Take yourself on an exploration of computer vision with our OpenCV with Python Quiz. Tailored for developers and enthusiasts, this quiz delves into the intricacies of OpenCV, a powerful library for image and video processing. Challenge yourself with questions covering image manipulation, feature extraction, and machine learning integration in Python. Whether you're a beginner or an experienced Python coder, this quiz provides a platform to test and enhance your OpenCV knowledge. Elevate your skills in computer vision and advance your proficiency in harnessing the capabilities of OpenCV with this engaging quiz. Take the challenge and deepen your understanding of image processing in Python.
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.
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(550, 700, 3)
(3, 550, 700)
(700, 550, 3)
(3, 700, 550)
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367,848
122,616
280,800
93,600
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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]])
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GBR
RGB
BRG
BGR
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Width=600, height=400, channels=3
Width=600, height=3, channels=400
Width=400, height=600, channels=3
width=3, width=600, channels=400
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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]])
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Crop = image[80:69, 1:48]
Crop = image[1:48, 80:69]
Crop = image[48:80, 48:69]
Crop = image[48:69, 1:80]
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Crop = image[9:97, 47:96]
Crop = image[47:96, 9:97]
Crop = image[9:47, 97:96]
crop = image[97:96, 9:47]
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