The iseefood from ibrahimnm

iseefood's Introduction

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

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
```

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

Import the package to your main.py file.
```
  from iSeefood import SeefoodAI
```
Instantiate a new object.
```
  example = SeefoodAI()
```
Pass an image to be processed (.png and .jpg only).
```
  example.process('../image.png')
```
Get the processing statistics.
```
  statistics = example.getScore()
```

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.