OpenCV 3 Computer Vision with Python Cookbook

How to do it...

1. Import the modules:

import cv2import numpy as np

2. Draw a test image:

img = np.zeros((500, 500), np.uint8)cv2.circle(img, (200, 200), 50, 255, 3)cv2.line(img, (100, 400), (400, 350), 255, 3)

3. Detect lines using the probabilistic Hough transform:

lines = cv2.HoughLinesP(img, 1, np.pi/180, 100, 100, 10)[0]

4. Detect circles using the Hough transform:

circles = cv2.HoughCircles(img, cv2.HOUGH_GRADIENT, 1, 15, param1=200, param2=30)[0]

5. Draw the detected lines and circles:

dbg_img = np.zeros((img.shape[0], img.shape[1], 3), np.uint8) for x1, y1, x2, y2 in lines:    print('Detected line: ({} {}) ({} {})'.format(x1, y1, x2, y2))    cv2.line(dbg_img, (x1, y1), (x2, y2), (0, 255, 0), 2) for c in circles: print('Detected circle: center=({} ...