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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Measure the distance of object to the camera

pic1

Distance of circle from camera

Some days ago, I was talking to my friends and one of them asked me if I can write a program to measure the distance of the object to the camera, so I told myself why not write a post about it on my blog. I got the idea of writing this code from Adrian’s blog. You can find the code of distance of object to the camera at the end of this post

In order to determine the distance from our camera to a known object or marker, I am going to utilize triangle similarity.

The triangle similarity goes something like this: Let’s say I have a marker or object with a known width W. Then I place this marker some distance D from my camera. I take a picture of my object using our camera and then measure the apparent width in pixels P. This allows me to derive the perceived focal length F of my camera:

F = (P x D) / W

For example, I place a 21 x 29cm piece of paper (vertically; W = 21) D = 20 cm in front of my camera and take a photo. When I measure the width of the piece of paper in the image, I notice that the perceived width of the paper is P = 133 pixels.

My focal length F is then:

F = (1338px x 20cm) / 21cm = 126.35

As I continue to move my camera both closer and farther away from the object/marker, I can apply the triangle similarity to determine the distance of the object to the camera:

D’= (W x F) / P

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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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Object tracking

Tracking of the blue object (and also my glasses which are blue as well ;D)

Tracking of the blue object (and also my glasses which are blue as well ;D)

  1. Take each frame of the video
  2. Convert from BGR to HSV color-space (HSV, it is more easier to represent a color than RGB color-space)
  3. We threshold the HSV image for a range of blue color
  4. Now extract the blue object, we can do whatever on that image we want.

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