TensorFlow Hub has a great sample for transferring the style of one image to another. You might have seen Munch's The Scream applied to a turtle, or Hokusai's The Great Wave off Kanagawa to the Golden Gate Bridge. It's fun to play with and I wondered how it would work for a timelapse video. I just posted my first attempt, four different shots of San Francisco and I think it turned out pretty well.
The four sequences were all shot on an A7C, one second exposure, ND3 filter and aperture/ISO as needed to hit one thousandth of a second before fitting the filter. Here's an example shot:
I didn't want The Scream or The Wave, so I asked my kids to pick two random pieces of art each so I could have a different style for each sequence:
The style transfer network wants a 256x256 style image so I cropped and resized the art as needed.
The sample code pulls images from URLs. I modified it to connect to Google Drive, iterate through a source folder of images and write the transformed images to a different folder. I'm running this in Google Colab which has the advantage that you get to use Google's GPUs and the disadvantage that it will disconnect, timeout, run out of memory etc. To work around this the modified code can be run as many times as needed to get through all the files and will only process input images that don't already exist in the output folder. Here's a gist of my adapted colab notebook:
One final problem is that the style transfer example produces square output images. I just set the output to 1920x1920 and then cropped a HD frame out the middle of each image to get around this.
Here's a more detailed workflow for the project:
I usually shoot timelapse with a neutral density filter to get some nice motion blur. When I shot this sequence it was the first time I'd used my filter on a new camera/lens and screwing in the filter threw off the focus enough to ruin the shots. Lesson learned - on this camera I need to nail the focus after attaching the filter. As I've been meaning to try style transfer for timelapse I decided to use this slightly bad sequence as the input. Generally for timelapse I shoot manual / manual focus, fairly wide aperture and ISO 100 unless I need to bump this up a bit to get to a 1 second exposure with the filter.
After shooting I use LRTimelapse and Lightroom 6 to edit the RAW photos. LRTimelapse reduces flicker and works well for applying a pan and/or zoom during processing as well. For this project I edited before applying the style transfer. The style transfer network preserves detail very well and then goes crazy in areas like the sky. Rather than zooming into those artifacts I wanted to keep them constant which I think gives a greater sense of depth as you zoom in or out.
Once the sequence is exported from Lightroom I cancel out of the LRTimelapse render window and switch to Google Colab. Copy the rendered sequence to the input folder and the desired style image and then run the notebook to process. If it misbehaves then Runtime -> Restart and run all is your friend.
To get to video I use ffmpeg to render each sequence. For this project at 24 frames per second and cropping a 1920x1080 frame from each of the 1920x1920 style transfer images.
Then DaVinci Resolve to edit the sequences together. I added a 2 second cross dissolve between each sequence and a small fade at the very beginning and end.
Finally, music. I use Filmstro Pro and for this video I used the track Durian.
Capture DropCam (Nest Cam) frames to Google Drive — Updated June 17, 2019: This is now broken for Nest/DropCam devices. It will still work for anything that has a web accessible image URL. Clint points out in the comments below that you can fix up the URL for Nest cams but it looks like you...
3D Printing a 72-58mm step down Camera Filter Adapter — I have a 58mm ND5 filter that I bought to photograph the 2017 solar eclipse. It worked pretty well for that with my Sony RX100 V, but now I want to use it with an RX10 IV (which has the advantage of a 600mm...
Long term solar powered time lapse camera using Arduino — After a few experiments I have a pretty decent platform for capturing long term time lapse footage. The system is designed to run for around a month and captures a photo every thirty seconds during daylight hours. It's a fairly...