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Image-to-Image Demo
Interactive Image Translation with pix2pix-tensorflow
Written by Christopher HesseFebruary 19th, 2017
edges2cats
Trained on about 2k stock cat photos and edges automatically generated from those photos. Generates cat-colored objects, some with nightmare faces. The best one I've seen yet was a cat-beholder.
Some of the pictures look especially creepy, I think because it's easier to notice when an animal looks wrong, especially around the eyes. The auto-detected edges are not very good and in many cases didn't detect the cat's eyes, making it a bit worse for training the image translation model.
edges2shoes
Trained on a database of ~50k shoe pictures collected from Zappos along with edges generated from those pictures automatically. If you're really good at drawing the edges of shoes, you can try to produce some new designs. Keep in mind it's trained on real objects, so if you can draw more 3D things, it seems to work better.
edges2handbags
Similar to the previous one, trained on a database of ~137k handbag pictures collected from Amazon and automatically generated edges from those pictures. If you draw a shoe here instead of a handbag, you get a very oddly textured shoe.
Implementation
The models were trained and exported with the pix2pix.py script from pix2pix-tensorflow. The interactive demo is made in javascript using the Canvas API and talks to a backend server that runs the images through Tensorflow. The backend server can run Tensorflow itself, or forward the requests to Cloud ML a hosted Tensorflow service run by Google.
The pre-trained models are available in the Datasets section on GitHub. All the ones released alongside the original pix2pix implementation should be available. The models can be exported from the pre-trained ones using the pix2pix.py script, and the exported models are linked from the server README on GitHub.
The edges for the cat photos were generated using Holistically-Nested Edge Detection and the functionality was added to process.py and the dependencies were added to the Docker image.
all code samples on this site are in the public domain unless otherwise stated
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