#coding:utf-8
import cv2
import numpy as np
#读取原始图像
img = cv2.imread('scenery.png')
#图像灰度处理
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#高斯滤波降噪
gaussian = cv2.GaussianBlur(gray, (5,5), 0)
#Canny算子
canny = cv2.Canny(gaussian, 50, 150)
#阈值化处理
ret, result = cv2.threshold(canny, 100, 255, cv2.THRESH_BINARY_INV)
#显示图像
cv2.imshow('src', img)
cv2.imshow('result', result)
cv2.waitKey()
cv2.destroyAllWindows()

#coding:utf-8
import cv2
import numpy as np
#读取原始图像
img = cv2.imread('nana.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
cv2.imshow('src', img)
cv2.imshow('dst', dst)
cv2.waitKey()
cv2.destroyAllWindows()

#coding:utf-8
import cv2
import math
import numpy as np
#读取原始图像
img = cv2.imread('scenery.png')
#获取图像行和列
rows, cols = img.shape[:2]
#设置中心点
centerX = rows / 2
centerY = cols / 2
print centerX, centerY
radius = min(centerX, centerY)
print radius
#设置光照强度
strength = 200
#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
cv2.imshow('src', img)
cv2.imshow('dst', dst)
cv2.waitKey()
cv2.destroyAllWindows()

#coding:utf-8
import cv2
import math
import numpy as np
#读取原始图像
img = cv2.imread('scenery.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
cv2.imshow('src', img)
cv2.imshow('dst', dst)
cv2.waitKey()
cv2.destroyAllWindows()

#coding:utf-8
import cv2
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) * 64)
    y = int(r/4 + int((b%32) / 4) * 64)
 #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 = cv2.imread('scenery.png')
lj_map = cv2.imread('table.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
cv2.imshow('src', img)
cv2.imshow('dst', dst)
cv2.waitKey()
cv2.destroyAllWindows()