opencv——图像边缘检测（上）sobel算子

1、Primitiv

# 原始图像梯度算子
import cv2
import numpy as np

moon = cv2.imread("./sudoku.jpg", 0)
row, column = moon.shape
moon_f = np.copy(moon)
moon_f = moon_f.astype("float")

Primary = np.zeros((row, column))

for x in range(row - 1):
    for y in range(column - 1):
        gx = abs(moon_f[x + 1, y] - moon_f[x, y])
        gy = abs(moon_f[x, y + 1] - moon_f[x, y])
        Primary[x, y] = gx + gy

sharp = moon_f + Primary

sharp = np.where(sharp < 0, 0, np.where(sharp > 255, 255, sharp))
sharp = sharp.astype("uint8")

cv2.imshow("src", moon)
cv2.imshow("Primary_sharp", sharp)
cv2.imwrite('./Primary_sharp.jpg', sharp)

cv2.waitKey()

2、Roberts

import cv2
import numpy as np

moon = cv2.imread("./sudoku.jpg", 0)
row, column = moon.shape
moon_f = np.copy(moon)
moon_f = moon_f.astype("float")

Roberts = np.zeros((row, column))

for x in range(row - 1):
    for y in range(column - 1):
        gx = abs(moon_f[x + 1, y + 1] - moon_f[x, y])
        gy = abs(moon_f[x + 1, y] - moon_f[x, y + 1])
        Roberts[x, y] = gx + gy

sharp = moon_f + Roberts

sharp = np.where(sharp < 0, 0, np.where(sharp > 255, 255, sharp))
sharp = sharp.astype("uint8")

cv2.imshow("src", moon)
cv2.imshow("Roberts_sharp", sharp)
cv2.imwrite('./Roberts_sharp.jpg', sharp)

cv2.waitKey()

3、Prewitt

import cv2
import numpy as np

moon = cv2.imread("./sudoku.jpg", 0)  # 读取图片，并且为灰度图
row, column = moon.shape  # 获取图片形状 （行， 列）

moon_f = np.copy(moon)
moon_f = moon_f.astype("float")
prewitt = np.zeros((row, column))
print(type(prewitt[0, 0]))

# 计算X轴和Y轴梯度
for x in range(1, row - 1):
    for y in range(1, column - 1):
        gx = abs((moon_f[x + 1, y - 1] + moon_f[x + 1, y] + moon_f[x + 1, y + 1]) - (
                moon_f[x - 1, y - 1] + moon_f[x - 1, y] + moon_f[x - 1, y + 1]))
        gy = abs((moon_f[x - 1, y + 1] + moon_f[x, y + 1] + moon_f[x + 1, y + 1]) - (
                moon_f[x - 1, y - 1] + moon_f[x, y - 1] + moon_f[x + 1, y - 1]))
        prewitt[x, y] = gx + gy

sharp = moon_f + prewitt
sharp = np.where(sharp < 0, 0, np.where(sharp > 255, 255, sharp))
sharp = sharp.astype("uint8")

cv2.imshow("src", moon)
cv2.imshow("prewitt_sharp", sharp)

cv2.waitKey()

4、Sobel

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
核大小(ksize)。如果ksize=-1,表示使用3X3的Scharr滤波器。
建议在处理速度相同的情况下，尽量使用Scharr滤波器。
"""
import cv2 as cv
import numpy as np


def nothing(x):
    pass


cv.namedWindow('Sobel x')
cv.namedWindow('Sobel y')

# 创建滑动条
cv.createTrackbar('xsize', 'Sobel x', 1, 15, nothing)
cv.createTrackbar('ysize', 'Sobel y', 1, 15, nothing)

# def sp_noise(image,prob):
#     '''
#     添加椒盐噪声
#     prob:噪声比例
#     '''
#     output = np.zeros(image.shape,np.uint8)
#     thres = 1 - prob
#     for i in range(image.shape[0]):
#         for j in range(image.shape[1]):
#             rdn = np.random.random()
#             if rdn < prob:
#                 output[i][j] = 0
#             elif rdn > thres:
#                 output[i][j] = 255
#             else:
#                 output[i][j] = image[i][j]
#     return output

img = cv.imread('./sudoku.jpg', 0)
# img = sp_noise(img, 0.009)

cv.imshow("src", img)
while True:
    kx = cv.getTrackbarPos('xsize', 'Sobel x')
    ky = cv.getTrackbarPos('ysize', 'Sobel y')
    if (kx % 2) == 0:
        kx += 1
    if (ky % 2) == 0:
        ky += 1
    # 参数 1,0 为只在 x 方向求一 导数 最大可以求 2 导数。
    sobelx = cv.Sobel(img, cv.CV_64F, 1, 0, ksize=kx)
    # 参数 0,1 为只在 y 方向求一 导数 最大可以求 2 导数。
    sobely = cv.Sobel(img, cv.CV_64F, 0, 1, ksize=ky)

    # 求绝对值
    sobelx = cv.convertScaleAbs(sobelx)
    sobely = cv.convertScaleAbs(sobely)

    sobel_xy = cv.addWeighted(sobelx, .5, sobely, .5, 1)
    k = cv.waitKey(24) & 0xFF
    if chr(k) == 'q':
        break
    cv.imshow('Sobel x', sobelx)
    cv.imshow('Sobel y', sobely)
    cv.imshow('sobel_xy', sobel_xy)

cv.destroyAllWindows()

5、soble_video

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import cv2 as cv


def nothing(x):
    pass


cv.namedWindow('Sobel x')
cv.namedWindow('Sobel y')
cv.namedWindow('Sobel_xy')

# 创建滑动条
cv.createTrackbar('xsize', 'Sobel x', 1, 15, nothing)
cv.createTrackbar('ysize', 'Sobel y', 1, 15, nothing)

cap = cv.VideoCapture(0)
while cap.isOpened():
    ret, frame = cap.read()
    # frame = cv.blur(frame, (5, 5))
    frame = cv.GaussianBlur(frame, (5, 5), -1)
    gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
    if (not ret):
        break
    kx = cv.getTrackbarPos('xsize', 'Sobel x')
    ky = cv.getTrackbarPos('ysize', 'Sobel y')
    if (kx % 2) == 0:
        kx += 1
    if (ky % 2) == 0:
        ky += 1

    # 参数 1,0 为只在 x 方向求一 导数 最大可以求 2 导数。
    sobelx = cv.Sobel(gray, cv.CV_64F, 1, 0, ksize=kx)
    # 参数 0,1 为只在 y 方向求一 导数 最大可以求 2 导数。
    sobely = cv.Sobel(gray, cv.CV_64F, 0, 1, ksize=ky)

    # 计算绝对值，并将结果转换为8位
    # 在输入数组的每个元素上，函数convertScaleAbs。顺序执行三个操作：缩放，取绝对值。值，转换为无符号8位类型
    sobelx = cv.convertScaleAbs(sobelx)
    sobely = cv.convertScaleAbs(sobely)

    sobel_xy = cv.addWeighted(sobelx, .5, sobely, .5, 1)
    k = cv.waitKey(24) & 0xFF
    if chr(k) == 'q':
        break
    cv.imshow('Sobel x', sobelx)
    cv.imshow('Sobel y', sobely)
    ret, img = cv.threshold(sobel_xy, 130, 255, cv.THRESH_BINARY_INV)
    cv.imshow('Sobel_xy', sobel_xy)

cv.destroyAllWindows()