This project will take a reference image and recreate it using a set of component images as 'pixels'.
Because apparently I didn't want to be able to type the project name without misspelling it a thousand times first.
Also because Carl Linnaeus.
Requirements:
- Python 3.6
- OpenCV for Python. If you're not working on ARM hardware (you're probably not), this should be installed with the rest of the package using opencv-python.
pip install git+git://github.com/NaturalHistoryMuseum/linnaeus.git#egg=linnaeusA more detailed example, along with all the files needed to run it, can be found in the example folder of this repo.
To run the builder, you will need:
- A reference image;
- A folder full of component images;
- Optionally, a configuration file to override the default settings.
You can then either write a script to run it (see the example), or use the command line interface.
The CLI is fully documented (try linnaeus -h), so here's a minimal example that doesn't cover all commands but will produce a composite:
An animated example of using the CLI and the expected output can be found here.
There are a few limited utilities included under linnaeus.utils.
For querying specific parts of the Data Portal API.
e.g. to find botany images (max. 100 per page):
from linnaeus.utils import API
for page in API.assets(resource_id=API.COLLECTIONS, offset=0, limit=100, collectionCode='bot'):
for media in page:
print(media)e.g. to search the collections dataset for zoology spirit records containing 'boops':
from linnaeus.utils import API
for page in API.collections(query='boops', collectionCode='zoo', kindOfObject='spirit'):
for record in page:
print(record.get('scientificName', 'Unknown'))This resizes and crops images to the pixel size defined in the config.
It is used to resize images automatically when creating a component, but it does not rotate or detect automatically.
If detect=True when downloading, each image will be searched for 'colourful' areas and each will be cropped down to the largest area. This is just a crude attempt to try and isolate interesting parts of the images.
e.g.
from linnaeus.utils import Formatter
from PIL import Image
img = Image.open('example-image.jpg')
# find the largest colourful area and crop down to that
img = Formatter.detect(img)
# rotate to landscape
img = Formatter.rotate(img)
# resize/crop to pixel size
img = Formatter.resize(img)This works in tandem with the API and the Formatter. It's used for bulk downloading sets of assets.
It's best to make your query as specific as possible when using this to avoid overloading the API - downloading all the images will take weeks.
As it downloads the images it will resize and crop them to the pixel size defined in the config.
e.g. download images of mineralogy specimens from a search for 'red':
from linnaeus.utils import Downloader
# create a downloader object with a target folder to save into
downloader = Downloader('specimens')
# find a list of assets - this is just a wrapper for the API
assets = downloader.search(query='red', collectionCode='min')
# download those assets into the target folder - do not try to detect
# the most interesting/colourful area when cropping/resizing
downloader.download(assets, detect=False)This helps to purge errant components from your input map.
from linnaeus.maputils.clean import clean_colour, clean_similar_to
clean_colour('specimens.json', False, s=150, st=20, v=234, vt=5)
clean_similar_to('solution_map.json', 'specimens.json', 40, 24, False)Small confession: it does cheat a little bit. The pixels are matched to the best possible component (without duplicates), but just to make it look a little nicer, the component's colour is tweaked before inserting it.
Here's an example of this (unadjusted on the left, adjusted on the right):
If you don't want this behaviour, add adjust=False into the Builder.fill() method:
solution = Builder.solve(reference_map, component_map)
canvas = Builder.fill(solution, adjust=False)
canvas.save('output.jpg')The main part of this project is the composite image creation - however, there are also some utilities for downloading and formatting images.
The core is divided into map generation and processing. These parts operate independently; processing does not call anything from generation and vice versa.
Map generation creates JSON-based 'maps' (which can be saved, loaded, and parsed as normal JSON) of the colour of each pixel in the reference image and the dominant colour of each provided component image.
Processing uses those maps to figure out which component image best matches each pixel (without repeating any), then builds an image from that.