Nanoscience tracker prototyped in Python

This project currently has the following dependences:

• OpenCV 4.4
• Scikit Learn
• Scikit Image
• Numpy
• Matplotlib

To install them using a conda environment:

# Create and set the environment
conda create mhpc
conda activate mhpc
# Install dependencies
conda install numpy matplotlib scikit-learn scikit-image shapely
conda install -c conda-forge opencv

The local tracker accepts one of the quadrants of the mcherry video sequence.

To download a sample for analysis:

cd data
./download-data.sh

To run an analysis over the sample (assume the sample name is loaded in SAMPLE):

cd src/LocalTracker
./main.py --input ../../data/mcherry/$SAMPLE --draw_detection=1 --draw_tracking=1

The global tracker creates a particle world with multi-scene capabilities.

To download a sample for analysis:

cd data
./download-data.sh

To run an analysis over the sample (assume the sample name is loaded in SAMPLE):

cd src/GlobalTracker
./main.py

The check the modifiers for main.py::

• ./main.py --help

This is the full demo of the project

To download a sample for analysis:

cd data
./download-data.sh

To run an analysis over the sample:

cd src/ares
# For single scene mode
./main.py --dataset=../data/mcherry/mcherry_single.json
# For multi-scene mode
./main.py --dataset=../data/mcherry/mcherry.json

The check the modifiers for main.py::

• ./main.py --help

Version: 0.1.0

Author: Luis G. Leon-Vega