An object detection, depth estimation and plane surface detection model, trained on a custom dataset containing images of hardhats, vests, masks and boots. Look at the documentation for more details.
clone this repo using
$ git clone https://github.com/firekind/project-fox --recurse-submodules
and refer to the jupyter notebooks in the root directory of the repo to find out how to train. Make sure the required packages are installed from the environment.yml file.
The directory structure of the dataset is like this:
data
├── images/
├── midas
│ ├── custom.data
│ └── depth
├── planercnn
│ ├── camera.txt
│ ├── custom.data
│ ├── masks
│ └── parameters
├── yolo
│ ├── custom.data
│ ├── custom.names
│ ├── images.shapes
│ ├── labels
│ ├── test.shapes
│ ├── test.txt
│ ├── train.shapes
│ └── train.txt
the images folder contains all the input images, midas/depth folder contains the depth image ground truths for the MiDAS portion of the model. The file midas/custom.data is something like this:
depth=./depth
images=../images
The planercnn/masks folder contain the plane segmentation images and the planercnn/parameters folder contain the plane parameters files (refer to this notebook file for more details). camera.txt is the same as the one used in the inference of vanilla PlaneRCNN. the planercnn/custom.data file is something like this:
masks=./masks
parameters=./parameters
images=../images
camera=./camera.txt
the yolo folder is structured the same way as mentioned in the YoloV3 repo.
Have a look at the journey here: https://firekind.github.io/project-fox