Instructions
Compile and read through the included Python tutorial.
2 questions.
Include code.
Feel free to include images or equations.
Please make this document anonymous.
Please use only the space provided and keep the page breaks. Please do not make new pages, nor remove pages. The document is a template to help grading.
If you really need extra space, please use new pages at the end of the document and refer us to it in your answers..
Questions
Q1: We wish to set all pixels that have a brightness of 10 or less to 0, to remove sensor noise. However, our code is slow when run on a database with 1000 grayscale images. Image: grizzlypeakg.png
import cv2 import numpy as np
Q1.1: How could we speed it up?
Q1.2: What factor speedup would we receive over 1000 images? Please measure it.
Ignore file loading; assume all images are equal resolution; don’t assume that the time taken for one image ×1000 will equal 1000 image computations, as single short tasks on multitasking computers often take variable time. A1.2: Factor speedup: 882.9766897090233
Q1.3: How might a speeded-up version change for color images? Please measure it.
Image: grizzlypeak.jpg
Q2: We wish to reduce the brightness of an image but, when trying to visualize the result, we see a brightness-reduced scene with some weird “corruption” of color patches. Image: gigi.jpg
import cv2 import numpy as np
I = cv2.imread(’gigi.jpg’).astype(np.uint8)
I = I - 40
cv2.imwrite(’result.png’, I)
Q2.1: What is incorrect with this approach? How can it be fixed while maintaining the same amount of brightness reduction?
import cv2 import numpy as np
I = cv2.imread(’gigi.jpg’).astype(np.uint8)
B = I < 40
I = I - 40
I[B] = 0
cv2.imwrite(’result.png’, I)
Q2.2: Where did the original corruption come from? Which specific values in the original image did it represent?
