Find simple shapes in an image

In this post I am going to solve this problem, how to find simple shapes like triangular and square ( or rectangular) in the image.  For simple shape like square or triangular I normally use this procedure:

1. Find Contours in the image ( image should be binary)
2. Approximate each contour using approxPolyDP function.
3. Check number of elements in the approximated contours of all the shapes to recognize the shape. For eg, triangular will have 3; for square or rectangle, it has to meet the following conditions:
* It is convex.
* It has 4 vertices.
* All angles are ~90 degree.
4. Assign the color, run the code for your test image, check its number, fill it with corresponding colors.

Assumptions: Shapes don’t overlap, both of them solid (meaning, there is no white pixels inside the shape (all shapes are black). There can be multiple shapes in the image and they can be rotated any arbitrary number of degrees, and they can be of any size. Important: Triangles are non-obtuse!

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Background subtraction (Opencv Python)

Background subtraction is a major preprocessing steps in many vision based applications. For example, consider the cases like visitor counter where a static camera takes the number of visitors entering or leaving the room, or a traffic camera extracting information about the vehicles etc. In all these cases, first you need to extract the person or vehicles alone. Technically, you need to extract the moving foreground from static background.

BackgroundSubtractorMOG: It is a Gaussian Mixture-based Background/Foreground Segmentation Algorithm. It was introduced in the paper “An improved adaptive background mixture model for real-time tracking with shadow detection” by P. KadewTraKuPong and R. Bowden in 2001. It uses a method to model each background pixel by a mixture of K Gaussian distributions (K = 3 to 5). The weights of the mixture represent the time proportions that those colours stay in the scene. The probable background colours are the ones which stay longer and more static.

While coding, we need to create a background object using the function, cv2.createBackgroundSubtractorMOG(). It has some optional parameters like length of history, number of gaussian mixtures, threshold etc. It is all set to some default values. Then inside the video loop, use backgroundsubtractor.apply() (in my code fgbg.apply()) method to get the foreground mask. (read more about this function here)

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Canny Edge Detection

Simpson family

The Canny Edge Detection is a popular edge detection algorithm. It was developed by John F. Canny in 1986. It is a multi-stage algorithm. Also known to many as the optimal detector, Canny algorithm aims to satisfy three main criteria:

  • Low error rate: Meaning a good detection of only existent edges.
  • Good localization: The distance between edge pixels detected and real edge pixels have to be minimized.
  • Minimal response: Only one detector response per edge.

Canny Edge Detection in OpenCV: cv2.Canny().

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Face and eyes detection using Haar Cascades

OpenCV algorithm is currently using the following Haar-like features which are the input to the basic classifiers:

Haar-like features

Haar-like features

Cascading is a particular case of ensemble learning based on the concatenation of several classifiers, using all information collected from the output from a given classifier as additional information for the next classifier in the cascade. Unlike voting or stacking ensembles, which are multi-expert systems, cascading is a multistage one.

stages Pictures source

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Hand detection using contour method

Contours can be explained simply as a curve joining all the continuous points (along the boundary), having same color or intensity. The contours are a useful tool for shape analysis and object detection and recognition.

There are 3 flags under cv2.findContours() category, but I am discussing only the first two – cv2.CHAIN_APPROX_NONE and cv2.CHAIN_APPROX_SIMPLE.

First one finds all the points on the contour or the boundary. But actually do we need all the points? For e.g. , you found the contour of a straight line. Do you need all the points on the line to represent that line? No, we need just two end points of that line. This is what second flag does. It removes all redundant points and compresses the contour.

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