1.遍历图像对其每个像素取反
import cv2 as cv import numpy as np img = cv.imread("d:/girl.jfif") cv.imshow("img", img) height = img.shape[0] width = img.shape[1] channels = img.shape[2] t1 = cv.getTickCount() for h in range(height): for w in range(width): for c in range(channels): img[h, w, c] = 255 - img[h, w, c] t2 = cv.getTickCount() t = (t2 - t1)/cv.getTickFrequency()*1000 print("%f ms"%t) cv.imshow("img2", img) cv.waitKey(0)
注意cv.getTickCount()的使用可以获得程序运行时间。
t = (t2 - t1)/cv.getTickFrequency()*1000
其中t的单位是毫秒
顺便一提，可以使用cv.bitwise_not(image)来实现对图像取反，比上面的代码要快几千倍。事实上，还有bitwise_and, bitwise_or, bitwise_xor等位操作的函数
2.构造一张蓝色的图
‘‘‘ 构造一张蓝色的图 ‘‘‘ import cv2 as cv import numpy as np img = np.zeros(shape=[400, 400, 3], dtype=np.uint8) img[:,:,0] = np.ones([400, 400])*255 cv.imshow("img", img) cv.waitKey(0)
三通道，bgr，所以只需要对第三维的索引0赋值。
3.构造单通道灰度图
‘‘‘ 构造一张灰色的图 ‘‘‘ import cv2 as cv import numpy as np img = np.ones(shape=[400, 400, 1], dtype=np.uint8) height = img.shape[0] width = img.shape[1] channels = img.shape[2] for h in range(height): for w in range(width): for c in range(channels): img[h, w, c] = (h+w)%255 cv.imshow("img", img) cv.waitKey(0)
结果很漂亮惹，懒得截图了
原文：https://www.cnblogs.com/loubin/p/12271412.html