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Crack Detection using OpenCV and Python |Crack detection image processing | Image Processing
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Peoject: Crack Detection in images
Demonstration of Crack Detection using Python IDE.
Steps:
Step1: Remove the inhomogeneous background illumination.
Step2: Apply a big gaussian blurring and subtract the original image from the blurred one.
Step3: Apply Sobel edge detection and a morphological close.
Step4: Remove the noise using blob analysis (e.g. remove regions with small areas).
Note: In principle, you have to convert the image between 0 and 1 before GaussianBlur. Additionally to get better results you also can apply a non-maximal suppression after Sobel.
Source Code & Link:
#Crackdetectionusingpython, #Python, #Sobeledgedetection, #Crackdetectionmethods, #Crackdetectionusingimageprocessing, #Crackdetection
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Demonstration of Crack Detection using Python IDE.
Steps:
Step1: Remove the inhomogeneous background illumination.
Step2: Apply a big gaussian blurring and subtract the original image from the blurred one.
Step3: Apply Sobel edge detection and a morphological close.
Step4: Remove the noise using blob analysis (e.g. remove regions with small areas).
Note: In principle, you have to convert the image between 0 and 1 before GaussianBlur. Additionally to get better results you also can apply a non-maximal suppression after Sobel.
Source Code & Link:
#Crackdetectionusingpython, #Python, #Sobeledgedetection, #Crackdetectionmethods, #Crackdetectionusingimageprocessing, #Crackdetection
====*====
For More Videos:
● Face Recognition based Attendance System
● Vehicle Detection And Counting Using Python
● Real-Time Face recognition
●Smile Detector Using python
●Face and Eye detection Using python
●Fire detection using python.
●Bird Detection Using python
●Student Management System Using Python and SQL
● Python Tutorial In Detail
● Data Visualisation Running Graph
● Computer Hacks and Tricks
====*====
Follow Me Here For More Help or Queries
====*====
SUBSCRIBE NOW for weekly videos on Python Tutorial, Computer Hacks and Tricks, Data Visualisation, Technology, and Many More.
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