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OpenCV Python Tutorial For Beginners 29 - Hough Line Transform using HoughLines method in OpenCV
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In this video on OpenCV Python Tutorial For Beginners, we are going to see Hough Line Transform using HoughLines method in OpenCV.
OpenCV implements two kind of Hough Line Transforms
The Standard Hough Transform (HoughLines method)
The Probabilistic Hough Line Transform (HoughLinesP method)
lines = cv.HoughLines(image, rho, theta, threshold)
image : source image.
lines : Output vector of lines. Each line is represented by a 2 or 3 element vector (ρ,θ) or (ρ,θ,votes) .
ρ is the distance from the coordinate origin (0,0) (top-left corner of the image). θ is the line rotation angle in radians . votes is the value of accumulator.
rho : Distance resolution of the accumulator in pixels.
theta : Angle resolution of the accumulator in radians.
threshold : Accumulator threshold parameter. Only those lines are returned that get enough votes ( greater then threshold ).
OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python.
it is Open Source and free. opencv is easy to use and install.
Starting with an overview of what the course will be covering, we move on to discussing morphological operations and practically learn how they work on images. We will then learn contrast enhancement using equalization and contrast limiting. Finally we will learn 3 methods to subtract the background from the video and implement them using OpenCV.
At the end of this course, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science.
Feel the real power of Python and programming! The course offers you a unique approach of learning how to code by solving real world problems.
References:
#ProgrammingKnowledge #ComputerVision #OpenCV
★★★Top Online Courses From ProgrammingKnowledge ★★★
★★★ Online Courses to learn ★★★
★★★ Follow ★★★
DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
OpenCV implements two kind of Hough Line Transforms
The Standard Hough Transform (HoughLines method)
The Probabilistic Hough Line Transform (HoughLinesP method)
lines = cv.HoughLines(image, rho, theta, threshold)
image : source image.
lines : Output vector of lines. Each line is represented by a 2 or 3 element vector (ρ,θ) or (ρ,θ,votes) .
ρ is the distance from the coordinate origin (0,0) (top-left corner of the image). θ is the line rotation angle in radians . votes is the value of accumulator.
rho : Distance resolution of the accumulator in pixels.
theta : Angle resolution of the accumulator in radians.
threshold : Accumulator threshold parameter. Only those lines are returned that get enough votes ( greater then threshold ).
OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python.
it is Open Source and free. opencv is easy to use and install.
Starting with an overview of what the course will be covering, we move on to discussing morphological operations and practically learn how they work on images. We will then learn contrast enhancement using equalization and contrast limiting. Finally we will learn 3 methods to subtract the background from the video and implement them using OpenCV.
At the end of this course, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science.
Feel the real power of Python and programming! The course offers you a unique approach of learning how to code by solving real world problems.
References:
#ProgrammingKnowledge #ComputerVision #OpenCV
★★★Top Online Courses From ProgrammingKnowledge ★★★
★★★ Online Courses to learn ★★★
★★★ Follow ★★★
DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
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