三 OpenCV 图像处理-1

import cv2 as cv

import numpy as np

#打印出所有的转换类型

flags = [item for item in dir(cv) if ‘COLOR_‘ in item]

print(flags)

import cv2 as cv

import numpy as np

cap = cv.VideoCapture(0)#打开摄像头

while(1):#循环追踪每一帧

    ret,frame = cap.read()#获取一帧

    hsv = cv.cvtColor(frame,cv.COLOR_BGR2HSV)#BGR->HSV

    #设定指定的颜色阀值：蓝色/或者其他，你要追踪的目标的颜色

    lower_blue = np.array([110,50,50])

    uper_blue = np.array([130,255,255])

    #根据阀值构建掩摸

    mask = cv.inRange(hsv,lower_blue,uper_blue)

    #对比原图和掩模进行位运算

    res = cv.bitwise_and(frame,frame,mask = mask)

    #显示图片

    cv.namedWindow(‘IMG-FRAME‘)

    cv.imshow(‘IMG-FRAME‘, frame)

    cv.namedWindow(‘IMG-MASK‘)

    cv.imshow(‘IMG-RES‘, mask)

    cv.namedWindow(‘IMG-RES‘)

    cv.imshow(‘IMG-RES‘, res)

    key = cv.waitKey(0)&0xff

    if key == 27:break

cv.destroyAllWindows()

###如果没有摄像头，直接对一张图片进行对指定颜色进行跟踪定位

import cv2 as cv

import numpy as np

#cap = cv.VideoCapture(0)#打开摄像头

#while(1):#循环追踪每一帧

frame = cv.imread(‘E:\\fruits.jpg‘)#cap.read()获取一帧

hsv = cv.cvtColor(frame,cv.COLOR_BGR2HSV)#BGR->HSV

#设定指定的颜色阀值：蓝色/或者其他，你要追踪的目标的颜色

lower_blue = np.array([110,50,50])

uper_blue = np.array([130,255,255])

#根据阀值构建掩摸

mask = cv.inRange(hsv,lower_blue,uper_blue)

#对比原图和掩模进行位运算

res = cv.bitwise_and(frame,frame,mask = mask)

#显示图片

cv.namedWindow(‘IMG-FRAME‘)

cv.imshow(‘IMG-FRAME‘, frame)

cv.namedWindow(‘IMG-MASK‘)

cv.imshow(‘IMG-RES‘, mask)

cv.namedWindow(‘IMG-RES‘)

cv.imshow(‘IMG-RES‘, res)

key = cv.waitKey(0)&0xff

if key == 27:input()

cv.destroyAllWindows()

import numpy as np

import cv2 as cv

##green = np.uint8([0,255,0])###转换失败，因为数据类型不一致

green = np.uint8([[[0,255,0]]])

hsv_green = cv.cvtColor(green,cv.COLOR_BGR2HSV)#BGR->HSV

print(hsv_green)

import numpy as np

import cv2 as cv

img = cv.imread(‘E:\\fruits.jpg‘)

height,width = img.shape[:2]

res = cv.resize(img,(1*width,1*height),interpolation=cv.INTER_AREA)

cv.imshow(‘res‘,res)

##cv.imshow(‘img‘,img)

cv.waitKey(0)

cv.destroyAllWindows()

import numpy as np

import cv2 as cv

img = cv.imread(‘E:\\fruits.jpg‘)

rows,cols = img.shape[:2]

M = np.float32([[1,0,100],[0,1,50]])

dst = cv.warpAffine(img,M,(cols,rows))

cv.imshow(‘img‘,dst)

cv.waitKey(0)

cv.destroyAllWindows()

import numpy as np

import cv2 as cv

img = cv.imread(‘E:\\fruits.jpg‘)

rows,cols = img.shape[:2]

M = cv.getRotationMatrix2D((rows/2,cols/2),90,1)

dst = cv.warpAffine(img,M,(rows,cols))

cv.imshow(‘res‘,dst)

##cv.imshow(‘img‘,img)

cv.waitKey(0)

cv.destroyAllWindows()

import numpy as np

import cv2 as cv

from matplotlib import pyplot as plt

img = cv.imread(‘E:\\fruits.jpg‘)

rows,cols = img.shape[:2]

pts1 = np.float32([[50,50],[200,50],[50,200]])

pts2 = np.float32([[10,10],[50,5],[10,200]])

M = cv.getAffineTransform(pts1,pts2)

dst = cv.warpAffine(img,M,(rows,cols))

cv.imshow(‘res‘,dst)

##cv.imshow(‘img‘,img)

cv.waitKey(0)

cv.destroyAllWindows()

import numpy as np

import cv2 as cv

from matplotlib import pyplot as plt

img = cv.imread(‘E:\\fruits.jpg‘)

rows,cols = img.shape[:2]

pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])

pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])

M = cv.getPerspectiveTransform(pts1,pts2)

dst = cv.warpPerspective(img,M,(300,300))

cv.imshow(‘res‘,dst)

##cv.imshow(‘img‘,img)

cv.waitKey(0)

cv.destroyAllWindows()

Threshold(src, dst, threshold, maxValue, thresholdType) → None

Applies a fixed-level threshold to array elements.

Parameters:   

src (CvArr) – Source array (single-channel, 8-bit or 32-bit floating point)

dst (CvArr) – Destination array; must be either the same type as src or 8-bit

threshold (float) – Threshold value

maxValue (float) – Maximum value to use with CV_THRESH_BINARY and CV_THRESH_BINARY_INV thresholding types

thresholdType (int) – Thresholding type (see the discussion)

AdaptiveThreshold(src, dst, maxValue, adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C, thresholdType=CV_THRESH_BINARY, blockSize=3, param1=5) → None

Applies an adaptive threshold to an array.

Parameters:   

src (CvArr) – Source image

dst (CvArr) – Destination image

maxValue (float) – Maximum value that is used with CV_THRESH_BINARY and CV_THRESH_BINARY_INV

adaptive_method (int) – Adaptive thresholding algorithm to use: CV_ADAPTIVE_THRESH_MEAN_C or CV_ADAPTIVE_THRESH_GAUSSIAN_C (see the discussion)

thresholdType (int) –

Thresholding type; must be one of

CV_THRESH_BINARY xxx

CV_THRESH_BINARY_INV xxx

blockSize (int) – The size of a pixel neighborhood that is used to calculate a threshold value for the pixel: 3, 5, 7, and so on

param1 (float) – The method-dependent parameter. For the methods CV_ADAPTIVE_THRESH_MEAN_C and CV_ADAPTIVE_THRESH_GAUSSIAN_C it is a constant subtracted from the mean or weighted mean (see the discussion), though it may be negative

import cv2 as cv

import numpy as np

from matplotlib import pyplot as plt

img = cv.imread(‘E:\\fruits.jpg‘,0)

##Threshold(src, dst, threshold, maxValue, thresholdType)

ret,thresh1 = cv.threshold(img,127,255,cv.THRESH_BINARY)

ret,thresh2 = cv.threshold(img,127,255,cv.THRESH_BINARY_INV)

ret,thresh3 = cv.threshold(img,127,255,cv.THRESH_TRUNC)

ret,thresh4 = cv.threshold(img,127,255,cv.THRESH_TOZERO)

ret,thresh5 = cv.threshold(img,127,255,cv.THRESH_TOZERO_INV)

##AdaptiveThreshold(src, dst, maxValue, adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C, thresholdType=CV_THRESH_BINARY, blockSize=3, param1=5)

thresh6 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,11,2)

thresh7 = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)

titles = [‘原图‘,‘二值化‘,‘转置二值化‘,‘截断化‘,‘超过阀值置0化‘,‘低于阀值置0化‘,‘C-均值自适应阀值‘,‘高斯权重-自适应阀值‘]

imgs = [img,thresh1,thresh2,thresh3,thresh4,thresh5,thresh6,thresh7]

##使用PLT显示

for i in range(len(imgs)):

     plt.subplot(3,3,i+1),plt.imshow(imgs[i],cmap = ‘gray‘)

     plt.title(titles[i])

     plt.xticks([]),plt.yticks([])#隐藏坐标

##解决中文不显示问题

plt.rcParams[‘font.sans-serif‘]=[‘SimHei‘]

plt.rcParams[‘axes.unicode_minus‘]=False

plt.show()

  import cv2

import numpy as np

from matplotlib import pyplot as plt

img = cv2.imread(‘E:\\fruits.jpg‘,0)

# global thresholding

ret1,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)

# Otsu‘s thresholding

ret2,th2 = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# Otsu‘s thresholding after Gaussian filtering

#（5,5）为高斯核的大小，0 为标准差

blur = cv2.GaussianBlur(img,(5,5),0)

# 阈值一定要设为 0！

ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

# plot all the images and their histograms

images = [img, 0, th1,

          img, 0, th2,

          blur, 0, th3]

titles = [‘Original Noisy Image‘,‘Histogram‘,‘Global Thresholding (v=127)‘,

‘Original Noisy Image‘,‘Histogram‘,"Otsu‘s Thresholding",

‘Gaussian filtered Image‘,‘Histogram‘,"Otsu‘s Thresholding"]

# 这里使用了 pyplot 中画直方图的方法，plt.hist, 要注意的是它的参数是一维数组

# 所以这里使用了（numpy）ravel 方法，将多维数组转换成一维，也可以使用 flatten 方法

#ndarray.flat 1-D iterator over an array.

#ndarray.flatten 1-D array copy of the elements of an array in row-major order.

for i in range(3):

     plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],‘gray‘)

     plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])

     plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)

     plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([])

     plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],‘gray‘)

     plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([])

plt.show()