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ABFS - Automatic Building Footprint Segmentation

Videos: Project Overview, Liquid Cooling Upgrade

Article: Data Science from Concept to Production: A Case Study of ABFS

ABFS hero banner

Installation

Installation Instructions

For this project, we'll use python 3.6.8. Go ahead and install pyenv if you don't already have it.

# * Install pyenv: https://github.com/pyenv/pyenv-installer
# * Add init commands to bash profile (bashrc, etc.)
# * Source shell before continuing

# Install proper version
pyenv install 3.6.8

Within the project directory, go ahead and setup a new virtual environment.

pyenv virtualenv 3.6.8 abfs-env
pyenv activate abfs-env

For GDAL, you'll need to install it separately through Homebrew/APT before installing the remaining requirements.

# Numpy must be installed BEFORE gdal (https://gis.stackexchange.com/a/274328)
pip install numpy

# On macOS:
brew install gdal

# On Debian/Ubuntu:
sudo apt-get install libgdal-dev
pip install \
  --global-option=build_ext \
  --global-option="-I/usr/include/gdal" GDAL==`gdal-config --version`

Now, go ahead and install the remaining dependencies.

pip install -r requirements.txt

For this program, you'll also need to decide whether to use a GPU-based backend.

# With CUDA-based GPU:
pip install tensorflow-gpu

# Without GPU:
pip install tensorflow

With these packages now available, install the command line utility.

python setup.py install

Verify it is installed properly by running the CLI.

abfs

If this returns an error about the command not being found, you may have to prepend the current python binary.

python -m abfs <COMMAND> <OPTIONS>

Usage

The entire program is operated from the command line utility. Here are some examples.

❯ abfs -h
usage: abfs [-h] {serve,train,tune,export,evaluate} ...

positional arguments:
  {serve,train,tune,export,evaluate}
    serve               Serve model as API
    train               Train a neural network
    tune                Tune the tolerance parameter on the validation set
    export              Export keras model
    evaluate            Evaluate keras model based on test data

optional arguments:
  -h, --help            show this help message and exit
Train
❯ abfs train -h
Using TensorFlow backend.
usage: abfs train [-h] [-lr LEARNING_RATE] [-s SIZE] [-e EPOCHS]
                  [-b BATCH_SIZE] [-mb MAX_BATCHES] [-gpus GPU_COUNT]

optional arguments:
  -h, --help            show this help message and exit
  -lr LEARNING_RATE, --learning-rate LEARNING_RATE
  -s SIZE, --size SIZE  Size of image
  -e EPOCHS, --epochs EPOCHS
  -b BATCH_SIZE, --batch-size BATCH_SIZE
                        Number of examples per batch
  -mb MAX_BATCHES, --max-batches MAX_BATCHES
                        Maximum batches per epoch
  -gpus GPU_COUNT, --gpu-count GPU_COUNT

❯ abfs train -lr 0.02 --batch-size 8 --epochs 150 -gpus 2
...
Tune (Includes Graph Output)
❯ abfs tune -h
usage: abfs tune [-h] [-w WEIGHTS_PATH] [-e MAX_EXAMPLES] [-s SIZE]
                 [-gpus GPU_COUNT]

optional arguments:
  -h, --help            show this help message and exit
  -w WEIGHTS_PATH, --weights-path WEIGHTS_PATH
                        Path to hdf5 model weights
  -e MAX_EXAMPLES, --max-examples MAX_EXAMPLES
                        Max number of examples to validate against
  -s SIZE, --size SIZE  Size of image
  -gpus GPU_COUNT, --gpu-count GPU_COUNT

❯ abfs tune -w checkpoints/<INSERT WEIGHTS HERE>.hdf5 -gpus 2
Tuning of the tolerance parameter will occur on 431 images.
Loading weights from checkpoints/<INSERT WEIGHTS HERE>.hdf5
Calculating F1-Scores... This may take perhaps even an hour if no GPU.
F1-Score calculation complete: 7.31 seconds
Plot has been saved to /tmp/tmpsyo8l1vi.png. Please open to view.
Tuned tolerance: 0.70 w/ median=0.6974 stdev=0.1722

F1-Score Tuning Results

Export
❯ abfs export -h
Using TensorFlow backend.
usage: abfs export [-h] [-s SIZE] [-o OUTPUT]

optional arguments:
  -h, --help            show this help message and exit
  -s SIZE, --size SIZE  Size of image
  -o OUTPUT, --output OUTPUT

❯ abfs export -s 512 -o architecture
Using TensorFlow backend.
Save to "models/architecture.json"
Evaluate
❯ abfs evaluate -h
Using TensorFlow backend.
usage: abfs evaluate [-h] [-w WEIGHTS_PATH] [-b BATCH_SIZE] [-s SIZE]

optional arguments:
  -h, --help            show this help message and exit
  -w WEIGHTS_PATH, --weights-path WEIGHTS_PATH
                        Path to hdf5 model weights
  -b BATCH_SIZE, --batch-size BATCH_SIZE
                        Number of examples per batch
  -s SIZE, --size SIZE  Size of image

❯ abfs evaluate -w checkpoints/unet-d82jd2-0020-0.19.hdf5
...
Loading weights from "checkpoints/unet-d82jd2-0020-0.19.hdf5"
Results:
[('loss', 0.1882165691484708), ...
Serve
❯ abfs serve -h
Using TensorFlow backend.
usage: abfs serve [-h] [-w WEIGHTS_PATH] [-m MODEL_PATH] [-a ADDRESS]
                  [-p PORT]

optional arguments:
  -h, --help            show this help message and exit
  -w WEIGHTS_PATH, --weights-path WEIGHTS_PATH
                        Path to hdf5 model weights
  -m MODEL_PATH, --model-path MODEL_PATH
                        Path to keras model JSON
  -a ADDRESS, --address ADDRESS
                        Address to bind server to
  -p PORT, --port PORT  Port for server to listen on

❯ abfs serve \
    --weights-path checkpoints/unet-d82jd2-0020-0.19.hdf5 \
    --model-path models/unet-d82jd2.json \
    --mapbox-api-key <INSERT KEY HERE>
Using TensorFlow backend.
Serving on 0.0.0.0:1337

Author

R. Christian Di Lorenzo. MIT License. (About Me)

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Automatic Building Footprint Segmentation: U-Net Production-Level API

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