#coding:utf-8
import cv2 as cv
import numpy as np

#读取原始图像
img = cv.imread('d:/paojie.png')

#图像灰度处理
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)

#高斯滤波降噪
gaussian = cv.GaussianBlur(gray, (5,5), 0)
 
#Canny算子
canny = cv.Canny(gaussian, 50, 150)

#阈值化处理
ret, result = cv.threshold(canny, 0, 255, cv.THRESH_BINARY_INV+cv.THRESH_OTSU)

#显示图像
#cv.imshow('src', img)
#cv.imshow('result', result)
cv.imshow('result',np.vstack((gray,result)))
cv.waitKey()
cv.destroyAllWindows()

#coding:utf-8
import cv2 as cv
import numpy as np

#读取原始图像
img = cv.imread('d:/paojie.png')

#获取图像行和列
rows, cols = img.shape[:2]

#新建目标图像
dst = np.zeros((rows, cols, 3), dtype="uint8")

#图像怀旧特效
for i in range(rows):
  for j in range(cols):
    B = 0.272*img[i,j][2] + 0.534*img[i,j][1] + 0.131*img[i,j][0]
    G = 0.349*img[i,j][2] + 0.686*img[i,j][1] + 0.168*img[i,j][0]
    R = 0.393*img[i,j][2] + 0.769*img[i,j][1] + 0.189*img[i,j][0]
    if B>255:
      B = 255
    if G>255:
      G = 255
    if R>255:
      R = 255
    dst[i,j] = np.uint8((B, G, R))
    
#显示图像
cv.imshow('result',np.vstack((img,dst)))
cv.waitKey()
cv.destroyAllWindows()

#coding:utf-8
import cv2 as cv
import math
import numpy as np

#读取原始图像
img = cv.imread('d:/paojie.png')

#获取图像行和列
rows, cols = img.shape[:2]

#设置中心点和光照半径
centerX = rows / 2 - 20
centerY = cols / 2 + 20
radius = min(centerX, centerY)

#设置光照强度
strength = 100

#新建目标图像
dst = np.zeros((rows, cols, 3), dtype="uint8")

#图像光照特效
for i in range(rows):
  for j in range(cols):
    #计算当前点到光照中心距离(平面坐标系中两点之间的距离)
    distance = math.pow((centerY-j), 2) + math.pow((centerX-i), 2)
    #获取原始图像
    B = img[i,j][0]
    G = img[i,j][1]
    R = img[i,j][2]
    if (distance < radius * radius):
      #按照距离大小计算增强的光照值
      result = (int)(strength*( 1.0 - math.sqrt(distance) / radius ))
      B = img[i,j][0] + result
      G = img[i,j][1] + result
      R = img[i,j][2] + result
      #判断边界 防止越界
      B = min(255, max(0, B))
      G = min(255, max(0, G))
      R = min(255, max(0, R))
      dst[i,j] = np.uint8((B, G, R))
    else:
      dst[i,j] = np.uint8((B, G, R))
    
#显示图像
cv.imshow('result',np.vstack((img,dst)))
cv.waitKey()
cv.destroyAllWindows()

#coding:utf-8
import cv2 as cv
import math
import numpy as np

#读取原始图像
img = cv.imread('d:/paojie.png')

#获取图像行和列
rows, cols = img.shape[:2]

#新建目标图像
dst = np.zeros((rows, cols, 3), dtype="uint8")

#图像流年特效
for i in range(rows):
  for j in range(cols):
    #B通道的数值开平方乘以参数12
    B = math.sqrt(img[i,j][0]) * 12
    G = img[i,j][1]
    R = img[i,j][2]
    if B>255:
      B = 255
    dst[i,j] = np.uint8((B, G, R))
    
#显示图像
cv.imshow('result',np.vstack((img,dst)))
cv.waitKey()
cv.destroyAllWindows()

#coding:utf-8
import cv2 as cv 
import numpy as np

#获取滤镜颜色
def getBGR(img, table, i, j):
  #获取图像颜色
  b, g, r = img[i][j]
  #计算标准颜色表中颜色的位置坐标
  x = int(g/4 + int(b/32) * 63)
  y = int(r/4 + int((b%32) / 4) * 63)
  #返回滤镜颜色表中对应的颜色
  return lj_map[x][y]

#读取原始图像
img = cv.imread('d:/paojie.png')
lj_map = cv.imread('lvjing.png')

#获取图像行和列
rows, cols = img.shape[:2]

#新建目标图像
dst = np.zeros((rows, cols, 3), dtype="uint8")

#循环设置滤镜颜色
for i in range(rows):
  for j in range(cols):
    dst[i][j] = getBGR(img, lj_map, i, j)
    
#显示图像
cv.imshow('result',np.vstack((img,dst)))

cv.waitKey()
cv.destroyAllWindows()