Contours can be seen as a curve joining all the points along the boundary of a certain shape. As they define the boundary of the shape, an analysis of these points can reveal key information for shape analysis and object detection and recognition. OpenCV provides many functions to properly detect and process contours. However, before diving into these functions, we are going to see the structure of a sample contour. For example, the following function simulates detecting a contour in a hypothetical image:
def get_one_contour():
"""Returns a 'fixed' contour"""
cnts = [np.array(
[[[600, 320]], [[563, 460]], [[460, 562]], [[320, 600]], [[180, 563]], [[78, 460]], [[40, 320]], [[77, 180]], [[179, 78]], [[319, 40]], [[459, 77]], [[562, 179]]], dtype=np.int32)]
return cnts
As you can see, a contour is an array composed...
