图像颜色加法
import cv2 as cv
import numpy as np
#读图
cao = cv.imread('E:\hqyj\code\opencv\images\cao.png')
pig = cv.imread('E:\hqyj\code\opencv\images\pig.png')
#饱和操作 cv.add(img1,img2) np.uint8
dst1 = cv.add(cao,pig)
#numpy直接相加 取模运算 对256取模 250+10=4
dst2 = cao+pig
# print(cao)
# print(pig)
# print(dst1)
cv.imshow('dst1',dst1)
cv.imshow('dst2',dst2)
x = np.uint8([[250]])
y = np.uint8([[10]])
xy1 = cv.add(x,y)
xy2 = x+y
print(xy1)
print(xy2)
dst3 = cv.addWeighted(cao,0.2,pig,0.8,0)
cv.imshow('dst3',dst3)
cv.waitKey(0)
cv.destroyAllWindows()
import cv2 as cv
import numpy as np
# 读取图片
img = cv.imread('E:\hqyj\code\opencv\images\pig.png')
# 颜色转换 cv.cvtColor(img, cv.COLOR_BGR2HSV)->图像，转换方式
# 灰度图
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
cv.imshow('gray',gray)
# 转hsv
hsv = cv.cvtColor(img,cv.COLOR_BGR2HSV)
cv.imshow('hsv',hsv)
# bgr转rgb
rgb = cv.cvtColor(img,cv.COLOR_BGR2RGB)
cv.imshow('rgb',rgb)
cv.waitKey(0)
cv.destroyAllWindows()
灰度化
import cv2 as cv
import numpy as np
#读取图像
pig = cv.imread('E:\hqyj\code\opencv\images\pig.png')
shape = pig.shape #(h,w,c)
img = np.zeros((shape[0],shape[1]),dtype = np.uint8)
#for循环遍历像素点
for i in range(shape[0]):
for j in range(shape[1]):
img[i,j] = max(pig[i,j,0],pig[i,j,1],pig[i,j,2])
cv.imshow('grey',img)
cv.waitKey(0)
cv.destroyAllWindows()
import cv2 as cv
import numpy as np
#读取图像
pig = cv.imread('E:\hqyj\code\opencv\images\pig.png')
shape = pig.shape #(h,w,c)
img = np.zeros((shape[0],shape[1]),dtype = np.uint8)
#for循环遍历像素点
for i in range(shape[0]):
for j in range(shape[1]):
#将int()：转换为更大的类型，防止溢出
img[i,j] =np.uint8((int(pig[i,j,0])+int(pig[i,j,1])+int(pig[i,j,2]))/3)
cv.imshow('grey',img)
cv.waitKey(0)
cv.destroyAllWindows()
import cv2 as cv
import numpy as np
#读取图像
pig = cv.imread('E:\hqyj\code\opencv\images\pig.png')
shape = pig.shape #(h,w,c)
img = np.zeros((shape[0],shape[1]),dtype = np.uint8)
#定义权重
wb,wg,wr = 0.114,0.587,0.299
#for循环遍历像素点
for i in range(shape[0]):
for j in range(shape[1]):
#将int()：转换为更大的类型，防止溢出
img[i,j] = round((wb * pig[i,j,0] + wg * pig[i,j,1] + wr * pig[i,j,2])//3)
cv.imshow('grey',img)
cv.waitKey(0)
cv.destroyAllWindows()
二值化
- 二值化（阈值法）格式：阈值法：
_,binary = cv2.threshold(img,thresh,maxval,type)
_和binary：cv2.threshold 函数返回两个值：一个是计算出的最佳阈值（retval），另一个是阈值处理后的图像（binary），_ 被用来忽略返回值中的第一个元素，即计算出的最佳阈值
img：输入图像，要进行二值化处理的灰度图。
thresh：设定的阈值。当像素值大于(或小于，取决于阈值类型)thresh时，该像素被赋予的值。
type：阈值处理的类型。
- 自适应二值化格式：
cv2.adaptiveThreshold(image_np_gray, maxval, adaptiveMethod, thresholdType, blockSize, c)
其中各个参数的含义如下：
maxval：最大阈值，一般为255
adaptiveMethod：小区域阈值的计算方式：
ADAPTIVE_THRESH_MEAN_C：小区域内取均值
ADAPTIVE_THRESH_GAUSSIAN_C：小区域内加权求和，权重是个高斯核
thresholdType：二值化方法，只能使用THRESH_BINARY、THRESH_BINARY_INV，也就是阈值法和反阈值法
blockSize：选取的小区域的面积，如7就是7*7的小块。(只能取奇数)
c：最终阈值等于小区域计算出的阈值再减去此值
import cv2 as cv
#读图
flower = cv.imread('E:\hqyj\code\opencv\images\\flower.png')
#灰度化处理
gray = cv.cvtColor(flower,cv.COLOR_BGR2GRAY)
gray = cv.resize(gray,(360,360))
cv.imshow('1.gray',gray)
#二值化 1.阈值法 cv.THRESH_BINARY
thresh,binary = cv.threshold(gray,127,255,cv.THRESH_BINARY)
print(thresh)
cv.imshow('2.binary',binary)
#2.反阈值法 cv.THRESH_BINARY_INV
_,binary_inv = cv.threshold(gray,127,255,cv.THRESH_BINARY_INV)
cv.imshow('3.binary_inv',binary_inv)
#3.截断阈值法 cv.THRESH_TRUNC
_,binary_trunc = cv.threshold(gray,170,255,cv.THRESH_TRUNC)
cv.imshow('4.binary_trunc',binary_trunc)
#4.低阈值零处理法 cv.THRESH_TOZERO
_,binary_tozero = cv.threshold(gray,127,255,cv.THRESH_TOZERO)
cv.imshow('5.binary_tozero',binary_tozero)
#5.超阈值零处理法 cv.THRESH_TOZERO_INV
_,binary_tozero_inv = cv.threshold(gray,127,255,cv.THRESH_TOZERO_INV)
cv.imshow('6.binary_tozero_inv',binary_tozero_inv)
#6.OTSU阈值法
shresh1,otsu=cv.threshold(gray,200,255,cv.THRESH_OTSU)
#7.OTSU+反阈值法
shresh2,otsu_inv = cv.threshold(gray,shresh1,255,cv.THRESH_BINARY_INV)
print(shresh1)
print(shresh2)
cv.imshow('7.otsu',otsu)
#自适应二值化 小区域计算 必须只能结合阈值法或反阈值法
#1.取均值
binary_adaptive = cv.adaptiveThreshold(gray,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,7,10)
#2.加权求和法 高斯核
adaptive_binary = cv.adaptiveThreshold(gray,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,10)
cv.imshow('8.binary_adaptive',binary_adaptive)
cv.imshow('9.adaptive_binary',adaptive_binary)
cv.waitKey(0)
cv.destroyAllWindows()
仿射变换
- 仿射变换流程
1.读图cv.imread()，调尺寸cv.resize()
2.获取仿射变换矩阵
1.旋转：M = cv.getRotationMatrix2D(center,angle,scale)
center：旋转中心点的坐标，格式为`(x,y)`。
angle：旋转角度，单位为度，正值表示逆时针旋转负值表示顺时针旋转。
scale：缩放比例，若设为1，则不缩放。
2.平移：M = np.float32([[1,0,tx],[0,1,ty]])
3.缩放：M = np.float32([[sx,0,0],[0,sy,0]])
4.剪切：M = np.float32([[shx,1,0],[1,shy,0]])
import cv2 as cv
#读图
cat = cv.imread('E:\hqyj\code\opencv\images\\cat1.png')
cat = cv.resize(cat,(520,520))
#获取旋转矩阵 cvv2.getRotationMatrix2D(center,angle,scale) 2x3
M = cv.getRotationMatrix2D((260,260),-45,1)
#仿射变换函数 cv.warpAffine(img,M,(w,h))
img = cv.warpAffine(cat,M,(520,520))
cv.imshow('img',img)
cv.waitKey(0)
cv.destroyAllWindows()
import cv2 as cv
import numpy as np
#读图
cat = cv.imread('E:\hqyj\code\opencv\images\\cat1.png')
cat = cv.resize(cat,(520,520))
#定义平移量
tx,ty = 80,120
#定义平移矩阵
M = np.float32([[1,0,tx],[0,1,ty]])
#仿射变换函数 M = cv.warpAffine(img,M,(w,h))
img = cv.warpAffine(cat,M,(520,520))
cv.imshow('img',img)
cv.waitKey(0)
cv.destroyAllWindows()
import cv2 as cv
import numpy as np
#读图
cat = cv.imread('E:\hqyj\code\opencv\images\\cat1.png')
cat = cv.resize(cat,(520,520))
#定义缩放量
sx,sy = 0.5,0.5
#定义缩放矩阵
M = np.float32([[sx,0,0],[0,sy,0]])
#仿射变换函数 cv.warpAffine(img,M,(w,h))
img = cv.warpAffine(cat,M,(520,520))
cv.imshow('img',img)
cv.waitKey(0)
cv.destroyAllWindows()
import cv2 as cv
import numpy as np
#读图
cat = cv.imread('E:\hqyj\code\opencv\images\\cat1.png')
cat = cv.resize(cat,(520,520))
#定义缩放量
shx,shy = 0.5,0.5
#定义缩放矩阵
M = np.float32([[shx,1,0],[1,shy,0]])
#仿射变换函数 cv.warpAffine(img,M,(w,h))
img = cv.warpAffine(cat,M,(520,520))
cv.imshow('img',img)
cv.waitKey(0)
cv.destroyAllWindows()