opencv计算机视觉学习笔记二

 

第三章 Opencv3处理图像

 

1 不一样色彩空间的转换

计算机视觉中三种常见的色彩空间:

灰度

BGR

HSV(hue色调 saturation饱合度 value黑暗程度)

2 傅里叶变换

快速傅里叶变换fft

离散傅里叶变换dft

 

高通滤波器heigh passfilter

检测图像的某个区域,根据像素和周围像素的亮度差值来提高该像素亮度的滤波器

示例代码以下:

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time    : 2016/11/29 12:23 # @Author  : Retacn # @Site    : 高通滤波器 # @File    : heighPassFilter.py # @Software: PyCharm import cv2
import numpy as np
from scipy import ndimage

#自定义核 kernel_3x3 = np.array([[-1, -1, -1],
                       [-1, 8, -1],
                       [-1, -1, -1]])

kernel_5x5 = np.array([[-1, -1, -1, -1, -1, ],
                       [-1, 1, 2, 1, -1],
                       [-1, 2, 4, 2, -1],
                       [-1, 1, 2, 1, -1],
                       [-1, -1, -1, -1, -1]])

#读入图像，转换为灰度格式 img=cv2.imread('../test.jpg',cv2.IMREAD_GRAYSCALE)

#卷积 k3=ndimage.convolve(img,kernel_3x3)
k5=ndimage.convolve(img,kernel_5x5)

#高经过滤 blurred=cv2.GaussianBlur(img,(11,11),0)
g_hpf=img-blurred

#显示图像 cv2.imshow('3x3',k3)
cv2.imshow('5x5',k5)
cv2.imshow('g_hpf',g_hpf)
cv2.waitKey()
cv2.destroyAllWindows()

 

 

 

 

 

 

 

低通滤波器low pass filter

在像素与周围像素的亮度差值小于一个特定值时,平滑该像素的亮度

3 建立模块

Filters.py文件,示例代码以下:

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time    : 2016/11/29 12:58 # @Author  : Retacn # @Site    : 滤波器 # @File    : filters.py.py # @Software: PyCharm import cv2
import numpy as np
import Three.utils #自定义工具类

 

Utils.py文件

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time    : 2016/11/29 12:59 # @Author  : Retacn # @Site    : 工具类 # @File    : utils.py.py # @Software: PyCharm import cv2
import numpy as np
from scipy import interpolate

 

 

4 边缘检测

经常使用函数

def Laplacian(src, 

            ddepth, 

            dst=None, 

            ksize=None, 

            scale=None, 

            delta=None, 

            borderType=None)

 

def Sobel(src, 

      ddepth, 

      dx,

      dy, 

      dst=None, 

      ksize=None, 

      scale=None, 

      delta=None, 

      borderType=None)

 

def Scharr(src, 

            ddepth, 

            dx, 

            dy, 

            dst=None, 

            scale=None, 

            delta=None, 

            borderType=None)

 

模糊滤波函数

1 平均

函数原型

def blur(src, #源图像

         ksize, #内核大小

         dst=None, #输出图像

         anchor=None, #中心锚点

         borderType=None)# 边界模式

2 高斯模糊

函数原型

def GaussianBlur(src, #输入图像

                  ksize, #高斯滤波模版大小

                  sigmaX, #横向滤波系数

                  dst=None, #输出图像

                  sigmaY=None,#纵向滤波系数 

                  borderType=None)

3 中值模糊

def medianBlur(src, #源图像

            ksize, #中值滤波器的模版的大小

            dst=None)#输出图像

4 双边滤波

def bilateralFilter(src, #输入图像

                  d, #每一个像素邻域的直径

                  sigmaColor, #颜色空间的标准误差

                  sigmaSpace, #坐标空间的标准误差

                  dst=None, #输出图像

                  borderType=None)#边缘点插值类型

 

示例代码以下:

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time    : 2016/11/29 12:58 # @Author  : Retacn # @Site    : 滤波器 # @File    : filters.py.py # @Software: PyCharm import cv2
import numpy as np
import Three.utils #自定义工具类 def strokeEdges(src,
                dst,
                blurKsize=7,#中值滤波ksize                 edgeKsize=5):#Laplacian算子ksize     if blurKsize>=3:
        #中值滤波         blurredSrc=cv2.medianBlur(src,blurKsize)
        #修改成灰度颜色空间         graySrc=cv2.cvtColor(blurredSrc,cv2.COLOR_BGR2GRAY)
    else:
        graySrc=cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
    cv2.Laplacian(graySrc,cv2.CV_8U,graySrc,ksize=edgeKsize)
    normalizedInverseAlpha=(1.0/255)*(255-graySrc)
    channels=cv2.split(src)
    for channel in channels:
        channel[:]=channel*normalizedInverseAlpha
    cv2.merge(channels,dst)

 

 

5 用定制内核做卷积

def filter2D(src, #输入图像

            ddepth, #图像深度

            kernel, #卷积核,单通道浮点矩阵

            dst=None, #输出图像

            anchor=None, #一个被滤波的点在核内的位置(中心)

            delta=None, 

      borderType=None)#边界类型

 

若是要对每一个通道使用不一样的核,必须用split()和merge()

示例代码以下:

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time    : 2016/11/29 12:58 # @Author  : Retacn # @Site    : 滤波器 # @File    : filters.py.py # @Software: PyCharm import cv2
import numpy as np
import Three.utils  # 自定义工具类 # 通常的卷积滤波器 class VConvolutionFilter(object):
    def __init__(self, kernel):
        self._kernel = kernel

    def apply(self, src, dst):
        cv2.filter2D(src, -1, self._kernel, dst)


# 特定的锐化滤波器 class SharpenFilter(VConvolutionFilter):
    def __init__(self):
        kernel = np.array([[-1, -1, -1],
                           [-1, 9, -1],
                           [-1, -1, -1]])
        VConvolutionFilter.__init__(self, kernel)


# 边缘检测滤波器 class FindEdgesFilter(VConvolutionFilter):
    def __init__(self):
        kernel = np.array([[-1, -1, -1],
                           [-1, 8, -1],
                           [-1, -1, -1]])
        VConvolutionFilter.__init__(self, kernel)


# 模糊滤波器 class BlurFilter(VConvolutionFilter):
    def __init__(self):
        kernel = np.array([[0.04, 0.04, 0.04, 0.04, 0.04],
                           [0.04, 0.04, 0.04, 0.04, 0.04],
                           [0.04, 0.04, 0.04, 0.04, 0.04],
                           [0.04, 0.04, 0.04, 0.04, 0.04],
                           [0.04, 0.04, 0.04, 0.04, 0.04]])
        VConvolutionFilter.__init__(self, kernel)


# 脊状和浮雕效果 class EmbossFilter(VConvolutionFilter):
    def __init__(self):
        kernel = np.array([[-2, -1, 0],
                           [-1, 1, 1],
                           [0, 1, 2]])
        VConvolutionFilter.__init__(self, kernel)


def strokeEdges(src,
                dst,
                blurKsize=7,  # 中值滤波ksize                 edgeKsize=5):  # Laplacian算子ksize     if blurKsize >= 3:
        # 中值滤波         blurredSrc = cv2.medianBlur(src, blurKsize)
        # 修改成灰度颜色空间         graySrc = cv2.cvtColor(blurredSrc, cv2.COLOR_BGR2GRAY)
    else:
        graySrc = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
    cv2.Laplacian(graySrc, cv2.CV_8U, graySrc, ksize=edgeKsize)
    normalizedInverseAlpha = (1.0 / 255) * (255 - graySrc)
    channels = cv2.split(src)
    for channel in channels:
        channel[:] = channel * normalizedInverseAlpha
    cv2.merge(channels, dst)

 

 

6 修改应用

#!/usr/bin/env python

# -*- coding: utf-8 -*-

# @Time   : 2016/11/28 14:45

# @Author : Retacn

# @Site   : cameo实现,有两种启动方法: run() 和 onkeypress()

# @File   : cameo.py

# @Software: PyCharm

 

 

import cv2

from Three import filters

from Two.cameo.managers importWindowManager,CaptureManager

 

class Cameo(object):

 

   def __init__(self):

       self._windowManager=WindowManager('Cameo',self.onkeypress)

 

       self._captureManager=CaptureManager(cv2.VideoCapture(0),self._windowManager,True)

      # self._curveFilter=filters.BGRPortraCurveFilter()

 

 

   def run(self):

       self._windowManager.createWindow()

       while self._windowManager.isWindowCreated:

           self._captureManager.enterFrame()

           frame=self._captureManager.frame

 

          # filters.strokeEdges(frame,frame)

          # self._curveFilter.apply(frame,frame)

 

           self._captureManager.exitFrame()

           self._windowManager.processEvents()

 

   def onkeypress(self,keycode):

       '''

           space-> 载图

           tab->启动和中止视频录制

           esc->退出应用

 

       :param keycode:

       :return:

       '''

       if keycode==32:#space

           self._captureManager.writeImage('screenshot.png')

       elif keycode==9:#tab

           if not self._captureManager.isWritingVideo:

                self._captureManager.startWritingVideo('screencast.avi')

           else:

               self._captureManager.stopWritingVideo()

       elif keycode==27:#esc

           self._windowManager.destroyWindow()

if __name__=='__main__':

Cameo().run()

 

 

 

 

7 canny边缘检测

示例代码以下:

import cv2
import numpy as np

#读入灰度图像
img=cv2.imread('../test.jpg',cv2.IMREAD_GRAYSCALE)
#边缘检测
cv2.imwrite('../canny.jpg',cv2.Canny(img,200,300))
#显示图像
cv2.imshow('canny',cv2.imread('../canny.jpg'))
cv2.waitKey()
cv2.destroyAllWindows()

 

8 轮廓检测

import cv2
import numpy as np

img=np.zeros((200,200,),dtype=np.uint8)
#将指定的区域设为白色
img[50:150,50:150]=255
#设定阈值
ret,thresh=cv2.threshold(img,127,255,0)
#查找轮廓
image,contours,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#更换颜色空间
color=cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
img=cv2.drawContours(color,contours,-1,(0,255,0),2)
cv2.imshow('contours',color)
cv2.waitKey()
cv2.destroyAllWindows()

 

9 边界框,最小矩形和最小闭圆的轮廓

import cv2
import numpy as np

img = cv2.pyrDown(cv2.imread('../contours.jpg', cv2.IMREAD_UNCHANGED))

ret, thresh = cv2.threshold(cv2.cvtColor(img.copy(),
                                         cv2.COLOR_BGR2GRAY),
                                         127,
                                         255,
                                         cv2.THRESH_BINARY)
image, contours, hier = cv2.findContours(thresh,
                                         cv2.RETR_EXTERNAL,
                                         cv2.CHAIN_APPROX_SIMPLE)

for c in contours:
    #绘制矩形边界框
    x, y, w, h = cv2.boundingRect(c)
    cv2.rectangle(img, (x, y), (x + w, x + y), (0, 255, 0), 2)

    #绘制最小矩形(红色)
    rect=cv2.minAreaRect(c)
    box=cv2.boxPoints(rect)
    box=np.int0(box)
    cv2.drawContours(img,[box],0,(0,0,255),3)

    #绘制小最闭圆
    (x,y),radius=cv2.minEnclosingCircle(c)
    center=(int(x),int(y))
    radius=int(radius)
    img=cv2.circle(img,center,radius,(0,255,0),2)
cv2.drawContours(img,contours,-1,(255,0,0),1)
cv2.imshow('contours',img)
cv2.waitKey()
cv2.destroyAllWindows()

 

 

10 凸轮廓与douglas-peucker

示例代码以下:

import cv2
import numpy as np

#读入图像
img=cv2.pyrDown(cv2.imread('../contours.jpg'),cv2.IMREAD_UNCHANGED)
#修改颜色空间,设置阈值
ret,thresh=cv2.threshold(cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY),
                         127,
                         255,
                         cv2.THRESH_BINARY)
#更换颜色空间
black=cv2.cvtColor(np.zeros((img.shape[0],img.shape[1]),
                    dtype=np.uint8),
                   cv2.COLOR_GRAY2BGR)
#检测轮廓
image,contours,hier=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

for cnt in contours:
    #轮廓的周长
    epsilon=0.01*cv2.arcLength(cnt,True)
    approx=cv2.approxPolyDP(cnt,epsilon,True)
    hull=cv2.convexHull(cnt)

    cv2.drawContours(black,[cnt],-1,(0,255,0),2)#绿,精确的轮廓
    cv2.drawContours(black,[approx],-1,(255,255,0),2)#蓝色 近似多边形
    cv2.drawContours(black,[hull],-1,(0,0,255),2)#红

cv2.imshow('hull',black)
cv2.waitKey()
cv2.destroyAllWindows()

 

 

11 直线和圆检测

函数原型:

def HoughLinesP(image, #源图像

               rho, #线段的几何表示1

               theta, #np.pi/180

               threshold, #阈值

               lines=None, 

               minLineLength=None, #最小直线长度

              maxLineGap=None)#最大线段间隙

直线检测,示例代码以下:

import cv2
import numpy as np

#读入图像
img=cv2.imread('../contours.jpg')
#转换颜色空间
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#边缘检测
edges=cv2.Canny(gray,50,120)
#最小直线长度
minLineLength=100
#最大线段间隙
maxLineGap=5
#直线检测
lines=cv2.HoughLinesP(edges,#须要处理的图像
                     1,
                     np.pi/180,
                     100,
                     minLineLength,
                     maxLineGap)

for x1,y1,x2,y2 in lines[1]:
    cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2)

#显示图像
cv2.imshow('edges',edges)
cv2.imshow('lines',img)
cv2.waitKey()
cv2.destroyAllWindows()

 

 

圆检测,示例代码以下:

import cv2
import numpy as np

#读入图像
img=cv2.imread('../circles.jpg')
#更换颜色空间
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#中值边滤
imgMb=cv2.medianBlur(gray,5)

#圆检测
circles=cv2.HoughCircles(imgMb,
                         cv2.HOUGH_GRADIENT,
                         1,
                         120,
                         param1=100,
                         param2=30,
                         minRadius=0,
                         maxRadius=0)
circles=np.uint16(np.around(circles))

for i in circles[0,:]:
    cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)
    cv2.circle(img,(i[0],i[1]),2,(0,0,255),3)

cv2.imwrite('../houghCircles.jpg',img)
cv2.imshow('../houghCircles.jpg',img)
cv2.waitKey()
cv2.destroyAllWindows()

 

 

 

 

12 检测其余形状

能够使用approxPloyDP

Cv2.findContours和cv2.approxyDP