What Is This?

A python module to recognize the existence of food in images by using a trained AI - SFCA.

Dependencies

In order to run the iSeefood module, you need to install the following packages:

• Numpy
• Tensorflow
• Pillow
• Nose
• Tornado

• You can just run the following command to install all required packages:

$ pip install -r requirements.txt

How To Use This

1. Download/ Clone the source code.

   NOTE: Make sure that you download all the saved_model data file (≈217MB).

   You can download the saved_model files:

   1. Manualy through the repository.

   2. By running the following command:

      $ git lfs pull

2. Copy the iSeefood file to your project directory.

   Recommended file structure:

   yourProject/
   ├── iSeefood/
   │   ├── README.md
   │   ├── SeefoodAI.py
   │   ├── __init__.py
   │   ├── samples/
   │   └── saved_model/
   │       ├── checkpoint
   │       ├── model_epoch5.ckpt.data-00000-of-00001
   │       ├── model_epoch5.ckpt.index
   │       └── model_epoch5.ckpt.meta
   ├── __init__.py
   └── main.py

3. Import the package to your main.py file.

     from iSeefood import SeefoodAI

4. Instantiate a new object.

     example = SeefoodAI()

5. Pass an image to be processed (.png and .jpg only).

     example.process('../image.png')

6. Get the processing statistics.

     statistics = example.getScore()

7. Evaluate the statistics.

     result = example.getResult(statistics)

Example

from iSeefood.SeefoodAI import SeefoodAI

example = SeefoodAI()
example.process("../cookies.png")
statistics = example.getScores()
result = example.getResult(statistics)
print  'Does the image contains food: ', result

Output

Does the image contains food:  True

⚠️ Notes

• SeefoodAI class assumes that the saved_models are stored in the following path:

  yourWorkingDirectory/iSeefood/saved_model/

• If you try to run your project from any location other than the project-base-path, the SeefoodAI class will not be able to find the saved_model directory!

• If you would like to manipulate the directory location, you will have to update saved_models/ direcotry path inside the SeefoodAI.py class. The saved_models directory is specified inside the __setup() function.

  def __setup(self):
    ......
    saver = tf.train.import_meta_graph(
              'yourCustomizedPath/iSeefood/saved_model/model_epoch5.ckpt.meta')
    saver.restore(self.sess, tf.train.latest_checkpoint('yourCustomizedPath/iSeefood/saved_model/'))
    ......

📃 Documentation

Function	Parameter	Return value	Description
process()	image_path:String	True/False:bool	If the image has been processed, then True. Else, then False.
getScores()	---	numpy.ndarray	Returns the data of the last processed image.
getResult()	scores:numpy.ndarray	True/False:bool	If there is food in the image, then True. Else, then False.

Built With

• Tensorflow - An E2E open source machine learning platform.
• Pytest - Testing framework.
• Travis CI - CI service.
• CodeFactor - Automated code review tool.
• Codecov - A code coverage measurement tool

Versioning

We use Github for versioning. For the versions available, see the tags on this repository.

Authors

• Ibrahim Almohaimeed - Github account

License

This project is licensed under the MIT License - see LICENSE for details.

Acknowledgments

• Machine Learning and Complex Systems Lab at Wright State University - GitHub repo