我认为这应该采取的情况下，如果你张贴一些样本图像将是有用的，但我会尝试回答无论如何.这里是从OpenCV文档

Canny( detected_edges, detected_edges, lowThreshold, lowThreshold*ratio, kernel_size );
where the arguments are:

detected_edges: Source image, grayscale
detected_edges: Output of the detector (can be the same as the input)
lowThreshold: The value entered by the user moving the Trackbar
highThreshold: Set in the program as three times the lower threshold (following Canny’s recommendation)
kernel_size: We defined it to be 3 (the size of the Sobel kernel to be used internally)

我通常使用highThreshold = 255 and lowThreshold = 255/3

正如Samer所说，这可以根据具体情况而定。这里有一些代码，它在opencv中使用了跟踪条，并在原始图像旁边显示canny图像，以便快速试验不同的阈值。

import cv2
import numpy as np 
import matplotlib.pyplot as plt 

def callback(x):
    print(x)

img = cv2.imread('your_image.png', 0) #read image as grayscale
    
    
canny = cv2.Canny(img, 85, 255) 

cv2.namedWindow('image') # make a window with name 'image'
cv2.createTrackbar('L', 'image', 0, 255, callback) #lower threshold trackbar for window 'image
cv2.createTrackbar('U', 'image', 0, 255, callback) #upper threshold trackbar for window 'image

while(1):
    numpy_horizontal_concat = np.concatenate((img, canny), axis=1) # to display image side by side
    cv2.imshow('image', numpy_horizontal_concat)
    k = cv2.waitKey(1) & 0xFF
    if k == 27: #escape key
        break
    l = cv2.getTrackbarPos('L', 'image')
    u = cv2.getTrackbarPos('U', 'image')

    canny = cv2.Canny(img, l, u)

cv2.destroyAllWindows()

你可以使用这个方程，它是有用的，你可以应用蓝色来增强它。

blurred_img = cv2.blur(img,ksize=(5,5))
med_val = np.median(img) 
lower = int(max(0 ,0.7*median_pix))
upper = int(min(255,1.3*median_pix))
edges = cv2.Canny(image=img, threshold1=lower,threshold2=upper)