4-10 边缘检测2

图片卷积和矩阵运算不是一回事。矩阵是行列式相乘。

import cv2
import numpy as np
import random
import math
img = cv2.imread('image2.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('src',img)
# sobel 1 算子模板 2 图片卷积 3 阈值判决
# [1 2 1           [ 1 0 -1 
#  0 0 0             2 0 -2
# -1 -2 -1 ]        1 0 -1 ]

# [1 2 3 4] [a b c d] a*1+b*2+c*3+d*4 = dst
# sqrt(a*a+b*b) = f>th(判决明显) 如果f>th,我们就认为是边缘;如果f<th,我们就认为是非边缘.
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst = np.zeros((height,width,1),np.uint8)
for i in range(0,height-2):
    for j in range(0,width-2):
        gy = gray[i,j]*1+gray[i,j+1]*2+gray[i,j+2]*1-gray[i+2,j]*1-gray[i+2,j+1]*2-gray[i+2,j+2]*1
        gx = gray[i,j]+gray[i+1,j]*2+gray[i+2,j]-gray[i,j+2]-gray[i+1,j+2]*2-gray[i+2,j+2]
        grad = math.sqrt(gx*gx+gy*gy)
        if grad>50: #梯度>阈值
            dst[i,j] = 255
        else:
            dst[i,j] = 0
cv2.imshow('dst',dst)
cv2.waitKey(0)

import cv2
import numpy as np
import random
import math
img = cv2.imread('image1.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('src',img)
# sobel 1 算子模板 2 图片卷积 3 阈值判决
# [1 2 1           [ 1 0 -1 
#  0 0 0             2 0 -2
# -1 -2 -1 ]        1 0 -1 ]

# [1 2 3 4] [a b c d] a*1+b*2+c*3+d*4 = dst
# sqrt(a*a+b*b) = f>th(判决明显) 如果f>th,我们就认为是边缘;如果f<th,我们就认为是非边缘.
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst = np.zeros((height,width,1),np.uint8)
for i in range(0,height-2):
    for j in range(0,width-2):
        gy = gray[i,j]*1+gray[i,j+1]*2+gray[i,j+2]*1-gray[i+2,j]*1-gray[i+2,j+1]*2-gray[i+2,j+2]*1
        gx = gray[i,j]+gray[i+1,j]*2+gray[i+2,j]-gray[i,j+2]-gray[i+1,j+2]*2-gray[i+2,j+2]
        grad = math.sqrt(gx*gx+gy*gy)
        if grad>50: #梯度>阈值
            dst[i,j] = 255
        else:
            dst[i,j] = 0
cv2.imshow('dst',dst)
cv2.waitKey(0)

import cv2
import numpy as np
import random
import math
img = cv2.imread('image0.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('src',img)
# sobel 1 算子模板 2 图片卷积 3 阈值判决
# [1 2 1           [ 1 0 -1 
#  0 0 0             2 0 -2
# -1 -2 -1 ]        1 0 -1 ]

# [1 2 3 4] [a b c d] a*1+b*2+c*3+d*4 = dst
# sqrt(a*a+b*b) = f>th(判决明显) 如果f>th,我们就认为是边缘;如果f<th,我们就认为是非边缘.
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst = np.zeros((height,width,1),np.uint8)
for i in range(0,height-2):
    for j in range(0,width-2):
        gy = gray[i,j]*1+gray[i,j+1]*2+gray[i,j+2]*1-gray[i+2,j]*1-gray[i+2,j+1]*2-gray[i+2,j+2]*1
        gx = gray[i,j]+gray[i+1,j]*2+gray[i+2,j]-gray[i,j+2]-gray[i+1,j+2]*2-gray[i+2,j+2]
        grad = math.sqrt(gx*gx+gy*gy)
        if grad>50: #梯度>阈值
            dst[i,j] = 255
        else:
            dst[i,j] = 0
cv2.imshow('dst',dst)
cv2.waitKey(0)

import cv2
import numpy as np
import random
import math
img = cv2.imread('image3.png',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('src',img)
# sobel 1 算子模板 2 图片卷积 3 阈值判决
# [1 2 1           [ 1 0 -1 
#  0 0 0             2 0 -2
# -1 -2 -1 ]        1 0 -1 ]

# [1 2 3 4] [a b c d] a*1+b*2+c*3+d*4 = dst
# sqrt(a*a+b*b) = f>th(判决明显) 如果f>th,我们就认为是边缘;如果f<th,我们就认为是非边缘.
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst = np.zeros((height,width,1),np.uint8)
for i in range(0,height-2):
    for j in range(0,width-2):
        gy = gray[i,j]*1+gray[i,j+1]*2+gray[i,j+2]*1-gray[i+2,j]*1-gray[i+2,j+1]*2-gray[i+2,j+2]*1
        gx = gray[i,j]+gray[i+1,j]*2+gray[i+2,j]-gray[i,j+2]-gray[i+1,j+2]*2-gray[i+2,j+2]
        grad = math.sqrt(gx*gx+gy*gy)
        if grad>50: #梯度>阈值
            dst[i,j] = 255
        else:
            dst[i,j] = 0
cv2.imshow('dst',dst)
cv2.waitKey(0)