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Home Page: https://stories.thinkingmachin.es/ai-art-holiday-cards/
License: GNU General Public License v2.0
Text to abstract art generation for the holidays!
Home Page: https://stories.thinkingmachin.es/ai-art-holiday-cards/
License: GNU General Public License v2.0
Write shell script for:
christmais.scripts.parse
)christmais.scripts.style_transfer
)Use them for usage instructions as required in #55
We might be using GCP to speed up processing (not yet sure). Easier to set everything up via Dockerfile
Hi @ibtingzon , I'll just write the workflow here so you can have a basis too (for easier syncs):
n
number of Artist
s, store them in a list called artists
(lol)PIL.Image
) using draw()
. We can use a list comprehension herePIL.Image
has a shape of (224, 224, 3)
. If you initialized n
number of Artist
s, we should stack them into a tensor of shape (n, 224, 224, 3)
. (Optional since predict()
can use PIL.Image
directly)Predictor
class and call predict()
on this tensor. Not sure if the current implementation does batch compute, if not, fix Predictor
. If successful, it should return a list
of shape (n,)
.Artist
. Optimize using the techniques that we have.artists
, we can get the gene by calling get_gene()
. This should give you a matrix for each artist (as far as I remember its shape (10, 26)
. Reshape them if necessary (np.ravel()
, etc. etc.).draw_from_gene()
for each Artist
again in the list artists
.# Generate 10 artists
artists = [Artist(seed, (224,224)) for i in range(10)]
# Draw an image for each artist
imgs = [artist.draw() for artist in artists]
# Compute fitness
target = 'iron'
fitness = [p.predict(img, target=target)[0] for img in imgs]
Add apt-get
installation for make dev
and make build
.
Requires testing output artifacts
For now, just write usage instructions on how to run the shell script.
We're just sourcing these dependencies from a CDN, so there's really no need to implement that. You can remove those and just have a single drawer
, similar to parser
and styler
instead.
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