```python
import numpy as np
import cv2

img = cv2.imread('opencv-logo.png')
imgray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ret, thresh = cv2.threshold(imgray, 127, 255, 0)
contours, hierarchy = cv2.findCountours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

print("Number of Contours = " + str(len(contours))

cv2.drawContours(img, contours, -1, (0, 255, 0) , 3)

cv2.imshow('Image', img)
cv2.imshow('Image gray', imgray)
cv2.waitkey(0)
cv2.destroyAllWindows()
```

University of Michigan - Ann Arbor

Contour maps are a tool that can be used to reduce an image to a set of lines where we can use pattern recognition to identify common features. This is used when identifying specific features in people's eyes or on a persons face. 
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Contours are the curves that join all the continuous points on the image. We usually use binary image when working with contours. To turn something to binary, we just use the ```cvtColor``` method and convert it to grayscale. 

This is very useful for shape analysis as well. You can download the openCV logo and try the following code example out on that image. It will outline the edges of all 9 shapes. 

Contour Maps

Code Example:

Thresholds help OpenCV gauge what is important and what can be ignored. There are many different methods to creating thresholds and each have varying benefits and downsides. So it is important to experiment with them before usage.

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