Be free and at leisure , Help grandma's roommate process the image . Spent a day studying cv2 Some of the functions in , Refer to other bloggers' excellent codes , On this basis, it is mixed and enriched , Made an image edge detection program , The threshold and other parameters can be adjusted in real time through the sliding bar , And can choose whether to save the image . Finally through pyinstaller Package the program into .exe Send the file to your roommate for use .
（ Learning image processing for the first time and writing articles for the first time , If there is a mistake , Welcome to correct ）
Use cv2 modular （opencv-python）
Enter instructions in the terminal ：pip install opencv-python install cv2
Import after successful installation cv2
import cv2
Enter three parameters , When the flag bit is 1 Time indicates that the method is used to process the image
By default Gauss filtering + Histogram equalization Methods
def image_processing(img, Gauss_flag=1, Color_flag=1, Gray_flag=0): # Image preprocessing
# Gaussian filter smoothes the image
if Gauss_flag == 1:
img = cv2.GaussianBlur(img, (3, 3), 0)
# Equalize the histogram of color image
if Color_flag == 1:
img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
# Convert a color image to a grayscale image , Histogram of balanced gray image
if Gray_flag == 1:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img = cv2.equalizeHist(img)
return img
Create a slider , Each control cv2.Canny Each parameter in the function
Canny(image, threshold1, threshold2, edges=None, apertureSize=None, L2gradient=None)
cv2.createTrackbar('threshold1', 'Canny', 50, 300, nothing) # threshold 1
cv2.createTrackbar('threshold2', 'Canny', 100, 300, nothing) # Larger threshold 2 It is used to detect obvious edges in the image
cv2.createTrackbar('apertureSize', 'Canny', 0, 2, nothing) # Sobel Operator size （3,5,7）
cv2.createTrackbar('L2gradient', 'Canny', 0, 1, nothing) # Parameters ( Boolean value )：true： Use more precise L2 norm （ The sum of the squares of the reciprocal in both directions is reopened ),false： Use L1 norm （ Add the absolute values of the two directional derivatives directly ）
Read and return the value of the slider position in real time
threshold1 = cv2.getTrackbarPos('threshold1', 'Canny') # threshold 1
threshold2 = cv2.getTrackbarPos('threshold2', 'Canny') # threshold 2
L2gradient = cv2.getTrackbarPos('L2gradient', 'Canny') # Parameters
aperturesize = cv2.getTrackbarPos('apertureSize', 'Canny') # Sobel Operator size
size = aperturesize * 2 + 3 # Sobel Operator size （3,5,7）
Use cv2 Medium Canny Function for edge detection , And display the image through the window
# Canny edge detection
img_edges = cv2.Canny(img, threshold1, threshold2, apertureSize=size, L2gradient=L2gradient)
# Show edge image
cv2.imshow('Canny', img_edges)
Test button , Press q sign out , Do not save the image ; Press s sign out , Save image .
（ The saved image path is the same as the original image , Named output.jpg）
if cv2.waitKey(1) == ord('q'): # sign out
break
elif cv2.waitKey(1) == ord('s'): # Save image
cv2.imwrite('\\'.join(img_path.split('\\')[:-1]) + '\\output.jpg', img_edges)
print(" Image saved successfully ")
break
Input the image address and three parameters of the image preprocessing function .
First, preprocess the image , And then edge detection .
if __name__ == "__main__":
img_path = input(" Please enter the picture address ( Such as E:\\Code\\xx.jpg)：")
guass_flag = int(input(" Whether Gaussian filtering is performed ( Input 1 Conduct , Input 0 Don't make )："))
color_flag = int(input(" Whether the color image is balanced ( Input 1 Conduct , Input 0 Don't make )："))
gray_flag = int(input(" Whether to balance the gray image ( Input 1 Conduct , Input 0 Don't make )："))
# Load the image
image = cv2.imread(img_path)
# Image preprocessing
img = image_processing(image, Gauss_flag=guass_flag, Color_flag=color_flag, Gray_flag=gray_flag)
# Show the original image
cv2.imshow('Original', image)
# Display the preprocessed image
cv2.imshow('Pretreatment', img)
# Image edge detection
image_canny(img)
Original image ：
Pre processed image ：
Image edge detection ：
Saved image ：
import cv2
def nothing(): # Define callback function
pass
def image_processing(img, Gauss_flag=1, Color_flag=1, Gray_flag=0): # Image preprocessing
# Gaussian filter smoothes the image
if Gauss_flag == 1:
img = cv2.GaussianBlur(img, (3, 3), 0)
# Equalize the histogram of color image
if Color_flag == 1:
img = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
# Histogram of balanced gray image
if Gray_flag == 1:
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Convert a color image to a grayscale image
img = cv2.equalizeHist(img)
return img
def image_canny(img): # Image edge detection
# Settings window
cv2.namedWindow('Canny')
# Create a slider , Control each parameter separately
cv2.createTrackbar('threshold1', 'Canny', 50, 300, nothing) # threshold 1
cv2.createTrackbar('threshold2', 'Canny', 100, 300, nothing) # Larger threshold 2 It is used to detect obvious edges in the image
# cv2.createTrackbar('apertureSize', 'Canny', 0, 2, nothing) # Sobel Operator size （3,5,7）
cv2.createTrackbar('L2gradient', 'Canny', 0, 1,
nothing) # Parameters ( Boolean value )：true： Use more precise L2 norm （ The sum of the squares of the reciprocal in both directions is reopened ),false： Use L1 norm （ Add the absolute values of the two directional derivatives directly ）
while (1):
# Returns the value of the position of the slider
threshold1 = cv2.getTrackbarPos('threshold1', 'Canny') # threshold 1
threshold2 = cv2.getTrackbarPos('threshold2', 'Canny') # threshold 2
L2gradient = cv2.getTrackbarPos('L2gradient', 'Canny') # Parameters
# aperturesize = cv2.getTrackbarPos('apertureSize', 'Canny') # Sobel Operator size
# size = aperturesize * 2 + 3 # Sobel Operator size （3,5,7）
# Canny edge detection
img_edges = cv2.Canny(img, threshold1, threshold2, L2gradient=L2gradient)
# Show edge image
cv2.imshow('Canny', img_edges)
if cv2.waitKey(1) == ord('q'): # Press q sign out
break
elif cv2.waitKey(1) == ord('s'): # Press s Save the image to the directory where the original image is located , Name it output.jpg, Quit again 
cv2.imwrite('\\'.join(img_path.split('\\')[:-1]) + '\\output.jpg', img_edges)
print(" Image saved successfully ")
break
cv2.destroyAllWindows()
if __name__ == "__main__":
img_path = input(" Please enter the picture address ( Such as E:\\Code\\xx.jpg)：") # Enter the original image address
guass_flag = int(input(" Whether Gaussian filtering is performed ( Input 1 Conduct , Input 0 Don't make )：")) # Input 1 For Gaussian filtering , Input 0 For not to proceed
color_flag = int(input(" Whether the color image is balanced ( Input 1 Conduct , Input 0 Don't make )：")) # Input 1 For color image equalization , Input 0 For not to proceed
gray_flag = int(input(" Whether to balance the gray image ( Input 1 Conduct , Input 0 Don't make )：")) # Input 1 For gray image equalization , Input 0 For not to proceed
# Load the image
image = cv2.imread(img_path)
# Image preprocessing
img = image_processing(image, Gauss_flag=guass_flag, Color_flag=color_flag, Gray_flag=gray_flag)
# Show the original image
cv2.imshow('Original', image)
# Display the preprocessed image
cv2.imshow('Pretreatment', img)
# Image edge detection
image_canny(img)
Enter instructions in the terminal ：pip install pyinstaller install pyinstaller
After successful installation, enter the command ：pyinstaller -F canny.py Package the files 
A literary creation 、 Difficult to adjust parameters , Ask for a little favor QAQ
Écrivez à votre cousin Python pour faire linterface graphique système de gestion des étudiants conception finale, lenseignant donne directement toutes les notes, la graduation est stable
PythonCréer un système de gest
