tfors

TensorFlow Object Recognition Service
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About The Project

tfors is a service that performs object recongition on images sent by a client. tfors let's you centralize any machine learning you have in your environment by having clients send images to a centralized server, perform object recognition, and then return the results to the client.

Built With

• Python
• Tensorflow

Getting Started

Docker Installation (Server)

Recommended way to host tfors https://hub.docker.com/r/tuxthepenguin/tfors

Traditional App Installation (Server)

1. Install pre-reqs

apt-get update && apt-get install -y --no-install-recommends \
  python3 \
  python3-pip \
  python-is-python3 \
  build-essential \
  python3-dev \
  git \
  protobuf-compiler \
  apt-transport-https \
  ca-certificates

2. Configure Object Detection & PIP packages

git clone --depth 1 https://github.com/tensorflow/models
cd models/research/
protoc object_detection/protos/*.proto --python_out=.
cp object_detection/packages/tf2/setup.py .
python -m pip install .
pip install httplib2

Traditional App Usage (Server)

python server.py -m "https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1" -l "C:\Users\Sam\Documents\git\tfors\mscoco_label_map.pbtxt" -d

Traditional App Arguments (Server)

parser.add_argument("-p", "--port", help="TCP Port to listen on. Example: 4949", type=int, default=4949)
parser.add_argument("-b", "--buffer", help="Buffer size. Example: 4096", type=int, default=4096)
parser.add_argument("-m", "--model", help="TensorFlow Hub model to use for inference. Example: https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1 or /path/to/centernet_resnet50v1_fpn_512x512_1", required=True)
parser.add_argument("-l", "--labels", help="Labels mapping file. Example: /path/to/mscoco_label_map.pbtxt or C:\path\to\mscoco_label_map.pbtxt", required=True)
parser.add_argument("-e", "--encoding", help="Set encoding. | Example : utf-8", default='utf-8')
parser.add_argument("-d", "--logging", help="Output logging information.", default=False, action='store_true')

Docker/Traditional App Installation (Client)

git clone https://github.com/tuxthepenguin84/tfors.git

Docker/Traditional App Usage (Client)

python client.py -f path\to\image.jpg -s server_ip

Docker/Traditional App Arguments (Client)

parser.add_argument("-f", "--file", help="Path to file, also accepts pipeline input", default=None) # accepts pipeline input
parser.add_argument("-s", "--server", help="The host/IP address of the server, required.", required=True)
parser.add_argument("-p", "--port", help="TCP Port to send to. | Example (default): 4949", type=int, default=4949)
parser.add_argument("-b", "--buffer", help="Buffer size. | Example (default): 4096", type=int, default=4096)
parser.add_argument("-e", "--encoding", help="Set encoding. | Example (default): utf-8", default='utf-8')
parser.add_argument("-c", "--objectclass", help="Object class(s) to recognize. | Example 1: cat | Example 2: car airplane 'potted plant' chair | Notes: When using multiple object classes, --maxresults is ignored", nargs='+', default=None)
parser.add_argument("-x", "--detectionbox", help="Detection box size (0.0 min - 1.0 max) [y_min, x_min, y_max, x_max] | Example : 0.0 0.0 0.5 0.5", nargs='+', default=None)
parser.add_argument("-m", "--minscore", help="Minimum detection score (percent). | Example (default): 60", type=int, default=60)
parser.add_argument("-a", "--maxresults", help="Max results returned. | Example (default): 1", type=int, default=1)
parser.add_argument("-u", "--output", help="Output type: silent, simple, detailed, json, onlyclass, onlyscore, onlybox, onlyclassfound, onlywithinbox, raw. | Example (default): simple", default="simple")
parser.add_argument("-d", "--logging", help="Output logging information.", default=False, action='store_true')

Systemd

See: tfors.service file

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Roadmap

• Return data to client in JSON format
• Return all inferenced class, score, and bounding box data to client

See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Sam Dockery - @SFTuxTweet - samueldockery@gmail.com

Project Link: https://github.com/tuxthepenguin84/tfors

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Acknowledgments

• Best-README-Template
• pythoncode-tutorials
• tensorflow/models
• tfhub.dev

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