https://github.com/icoxfog417/mlimages

prepare the image data set for machine learning
https://github.com/icoxfog417/mlimages

computer-vision machine-learning

Last synced: 6 days ago
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prepare the image data set for machine learning

• Host: GitHub
• URL: https://github.com/icoxfog417/mlimages
• Owner: icoxfog417
• License: mit
• Created: 2016-02-20T07:18:24.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2022-12-26T20:25:39.000Z (almost 2 years ago)
• Last Synced: 2024-10-31T06:49:47.386Z (11 days ago)
• Topics: computer-vision, machine-learning
• Language: Python
• Size: 1.11 MB
• Stars: 24
• Watchers: 5
• Forks: 12
• Open Issues: 11
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# mlimages

gather and create image dataset for machine learning.

![imagenet](./doc/mlimages.PNG)

## How to use

```

pip install mlimages

```

Or clone the repository. Then you can execute examples.

If you want to do fine tuning, you can download pretrained model in `examples/pretrained` by [git lfs](https://git-lfs.github.com/).

This tool dependes on Python 3.5 that has async/await feature!

## Gather Images

Please make python file in your project folder as below.

```py

import mlimages.scripts.gather_command as cmd

if __name__ == "__main__":

    ps = cmd.make_parser()

    args = ps.parse_args()

    cmd.main(args)

```

### Imagenet

Confirm the **WordnetID** on the [ImageNet site](http://image-net.org/synset)

![imagenet](./doc/imagenet.PNG)

Then download it.

```

python your_script_file.py -p path/to/data/folder -imagenet --wnid n11531193

```

## Labeling

You can create training data from images data folder.

Please make python file in your project folder as below.

```py

import mlimages.scripts.label_command as cmd

if __name__ == "__main__":

    ps = cmd.make_parser()

    args = ps.parse_args()

    cmd.main(args)

```

Then run it.

```

python label.py path/to/images/folder --out path/to/training_data.txt

```

## Training

Now, you have images and training_data.txt.

But you have to do some pre-processing to train your model. For example...

* resize image

* normalize image

* sometimes change color...

:sob:

Don't warry. mlimages supports you!

```py

from mlimages.model import LabelFile, ImageProperty

lf = LabelFile("path/to/training_data.txt", img_root="path_to_your_image_folder")

prop = ImageProperty(width=32, gray_scale=True)

td = lf.to_training_data(prop)

td.make_mean_image("path/to/mean_image")  # store mean image to normalize

for d in td.generate():

    # d is numpy array that adjusted according to ImageProperty, and normalized by mean_image!

    # only you have to do is train the model by it!

    print(d)

```

And also, you can restore the image from data.

```py

image = td.result_to_image(numpy_array, label)

```