cars-edge-detection-plugin 0.1.0

Cars plugin to use the MoGe2 model for edge detection

Edge Detection plugin for CARS

This plugin enables the use of the MoGe2 model in CARS, for higher accuracy building reconstruction.

• Context
• Installation
• Using the new pipeline

Context

This plugin is made to be used in conjunction with CARS, the stereo-reconstruction software. More information can be found over at CARS's GitHub page.

Installation

First clone this repository, using :

$ git clone --recurse-submodules [e-mail]:dali/cars-park/cars-plugins/cars-edge-detection-plugin.git
$ cd cars-edge-detection-plugin   

Note: --recurse-submodules is required. This project vendors MoGe as a submodule, and MoGe itself vendors utils3d and pipeline as nested submodules. A plain git clone will leave those directories empty, causing build failures.

If you already cloned the repository without submodules, initialize them before installing:

$ git submodule update --init --recursive

You can then create a virtual environment and install the plugin, which will install CARS automatically :

$ python3 -m venv venv
$ source venv/bin/activate
$ make install   

Or install the plugin in your own environment, if it already has CARS :

$ source your/own/env/activate
$ pip install .

Once installed, don't forget to download a MoGe2 model, for example Ruicheng/moge-2-vitl-normal, using this command :

$ cars-download-moge2 --model vitl-normal

Or via any other means if you don't have a direct access to the internet. The plugin will attempt to download the vitl-normal model on first import, providing at least the default model. If working from an environment such as the TREX cluster, an option is to directly use wget to fetch the model file, then move it to its proper place for the plugin to recognize it :

# fetch the model
$ wget https://huggingface.co/Ruicheng/moge-2-vitl-normal/resolve/main/model.pt

# move the model to the right place
# it should be under cars_edge_detection_plugin/applications/depth_map_generation/models with the proper name for each model :
#  - moge-2-vitl-normal.pt
#  - moge-2-vitb-normal.pt
#  - moge-2-vits-normal.pt
$ mkdir [your/plugin/installation/path/]cars_edge_detection_plugin/applications/depth_map_generation/models
$ mv ./model.pt [your/plugin/installation/path/]cars_edge_detection_plugin/applications/depth_map_generation/models/moge-2-vitl-normal.pt

Using the new pipeline

Though this pipeline is intended to be used within CARS's meta pipeline, it can still be used as a stand-alone pipeline by providing the right configuration.

Once your configuration file is ready, you can launch the pipeline using CARS :

$ cars configfile.yaml

Configuration

The edge detection pipeline can be enabled by setting the pipeline parameter in the global advanced section of the CARS configuration.

A minimal example configuration is shown below:

input:
  sensors:
    one: # sensor image path
    two: # sensor image path
pipeline: edge_detection
output:
  directory: outresults

The pipeline operates on image pairs. By default, edge detection is only computed where required by downstream applications, meaning on the left images only.

Additional options specific to the edge detection pipeline can be configured under the edge_detection section. For example, edge detection can also be applied to right images, and the MoGe2-based depth map generation application can be configured as follows:

input: ...
advanced: ...
output: ...
edge_detection:
  advanced:
    save_intermediate_data: false
    right_image_edge_detection: true
  applications:
    depth_map_generation:
      method: moge2
      model: Ruicheng/moge-2-vitl-normal
      save_intermediate_data: true
      edge_threshold: 0.7

The model parameter can reference either a local MoGe2 checkpoint or a Hugging Face model identifier.

If save_intermediate_data is set to false, only the edge map will be created in the output folder. Else, all by-products (depth map, normal map, tile_id) will be saved in the dump_dir folder.