Opencv With Python Quiz
- 1.What does the import cv2 statement do?
- A.
Imports the SciPy library for numerical processing.
- B.
Imports the NumPy library for numerical processing.
- C.
Imports our OpenCV Python bindings.
- D.
Displays an image to our screen.
- 2.Given the following NumPy array shape, how would we interpret the width, height, and number of channels in the image: (400, 600, 3):
- A.
Width=600, height=400, channels=3
- B.
Width=600, height=3, channels=400
- C.
Width=400, height=600, channels=3
- D.
width=3, width=600, channels=400
- 3.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?
- A.
(550, 700, 3)
- B.
(3, 550, 700)
- C.
(700, 550, 3)
- D.
(3, 700, 550)
- 4.We have an image that is 393 pixels wide and 312 tall. How many total pixels are in the image?
- A.
367,848
- B.
122,616
- C.
280,800
- D.
93,600
- 5.OpenCV stores RGB pixels in what order?
- A.
GBR
- B.
RGB
- C.
BRG
- D.
BGR
- 6.The RGB tuple (255, 0, 0) codes for red. But OpenCV would actually interpret this color as:
- A.
Orange
- B.
Blue
- C.
Green
- D.
Yellow
- 7.Translation: 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?
- A.
M = np.float32([[1, 0, -20], [0, 1, 10]])
- B.
M = np.float32([[1, 0, 20], [0, 1, -10]])
- C.
M = np.float32([[1, 0, -10], [0, 1, 20]])
- D.
M = np.float32([[1, 0, 10], [0, 1, -20]])
- 8.Translation: 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?
- A.
M = np.float32([[1, 0, -180], [0, 1, -30]])
- B.
M = np.float32([[1, 0, -180], [0, 1, 30]])
- C.
M = np.float32([[1, 0, 30], [0, 1, 180]])
- D.
M = np.float32([[1, 0, -30], [0, 1, -180]])
- 9.Cropping: I want to extract a rectangular region from my image starting at x=9, y=47 and ending at x=97, y=96. What is the correct line of code to perform this cropping?
- A.
Crop = image[9:97, 47:96]
- B.
Crop = image[47:96, 9:97]
- C.
Crop = image[9:47, 97:96]
- D.
crop = image[97:96, 9:47]
- 10.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?
- A.
Crop = image[80:69, 1:48]
- B.
Crop = image[1:48, 80:69]
- C.
Crop = image[48:80, 48:69]
- D.
Crop = image[48:69, 1:80]
Questions: 37 | Attempts: 6630 | Last updated: Dec 21, 2020
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