import cv2
img = cv2.imread("01.jpg")
imgGrey = cv2.imread("01.jpg",0)
cv2.imshow("img",img)
cv2.imshow("imgGrey",imgGrey)
cv2.waitKey()
cv2.imwrite("Copy.jpg",img)
cv2.namedWindow(窗口名,属性) 创建一个窗口
属性—指定窗口大小模式:
cv2.WINDOW_AUTOSIZE:根据图像大小自动创建大小
cv2.WINDOW_NORMAL:窗口大小可调整
cv2.destoryAllWindows(窗口名) 删除任何建立的窗口
import cv2
img = cv2.imread("01.jpg")
cv2.namedWindow("img",cv2.WINDOW_NORMAL)
cv2.imshow("img",img)
cv2.waitKey()
cv2.destroyAllWindows()
img.shape 返回图像高(图像矩阵的行数)、宽(图像矩阵的列数)和通道数3个属性组成的元组,若图像是非彩色图,则只返回高和宽组成的元组。
import cv2
import numpy as np
img = cv2.imread("01.jpg")
imgGrey = cv2.imread("01.jpg",0)
sp1 = img.shape
sp2 = imgGrey.shape
mean_img_value = np.mean(img)
print(sp1)
print(sp2)
print(mean_img_value)
图像矩阵img的size属性和dtype分别对应图像的像素总数目和图像数据类型。一般情况下,图像的数据类型是uint8。
import cv2
img = cv2.imread("01.jpg")
imgSize = img.size
print imgSize
ty = img.dtype
print ty
使用numpy.zeros()函数生成指定大小的空图像
import cv2
import numpy as np
img = cv2.imread("01.jpg")
imgZero = np.zeros(img.shape,np.uint8)
imgFix = np.zeros((300,500,3),np.uint8)
# imgFix = np.zeros((300,500),np.uint8)
cv2.imshow("img",img)
cv2.imshow("imgZero",imgZero)
cv2.imshow("imgFix",imgFix)
cv2.waitKey()
OpenCV中图像矩阵的顺序是B、G、R。可以直接通过坐标位置访问和操作图像像素。
import cv2
img = cv2.imread("01.jpg")
numb = img[50,100]
print numb
img[50,100] = (0,0,255)
cv2.imshow("img",img)
cv2.waitKey()
分开访问图像某一通道像素值也炒鸡方便:
import cv2
img = cv2.imread("01.jpg")
img[0:100,100:200,0] = 255
img[100:200,200:300,1] = 255
img[200:300,300:400,2] = 255
cv2.imshow("img",img)
cv2.waitKey()
嗯,不得不说Python是一个很神奇的存在。
Python中,更改图像某一矩形区域的像素值也很方便:
import cv2
img = cv2.imread("01.jpg")
img[0:50,1:100] = (0,0,255)
cv2.imshow("img",img)
cv2.waitKey()
分离图像通道可以使用cv2中的split函数,合并使用merge函数。
import cv2
img = cv2.imread("01.jpg")
b , g , r = cv2.split(img)
# b = cv2.split(img)[0]
# g = cv2.split(img)[1]
# r = cv2.split(img)[2]
merged = cv2.merge([b,g,r])
cv2.imshow("Blue",b)
cv2.imshow("Green",g)
cv2.imshow("Red",r)
cv2.imshow("Merged",merged)
cv2.waitKey()
使用putText函数在图片上输出文字,函数原型:
putText(img, text, org, fontFace, fontScale, color, thickness=None, lineType=None, bottomLeftOrigin=None)
import cv2
img = cv2.imread("01.jpg")
cv2.putText(img,"Print some text to img",(100,100),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255))
cv2.imshow("img",img)
cv2.waitKey()
缩放使用cv2.resize()函数,resize函数里的size第一个是宽(列),第二个是高(行)。
import cv2
img = cv2.imread("01.jpg")
cv2.imshow("img",img)
img1 = cv2.resize(img,(300,100),cv2.INTER_AREA)
hight, width = img.shape[0:2]
img2 = cv2.resize(img,(width,hight))
cv2.imshow("img1",img1)
cv2.imshow('img2',img2)
cv2.waitKey()
"cv2.INTER_AREA"是定义的缩放插值方法。共有5种:
为了最好的缩放效果,缩小推荐使用 "cv2.INTER_AREA"; 扩大推荐使用 “cv2.INTER_CUBIC”
import cv2
img = cv2.imread('0021.jpg')
height = img.shape[0]
weight = img.shape[1]
channels = img.shape[2]
for row in range(height): # 遍历高
for col in range(weight): # 遍历宽
for c in range(channels): # 遍历各通道
value = img[row, col, c]
imgs[row, col, c] = 255-imgs[row, col, c] #操作像素
cv2.imshow('result',imgs)
cv2.waitKey()
import cv2
img = cv2.imread('timg.jpg',0) # 按灰度图读入
# ret 是自动选择的大津分割阈值 img_binary 是二值化图像
ret, img_binary = cv2.threshold(img,0,255,cv2.THRESH_OTSU) # 大津阈值
# ret, binary = cv.threshold(img, 100, 255, cv.THRESH_BINARY) # 指定阈值
cv2.imshow('Source image',img)
cv2.imshow('THRESH_OTSU',img_binary)
cv2.waitKey()