omputer Science Use only NumPy to create the Normalize Class: Image Normalization is a technique used to touch up images that are too dark or too light for the human viewing experience. Implement a version that outputs images with full dynamic range (0–255 pixel intensity). In other words, for each color channel (r, g, b), rescale the range of pixel values for that channel to include both 0 and 255. After normalizing, there should be at least one pixel with a red value of 0, at least one pixel with a red value of 255, at least one pixel with a green value of 0, etc. To normalize a list of numbers nums to the range 0–n, set nums[i] = (nums[i]−min(nums))/(max(nums)−min(nums))∗n for all i. Be careful! When max(nums) == min(nums), you should not touch that list (why?

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
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Use only NumPy to create the Normalize Class: Image Normalization is a technique used to touch up images that are too dark or too light for the human viewing experience. Implement a version that outputs images with full dynamic range (0–255 pixel intensity). In other words, for each color channel (r, g, b), rescale the range of pixel values for that channel to include both 0 and 255. After normalizing, there should be at least one pixel with a red value of 0, at least one pixel with a red value of 255, at least one pixel with a green value of 0, etc. To normalize a list of numbers nums to the range 0–n, set nums[i] = (nums[i]−min(nums))/(max(nums)−min(nums))∗n for all i. Be careful! When max(nums) == min(nums), you should not touch that list (why?).

class Normalize(ImageEffect):
def apply(pixels: np.ndarray) -> np.ndarray:
Transcribed Image Text:class Normalize(ImageEffect): def apply(pixels: np.ndarray) -> np.ndarray:
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