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