README.md

Video-Depthify

Collection of scripts for generating depth maps for videos using machine learning.

Running on Google Colab

You can run the whole process directly from your browser without setting anything up locally, thanks to Google Colab.

June 2021 update

Use the following Colab notebook to use BoostingMonocularDepth instead of MiDaS for even more detailed (and slower) depth maps.

Running locally

Requirements

• Python/Conda 3 environment (virtualenv recommended)

pip3 install torch torchvision opencv-python timm Pillow numpy

• ffmpeg
• Unix system recommended (WSL on Windows)

Setup

1. Copy depth.py, average.py and merge.py into a folder with the video you wish to depthify. In our case we are using a short, royalty-free video of an elephant.
2. In the same folder, create empty folders rgb, depth, averaged, and merged.

Your folder structure should look like this:

.
├── averaged
├── average.py
├── depth
├── depth.py
├── Elephant.mp4
├── merged
├── merge.py
└── rgb

Steps

1. Get the FPS of the source video. We will need this to put the video back together.

ffmpeg -i Elephant.mp4 2>&1 | sed -n "s/.*, \(.*\) fp.*/\1/p"

2. Split the video into individual frames (this will populate the rgb folder).

ffmpeg -i Elephant.mp4 -qmin 1 -qscale:v 1 ./rgb/%06d.jpg

3. Run the depth inferrence (this will populate the depth folder).

For faster (but lower quality) depth map generation you can switch to a lighter model by changing line 13 to False.

python3 depth.py

4. [Optional step] Run the frame average to reduce the flicker betweeen individual frames (this will populate the averaged folder).

See Elephant_depth_averaged_sound.mp4 vs. Elephant_depth_sound.mp4 to see the effect of this step.

python3 average.py

5. Merge the depthmaps with the original frames (this will populate the merged folder).

python3 merge.py

6. Combine the merged frames into a video file. (substitute 25 fps with the number you got from step 1).

ffmpeg -framerate 25 -i ./merged/%06d.jpg -vcodec libx264 -pix_fmt yuv420p Elephant_depth.mp4

7. Copy the audio track from the original video.

ffmpeg -i Elephant_depth.mp4 -i ./Elephant.mp4 -c copy -map 0:0 -map 1:1 -shortest Elephant_depth_sound.mp4

8. Voilà

MiDaS result:

BoostingMonocularDepth result:

BoostingMonocularDepth does not always provide better results compared to vanilla MiDaS, as you can see on the following example.

MiDaS result:

BoostingMonocularDepth result: