Python利用OpenCV和skimage实现图像边缘检测

cv2.Canny(image, threshold1, threshold2, edges=None, apertureSize=None, L2gradient=None):
# 用自定义梯度
cv2.Canny(dx, dy, threshold1, threshold2[, edges[, L2gradient]]) -> edges

def opencv_canny(image):
    # 高斯模糊  降低噪声
    blurred = cv.GaussianBlur(image, (5, 5), 0)
    # 转为灰度图像
    gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
    # 计算x y 方向梯度
    grad_x = cv.Sobel(gray, cv.CV_16SC1, 1, 0)
    grad_y = cv.Sobel(gray, cv.CV_16SC1, 0, 1)
    edge_output = cv.Canny(grad_x, grad_y,  60, 120)
    # 英文字体   Times New Roman
    plt.rcParams['font.sans-serif'] = ['Times New Roman']

    # 可视化结果
    plt.figure(figsize=(8, 4), dpi=500)
    plt.subplot(121)
    plt.imshow(gray, cmap='gray')
    plt.title('Original Image', fontsize=18)
    plt.xticks([]), plt.yticks([])
    plt.subplot(122)
    plt.imshow(edge_output, cmap='gray')
    plt.title('Edge Image', fontsize=18)
    plt.xticks([]), plt.yticks([])
    plt.savefig("002.png", dpi=500)
    plt.show()


if __name__ == "__main__":
    # 读取图像  传入
    src = cv.imread("Lenna.png")
    opencv_canny(src)

import numpy as np
from skimage.io import imread
from skimage.feature import canny
import matplotlib.pyplot as plt

# 读取图像
img = imread("Lenna.png", as_gray=True)
# 高斯模糊  降低噪声
img = cv.GaussianBlur(img, (5, 5), 0)

# Canny边缘检测
edges = canny(img, sigma=1.6)

# 可视化结果
plt.rcParams['font.sans-serif'] = ['Times New Roman']
plt.figure(figsize=(8, 4), dpi=500)
plt.subplot(121)
plt.imshow(img, cmap='gray')
plt.title('Original Image', fontsize=18)
plt.xticks([]), plt.yticks([])
plt.subplot(122)
plt.imshow(edges, cmap='gray')
plt.title('Edge Image', fontsize=18)
plt.xticks([]), plt.yticks([])

plt.show()

import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from skimage import feature


# 产生带有噪声的举行图案
im = np.zeros((128, 128))
im[32:-32, 32:-32] = 1
im = ndi.rotate(im, 15, mode='constant')  # 旋转一定角度
im = ndi.gaussian_filter(im, 4)
im += 0.2 * np.random.random(im.shape)

# Compute the Canny filter for two values of sigma
edges1 = feature.canny(im, sigma=1)
edges2 = feature.canny(im, sigma=3)

# display results
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 4),
                                    sharex=True, sharey=True, dpi=500)

ax1.imshow(im, cmap=plt.cm.gray)
ax1.axis('off')
ax1.set_title('Noisy image', fontsize=20)

ax2.imshow(edges1, cmap=plt.cm.gray)
ax2.axis('off')
ax2.set_title(r'Canny filter, $\sigma=1$', fontsize=20)

ax3.imshow(edges2, cmap=plt.cm.gray)
ax3.axis('off')
ax3.set_title(r'Canny filter, $\sigma=3$', fontsize=20)

fig.tight_layout()

plt.show()

skimage.feature.canny(image, sigma=1.0,
                      low_threshold=None, high_threshold=None,
                      mask=None, use_quantiles=False)