /tiles/internal_metadata)
          - [x] endpoints for serving tiles (/tiles/zxy, /tiles/fzxy)
          - [x] cache management - tile sources should be cached so that we don't open a file for each tile
          - [x] endpoint for regions
          - [x] endpoint for thumbnails
          - [x] thumbnail caching
          - [x] endpoint for individual pixels
          - [x] endpoint for histograms
          - [x] some diagnostic and settings endpoints (list available sources, set whether to automatically use large_images and the size of small images that can be used)
        - [x] Support for django's FileFeild
        - [x] Support for S3FileField
        - [x] Ship an easily extensible SSR template for tile viewing with CesiumJS
        - [x] Support for using file URLs with GDAL's VSI
        - [x] Provide OpenAPI documentation in swagger
        
        ### Phase 2
        
        - [ ] Handle band/component selection styling for tile serving and thumbnails
          - e.g., use channels 3,7,5 for Red Green Blue
          - endable linear/discrete color modes
        - [ ] Tie large-image's caching into Django's cache (might require upstream work in large-image)
        - [ ] Provide some sort of endpoint to check if an image is a valid COG
        - [ ] Create a secondary app with celery tasks for converting images to COG
        - [ ] Refactor/prototpye RGD's ChecksumFile model as a FieldFile subclass
        - [ ] Support GeoDjango's [`GDALRaster`](https://docs.djangoproject.com/en/4.0/ref/contrib/gis/gdal/#django.contrib.gis.gdal.GDALRaster)
        
        ### Phase 3 and onward
        
        Incorporate more features from large-image.
        
        Things that would require implementing tasks with celery:
        
        - [ ] ability to convert images via large_image_converter
        - [ ] async endpoint for regions
        
        Things I'm unsure about:
        
        - [ ] endpoints for associated images
        - [ ] ability to precache thumbnails (the thumbnail jobs endpoints)
        - [ ] endpoints for serving tiles in deepzoom format
        
        Things I think should be implemented downstream:
        
        - endpoint or method to make / unmake a Django file field into a large_image item
        - fuse-like ability to access filefields as os-level files (until implemented, s3 files will need to be pulled locally to serve them, which is inefficient)
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Web Environment
Classifier: Framework :: Django :: 3.0
Classifier: Framework :: Django
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Provides-Extra: colormaps
