opencv計算機視覺學習筆記二

 

第三章 Opencv3處理圖像

 

1 不一樣色彩空間的轉換

計算機視覺中三種常見的色彩空間:

灰度

BGR

HSV(hue色調 saturation飽合度 value黑暗程度)

2 傅里葉變換

快速傅里葉變換fft

離散傅里葉變換dft

 

高通濾波器heigh passfilter

檢測圖像的某個區域,根據像素和周圍像素的亮度差值來提高該像素亮度的濾波器

示例代碼以下:

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

#自定義核 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]])

#讀入圖像，轉換爲灰度格式 img=cv2.imread('../test.jpg',cv2.IMREAD_GRAYSCALE)

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

#高經過濾 blurred=cv2.GaussianBlur(img,(11,11),0)
g_hpf=img-blurred

#顯示圖像 cv2.imshow('3x3',k3)
cv2.imshow('5x5',k5)
cv2.imshow('g_hpf',g_hpf)
cv2.waitKey()
cv2.destroyAllWindows()

 

 

 

 

 

 

 

低通濾波器low pass filter

在像素與周圍像素的亮度差值小於一個特定值時,平滑該像素的亮度

3 建立模塊

Filters.py文件,示例代碼以下:

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

 

Utils.py文件

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

 

 

4 邊緣檢測

經常使用函數

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)

 

模糊濾波函數

1 平均

函數原型

def blur(src, #源圖像

         ksize, #內核大小

         dst=None, #輸出圖像

         anchor=None, #中心錨點

         borderType=None)# 邊界模式

2 高斯模糊

函數原型

def GaussianBlur(src, #輸入圖像

                  ksize, #高斯濾波模版大小

                  sigmaX, #橫向濾波係數

                  dst=None, #輸出圖像

                  sigmaY=None,#縱向濾波係數 

                  borderType=None)

3 中值模糊

def medianBlur(src, #源圖像

            ksize, #中值濾波器的模版的大小

            dst=None)#輸出圖像

4 雙邊濾波

def bilateralFilter(src, #輸入圖像

                  d, #每一個像素鄰域的直徑

                  sigmaColor, #顏色空間的標準誤差

                  sigmaSpace, #座標空間的標準誤差

                  dst=None, #輸出圖像

                  borderType=None)#邊緣點插值類型

 

示例代碼以下:

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time    : 2016/11/29 12:58 # @Author  : Retacn # @Site    : 濾波器 # @File    : filters.py.py # @Software: PyCharm import cv2
import numpy as np
import Three.utils #自定義工具類 def strokeEdges(src,
                dst,
                blurKsize=7,#中值濾波ksize                 edgeKsize=5):#Laplacian算子ksize     if blurKsize>=3:
        #中值濾波         blurredSrc=cv2.medianBlur(src,blurKsize)
        #修改成灰度顏色空間         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 用定製內核做卷積

def filter2D(src, #輸入圖像

            ddepth, #圖像深度

            kernel, #卷積核,單通道浮點矩陣

            dst=None, #輸出圖像

            anchor=None, #一個被濾波的點在覈內的位置(中心)

            delta=None, 

      borderType=None)#邊界類型

 

若是要對每一個通道使用不一樣的核,必須用split()和merge()

示例代碼以下:

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

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


# 特定的銳化濾波器 class SharpenFilter(VConvolutionFilter):
    def __init__(self):
        kernel = np.array([[-1, -1, -1],
                           [-1, 9, -1],
                           [-1, -1, -1]])
        VConvolutionFilter.__init__(self, kernel)


# 邊緣檢測濾波器 class FindEdgesFilter(VConvolutionFilter):
    def __init__(self):
        kernel = np.array([[-1, -1, -1],
                           [-1, 8, -1],
                           [-1, -1, -1]])
        VConvolutionFilter.__init__(self, kernel)


# 模糊濾波器 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)


# 脊狀和浮雕效果 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,  # 中值濾波ksize                 edgeKsize=5):  # Laplacian算子ksize     if blurKsize >= 3:
        # 中值濾波         blurredSrc = cv2.medianBlur(src, blurKsize)
        # 修改成灰度顏色空間         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 修改應用

#!/usr/bin/env python

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

# @Time   : 2016/11/28 14:45

# @Author : Retacn

# @Site   : cameo實現,有兩種啓動方法: 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-> 載圖

           tab->啓動和中止視頻錄製

           esc->退出應用

 

       :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邊緣檢測

示例代碼以下:

import cv2
import numpy as np

#讀入灰度圖像
img=cv2.imread('../test.jpg',cv2.IMREAD_GRAYSCALE)
#邊緣檢測
cv2.imwrite('../canny.jpg',cv2.Canny(img,200,300))
#顯示圖像
cv2.imshow('canny',cv2.imread('../canny.jpg'))
cv2.waitKey()
cv2.destroyAllWindows()

 

8 輪廓檢測

import cv2
import numpy as np

img=np.zeros((200,200,),dtype=np.uint8)
#將指定的區域設爲白色
img[50:150,50:150]=255
#設定閾值
ret,thresh=cv2.threshold(img,127,255,0)
#查找輪廓
image,contours,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
#更換顏色空間
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 邊界框,最小矩形和最小閉圓的輪廓

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:
    #繪製矩形邊界框
    x, y, w, h = cv2.boundingRect(c)
    cv2.rectangle(img, (x, y), (x + w, x + y), (0, 255, 0), 2)

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

    #繪製小最閉圓
    (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 凸輪廓與douglas-peucker

示例代碼以下:

import cv2
import numpy as np

#讀入圖像
img=cv2.pyrDown(cv2.imread('../contours.jpg'),cv2.IMREAD_UNCHANGED)
#修改顏色空間,設置閾值
ret,thresh=cv2.threshold(cv2.cvtColor(img.copy(),cv2.COLOR_BGR2GRAY),
                         127,
                         255,
                         cv2.THRESH_BINARY)
#更換顏色空間
black=cv2.cvtColor(np.zeros((img.shape[0],img.shape[1]),
                    dtype=np.uint8),
                   cv2.COLOR_GRAY2BGR)
#檢測輪廓
image,contours,hier=cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

for cnt in contours:
    #輪廓的周長
    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)#綠,精確的輪廓
    cv2.drawContours(black,[approx],-1,(255,255,0),2)#藍色 近似多邊形
    cv2.drawContours(black,[hull],-1,(0,0,255),2)#紅

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

 

 

11 直線和圓檢測

函數原型:

def HoughLinesP(image, #源圖像

               rho, #線段的幾何表示1

               theta, #np.pi/180

               threshold, #閾值

               lines=None, 

               minLineLength=None, #最小直線長度

              maxLineGap=None)#最大線段間隙

直線檢測,示例代碼以下:

import cv2
import numpy as np

#讀入圖像
img=cv2.imread('../contours.jpg')
#轉換顏色空間
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#邊緣檢測
edges=cv2.Canny(gray,50,120)
#最小直線長度
minLineLength=100
#最大線段間隙
maxLineGap=5
#直線檢測
lines=cv2.HoughLinesP(edges,#須要處理的圖像
                     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)

#顯示圖像
cv2.imshow('edges',edges)
cv2.imshow('lines',img)
cv2.waitKey()
cv2.destroyAllWindows()

 

 

圓檢測,示例代碼以下:

import cv2
import numpy as np

#讀入圖像
img=cv2.imread('../circles.jpg')
#更換顏色空間
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#中值邊濾
imgMb=cv2.medianBlur(gray,5)

#圓檢測
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 檢測其餘形狀

能夠使用approxPloyDP

Cv2.findContours和cv2.approxyDP