OpenCV 3 Computer Vision with Python Cookbook

How to do it...

For this recipe, you need to perform the following steps:

1. You can easily read an image with the cv2.imread function, which takes path to image and optional flags:

import argparse
import cv2
parser = argparse.ArgumentParser()
parser.add_argument('--path', default='../data/Lena.png', help='Image path.')
params = parser.parse_args()
img = cv2.imread(params.path)

2. Sometimes it's useful to check whether the image was successfully loaded or not:

assert img is not None  # check if the image was successfully loaded
print('read {}'.format(params.path))
print('shape:', img.shape)
print('dtype:', img.dtype)

3. Load the image and convert it to grayscale, even if it had many color channels originally:

img = cv2.imread(params.path, cv2.IMREAD_GRAYSCALE) ...