This project is inspired by a hands-on lecture (Brownian motion exp.) in school. The main goal of this project is to extend self-learning programing skill and provide tools to optimize the workflow. Currently, looking forward to share with junior students!

1. Organize input data (tif images) by group and store in respective folder

Folder_Name_List = ['1', '2', '3', '4', '5','Free','Laser']

2. Program Setup

# Step1 Clone this repository to your folder 
$ git clone https://github.com/sc0210/Particle-Tracking-OpenCV.git

# Step2 Move current working directory into the folder.
$ cd Particle-Tracking-OpenCV

# Step3 Install package used this project 
$ pip install -r requirements

# Step4 Execute main program (data must involve in the same folder path)
$ python main.py

3. Analyze done! Check up the result in ./Export

1. Add different mode of tracking methods
2. Add export excel sheet of particle tracking results
3. Revised the computing algorithm

(last updated 8/24)

• Part 1 Develop tools with funcitons listed bellow

  • Read several types(tif, jpg, png) of image

    - ReadGrayImg(RscPath, show=False)
    

  • Convert sequences of images into animation

    - IMG2MP4(SrcFolder,OutFolder,OutName,FPS=5)
    - PNG2GIF(SrcFolder,OutFolder,OutName,ImgFormat="png", duration=120)
    

  • Image preprocessing (kernel/ filter) (edge detection/ blur/ sharpen/ fill)

    - dog(img,size=(0,0),k=1.6,sigma=0.5,gamma=1)
    - xdog(img,sigma=0.5,k=1.6, gamma=1,epsilon=1,phi=1)
    - xdog_garygrossi(img,sigma=0.5,k=200, gamma=0.98,epsilon=0.1,phi=10)
    

  • Relation beetween sequentail of images

    - normxcorr2(template,image,mode="full")
    - Track(SrcFolder,OutFolder,OutName="test",SavePlot=True)
    

  • Coefficient of viscosity
  • Graph the in XY cororidnated system

    - MSD(X,Y,OutFolder,filename,length,ImgShow=False)
    - MDD(X,Y,OutFolder,filename,length,ImgShow=False)
    

• Part 2 Organized and record the process

  • Github -> Create this repository!
  • TA (teaching material, demo code, ppt)

1. HoughCircles() (OpenCV document) [https://reurl.cc/0XZbxb]
2. Canny edge detection (OpenCV document) [https://reurl.cc/GEK9xy]
3. Python OpenCV 影像二值化 Image Thresholding [https://reurl.cc/D3Ax9e]
4. cv2霍夫圓環檢測（HoughCircle）[https://reurl.cc/KQ02k9]
5. Git remote connection [https://reurl.cc/rR50xZ]