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deprecated

this program is now maintained in

https://github.com/totti0223/deepstomata/

bmicp (stomatal pore quantifier)

A three step image analysis program for quantification of stomatal aperture from bright field images.

  1. Identifying the coordinate of the stomata by HOG + SVM.
  2. Classifying the status (open, partially open, closed, false positive) by CNN.
  3. Pore quantification responsive to the object status.

  1. licence is not determined yet.
  2. planning to submit an article soon.
  3. contact me if you want any details. (Jan,27,2018)

Author

Yosuke Toda, Ph.D (Agriculture)

[email protected]

Post Doctoral Researcher

Lab of Plant Physiology

Department of Science

Nagoya University, Japan

Requirements

python>3

matplotlib==1.5.1

numpy==1.11.2

scipy==0.18.1

scikit_image==0.12.3

tensorflow==0.10.0rc0

Pillow==3.2.0

common==0.1.2

cv2==1.0

dlib==19.1.0

setuptools==32.3.1

Installation

  1. Download this repository.

  2. Unzip.

  3. Open terminal.

  4. Move to the Unzipped directory.

pip install .

Note

  • Tensorflow must not be ver. 1.0.. Codes are not compatible.

  • Several packages such as cv2 and dlib cannot be installed via pip in anaconda environment. In such cases, comment out the requirements.txt like the following

#cv2 ==1.0
#dlib == 19.1.0

and install respectively via conda install

Usage

  • In terminal
python
import bmicp
bmicp.cui("PATH/TO/THE/DIRECTORY_OR_IMAGES")

Example

1

Analyze a directory containing 4 jpeg images in the example folder

import bmicp
bmicp.cui("PATH_TO_THE_EXAMPLE_FOLDER/examples")

ex1

2

Result overview in the terminal.

ex2

3

Result directory overview.

ex3

annotated/IMAGE_stomata_position.jpg

Image with stomata position.

annotated/IMAGE_stomata_tiled.jpg

Montage image of stomata candidate that is same size as input image.

annotated/IMAGE_classified.jpg

Image with stomata position, class percentage.

annotated/IMAGE_all.jpg

Image with stomata position, class percentage, and segmented stomatal pore.

FOLDERNAME_all.csv

CSV files with quantified stomatal pores.

FOLDERNAME_count.csv

Statistics of classified object per image (no. of open, partially open, closed, false positive per image).

Plans

  • Migrating CNN code from tensorflow to keras

  • GUI

  • Registrating the package to PyPi (Packaging the CNN model exceeds the upload size limit of PyPI)

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