Python 多幅图像的边缘检测

嗨，我有一组图像，我想同时对所有图像进行边缘检测。我可以为每个图像手动执行，但我认为这不是正确的方法。如何一次对所有图像执行此操作？我认为它应该在一个循环中，但我不知道如何实现它

我读过多幅灰度图像，现在我想对所有图像进行边缘检测。我们如何为Canny函数选择最大值和最小值参数。可以访问这些图像

---

要自动选择

cv2.Canny（）

的最大值和最小值，可以使用Adrian Rosebrock在其博客中创建的

auto\u Canny（）

函数。其思想是计算图像中像素强度的中值，然后取该中值来确定

下限

和

上限

阈值。想了解更详细的解释，请查看他的博客。下面是函数

def auto_canny(image, sigma=0.33):
    # Compute the median of the single channel pixel intensities
    v = np.median(image)

    # Apply automatic Canny edge detection using the computed median
    lower = int(max(0, (1.0 - sigma) * v))
    upper = int(min(255, (1.0 + sigma) * v))
    return cv2.Canny(image, lower, upper)

---

要对多个图像执行边缘检测，可以使用

glob

库对每个图像进行迭代，应用canny边缘检测，然后保存图像。这是结果

def auto_canny(image, sigma=0.33):
    # Compute the median of the single channel pixel intensities
    v = np.median(image)

    # Apply automatic Canny edge detection using the computed median
    lower = int(max(0, (1.0 - sigma) * v))
    upper = int(min(255, (1.0 + sigma) * v))
    return cv2.Canny(image, lower, upper)

import cv2
import numpy as np
import glob

def auto_canny(image, sigma=0.33):
    # Compute the median of the single channel pixel intensities
    v = np.median(image)

    # Apply automatic Canny edge detection using the computed median
    lower = int(max(0, (1.0 - sigma) * v))
    upper = int(min(255, (1.0 + sigma) * v))
    return cv2.Canny(image, lower, upper)

# Read in each image and convert to grayscale
images = [cv2.imread(file,0) for file in glob.glob("images/*.jpg")]

# Iterate through each image, perform edge detection, and save image
number = 0
for image in images:
    canny = auto_canny(image)
    cv2.imwrite('canny_{}.png'.format(number), canny)
    number += 1