图像阀值

本文内容：

　　·空间颜色转换

　　·物体跟踪

　　·几何变换

　　·图像阀值

1、颜色空间转换

内容：颜色空间转换（BGR<->Gray、BGR<->HSV）/获取图形中特定颜色的物体/

函数：cv2.cvtColor(input_img,flag):flag:转换类型:cv2.COLOR_BGR2GRAY,cv2.COLOR_BGR2HSV等有261多种

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)

HSV：色度、饱和度、亮度

Gray：灰度图

BGR：B/G/R三通道图

·2、物体跟踪

算法：

由于HSV的颜色空间比BGR空间更容易表示特定的颜色，故我们将BGR空间转换成HSV，
然后设置HSV的阀值到指定的颜色范围，
然后获取到指定颜色范围的物体，
达到物体追踪目的，进行操作，如画圈

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()

问题1：追踪的目标仍有些噪音，应该优化消减噪音

问题2：如何找到我要跟踪的目标的HSV值呢？

解决方法：将你的目标图的BGR值，通过颜色空间转换即可。例如绿色

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)

·3、几何变换 Geometric Image Transformations

参考http://opencv.jp/opencv-2.2_org/py/imgproc_geometric_image_transformations.html

原理：: \texttt{dst} \rightarrow \texttt{src}" v:shapes="_x0000_i1031">

内容：移动、旋转、仿射、扩展放缩

函数：cv2.getPerspectiveTransform 透视转换

cv2.warpAffine 变形-仿射参数为2X3矩阵

cv2.warpPerspective 变形-透视参数为3X3矩阵

cv2. getRotationMatrix2D 旋转

·放缩

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()

·平移

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()

·旋转

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()

·仿射变换warpAffine

对指定的三点，重新给出变形后的位置，得出新图像，原理：T = M X 求出X后应用到整个图片

可以平移、旋转、放大缩小等变形操作，只要给出变形前的三点与变形后的三点即可

参考：http://blog.csdn.net/qq_18343569/article/details/47953505

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()

·透视变换 warpPerspective

投影映射

参考http://blog.csdn.net/qq_18343569/article/details/47953843

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()

·4图像阀值

分类：简单图像阀值、自适应阀值，Otsu’s二值化

作用：分割，找出不同的物体、目标

·简单图像阀值（thresh 削峰阀值）

类型：

API

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)

·自适应阀值

不同部分具有不同的亮度，可以使用自适应阀值

Adapter-Mothod有：

-cv.ADAPTIVE_THRESH_MEAN_C:阀值取自相邻域的平均值

-cv.ADAPTIVE_THRESH_GAUSSIAN_C:阀值取自相邻域的加权和，权重为一个高斯窗口

API:

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

代码样例

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()

对应不同的thresh方法，可采样不同的threshold值，但是，怎么样选取threshold值呢？不同的值得到的不同结果，而那个才是好坏，就要不停去尝试。如果一幅双峰图像，如何选择其中一个峰作为阀值？这时候就需要Otus二值化来自动根据直方图计算出一个阀值

·OTSU 二值化阀值

原理参考：http://www.tuicool.com/articles/uUJFN3f

根据权重方差均值等找到一个阀值T，即所谓的自动二值化阀值

算法源码：

例子

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()

posted on 2017-06-30 11:10 freeo 阅读(1961) 评论(0) 编辑收藏举报