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)