This is my project during Mitacs internship at the university of Saskatchewan.
Introduction
The program automatically detects cells in the ALL-IDB dataset.
Proposed Method
Counting problem
Classification
Requirements
- numpy >= 1.9
- opencv >= 2.4.10
- skimage >= 0.12
- matplotlib >= 1.4.3
- pymorph >= 0.96
- spams (install from source)
Programs
Counting Framework
Usage
Run python program_nolearing.py -h for more details about using the program.
Examples
python program_nolearing.py -d training.txt -v: count all images which are located in file training.txt and visualize the result.
python program_nolearing.py -d training.txt -o result.txt: count all images which are located in file training.txt and write the result to file result.txt
To load data from training.txt or problem.txt, you must download the dataset and copy all images and xyc files into folder data. Because of the copyright of the dataset, I can not make a copy version in this repository.
Learning Framework
Usage
Run python program_fusion.py <training path> <testing-path>
Examples
python program_fusion.py train/allidb_1.txt test/allidb_1.txt
ALL IDB 2
Because the framework is in progress as well as this version is not the final one. To evaluate the performace of the ALL-IDB 2 dataset, you have to use these scripts which have the "allidb2_" prefix.
For example, to run the fusion of classifiers framework, type this script to your console terminal:
python allidb2_fusion train/allidb2_1.txt test/allidb2_1.txt or
python allidb2_fusion it uses file train/allidb2_1.txt as training samples and file test/allidb2_1.txt as testing samples.
Development
[Updating]