Contour detection

A vital task in computer vision is contour detection. We want to detect contours or outlines of subjects contained in an image or video frame, not only as an end in itself but also as a step toward other operations. These operations are, namely, computing bounding polygons, approximating shapes, and generally calculating regions of interest (ROIs). ROIs considerably simplify interaction with image data because a rectangular region in NumPy is easily defined with an array slice. We will be using contour detection and ROIs a lot when we explore the concepts of object detection (including face detection) and object tracking in later chapters.

Let's familiarize ourselves with the contour detection API via an example:

import cv2
import numpy as np
img = np.zeros((200, 200), dtype=np.uint8)
img[50:150, 50:150] = 255
ret, thresh = cv2.threshold(img, 127, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,
                                       cv2.CHAIN_APPROX_SIMPLE...