OpenCV从基础到入门（基于python）

  • 开发
  • 27
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\lenna.png",cv2.IMREAD_UNCHANGED )
# cv2.imshow("original",a)
# b,g,r = cv2.split(a)
# cv2.imshow("b",b)
# cv2.imshow("g",g )
# cv2.imshow("r",r)
# m = cv2.merge([r,g,b])
# cv2.imshow("merge",m)
# cv2.waitKey()
# cv2.destroyAllWindows()

'''图像拆分与合并'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\lenna.png",cv2.IMREAD_UNCHANGED )
# cv2.imshow("original",a)
# b,g,r = cv2.split(a)
# rows,cols,chns = a.shape
# b = np.zeros((rows,cols),a.dtype)
# g = np.zeros((rows,cols),a.dtype)
# r = cv2.split(a)[0]
# m = cv2.merge([b,g,r])
# cv2.imshow("merge",m)
# cv2.waitKey()
# cv2.destroyAllWindows()

# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\lena.bmp ",cv2.IMREAD_UNCHANGED )
# b = a
# result1 = a+b
# result2 = cv2.add(a,b)
# cv2.imshow("orignial",a)
# cv2.imshow("zhijie",result1)
# cv2.imshow("OpenCV",result2)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''图像融合'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\gaO.png ",cv2.IMREAD_UNCHANGED )
# b = cv2.imread("D:\\img\\hi.png ",cv2.IMREAD_UNCHANGED)
# result = cv2.addWeighted(a,0.7,b,0.3,0)
# cv2.imshow("ronghe",result)
# cv2.waitKey()
# cv2.destroyAllWindows()

'''图像类型转换'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\lenna.png ",cv2.IMREAD_UNCHANGED )
# zhuan = cv2.cvtColor(a,cv2.COLOR_BGR2RGB)
# b,g,r = cv2.split(zhuan)
# cv2.imshow("b",b)
# cv2.imshow("a",a)
# cv2.imshow("g",g)
# cv2.imshow("r",r)
# cv2.imshow("zhuan",zhuan)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''图像翻转'''
# import cv2
# a = cv2.imread("D:\\img\\gao.png ",cv2.IMREAD_UNCHANGED )
# b = cv2.flip(a,0)
# c = cv2.flip(a,1)
# d = cv2.flip(a,-1)
# cv2.imshow("a",a)
# cv2.imshow("b",b)
# cv2.imshow("c",c)
# cv2.imshow("d",d)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''图像阈值化处理'''
# import cv2
# a = cv2.imread("D:\\img\\Lena.bmp",cv2.IMREAD_UNCHANGED )
# r,b = cv2.threshold(a,128,255,cv2.THRESH_BINARY)
# m,c = cv2.threshold(a,127,255,cv2.THRESH_BINARY_INV)
# n,d = cv2.threshold(a,127,255,cv2.THRESH_TRUNC)
# p,e = cv2.threshold(a,127,255,cv2.THRESH_TOZERO)
# o,f = cv2.threshold(a,127,255,cv2.THRESH_TOZERO_INV)
# cv2.imshow("erjinzhi",b)
# cv2.imshow("fanerjinzhi",c)
# cv2.imshow("original",a)
# cv2.imshow("jieduan",d)
# cv2.imshow("yuzhi",e)
# cv2.imshow("fanyuzhi",f)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''均值滤波'''
# import cv2
# a = cv2.imread("D:\\img\\gao.png ",cv2.IMREAD_UNCHANGED )
# result = cv2.blur(a,(5,5))
# result1 = cv2.blur(a,(100,100))
# cv2.imshow("original",a)
# cv2.imshow("lvbo",result)
# cv2.imshow("lvbo1",result1)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''高斯滤波'''
# import cv2
# a = cv2.imread("D:\\img\\gao.png ",cv2.IMREAD_UNCHANGED )
# result = cv2.GaussianBlur(a,(31,31),0)
# cv2.imshow("original",a)
# cv2.imshow("result",result)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''中值滤波'''
# import cv2
# a = cv2.imread("D:\\img\\gao.png ",cv2.IMREAD_UNCHANGED )
# result = cv2.medianBlur(a,13)
# cv2.imshow("original",a)
# cv2.imshow("result",result)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''图像膨胀'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\dilation.bmp",cv2.IMREAD_UNCHANGED )
# kernel = np.ones((5,5),np.uint8 )
# result = cv2.dilate(a,kernel,iterations= 4)
# cv2.imshow("original",a)
# cv2.imshow("result",result)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''图像腐蚀'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\erode.bmp",cv2.IMREAD_UNCHANGED )
# kernel = np.ones((5,5),np.uint8 )
# result = cv2.erode(a,kernel)
# result2 = cv2.erode(a,kernel,iterations= 5)
# cv2.imshow("original",a)
# cv2.imshow("result",result )
# cv2.imshow("result2",result2 )
# cv2.imwrite("D:\\img\\erode2.bmp",result2 )
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''开运算'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\lenaNoise.png",cv2.IMREAD_UNCHANGED )
# kernel = np.ones((5,5),np.uint8)
# result = cv2.morphologyEx(a,cv2.MORPH_OPEN ,kernel)
# cv2.imshow("original",a)
# cv2.imshow("result",result)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''闭运算'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\gao.png ",cv2.IMREAD_UNCHANGED )
# kernal = np.ones((5,5),np.uint8 )
# result = cv2.morphologyEx(a,cv2.MORPH_CLOSE ,kernal )
# cv2.imshow("original",a)
# cv2.imshow("result",result)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''梯度运算'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\erode2.bmp",cv2.IMREAD_UNCHANGED )
# kernal = np.ones((3,3),np.uint8 )
# tidu = cv2.morphologyEx(a,cv2.MORPH_GRADIENT ,kernal)
# cv2.imshow("tidu",tidu)
# cv2.imshow("o",a)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''礼帽运算'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\erode.bmp ",cv2.IMREAD_UNCHANGED )
# kernal = np.ones((5,5),np.uint8 )
# result = cv2.morphologyEx(a,cv2.MORPH_TOPHAT ,kernal)
# cv2.imshow("original",a)
# cv2.imshow("limao",result)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''黑帽运算'''
# import cv2
# import numpy as np
# a = cv2.imread("D:\\img\\erode.bmp ",cv2.IMREAD_UNCHANGED )
# kernal = np.ones((5,5),np.uint8 )
# result = cv2.morphologyEx(a,cv2.MORPH_BLACKHAT ,kernal )
# cv2.imshow("original",a)
# cv2.imshow("result",result)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''转bmp'''
# import cv2
# a = cv2.imread("D:\\img\\gao.png",cv2.IMREAD_GRAYSCALE)
# cv2.imshow("sobel",a)
# cv2.imwrite("D:\\img\\gaogao.bmp",a)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''Sobel算子'''
# import cv2
# a = cv2.imread("D:\\img\\lena.bmp",cv2.IMREAD_UNCHANGED )
# # result = cv2.Sobel(a,-1,1,0)
# # result1 = cv2.Sobel(a,cv2.CV_64F,1,1)
#
# dx = cv2.Sobel(a,cv2.CV_64F ,1,0)
# dy = cv2.Sobel(a,cv2.CV_64F ,0,1)
# result2 = cv2.convertScaleAbs(dx )
# result4 = cv2.convertScaleAbs(dy )
# result3 = cv2.addWeighted(result2 ,0.5,result4 ,0.5,0)
# cv2.imshow("original",a)
# # cv2.imshow("sobel",result )
# # cv2.imshow("sobel1",result1 )
# # cv2.imshow("sobel2",result2 )
# cv2.imshow("sobel3",result3 )
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''scharr算子'''
# import cv2
# a = cv2.imread("D:\\img\\gaogao.bmp",cv2.IMREAD_UNCHANGED )
# scharrx = cv2.Scharr(a,cv2.CV_64F ,1,0)
# scharry = cv2.Scharr(a,cv2.CV_64F ,0,1)
# resultx = cv2.convertScaleAbs(scharrx )
# resulty = cv2.convertScaleAbs(scharry )
# result = cv2.addWeighted(resultx,0.5,resulty ,0.5,0)
# cv2.imshow("original",a)
# cv2.imshow("result",result )
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''laplacian算子'''
# import cv2
# a = cv2.imread("D:\\img\\suanzi.bmp ",cv2.IMREAD_UNCHANGED )
# laplacian = cv2.Laplacian(a,cv2.CV_64F )
# laplacian = cv2.convertScaleAbs(laplacian )
# cv2.imshow("original",a)
# cv2.imshow("laplacian",laplacian )
# cv2.waitKey()
# cv2.destroyAllWindows()
#  '''canny边缘检测'''
# import cv2
# a = cv2.imread("D:\\img\\gaogao.bmp",cv2.IMREAD_UNCHANGED )
# canny = cv2.Canny(a,50,60)
# cv2.imshow("original",a)
# cv2.imshow("canny",canny )
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''向下取样'''
# import cv2
# a = cv2.imread("D:\\img\\gao.png",cv2.IMREAD_UNCHANGED )
# result = cv2.pyrDown(a)
# result1 = cv2.pyrDown(result)
# cv2.imshow("result",result)
# cv2.imshow("result1",result1)
# cv2.imshow("original",a)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''向上取样'''
# import cv2
# a = cv2.imread("D:\\img\\lena.bmp ",cv2.IMREAD_UNCHANGED )
# result = cv2.pyrUp(a)
# result1 = cv2.pyrUp(result)
# cv2.imshow("result",result)
# cv2.imshow("result1",result1)
# cv2.imshow("original",a)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''向上取样和向下取样的可逆性研究'''
# import cv2
# a = cv2.imread("D:\\img\\lena.bmp ",cv2.IMREAD_UNCHANGED )
# result = cv2.pyrUp(a)
# result = cv2.pyrDown(result)
# result1 = cv2.pyrDown(a)
# result1 = cv2.pyrUp(result1)
# cv2.imshow("result",result)
# cv2.imshow("result1",result1)
# cv2.imshow("original",a)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''拉普拉斯金字塔'''
# import cv2
# o = cv2.imread("D:\\img\\lena.bmp ",cv2.IMREAD_UNCHANGED )
# od = cv2.pyrDown(o)
# odu = cv2.pyrUp(od)
# laplacian = o - odu
# cv2.imshow("first",laplacian )
# cv2.imshow("original",o )
# o1 = od
# o2d = cv2.pyrDown(o1)
# o2du = cv2.pyrUp(o2d)
# laplacian2 = o1 - o2du
# cv2.imshow("second",laplacian2 )
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''图像轮廓'''
# import cv2
# a = cv2.imread("D:\\img\\lena.bmp ")
# gray = cv2.cvtColor(a,cv2.COLOR_BGR2GRAY )
# ret,binary = cv2.threshold(gray ,127,255,cv2.THRESH_BINARY )
# image , contours,hierarchy =cv2.findContours(binary ,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE )
# co = a.copy()
# result = cv2.drawContours(co,contours ,-1,(0,0,255),1)
# cv2.imshow("original",a)
# cv2.imshow("gray",gray)
# cv2.imshow("result")
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''图像轮廓'''
# import cv2
# o = cv2.imread("D:\\img\\gao.png ",cv2.IMREAD_UNCHANGED )
# gray = cv2.cvtColor(o,cv2.COLOR_BGR2GRAY )
# ret,binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY )
# # image, contours,heirarchy = cv2.findContours(binary ,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_NONE )
# '''高版本中image参数不再需要'''
# contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# co = o.copy()
# result = cv2.drawContours(co,contours ,-1,(0,255,0),1)
# cv2.imshow("original",o)
# cv2.imshow("result",result)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''绘制直方图'''
# import cv2
# import matplotlib.pyplot as plt
# a = cv2.imread("D:\\img\\gao.png  ")
# cv2.imshow("original",a)
# plt.hist(a.ravel() ,256)
# plt.show()
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''OpenCV绘制直方图'''
# import cv2
# a = cv2.imread("D:\\img\\lena.bmp ")
# hist =cv2.calcHist([a],[0],None,[256],[0,255],None)
# print(hist )
# cv2.imshow("hist",hist)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''OpenCV绘制直方图'''
# import numpy as np
# import matplotlib.pyplot  as plt
# a = cv2.imread("D:\\img\\gao.png ")
# # x = np.arange(0,5,0.1)
# # y = np.sin(x)
# # plt.plot(x,y)
# # y = [0.1,0.2,0.3,0.5,0.9,2,4,7,8,12,45]
# # plt.plot(y)
# hist1 = cv2.calcHist([a],[0],None ,[256],[0,255])
# hist2 = cv2.calcHist([a],[1],None ,[256],[0,255])
# hist3 = cv2.calcHist([a],[2],None ,[256],[0,255])
# plt.plot(hist1,color='b')
# plt.plot(hist2,color='g')
# plt.plot(hist3,color='r')
# plt.show()

# '''使用掩膜绘制直方图'''
# import cv2
# import numpy as np
# import matplotlib .pyplot  as plt
# a = cv2.imread("D:\\img\\lena.bmp ",cv2.IMREAD_GRAYSCALE  )
# mask = np.zeros(a.shape,np.uint8 )
# mask[50:80,50:80] = 255
# histm = cv2.calcHist([a],[0],mask,[256],[0,255])
# histn = cv2.calcHist([a],[0],None ,[256],[0,255])
# plt.plot (histm)
# plt.plot (histn)
# cv2.imshow("origina",a)
# cv2.imshow("origin",mask  )
# plt .show()
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''图像掩膜处理'''
# import cv2
# import numpy as np
# import matplotlib .pyplot  as plt
# a = cv2.imread("D:\\img\\lena.bmp ",cv2.IMREAD_GRAYSCALE )
# mask = np.zeros(a.shape ,np.uint8 )
# mask[30:100,30:100] = 255
# mi = cv2.bitwise_and(a,mask)
# cv2.imshow("original",a)
# cv2.imshow("mask",mask )
# cv2.imshow("mi",mi )
# hisk = cv2.calcHist([a],[0],mask,[256],[0,255])
# plt.plot(hisk)
# plt.show()
# cv2.waitKey()
# cv2.destroyAllWindows()

'''直方图均衡化处理'''
# import cv2
# # import numpy as np  '''cv2和matplotlib库都依赖于numpy,建议导入'''
# import matplotlib .pyplot as plt
# img = cv2.imread("D:\\img\\gao.png  ",cv2.IMREAD_GRAYSCALE )
# equ = cv2.equalizeHist(img)
# plt.hist(img.ravel(),256 )
# plt.figure()
# plt.hist (equ.ravel(),256)
# plt.show()
# cv2.imshow("original",img)
# cv2.imshow("equ",equ)
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''plt.imshow函数'''
# import cv2
# import matplotlib . pyplot  as plt
# img = cv2.imread("D:\\img\\lenna.png " )
# cvt = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY )
# plt.subplot (2,2,1) , plt.imshow (img),plt.axis('off')
# plt.subplot (2,2,2) , plt.imshow (img,cmap = plt.cm.gray),plt.axis('off')
# plt.subplot (2,2,3) , plt.imshow (cvt),plt.axis('off')
# plt.subplot (2,2,4) , plt.imshow (cvt,cmap = plt.cm.gray),plt.axis('off')
# plt.show()
# cv2.waitKey()
# cv2.destroyAllWindows()

# '''imshow函数显示彩色图像'''
# import cv2
# import matplotlib .pyplot  as plt
# import numpy as np
# a = cv2.imread("D:\\img\\lenna.png ")
# '''方法一直接对图片空间进行转化，BGR2RGB'''
# cvt = cv2.cvtColor(a,cv2.COLOR_BGR2RGB )
# plt.subplot(1,2,1),plt.imshow(cvt),plt.axis ('off')
# plt.subplot(1,2,2),plt.imshow(a),plt.axis ('off')
# '''方法2将图片通道拆分后再进行RGB合并'''
# b,g,r = cv2.split(a)
# result = cv2.merge([r,g,b])
# plt.subplot (1,2,1),plt.imshow(a),plt.axis('off')
# plt.subplot (1,2,2),plt.imshow(result),plt.axis('off')
# plt.show()
# cv2.waitKey()
# cv2.destroyAllWindows()

'''imshow函数显示灰度图像'''
import cv2
import matplotlib .pyplot  as plt
import numpy as np

# # '''方法一直接以灰度图像显示'''
# # a = cv2.imread("D:\\img\\lena.bmp  ",cv2.IMREAD_GRAYSCALE )
# # plt.subplot(1,2,2),plt.imshow(a,cmap = plt.cm.gray),plt.axis ('off')

# '''方法二将BGR转灰度图像后显示'''
# a = cv2.imread("D:\\img\\lena.bmp ")
# cvt = cv2.cvtColor(a,cv2.COLOR_BGR2GRAY )
# plt.subplot (1,2,1),plt.imshow(cvt),plt.axis('off')
# plt.subplot (1,2,2),plt.imshow(cvt,cmap= plt.cm.gray),plt.axis('off')

# plt.show()
# cv2.waitKey()
# cv2.destroyAllWindows()
阅读全部

相关推荐

  1. OpenCV从基础到入门（基于python）

    2024-07-20 20:26:01       28 阅读

  4. Python开发入门：从基础到实践的全方位探索

    2024-07-20 20:26:01       33 阅读

  7. OpenCV入门1：Python基础编程

    2024-07-20 20:26:01       48 阅读

  9. MATLAB基础：从入门到熟悉

    2024-07-20 20:26:01       63 阅读

最近更新

  3. docker php8.1+nginx base 镜像 dockerfile 配置

    2024-07-20 20:26:01       171 阅读

  4. Could not load dynamic library ‘cudart64_100.dll‘

    2024-07-20 20:26:01       189 阅读

  9. 在Django里面运行非项目文件

    2024-07-20 20:26:01       157 阅读

  19. Python语言-面向对象

    2024-07-20 20:26:01       170 阅读

  20. 如何分清楚常见的 Git 分支管理策略Git Flow、GitHub Flow 和 GitLab Flow

    2024-07-20 20:26:01       156 阅读

热门阅读

  5. 运维 | Linux 系统中 MySQL 的安装与使用记录

    2024-07-20 20:26:01       30 阅读

  10. GPT-4o 与 GPT-4o Mini：两者的区别和特点

    2024-07-20 20:26:01       33 阅读

  13. GO Channel使用详解（各种场景下的最佳实践）

    2024-07-20 20:26:01       27 阅读

  18. Linux输出重定向到文件立即输出

    2024-07-20 20:26:01       33 阅读

  21. buuctf-reverse write-ups (1)

    2024-07-20 20:26:01       24 阅读

  22. Android init.rc各阶段的定义和功能

    2024-07-20 20:26:01       36 阅读

  23. tebi.io免费对象存储，可托管静态网站

    2024-07-20 20:26:01       34 阅读

  25. 【vueUse库Array模块各函数简介及使用方法--下篇】

    2024-07-20 20:26:01       32 阅读

  26. 前端路由History 和 Hash模式的区别以及Vue项目打包后显示白屏，路由router-view不加载问题

    2024-07-20 20:26:01       33 阅读

  28. 面经学习（厦门安全狗实习）

    2024-07-20 20:26:01       27 阅读

  29. 【项目-轻量级Web Server 定时器模块】

    2024-07-20 20:26:01       25 阅读