https://github.com/hlgirard/xptools

An experimentalist's toolbox to display images, detect particles, bubbles and crystals and analyze their shape, size and distibution.
https://github.com/hlgirard/xptools

bubble crystal droplet image-analysis image-segmentation video-processing

Last synced: about 2 months ago
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An experimentalist's toolbox to display images, detect particles, bubbles and crystals and analyze their shape, size and distibution.

• Host: GitHub
• URL: https://github.com/hlgirard/xptools
• Owner: hlgirard
• License: mit
• Created: 2018-10-30T16:12:40.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2019-03-20T14:47:46.000Z (almost 6 years ago)
• Last Synced: 2024-09-29T05:54:02.843Z (4 months ago)
• Topics: bubble, crystal, droplet, image-analysis, image-segmentation, video-processing
• Language: Python
• Homepage:
• Size: 18.4 MB
• Stars: 3
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# xptools

[![PyPI version](https://badge.fury.io/py/xptools.svg)](https://badge.fury.io/py/xptools)

Analysis tools for experimental sciences to display images, detect particles (cells, bubbles, crystals), moving fronts and analyze their shape, size and distibution.

## Installation

xptools is on PyPI so you can install it straight from pip with the command

```

pip install xptools

```

Alternatively, you can clone the repository to your local computer and install from source

```

git clone https://github.com/hlgirard/xptools

cd xptools

pip install .

```

## Scripts

- analyze_front - This scipt takes a directory containing video files. For each file, it asks the user to select a region of interest

and processes the selected area with a minimum threshold to find the largest area. It then plots the height of this area as

a function of time.

```

analyze_front --plotly --scale 60 --framerate 30 moviedirectory/

```

![screenshot](docs/screenshots/analyze_front_ex-01.jpg)

- analyze_bubbles - This script takes a movie (or directory of movies) showing bubbles on a surface (bright on dark). It uses a watershed segmentation algorithm to identify the bubbles and characterize their size. It then plots the bubble density and mean size as a function of time.

```

analyze_bubbles --plotly --scale 60 bubble_movie.avi

```

![screenshot](docs/screenshots/analyze_bubbles_ex-01.jpg)

- analyze_crystals - This script takes a directory containing pictures of droplets containing crystals (under cross polarization). It uses a thresholding algorithm to segment the crystals, count them and measure their size.

```

analyze_crystals --plotly --key funct_key.txt imagedirectory/

```

![screenshot](docs/screenshots/analyze_crystals_ex-01.jpg)

- display_image_matrix - Arranges all the images in a directory as a matrix and saves the resulting image

```

display_image_matrix --lines 10 --compress imagedirectory/

```

## Utilities

Several utilities are included in the submodule utils including:

- select_roi:select_rectangle - Prompts the user to make a rectangular selection on the passed image and returns the coordinates of the selection.

- videotools:open_video - Opens a video file as a list of np.arrays each containing one frame of the video.

- videotools:determine_threshold - Determines the threshold to use for a video based on the minimum threshold algorithm.

- videotools:obtain_cropping_boxes - Prompts the user to select the region of interest for each video file. Returns a dataframe containing the coordinates of the selected area for each file.

- imagetools:open_all_images - Opens all the images in a folder and returns a list of cv2 images.

### Example usage

```python

import pandas as pd

from xptools.utils import videotools

video_list = ['Film1.avi','Film2.avi']

dict_crop = videotools.obtain_cropping_boxes(video_list)

for key, val in dict_crop:

    stack = videotools.open_video(key)

    (minRow, minCol, maxRow, maxCol) = val

    stack = [img[minRow:maxRow,minCol:maxCol] for img in stack]

    process(stack)

```

## Notebooks

- SegmentationBroadSpectrum.ipynb - Tests different image segmentation techniques to determine which is most appropriate

- SegmentationFocused.ipynb - Implements a specific analysis and plots the resulting size and number distributions for the particles

- Watershed_Segmentation.ipynb - Implements Watershed segmentation.

## Credits

Code for display_image_matrix adapted from https://gist.github.com/pgorczak/95230f53d3f140e4939c#file-imgmatrix-py

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.