Metadata-Version: 2.1
Name: pyGUS_bio
Version: 1.8
Summary: Quantify GUS Stain Images
Author-Email: Ping Wu <wpwupingwp@outlook.com>
License: GNU AGPL v3
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU Affero General Public License v3
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Project-URL: Homepage, https://github.com/wpwupingwp/pyGUS
Requires-Python: >=3.8
Requires-Dist: colour-science==0.4.4
Requires-Dist: coloredlogs==15.0.1
Requires-Dist: matplotlib==3.5.2
Requires-Dist: numpy==1.24.2
Requires-Dist: opencv-contrib-python==4.6.0.66
Requires-Dist: scipy==1.9.3
Requires-Dist: tqdm==4.64.1
Description-Content-Type: text/markdown

# pyGUS

[![PyPI version](https://badge.fury.io/py/pyGUS.svg)](https://badge.fury.io/py/pyGUS)

# Quick start

Download [the package](https://github.com/wpwupingwp/pyGUS/releases),
unzip, and then double-click `pyGUS.exe` or `pyGUS`.

__OR__

Make sure you have [Python](https://www.python.org/) (3.8 or higher) installed.

Open terminal, run

   ```
   # Install, using pip (recommended)
   pip install pyGUS_bio --user
   # Or, use conda
   conda install -c wpwupingwp pyGUS_bio
   # Run
   # Windows
   python -m pyGUS
   # Linux and MacOS
   python3 -m pyGUS
   ```

# Table of Contents

* [Quick start](#quick-start)
* [Feature](#feature)
* [Prerequisite](#prerequisite)
    * [Hardware](#hardware)
    * [Software](#software)
* [Installation](#installation)
    * [Portable](#portable)
    * [Install with pip](#Install-with-pip)
* [Usage](#usage)
    * [Desktop](#desktop)
    * [Command line](#command-line)
    * [Photo tips](#photo-tips)
    * [Input](#input)
    * [Output](#output)
* [Utilities](#utilities)
    * [extract_info.py](#extractinfopy)
    * [stats.py](#statspy)
* [Performance](#performance)
* [Citation](#citation)
* [Flowchart](#flowchart)

# Feature

:heavy_check_mark: Quantify GUS expression in stain images for multiple species.

:heavy_check_mark: Identify expressed regions and no expressed regions.

:heavy_check_mark: Use negative and positive references to calibrate expression 
value.

:heavy_check_mark: Support Macbeth color checker to calibrate color bias.

# Prerequisite

## Hardware

The program could run in normal computers and have no extra requirements for 
memory, CPU, et al.

Currently, macOS, Linux and Microsoft Windows systems are supported.

## Software

For the portable version, nothing needs to be installed manually.

For installing from pip, [Python](https://www.python.org/downloads/) is
required. Notice that the python version should be **3.8** or higher.

:white_check_mark: All third-party dependencies will be automatically
installed with the Internet, including `opencv-contrib-python`, `numpy`, 
`scipy`, `matplotlib`, `coloredlogs` (python packages).

# Installation

## Portable

Download from the [link](https://github.com/wpwupingwp/pyGUS/releases),
unpack and run.

## Install with pip

1. Install [Python](https://www.python.org/downloads/). *3.8 or newer* is
   required.

2. Open the command line, run

```
pip3 install pyGUS_bio --user
```

# Usage

## Desktop

If installed with pip,

```
# Windows
python -m pyGUS
# Linux and MacOS
python3 -m pyGUS
```

If you use the portable version, double-click the `pyGUS.exe`. Then click the
button to choose which mode to run.

## Command line

:exclamation: In Linux and macOS, Python2 is `python` and Python3 is
`python3`. However, in Windows, Python3 is called `python`, too. Please
notice the difference and make sure you use `Python 3` instead of `Python 2`.

* Show help information

 ```
 # Windows
 python -m pyGUS -h 
 # Linux and MacOS
 python3 -m pyGUS -h 
 ```

* Run

 ```powershell
 # Windows
 #   mode 1
 python -m pyGUS -mode 1 -ref1 [file1] -ref2 [file2] -images [files3] [file4] ...
 #   mode 2
 python -m pyGUS -mode 2 -ref1 [file1] -images [file2] [file3] ...
 #   mode 3
 python -m pyGUS -mode 3 -ref1 [file1] -ref2 [file2] -images [files3] [file4] ...
 #   mode 4
 python -m pyGUS -mode 4 -images [file1] [file2] ...
 # Linux and macOS
 #   mode 1
 python3 -m pyGUS -mode 1 -ref1 [file1] -ref2 [file2] -images [files3] [file4] ...
 #   mode 2
 python3 -m pyGUS -mode 2 -ref1 [file1] -images [file2] [file3] ...
 #   mode 3
 python3 -m pyGUS -mode 3 -ref1 [file1] -ref2 [file2] -images [files3] [file4] ...
 #   mode 4
 python3 -m pyGUS -mode 4 -images [file1] [file2] ...
 ```

## Input

All images should use white background.

### Negative reference

One plant (whole or partial) with low expression or no expression.

### Positive reference

One plant (whole or partial) with high expression.

### Target images

Plant images for  analysis.

### Photo tips
**These tips are for better results, not strict rules.**

#### Equipment

Cameras and stereo microscopes are recommended. Smartphones with camera
functions are not recommended, especially models with built-in color grading.
To reduce the effects of perspective distortion, lens with medium focal length
(e.g. 50 mm in full-frame) is recommended. Make sure lens and camera sensor
are free of stains.

#### Background

**White** board or **white** paper with no debris, no wrinkles, no obvious
reflection

#### Lighting

It is recommended to use the bottom lighting of the whiteboard. When
using indoor lighting, the international standard color rendering index (Ra
index) of the bulb is required to be no less than 85, and the color
temperature is between 4000 k to 7000 k; when using sunlight, avoid morning
and evening. Close overhead lighting can cause significant reflections and is
not recommended

#### Focus

The whole subject is not out of focus. Narrow down the aperture when
shooting with a camera; adjust focus and magnification when using a stereo
microscope to ensure that the edges of the subject are sharp

#### White balance

The recommended color temperature is around 6500k, with a
minimum of 4000k and a maximum of 7000k. When the color temperature cannot be
adjusted, adjust other parameters until the background is displayed in white
without obvious color cast.

#### Exposure compensation

When using camera, the exposure compensation can be set
to +1EV or +2EV to reduce the color cast of the paper. It could also reduce
the influence of the transmittance of color cards or plants

#### Layout

Based on the size of the plant, adjust the lens-to-plant distance to make the
plant cover the frame more than 50%. The main body does not cross the border,
and the distance is not less than 10% of the screen width.

When there is only a single subject, place it in the center of the frame. When
two subjects (two plants or plants with color card), place them on the left
and the right, with interval between them not less than one-third of the width
of the plant. They do not intersect, and they are not surrounded by each other
(especially one with the long roots); there is no breakage, and the leaves do
not overlap as much as possible.

#### File

No toning processing. The recommended minimum resolution is 500\*500 px. When
shooting with the camera, vignetting and distortion compensation can be turned
on, and other adjustments (including white point, black point, contrast,
brightness, style, etc.) should be turned off.

## Mode
### Normal mode

The `normal mode` requires _negative reference_ (`-ref1`),  _positive reference_
(`-ref2`) and target images (`-images`, one or more). Each image only have one 
object.

This mode requires users to add marks on the image:
1. left click mouse to add _white_ dots on the target plant
2. right click mouse to finish or press `Esc` to reset dots
3. drag to draw a box _around_ the target plant. Do not cross over the plant.
Do not draw near the border of the image, too.
4. right click mouse to finish or press `Esc` to reset the box
### With reference mode

The `with reference mode` requires one reference image (`-ref1`) with _negative 
reference on left and positive reference on right_. In each target images 
(`-images`, one or more), target plants are on left, positive references are on 
right separately.

This mode requires users to add marks on the image:
1. left click mouse to add _white_ dots on the target plant and the reference.
2. right click mouse to finish or press `Esc` to reset dots
3. drag to draw a box _around_ the target plant and the reference. Do not cross 
over the plant. Do not draw near the border of the image, too.
4. right click mouse to finish or press `Esc` to reset the box
### With color checker mode

The `with color checker mode` requires _negative reference_ (`-ref1`),  
_positive reference_ (`-ref2`) and target images (`-images`, one or more). 
In each image, the plant is on left and a Macbetch color checker is on right.

This mode requires users to add marks on the image:
1. left click mouse to add _white_ dots on the target plant and the color 
checker.
2. right click mouse to finish or press `Esc` to reset dots
3. drag to draw a box _around_ the target plant and the color checker. Do not 
cross over the plant. Do not draw near the border of the image, too.
4. right click mouse to finish or press `Esc` to reset the box
### Manually mode

The `Manually mode` requires one or more target images (`-images`). Users select
two references and target regions by mouse and press any key to continue to next
image or to start running.

## Output

`.csv` files: **csv** format table. Storage all analysis results.

`-masked.png` files: Original image with alpha channel. Background is set to
fully transparent, expressed regions are set to fully opaque.

`-select.png` files: In mode 4, the selected regions were filled with different
color. <span style="color:red">Red</span> represents negative reference, 
<span style="color:yellow">yellow</span> is for positive reference,
and <span style="color:cyan">cyan</span> is for the target region.

`-calibrate.png` files: Images calibrated with color checker.

`-fill.png` files: Images filled with different colors.
<span style="color:blue">Blue</span> means target,
<span style="color:red">red</span> means background, <span style="color:yellow">
yellow</span> means regions inner target but have different expression,
<span style="color:green">green</span> means darker regions
inner target.

# Utilities
## extract_info.py
This program can extract the alpha channel information stored in the image
output by pyGUS and generate a black and white image. Among them, black
represents the background part of the original image recognized by pyGUS, gray 
represents the plant part, and white represents the part with GUS signal 
recognized by the program.

Usage:
```powershell
# Windows
python extract_info.py [image filename]
# Linux and macOS
python3 extract_info.py [image filename]
```
## stats.py
This program is used to statistically analyze the difference in GUS signals 
between different groups of sample photos and perform significance test (t-test). 
Considering that users may prefer different statistical tools, this program is 
an auxiliary tool and is not integrated into the pyGUS. The program requires the 
user to provide a CSV format (exportable from Microsoft Excel, Notepad or any
other text editor) list as input, with the following format:
```text
Filename3,Sample1,Group1
Filename4,Sample2,Group1
Filename5,Sample3,Group2
Filename6,Sample4,Group2
```
`Filename` represents filename of pyGUS input images, `Sample1` represents 
sample ID and `Group1` represents the group of samples. Sample IDs should be 
unique. 

Note that please do not contain comma or space in each column. For example:
```text
A1.png,A1,control
A2.png,A2,control
A3.png,A3,control
B1.png,B1,treatment
B2.png,B2,treatment
B3.png,B3,treatment
```

Usage:
```powershell
# Windows
python stats.py [filename]
# Linux and macOS
python3 stats.py [filename]
 #>
```


# Performance

It depends on hardware and image size. Normally one image cost seconds or no 
more than one minute. The program could detect and utilize multi cores of CPU.

# Citation

Unpublished.

# License

The software itself is licensed under
[AGPL-3.0](https://github.com/wpwupingwp/pyGUS/blob/master/LICENSE)
(**not include third-party software**).

# Q&A

Please submit your questions in the
[Issue](https://github.com/wpwupingwp/pyGUS/issues) page :smiley:

* Q: I got the error message that I don't have `tkinter `module installed.

  A: If you want to run GUI on Linux or macOS, this error may happen because the
  Python you used did not include tkinter as default package (kind of weird). Run
  ```
  # Debian and Ubuntu
  sudo apt install python3-tk
  # CentOS
  sudo yum install python3-tk
  ```
  may help.

  For macOS users or linux users without root privilege, please try to install the
  newest version of Python or to use conda, see
  [conda](https://docs.conda.io/en/latest/miniconda.html)
  and [Python](https://www.python.org/download/mac/tcltk/) for details.

* Q: It says my input is invalid, but I'm sure it's OK!

  A: Please check your files' path and filename. The `space` or Chinese 
characters in the folder name or filename may cause this error.

## Flowchart

```mermaid
flowchart TB
    subgraph Input
        m1((Normal mode))
        m2((With reference mode))
        m3((With color checker mode))
        m4((Manually mode))
        targets[Target images]
        c[Macbeth Color checker]
        ref1[Positive reference]
        ref2[Negative reference]
        ref3[Negative and Positive reference]
        targets
        ref1 & ref2 & targets --> m1
        ref3 & targets --> m2
        m3 --> c --> ref1 & ref2 & targets
        m4 --> ref1 & ref2 & targets
        style c fill:#399
        style m1 fill:#393
        style m2 fill:#393
        style m3 fill:#393
        style m4 fill:#393
    end
    
```

```mermaid
flowchart LR
    subgraph Process
        g1[Read image]
        g1.5[[Color calibration]]
        g2[Split to R, G, B channels]
        g3[Generate gray copy]
        g4[Get edge]
        g7[Calculate]
        g8[Output table and figure]
        g1 --> g1.5 --> g2 --> g3 --> g4 --> g7 --> g8 
        style g1.5 fill:#59f
        style g7 fill:#557788
        style g8 fill:#59f
    end
    subgraph g1.5[Color calibration]
        direction TB
        c1[Detect Macbeth color checker]
        c2[Generate CCM matrix]
        c3[Apply matrix to original whole image]
        c4[Generate calibrated image]
        c1 --> c2 --> c3 --> c4
    end
    subgraph g4[Get mask]
        direction LR
        g41[cfm] --> g42[binary] --> g43[split]
    end
    subgraph g7[Calculate]
        direction LR
        neg & pos --> target --- Mean & Area & Ratio
    end

```
