RGB的像素值都在[0,255],如果我們想給一個(gè)250的像素再加十個(gè)像素會(huì)怎么樣？

NumPy將執(zhí)行模算術(shù)和“環(huán)繞”。比如250 再加10像素，會(huì)繞回到4，
OpenCV 將執(zhí)行剪切并確保像素值永遠(yuǎn)不會(huì)超出范圍[0,255]

NumPy will perform modulo arithmetic and “wrap around”.
OpenCV, on the other hand, will perform clipping and ensure pixel values never fall outside the range [0, 255].

from __future__ import print_function
import numpy as np 
import argparse
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-i","--image",required =True, help="Path to the image")
args = vars(ap.parse_args())

image = cv2.imread(args["image"])
cv2.imshow("Original",image)

print("max of 255:{}".format(cv2.add(np.uint8([200]),np.uint8([100]))))
print("min of 0:{}".format(cv2.subtract(np.uint8([50]),np.uint8([100]))))

print("wrap around :{}".format(np.uint8([200])+np.uint8([100])))
print("wrap around :{}".format(np.uint8([50])-np.uint8([100])))

運(yùn)行結(jié)果如下:

圖·1

M =np.ones(image.shape, dtype="uint8")*100
# defines a NumPy array of ones, with the same
# size as our image.
#為了用100的值而不是1來(lái)填充我們的矩陣，我們簡(jiǎn)單地把1的矩陣乘以100。
added = cv2.add(image,M)
cv2.imshow("Added",added)

M = np.ones(image.shape,dtype ="uint8")*50
subtracted = cv2.subtract(image,M)
cv2.imshow("Subtraced",subtracted)
cv2.waitKey(0)

圖·2

一件事情的畢業(yè)，永遠(yuǎn)是另一件事情的開(kāi)啟.
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