python opencv 快速均值平滑

 

import cv2
import numpy as np
from datetime import datetime
from scipy import signal #二维离散卷积运算
import math
def printWithdate(str):
    time2 = datetime.now()
    time2a = time2.strftime("%Y-%m-%d %H:%M:%S")
    print(time2a+' '+str)
#python opencv均值平滑
def integral(image):
    '''
    代码实现积分运算(展示原理)--即任意一个位置的积分等于该位置左上角所有值的和
    '''
    rows,cols = image.shape
    #行积分运算
    inteImgC=np.zeros((rows,cols),np.float32)
    for r in range(rows):
        for c in range(cols):
         if c == 0:
            inteImgC[r][c] = image[r][c]
         else:
            inteImgC[r][c] = inteImgC[r][c-1]+image[r][c]
    #列积分运算
    inteImg = np.zeros(image.shape,np.float32)
    for c in range(cols):
       for r in range(rows):
          if r==0:
             inteImg[r][c] = inteImgC[r][c]
          else:
             inteImg[r][c] = inteImg[r-1][c]+inteImgC[r][c]
    #上边盒左边进行补零
    inteImg_0 = np.zeros((rows+1,cols+1),np.float32)
    inteImg_0[1:rows+1,1:cols+1] = inteImg
    return inteImg_0
def fastMeanblur(img,winSize,borderType = cv2.BORDER_DEFAULT):
   '''
   代码实现均值平滑，展示原理。
   '''
   halfH = int((winSize[0]-1)/2)
   halfW = int((winSize[1]-1)/2)
   ratio = 1.0/(winSize[0]*winSize[1])
   #边界扩充
   padImg = cv2.copyMakeBorder(img,halfH,halfH,halfW,halfW,borderType)
   #图像积分
   padImg = integral(padImg)
   #图像高，宽
   rows,cols = img.shape
   #均值滤波后的结果
   meanBlurImg = np.zeros(img.shape,np.float32)
   r,c = 0,0
   for h in range(halfH,halfH+rows,1):
      
      for w in range(halfW,halfW+cols,1):
         
         meanBlurImg[r][c]=(padImg[h+halfH+1][w+halfW+1]
                            +padImg[h-halfH][w-halfW]
                            -padImg[h+halfH+1][w-halfW]
                            -padImg[h-halfH][w+halfW+1])*ratio
         c+=1
      r+=1
      c=0
   return meanBlurImg
      


if __name__=="__main__":
    printWithdate("hello")
     
    try: #异常捕捉 ，python 捕捉 imread路径错误
       img = cv2.imread(r'dayan.png',cv2.IMREAD_GRAYSCALE) #不会抛出异常，返回none
    except FileNotFoundError:
       print('没有找到文件')
    except IOError as e:
       print('img read error')
    except Exception as e:
       print('未知错误 error')
    if img is None:
       print("img is None,程序结束")
       quit() #程序直接退出不执行后续代码

    # cv2.imshow('img',img)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()
    #图像的积分后，通过定义函数fastMeanBlur来实现均值平滑，其中image是输
#入矩阵，winSize为平滑窗口尺寸，宽、高均为奇数，borderType为边界扩充类型。如果
#在图像的边界进行的处理是补零操作，那么随着窗口的增大，平滑后黑色边界会越来越
#明显，所以在进行均值平滑处理时，比较理想的边界扩充类型是镜像扩充
     
    meanImg = fastMeanblur(img,winSize=(7,7))
   # 图像进行灰度级显示
    meanImg = np.round(meanImg)
    meanImg = meanImg.astype(np.uint8)
    #卷积结果的数据类型是浮点型，假设输入的图像是8位图，进行
    #高斯卷积后，需要将结果用命令asty pe（numpy.uint8）进行数据类型转换，从而进行灰
    #度级显示，否则用imshow显示时为黑色
    cv2.imshow("GaussBlur",meanImg)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
   #  new_img = np.zeros(img.shape,np.uint8)
   #  H,W = img.shape
    
   #  for x in range(H):
   #      for y in range(W):
   #       if img[x][y]<245: #
   #          new_img[x][y] = 0  #0 黑色
   #       else:
   #          new_img[x][y] = 255 # 亮，白色

   #  cv2.imwrite('lrc_2.jpg',new_img)
     
   #  cv2.imshow('a',new_img)
   #  cv2.waitKey(0)
   #  cv2.destroyAllWindows()
    
    

 

翻译

搜索

复制