Another vital task in computer vision is contour detection, not only because of the obvious aspect of detecting contours of subjects contained in an image or video frame, but because of the derivative operations connected with identifying contours.
These operations are, namely, computing bounding polygons, approximating shapes, and generally calculating regions of interest, which considerably simplify interaction with image data because a rectangular region with NumPy is easily defined with an array slice. We will be using this technique a lot when exploring the concept of object detection (including faces) and object tracking.
Let's go in order and familiarize ourselves with the API first with 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)
image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
color = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
img = cv2.drawContours(color, contours, -1, (0,255,0), 2)
cv2.imshow("contours", color)
cv2.waitKey()
cv2.destroyAllWindows()Firstly, we create an empty black image that is 200x200 pixels in size. Then, we place a white square in the center of it utilizing ndarray's ability to assign values on a slice.
We then threshold the image, and call the findContours() function. This function has three parameters: the input image, hierarchy type, and the contour approximation method. There are a number of aspects that are of particular interest in this function:
- The function modifies the input image, so it would be advisable to use a copy of the original image (for example, by passing
img.copy()). - Secondly, the hierarchy tree returned by the function is quite important:
cv2.RETR_TREEwill retrieve the entire hierarchy of contours in the image, enabling you to establish "relationships" between contours. If you only want to retrieve the most external contours, usecv2.RETR_EXTERNAL. This is particularly useful when you want to eliminate contours that are entirely contained in other contours (for example, in a vast majority of cases, you won't need to detect an object within another object of the same type).
The findContours function returns three elements: the modified image, contours, and their hierarchy. We use the contours to draw on the color version of the image (so that we can draw contours in green) and eventually display it.
The result is a white square with its contour drawn in green. Spartan, but effective in demonstrating the concept! Let's move on to more meaningful examples.