LAB: Grayscale Image Segmentation -Gear

LAB: Grayscale Image Segmentation

I. Introduction

Goal: Defective Gear Inspection System

Plastic gears are widely used in many applications, including toys, RC cars, and plastic-based hardware. Since they are made of plastic, it is fragile and can have broken gear teeth. You are asked to develop a machine vision system that can inspect defective plastic gears.

Download the test image

After analyzing histogram, applying thresholding and morphology, we can identify and extract the target objects from the background by finding the contours around the connected pixels.

II. Procedure

• Design algorithms to detect defective gear for given images.

  • Include a flowchart to explain the algorithm flow.

• You should apply image processing algorithms, which you have learnt in class, as much as possible.

• If you want to use other algorithms that were not covered in class, then you should briefly explain how that algorithm works.

• For the output, you should calculate the following

  • Number of defective teeth

  • Diameter of the gear

  • Quality Inspection (Pass or Fail)

• You must explain each process with appropriate results

• You MUST include all the following in the report.

  • A simple flowchart to explain your algorithm

  • Apply appropriate filters to enhance image

  • Explain how the appropriate threshold value was chosen (if used)

  • Apply the appropriate morphology method to segment parts (if necessary)

    • Find the contour and draw the segmented objects. See Appendix

  • Explain how to determine the defective teeth

Output Examples

Examples: Output images of each process

III. Report

You are required to write a concise lab report and submit the program files.

First, read LAB Report Instruction

Lab Report:

• Use the given Lab Report Template

• Show what you have done with concise explanations and example results of each necessary process

• In the appendix, show your source code.

• You must write the report in markdown file (*.md),

  • Recommend (Typora 0.x < 1.x) or Notion

  • When embedding images

    Option 1) If you are using local path images: You must include local image folder with the report in zip file

    Option 2) Use online link for images.

• Submit in both PDF and original documentation file/images

Source Code:

• Zip all the necessary source files.

• Only the source code files. Do not submit visual studio project files etc.

Appendix

Tip: (contour_demo.cpp)

// OpenCV - use findCountours function

C++: void findContours (InputOutputArray image, OutputArrayOfArrays contours, int mode, int method, Point offset=Point())

C++: void drawContours(InputOutputArray image, InputArrayOfArrays contours, int contourIdx, const Scalar& color, int thickness=1, int lineType=8, InputArrayhierarchy=noArray(), int maxLevel=INT_MAX, Point offset=Point() )

// Example code
// dst: binary image
 vector<vector<Point>> contours;
 vector<Vec4i> hierarchy;

  /// Find contours
 findContours( dst, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
  
 /// Draw all contours excluding holes
 Mat drawing( dst.size(), CV_8U,Scalar(255));
 drawContours( drawing, contours, -1, Scalar(0), CV_FILLED);
   
 cout<<" Number of coins are ="<<contours.size()<<endl;
 
 for( int i = 0; i< contours.size(); i++ )
 {
      printf(" * Contour[%d] -  Area OpenCV: %.2f - Length: %.2f \n", i, contourArea(contours[i]),                          arcLength( contours[i], true ) );       
 }