#实质是卷积运算
#1、灰度处理，2、高斯滤波，3、边缘检测
import cv2
import numpy as np
import random

img = cv2.imread('car.jpg', 1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('src',img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  #灰度化
imgG = cv2.GaussianBlur(gray, (3,3), 0) #高斯滤波,(3,3)是模板大小
dst = cv2.Canny(img, 50, 50)  #经过卷积之后大于50则认为是边缘点
cv2.imshow('dst',dst)
cv2.waitKey(0)

#sobel算子：算子模板（两种）、图片卷积、阈值判决
#模板：水平方向：[1 2 1            竖直方向： [1 0 -1
#               0 0 0                        2 0 -2
#              -1 -2 -1]                     1 0 -1]
import cv2
import numpy as np
import random
import math

img = cv2.imread('car.jpg', 1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
cv2.imshow('src',img)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  #灰度化
dst = np.zeros((height, width, 1), np.uint8)
for i in range(0, height-2):     #height-m防止越界
    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]*1 + gray[i+1,j]*2 +gray[i+2,j]*1 - gray[i,j+2]*1 - gray[i+1,j+2]*2-gray[i+2,j+2]*1
        grad = math.sqrt(gy*gy + gx*gx)
        if grad > 50:
            dst[i,j] = 255
        else:
            dst[i,j] = 0
cv2.imshow('dst',dst)
cv2.waitKey(0)