OpenCV图像梯度处理与边缘检测的实战指南

import cv2
import numpy as np

# 自定义卷积核
kernel = np.array([[-1, 0, 1],
                   [-2, 0, 2],
                   [-1, 0, 1]], dtype=np.float32)

# 读取图像
img = cv2.imread("./opencv_work/src/shudu.png")

# 使用卷积核进行边缘检测
img2 = cv2.filter2D(img, -1, kernel=kernel)  # 垂直边缘检测
img3 = cv2.filter2D(img, -1, kernel=kernel.T)  # 水平边缘检测

# 显示结果
cv2.imshow("Original Image", img)
cv2.imshow("Vertical Edges", img2)
cv2.imshow("Horizontal Edges", img3)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取图像并转换为灰度
img = cv2.imread("./opencv_work/src/shudu.png", cv2.IMREAD_GRAYSCALE)

# 使用 Sobel 算子进行边缘检测
img2 = cv2.Sobel(img, -1, dx=1, dy=0, ksize=3)  # 水平边缘检测
img3 = cv2.Sobel(img, -1, dx=0, dy=1, ksize=3)  # 垂直边缘检测

# 显示结果
cv2.imshow("Original Image", img)
cv2.imshow("Horizontal Edges (Sobel)", img2)
cv2.imshow("Vertical Edges (Sobel)", img3)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取图像并转换为灰度
img = cv2.imread("./opencv_work/src/shudu.png", cv2.IMREAD_GRAYSCALE)

# 使用 Laplacian 算子进行边缘检测
img2 = cv2.Laplacian(img, -1, ksize=3)

# 显示结果
cv2.imshow("Original Image", img)
cv2.imshow("Edges (Laplacian)", img2)
cv2.waitKey(0)
cv2.destroyAllWindows()

import cv2

# 读取图像并转换为灰度
img = cv2.imread("./src/face.png", cv2.IMREAD_GRAYSCALE)

# 使用 Canny 算法进行边缘检测
img_canny = cv2.Canny(img, 100, 150)

# 显示结果
cv2.imshow("Original Image", img)
cv2.imshow("Edges (Canny)", img_canny)
cv2.waitKey(0)
cv2.destroyAllWindows()