This repository contains a small script to merge the capabilities of two different algorithms:
By using the 3d-photo-inpainting method we can brign an extra dimension to our picture, which will eventually help yolo to better detect "occluded" objects and improve the accuracy of the detection (for some of the frames).
Clone the respective repositories that you can find on their original GitHub (see links above) and follow their instructions to test that the code is properly set up and up and running.
Inside the 3d-photo-inpainting folder change the argument.yml to:
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src_folder: ../images
video_folder: ../videos
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Inside the darknet folder change the Makefile to (before bulding the project):
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OPENCV=1
...
Place your test images inside the images folder and run the following command:
python yolt.py
This will save the output of the 3d-in-painting method in the videos folder, with the following: four rendered videos with zoom-in, swing, and circle motion and dolly zoom-in effect, respectively). Once this step is completed, the program will run the YOLOv4 detector on the rendered videos.
The resulting videos will be allocated in the videos folder.
If you are using CPU instead of GPU, in argument.yml inside the 3d-photo-inpainting folder change the gpu_ids to a negative number, e.g.:
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gpu_ids: -1
...
For simplicity, you can continue working on the venv created with conda for the 3d-photo-inpainting and install the necessary packages to run darknet, most likely you would only have to install OpenCV.
To make this run on MacOS (YOLO only comes for Windows and Linux distribution), make sure that you have Homebrew installed, and install the following package:
brew install pkg-config
Then you have to install the OpenCV dependency as well, running the following command:
brew install opencv