一.像素逻辑运算:
如下图的一个取反(not)操作:
具体操作:
"像素逻辑运算" import cv2 as cv import numpy as np def add_demo(m1,m2): dst = cv.add(m1,m2) cv.imshow('add',dst) def subtract_demo(m1,m2): dst = cv.subtract(m1,m2) cv.imshow('subtract',dst) def divide_demo(m1,m2): dst = cv.divide(m1,m2) cv.imshow('divide',dst) def multiply_demo(m1,m2): dst = cv.multiply(m1,m2) cv.imshow('multipy',dst) #均值 def mean_demo(m1,m2): m1 = cv.mean(m1) m2 = cv.mean(m2) print(m1,m2) #与运算 def logic_and(m1,m2): dst = cv.bitwise_and(m1,m2) cv.imshow('logic-and',dst) #或运算 def logic_or(m1,m2): dst = cv.bitwise_or(m1,m2) cv.imshow('logic-or',dst) #非运算 def logic_not(m1,m2): dst1 = cv.bitwise_not(m1) dst2 = cv.bitwise_not(m2) cv.imshow('logic-not1',dst1) cv.imshow('logic-not2',dst2) #对比度与亮度的调节 def contrast_bright_demo(image,c,b): h,w,ch = image.shape blank = np.zeros([h,w,ch],image.dtype) dst = cv.addWeighted(image,c,blank,1-c,b) cv.imshow('c_b',dst) src1= cv.imread(r"D:opencv-test ed.png") src2= cv.imread(r"D:opencv-testlack.png") print(src1.shape) print(src2.shape) # cv.namedWindow("input image",cv.WINDOW_AUTOSIZE) cv.imshow("red",src1) cv.imshow("black",src2) # add_demo(src1,src2) # multiply_demo(src1,src2) # divide_demo(src1,src2) # mean_demo(src1,src2) # logic_and(src1,src2) # logic_or(src1,src2) logic_not(src1,src2) # subtract_demo(src1,src2) # contrast_bright_demo(src1,1.2,10) cv.waitKey(0) cv.destroyAllWindows()
二.ROI与泛洪填充
ROI：ROI区域即一张图上你感兴趣的区域，可以用数组切片的方式选出图像的特定区域，对ROI区域的操作，也会反应在原图上。
比如选取原图的一块区域操作后变成灰色:
代码:
import cv2 as cv import numpy as np src= cv.imread(r"D:opencv-test ed.png") cv.namedWindow("input image",cv.WINDOW_AUTOSIZE) cv.imshow("input image",src) print(src.shape) face =src[50:100,50:100] gray = cv.cvtColor(face,cv.COLOR_BGR2GRAY) backface = cv.cvtColor(gray,cv.COLOR_GRAY2BGR) #还原三通道 src[50:100,50:100]=backface # cv.imshow('face',face) cv.imshow('face',src)
泛洪填充：即指定图中某一像素点，以该像素点为基准点，设置像素值的上限与下线，所有处于上下限范围内的像素，都会被填充为指定的新颜色。
代码如下:
import cv2 as cv import numpy as np def fill_demo(image): image= image.copy() h,w = image.shape[:2] mask = np.zeros([h+2,w+2],np.uint8) cv.floodFill(image,mask,(30,30),(0,255,255),(100,100,100),(50,50,50),cv.FLOODFILL_FIXED_RANGE) # (0, 255, 255) yellow '''
FLOODFILL_FIXED_RANGE- 改变图像，泛洪填充 floodFill(image,mask,seedPoint,newVal,rect,loDiff,upDiff,flags) seedPoint:种子像素，填充的起始点像素 newVal：要填充的颜色 loDiff:规定像素值的下限 upDiff:规定像素值的上限 seedPoint.BGR - loDiff <= 被填充像素值 <= seedPoint.BGR + upDiff 即 seedPoint的BGR各减去loDiff <= 要填充的颜色范围 <= seedPoint的BGR各加上upDiff ''' cv.imshow('fill',image)
二值填充:通过限定mask中像素值为0的区域来规定填充区域。
def fill_binary(): image = np.zeros([400,400,3],np.uint8) image[100:300,100:300,:] = 255 cv.imshow('pre',image)
mask = np.ones([402,402,1],np.uint8) mask[101:301,101:301] = 0 cv.floodFill(image,mask,(200,200),(0,0,255),cv.FLOODFILL_MASK_ONLY) cv.imshow('aft',image)
注: 在进行二值填充是 mask层必须全部初始化为1 想填充的区域 初始化为0
