ridge-detection 3.0.0
ridge / line detection algorithm
License: MIT
Requires Python: >3.4.0

# ridge_detection
This package implements and extends the ridge / line detection algorithm described in:

Steger, C., 1998. An unbiased detector of curvilinear structures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(2), pp.113–125.  

It is basically the translation from java to python of https://github.com/thorstenwagner/ij-ridgedetection.
For more informations about the params visit "https://imagej.net/Ridge_Detection".

## **Please pay attention that we removed some part of the code and some parameters** 

HOW RUN IT
-
Starting form the version 2.0.0 a GUI is provided. This is the running main

    from ridge_detection.gui import create_win

    if __name__ == "__main__":
        create_win()

The core part of the tool is the LineDetector class. That is callable from config file (see below) or with dict variable which must have the same format

An example script is:

    from ridge_detection.lineDetector import LineDetector
    from ridge_detection.params import Params,load_json
    from ridge_detection.basicGeometry import reset_counter
    from ridge_detection.helper import displayContours,save_to_disk
    from argparse import ArgumentParser
    from datetime import datetime
    from PIL import Image
    from  mrcfile import open as mrcfile_open


    def run():
        start=datetime.now()
        parser = ArgumentParser("ridge detection parser tool")
        parser.add_argument(dest="config_filename",type=str, nargs='?',help="name of the config_file to use. Default value is 'example_config.json'")
        args=parser.parse_args()
        config_filename = args.config_filename if args.config_filename is not None else "example_config.json"
        json_data=load_json(config_filename)
        params = Params(config_filename)

        try:
            img=mrcfile_open(json_data["path_to_file"]).data
        except ValueError:
            img=Image.open(json_data["path_to_file"])

        detect = LineDetector(params=config_filename)
        result = detect.detectLines(img)
        resultJunction =detect.junctions
        out_img,img_only_lines = displayContours(params,result,resultJunction)      
        if params.get_saveOnFile() is True:
            save_to_disk(out_img,img_only_lines)

        print(" TOTAL EXECUTION TIME: " + str(datetime.now()-start))

    if __name__ == "__main__":
        run()

From the command line:

    python main.py name_your_config_file.json

EXAMPLE CONFIG FILE
-
It has to be a json format

    {
        "path_to_file": "../example.tif",
        "mandatory_parameters": {
            "Sigma": 3.39,
            "Lower_Threshold": 0.34,
            "Upper_Threshold": 1.02,
            "Maximum_Line_Length": 0,
            "Minimum_Line_Length": 0,
            "Darkline": "LIGHT",
            "Overlap_resolution": "NONE"
        },

        "optional_parameters": {
            "Line_width": 10.0,
            "High_contrast": 200,
            "Low_contrast": 80
        },

        "further_options": {
            "Correct_position": true,
            "Estimate_width": true,
            "doExtendLine": true,
            "Show_junction_points": true,
            "Show_IDs": false,
            "Display_results": true,
            "Preview": true,
            "Make_Binary": false,
            "save_on_disk": true
        }
	}


PARAMETER SELECTION

There are three parameters which have to be specified. These are the mandatory parameters. The optional parameters can be used to estimate the mandatory parameters


MANDATORY PARAMETERS:
-

SIGMA  -->  Determines  the sigma for the derivatives. It depends on the line width

LOWER THRESHOLD --> Line points with a response smaller as this threshold are rejected 

UPPER THRESHOLD --> Line points with a response larger as this threshold are accepted. 

DARKLINE (true/false) --> This parameter determines whether dark or bright lines are extracted. 

OVERLAP RESOLUTION (None/Slope) --> You can select a method to attempt automatic overlap resolution. The accuracy of this method will depend on the structure of your data.
 * NONE --> The default behavior: no assumption of overlap is made. Any point of potential intersection will be treated as an end point for the ridges involved.
 * SLOPE --> This method makes the assumption that when two ridges overlap, it is more likely that they will continue on their path than make turns. **This is best suited to datasets with brief periods of overlap!** If two ridges have a significant portion of overlap, the accuracy of this method will rapidly diminish. 

OPTIONAL PARAMETERS: 
-
They are used to estimate the mandatory parameters. It is able starting from the version 3.0.0

The formula are written using latex. You cannot see them on git. 


LINE WIDTH (w)  --> The line diameter in pixels. It estimates the mandatory parameter 'Sigma' by: <img src="https://latex.codecogs.com/svg.latex?{\color{White} \frac{w}{2\sqrt{3}}+0.5}"/>

HIGH CONTAST (Bupper) -->  Highest grayscale value of the line. It estimates the mandatory parameter 'Upper threshold' by: <img src="https://latex.codecogs.com/svg.latex?{\color{White} 0.17*\frac{2*Bupper*\frac{w}{2}}{\sqrt{2\pi}\sigma^{3}} *   e^{-\frac{(\frac{w}{2})^{2}}{2\sigma ^{2}}} } "/>

LOW CONTAST  (Blow) --> Lowest grayscale value of the line. It estimates the mandatory parameter 'Lower threshold' by: <img src="https://latex.codecogs.com/svg.latex?{\color{White} 0.17*\frac{2*Blow*\frac{w}{2}}{\sqrt{2\pi}\sigma^{3}} *   e^{-\frac{(\frac{w}{2})^{2}}{2\sigma ^{2}}} }"/>

FURTHER OPTIONS (True/False)
-

CORRECTION POSITION --> Correct the line position if it has different contrast on each side of it

ESTIMATE WIDTH: If this option is selected the width of the line is estimated.

SHOW JUNCTION POINTS: If this option is selected the junctions points will be displayed.

SHOW IDs: The ID of each line will be shown.

DISPLAY RESULTS: If this option is selected, all contours and junctions are filled into a results table.