python openCV

What does the import cv2 statement do?

• Imports the SciPy library for numerical processing.

• Imports the NumPy library for numerical processing.

• Imports our OpenCV Python bindings.

• Displays an image to our screen.

We have an image that is 393 pixels wide and 312 tall. How many total pixels are in the image?

• 367,848

• 122,616

• 280,800

• 93,600

OpenCV stores RGB pixels in what order?

• BGR

• GBR

• RGB

• BRG

The RGB tuple (255, 0, 0) codes for red. But OpenCV would actually interpret this color as:

• Orange

• Blue

• Green

• Yellow

Cropping: Now, suppose I want to extract a second rectangular region from my image starting at x=1, y=48 and ending at x=80, y=69. What is the correct line of code to perform this cropping?

• Crop = image[80:69, 1:48]

• Crop = image[1:48, 80:69]

• Crop = image[48:80, 48:69]

• Crop = image[48:69, 1:80]

Given the following NumPy array shape, how would we interpret the width, height, and number of channels in the image: (400, 600, 3):

• Width=600, height=400, channels=3

• Width=600, height=3, channels=400

• Width=400, height=600, channels=3

• width=3, width=600, channels=400

Suppose our image has a width of 700 pixels, a height of 550 pixels, and 3 channels, one for each Red, Green, and Blue component. How would we express this image as a NumPy array shape?

• (550, 700, 3)

• (3, 550, 700)

• (700, 550, 3)

• (3, 700, 550)

We have an image that is 393 pixels wide and 312 tall. How many total pixels are in the image?

• 367,848

• 122,616

• 280,800

• 93,600

What is the correct line of code that defines a translation matrix that shifts an image 10 pixels up and 20 pixels to the right?

• M = np.float32([[1, 0, -20], [0, 1, 10]])

• M = np.float32([[1, 0, 20], [0, 1, -10]])

• M = np.float32([[1, 0, -10], [0, 1, 20]])

• M = np.float32([[1, 0, 10], [0, 1, -20]])

What is the correct line of code that defines a translation matrix that shifts an image 30 pixels down and 180 pixels to the left?

• M = np.float32([[1, 0, -180], [0, 1, -30]])

• M = np.float32([[1, 0, -180], [0, 1, 30]])

• M = np.float32([[1, 0, 30], [0, 1, 180]])

• M = np.float32([[1, 0, -30], [0, 1, -180]])