Python + OpenCV achieve image edge detection, image filter function

Used in the Python Jupyter Notebook achieve the following edge detection code, filtering the image, the process to achieve image processing out certain functions may be a bit rough. Install on opencv libraries can refer to: Python installation under opencv libraries and some of the issues summarized .

First, edge extraction

Import CV2 # Import opencv library 

# read original gradation image 
Image cv2.imread = ( " test.bmp " ) 
cv2.imshow ( " Image " , Image) # original image named "image" is displayed 

# threshold image division processing, image processing is about to black and white binary image 
RET, image1 = cv2.threshold (image, 80, 255, cv2.THRESH_BINARY)   # binary (black and white binary), the representative RET threshold, low threshold is 80, 255 is a high threshold 
cv2.imshow ( ' image1 ' , image1) # picture after the thresholding process name "image1" is displayed 

# binary image color-inverted, the inverted image pixel 
height, width = image1.shape # return to image size 
image2 = image1.copy ()
for I in Range (height):
     for J in Range (width): 
        image2 [I, J] = (255- image1 [I, J]) 
cv2.imshow ( ' image2 ' , image2) # The reaction color after treatment image named "image2" displayed 

# edge extraction using Canny function 
image2_3 = cv2.Canny (image2,80,255) # set 80 to a low threshold, a high threshold 255 
cv2.imshow ( ' image2_3 ' , image2_3) # edge extraction after the image is named "image2_3" is displayed 

# again color-inverted image of the edge extraction is black, the remainder is white, the same method image2 
height1, width1 = image2_3.shape 
Image3 = image2_3.copy ()
for I in Range (height1):
     for J in Range (width1): 
        Image3 [I, J] = (255- image2_3 [I, J]) 
cv2.imshow ( ' Image3 ' , Image3) # after the inverse color edge extraction picture named "image3" displayed 
cv2.waitKey (0) # wait for keyboard input, do not enter an infinite waiting 
cv2.destroyAllWindows ()   # destroy all windows

operation result:

 

 

 

 

 

 

 

 

 

 

Second, the image filter

Import CV2
 Import numpy
 Import matplotlib.pyplot AS plt
 from PIL Import Image, ImageDraw, ImageFont 

# to add Chinese characters to the picture 
DEF ImgText_CN (img, text, left, Top, textColor = (0, 255, 0), textSize = 20 ):
     iF (isinstance (img, numpy.ndarray)):   # determine whether the OpenCV image type 
        img = Image.fromarray (cv2.cvtColor (img, cv2.COLOR_BGR2RGB)) 
    Draw = ImageDraw.Draw (img) 
    fontText = ImageFont. TrueType ( " font / simhei.ttf " , the textSize, encoding = " UTF-. 8 ") 
    Draw.text ((left, Top), text, textColor, font = fontText)
 return cv2.cvtColor (numpy.asarray (img), cv2.COLOR_RGB2BGR) 

# read the original gray-scale images 
src = cv2.imread ( " test2 .bmp " ) 
cv2.imshow ( " the src " , the src) # original image named" the src "shown 
src_cn = ImgText_CN (the src, " original image " , 5, 5, (255, 0, 0), 20 ) 
cv2.imshow ( " src_cn " , src_cn) # adding text src named "src_cn" is displayed and 
# mean filter 
des cv2.blur = (src, (5, 5 )) 
cv2.imshow ( "des " , des) # The mean filtering process after the src named" des "and displayed 
des_cn = ImgText_CN (des, " mean filter " ,. 5,. 5, (255, 0, 0), 20 is ) 
cv2.imshow ( " des_cn " , des_cn) # adding text des named "des_cn" and displayed 
# median filtering 
Med = cv2.medianBlur (the src,. 5 ) 
cv2.imshow ( " Med " , Med) # the median filter after src named "med" and displayed 
med_cn = ImgText_CN (Med, " median filtering " ,. 5,. 5, (255, 0, 0), 20 is ) 
cv2.imshow ( " med_cn " , med_cn) #Adding text med named "med_cn" and displayed 
# Gaussian 
Gauss = cv2.GaussianBlur (src, (5, 5 ), 0) 
cv2.imshow ( " Gauss " , Gauss) # after the Gaussian filter processing src named "gauss" and displayed 
gauss_cn = ImgText_CN (gauss, " Gaussian " ,. 5,. 5, (255, 0, 0), 20 is ) 
cv2.imshow ( " gauss_cn " , gauss_cn) # adding text gauss named is "gauss_cn" and displayed 
# Gaussian edge detection 
gaussedge = cv2.Canny (gauss, 0,50 ) 
cv2.imshow ( " gaussedge " , gaussedge) # the gauss rear edge detection processing named "gaussedge" and displayed
= ImgText_CN gaussedge_cn (gaussedge, " Gaussian edge detection " ,. 5,. 5, (0, 255, 0), 20 is ) 
cv2.imshow ( " gaussedge_cn " , gaussedge_cn) # adding text gaussedge named "gaussedge_cn" and displayed 
cv2.waitKey (0)

operation result: