Note: Interested in a more in-depth look at contour approximation? Be sure to check out the PyImageSearch Gurus course where I discuss computer vision and image processing fundamentals such as contours and connected-component analysis in detail. In order to perform contour approximation, we first compute the perimeter of the contour ( Line 11), followed by constructing the actual contour approximation ( Line 12).Ĭommon values for the second parameter to cv2.approxPolyDP are normally in the range of 1-5% of the original contour perimeter. This leads to a resulting approximated curve that consists of a subset of points that were defined by the original curve.Ĭontour approximation is actually already implemented in OpenCV via the cv2.approxPolyDP method. This algorithm is commonly known as the Ramer-Douglas-Peucker algorithm, or simply the split-and-merge algorithm.Ĭontour approximation is predicated on the assumption that a curve can be approximated by a series of short line segments. In order to perform shape detection, we’ll be using contour approximation.Īs the name suggests, contour approximation is an algorithm for reducing the number of points in a curve with a reduced set of points - thus the term approximation. We then have our detect method on Line 8 which requires only a single argument, c, the contour (i.e., outline) of the shape we are trying to identify. We’ll skip the _init_ constructor here since nothing needs to be initialized. Line 4 starts the definition of our ShapeDetector class. # initialize the shape name and approximate the contourĪpprox = cv2.approxPolyDP(c, 0.04 * peri, True) Open up the shapedetector.py file and insert the following code: # import the necessary packages Let’s go ahead and define our ShapeDetector.
#Chessboard pdf open cv face detection code
The first step in building our shape detector is to write some code to encapsulate the shape identification logic.
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#Chessboard pdf open cv face detection install
Inside this module we have shapedetector.py which will store our implementation of the ShapeDetector class.įinally, we have the detect_shapes.py driver script that we’ll use to load an image from disk, analyze it for shapes, and then perform shape detection and identification via the ShapeDetector class.īefore we get started, make sure you have the imutils package installed on your system, a series of OpenCV convenience functions that we’ll be using later in this tutorial: $ pip install imutils
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Looking for the source code to this post? Jump Right To The Downloads Section OpenCV shape detectionīefore we get started with this tutorial, let’s quickly review our project structure: |- pyimagesearchĪs you can see, we have defined a pyimagesearch module.