这是一个美国的Python计算机视觉代写
1. Image Histogram
a. (20 pts) Build your own implementation of the histogram equalization function from
scratch, which takes an image as its input and returns an image that has been histogram
equalized.
b. (5 pts) Perform your function on an image of your interest (you can pick any image you
like) and compare the result with that of using OpenCV function cv2.equalizeHist(·).
2. Image Smoothing
a. (20 pts) Build your own implementation of the Gaussian smoothing function from scratch,
which takes an image, a filter size, and sigma (standard deviation) as its inputs and returns
an image that has been blurred. Please note that
a. cv2.getGaussianKernel(), cv2.filter2D(), cv2.sepFilter2D(), cv2.GaussianBlur()
are NOT allowed to be used in your implementation.
b. (Optional) 10 extra points will be given if separable 1D Gaussian kernels are used
to reduce the computational cost.
b. (5 pts) Perform your function on an image of your interest (you can pick any image you
like) and compare the result with that using OpenCV function cv2.GaussianBlur(·).
What to Submit?
1. Python source codes in “.ipynb” format. Please note that
a. don’t use .py format
b. use relative file paths to load (save) images from (to) disk.
c. comment some important code lines,
2. Input images used.
3. Output images generated.
4. Please zip all documents as yourname_assignment1.zip and submit it to Canvas.
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