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Image and video analysis tools for experimental sciences

Project description


PyPI version

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


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
cd xptools
pip install .


  • 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/


  • 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


  • 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/


  • 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/


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

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]


  • 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.


Code for display_image_matrix adapted from


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

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