tensorflow_object_detection_webcam

Webcam Implementation of Tensorflow Object Detection

Tensorflow Object Detection API

Creating accurate machine learning models capable of localizing and identifying multiple objects in a single image remains a core challenge in computer vision. The TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. At Google we’ve certainly found this codebase to be useful for our computer vision needs, and we hope that you will as well.

Contributions to the codebase are welcome and we would love to hear back from you if you find this API useful. Finally if you use the Tensorflow Object Detection API for a research publication, please consider citing:

# Installation

Dependencies

Tensorflow Object Detection API depends on the following libraries:

• Protobuf 3+
• Python-tk
• Pillow 1.0
• lxml
• tf Slim (which is included in the "tensorflow/models/research/" checkout)
• Jupyter notebook
• Matplotlib
• Tensorflow
• Cython
• cocoapi

For detailed steps to install Tensorflow, follow the Tensorflow installation instructions. A typical user can install Tensorflow using one of the following commands:

# For CPU
pip install tensorflow
# For GPU
pip install tensorflow-gpu

The remaining libraries can be installed on Ubuntu 16.04 using via apt-get:

sudo apt-get install protobuf-compiler python-pil python-lxml python-tk
sudo pip install Cython
sudo pip install jupyter
sudo pip install matplotlib

Alternatively, users can install dependencies using pip:

sudo pip install Cython
sudo pip install pillow
sudo pip install lxml
sudo pip install jupyter
sudo pip install matplotlib

COCO API installation

Download the cocoapi and copy the pycocotools subfolder to the tensorflow/models/research directory if you are interested in using COCO evaluation metrics. The default metrics are based on those used in Pascal VOC evaluation. To use the COCO object detection metrics add metrics_set: "coco_detection_metrics" to the eval_config message in the config file. To use the COCO instance segmentation metrics add metrics_set: "coco_mask_metrics" to the eval_config message in the config file.

git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
make
cp -r pycocotools <path_to_tensorflow>/models/research/

Protobuf Compilation

The Tensorflow Object Detection API uses Protobufs to configure model and training parameters. Before the framework can be used, the Protobuf libraries must be compiled. This should be done by running the following command from the tensorflow/models/research/ directory:

# From tensorflow/models/research/
protoc object_detection/protos/*.proto --python_out=.

Add Libraries to PYTHONPATH

When running locally, the tensorflow/models/research/ and slim directories should be appended to PYTHONPATH. This can be done by running the following from tensorflow/models/research/:

# From tensorflow/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

Note: This command needs to run from every new terminal you start. If you wish to avoid running this manually, you can add it as a new line to the end of your ~/.bashrc file, replacing `pwd` with the absolute path of tensorflow/models/research on your system.

Testing the Installation

You can test that you have correctly installed the Tensorflow Object Detection
API by running the following command:

python object_detection/builders/model_builder_test.py