How to detect pits on smart card contact pins using diplib

Question

During the smart card manufacture process, a probe stucks could leave pits on contact pins. I try to use dip.tophat and dip.HysteresisThreshold to extract the pits, this works but found more local height outside or near the edge of the contact pins. How could I filter those extra local height out?

import diplib as dip

height = dip.ImageRead('my2.png')

local_height = dip.Tophat(height, 9)

dip.viewer.ShowModal(local_height)

inselbergs = dip.HysteresisThreshold(local_height, 20, 30)

dip.viewer.ShowModal(inselbergs)

dip.viewer.ShowModal(dip.Overlay(height, inselbergs))

labels = dip.Label(inselbergs, minSize = 3, maxSize = 20)

dip.viewer.ShowModal(dip.Overlay(height, labels))

dip.ImageWrite(dip.Overlay(height, labels), 'out.jpg')

result = dip.MeasurementTool.Measure(labels, features=['Size', 'Center'])
print(result)

two stucks probe leave the pits on contact pins

I want to filter out extra local height

Also I would like to detect scratchs on the pin contacts

import diplib as dip

height = dip.ImageRead('my2.png')

vess = dip.FrangiVesselness(height, sigmas=0.6, polarity='black')
dip.viewer.ShowModal(vess)

myout = dip.HysteresisThreshold(vess,0.2,0.4)
dip.viewer.ShowModal(dip.Overlay(height, myout))

labels = dip.Label(myout, minSize = 3, maxSize = 30)
dip.viewer.ShowModal(dip.Overlay(height, labels))

dip.ImageWrite(dip.Overlay(height, labels), 'out2.jpg')

dip::FrangiVesselness find so many lines

I want to filter out the extra local height outside or near the pin contacts, I want to filter out the extra lines which didn't represent the scratchs. The pre-punched hole on bottom left of each module is so problematic, it must be filtered out also

English is not my primiary language, so in my country ocr equals to computer vision — HotCat, Mar 13 '23 at 16:37
If you read the mouse over tip of `ocr` tag above you will see that it typically is used as the abbreviation for "Optical Character Recognition", which might be misleading in your case. — Markus, Mar 13 '23 at 16:46
sorry for the inconvenience, this is my first day registering stackOverflow and posting the first question. I am not familiar with the feature of the editor as well as English culture — HotCat, Mar 13 '23 at 17:00
You need to detect the contact areas first, then limit the other detections to be within those areas. This looks like something you could do just by color (gray vs black) and size (fill in holes thinner than some width). Your approach otherwise is quite good. — Cris Luengo, Mar 13 '23 at 17:24

HotCat · Accepted Answer · 2023-03-16T05:14:13.550

You need to detect the contact areas first, then limit the other detections to be within those areas

So I take the advice from the author of diplib, also I enhance the light source and change the background from white to black. The following is what I did so far


import diplib as dip
import math

img = dip.ImageRead('my2.png')
obj = dip.Threshold(img)[0]

dip.viewer.Show(obj)

obj3 = dip.Erosion(obj, 6)

# This operator stablize the ultimate result tremendously
obj3 = dip.ClosingByReconstruction(obj3, 6)

dip.viewer.Show(obj3)

obj4 = img.Copy()
obj4.Mask(obj3)

dip.viewer.Show(obj4)

lab = dip.Label(obj3, minSize = 1700, maxSize = 6000)
msr = dip.MeasurementTool.Measure(lab,img,['Size'])
print(msr)

local_height = dip.Tophat(obj4, 9)

# I don't know why Tophat produce some strange inselbergs, so filter them all
mask = local_height < 100
dip.viewer.Show(mask)

local_height = local_height.Copy()
local_height.Mask(mask)

dip.viewer.Show(local_height)
inselbergs = dip.HysteresisThreshold(local_height, 20, 50)

dip.viewer.Show(dip.Overlay(obj4, inselbergs))

labels = dip.Label(inselbergs, minSize = 3, maxSize = 16)
dip.viewer.Show(dip.Overlay(obj4, labels))

# The result is greatly improved, but I still have to find the pre-punched holes as the mask to filter out the glitchs near them
dip.ImageWrite(dip.Overlay(obj4, labels), 'out3.jpg')

This result is not bad, although extra work still needed to filter out the spots near the pre-punched hole area. Diplib is awesome,provide the nice abstraction to manipulate images.But at the price of relative slow processing time compare to OpenCV

At last, I leave some code to find the pre-punched holes mask

import diplib as dip
import math

img = dip.ImageRead('my2.png')

# Extract object
obj = dip.Threshold(img)[0]

dip.viewer.Show(obj)

obj3 = dip.Dilation(obj, 5)

dip.viewer.Show(~obj3)

obj4 = dip.EdgeObjectsRemove(~obj3)
dip.viewer.Show(obj4)

lab = dip.Label(obj4, minSize = 500, maxSize = 700)
dip.viewer.Show(lab)

msr = dip.MeasurementTool.Measure(lab,img,['Size'])
print(msr)

I am a Haskeller, ultimately I would like to integrate the code to my own machine vision framework. So I rewrite the algorithm in C++ so that I can easily integrated using inline-c-cpp framwork


#include "diplib.h"
#include "dipviewer.h"
#include "diplib/simple_file_io.h"
#include "diplib/linear.h"       // for dip::Gauss()
#include "diplib/segmentation.h" // for dip::Threshold()
#include "diplib/regions.h"      // for dip::Label()
#include "diplib/measurement.h"
#include "diplib/mapping.h"      // for dip::ContrastStretch() and dip::ErfClip()
#include "diplib/binary.h"
#include "diplib/testing.h"
#include "diplib/generation.h"
#include "diplib/statistics.h"
#include "diplib/morphology.h"
#include "diplib/detection.h"
#include "diplib/display.h"

int main() {

  dip::testing::Timer timer;
  dip::Image circle;

  timer.Reset();

  auto a = dip::ImageRead("./my2.jpg");

  dip::Image b, c, d, e, f, g, labels;
  dip::Threshold(a, b);

  c = dip::ClosingByReconstruction(b,6);

  d = dip::Erosion(c,1);

  f = a.Copy();

  f.At(~d).Fill(0);

  e = dip::FrangiVesselness(f, {0.67});
  
  dip::viewer::Show(e);

  g = dip::HysteresisThreshold(e,0.12,0.4);

  dip::viewer::Show(g);

  labels = dip::Label(g, 0, 3, 30);

  dip::MeasurementTool measurementTool;
  auto msr = measurementTool.Measure(labels, f, {"Radius"});

  std::cout << msr << '\n';

  dip::Image feature = dip::ObjectToMeasurement(labels, msr[ "Radius" ]);

  // filter out extra regions through Radius.StdDev
  labels.At( feature[3] > 0.0002 ) = 0; 

  dip::viewer::Show(dip::Overlay(a,labels));

  dip::viewer::Spin();

  return 0;
}

The following is the C++ make file

cmake_minimum_required(VERSION 2.8)
project( detection )
find_package( DIPlib REQUIRED )
include_directories( ${OpenCV_INCLUDE_DIRS} )
add_executable( detection detection.cpp )
target_link_libraries( detection DIP DIPviewer ${OpenCV_LIBS} )

How to detect pits on smart card contact pins using diplib

1 Answers1