ODDS - Overhead Structure Detection and Data Science

The goal of this project is to ensure up-to-date data quality control with SDG&E's databases of electric pole assets. Our project seeks to create a more efficient way of ensuring that the databases are up-to-data by utilizing the Google Street View API along with a pole-detection model trained using DETR. Our model will traverse the streets between 2 given longitude and latitude coordinate pairs and count the number of wooden and metal electric poles it identifies. The counted poles are compared to the number on record in SDG&E's database which would be a good indicator of whether an update is necessary.

Data Sources:

Training images are obtained using the Google Street View Static API.

Running python scripts/image_collection/collect_images.py will download all the training images into the images folder into the data directory of this repository.

Setup

Create a .env file with variables from Google Cloud:

API_KEY='your_api_key'
SECRET='your_secret_key'

Conda Environment

After cloning repository, navigate to root level and run:

conda env create -f environment.yml

DETR Model

You must clone the DETR repository in order to train the model:

git clone https://github.com/woctezuma/detr.git
cd detr
git checkout finetune
cd ..

Create Training/Validation Datasets and Prepare files to train model

After downloading images folder into the data directory, split the data into training/validation set by running:

python scripts/cocosplit.py --having-annotations --multi-class -s 0.8 annotations/combined-everyone.json data/custom/annotations/custom_train.json data/custom/annotations/custom_val.json

Train/val split script derived from Cocosplit Repo.

To split the images into its train and validation set, run:

python scripts/train-val-split.py

After both of these scripts have been ran, the annotations and training/validation will be saved in:

├── data/custom/                
│   ├── annotations
│       ├── custom_train.json
│       ├── custom_val.json
│   ├── train2017
│   ├── val2017
│

Train the Model

In order to train the model, run the following:

python detr/main.py \
  --dataset_file "custom" \
  --coco_path "data" \
  --output_dir "entire_workflow/models" \
  --resume "detr/detr-r50_no-class-head.pth" \
  --num_classes 2 \
  --epochs 20 \
  --device cuda

The parameters preceded by "--" may be modified accordingly such as the number of epochs to train for.

Important

A GPU is required to timely train the model.

After the model is finished training, output files will be saved into detr/outputs, and from there, move the model into the entire_workflow/models folder.

DETR2

If you are willing to train DETR2 model manually, go to scripts/model_dev and refer to detr2_finetuning.ipynb.

PostgreSQL Docker Container

1. Run lsof -i :5432 to see if anything is currently occupying that port.
   • If so, run sudo kill [pid] to get rid of it.
2. Run docker build -t dsc180b-image .
3. Run docker run -p 5432:5432 --name dsc180b-container -d dsc180b-image

Running the Traversal Model Script

In order to run the model to identify wooden and metal poles along the streets of 2 coordiante pairs, run:

python entire_workflow/pole_workflow.py [coordinate pair 1] [coordinate pair 2]

Important

Depending on your GPU and CUDA availability, the pipeline will use 2 different models. If your device has accessible GPU and CUDA set up, please run set up codes in entire_workflow/setup.ipynb.

Note

Example:

python entire_workflow/pole_workflow.py '32.7077092,-117.0841702' '32.7077089,-117.08301'

Results will be stored in entire_workflow/results.txt and images with bounding boxes are stored in entire_workflow/temp_bb_images.

Flask Server

To open a flask server for interactive demo, run:

python flask-app.py