import cv2
import numpy as np
img = cv2.imread('image1.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# newP = gray0-gray1+150 加上150是为了增强图片的浮雕灰度等级
# 相邻像素相减是为了突出灰度的凸片,即它的边缘特征.
dst = np.zeros((height,width,1),np.uint8)
for i in range(0,height):
    for j in range(0,width-1):
        grayP0 = int(gray[i,j)# 表明我们的当前像素 获取当前的灰度值
        grayP1 = int(gray[i,j+1])# 表明下一个像素
        newP = grayP0-grayP1+150
        if newP > 255:
            newP = 255
        if newP < 0:
            newP = 0
        dst[i,j] = newP
cv2.imshow('dst',dst)
cv2.waitKey(0)

150就是当前的灰度值，边沿细节就是我们的相邻像素之差。

4-11 浮雕效果_灰度值

import cv2
import numpy as np
img = cv2.imread('image2.jpg',1)
imgInfo = img.shape
height = imgInfo[0]
width = imgInfo[1]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# newP = gray0-gray1+150 加上150是为了增强图片的浮雕灰度等级
# 相邻像素相减是为了突出灰度的凸片,即它的边缘特征.
dst = np.zeros((height,width,1),np.uint8)
for i in range(0,height):
    for j in range(0,width-1):
        grayP0 = int(gray[i,j])# 表明我们的当前像素 获取当前的灰度值
        grayP1 = int(gray[i,j+1])# 表明下一个像素
        newP = grayP0-grayP1+150
        if newP > 255:
            newP = 255
        if newP < 0:
            newP = 0
        dst[i,j] = newP
cv2.imshow('dst',dst)
cv2.waitKey(0)

4-11 浮雕效果_灰度_02