opencv图像处理学习

opencv特效处理
1 毛玻璃

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

#读取原始图像
src = cv2.imread('4.jpg')

#新建目标图像
dst = np.zeros_like(src)

#获取图像行和列
rows, cols = src.shape[:2]

#定义偏移量和随机数
offsets = 5
random_num = 0

#毛玻璃效果: 像素点邻域内随机像素点的颜色替代当前像素点的颜色
for y in range(rows - offsets):
    for x in range(cols - offsets):
        random_num = np.random.randint(0,offsets)
        dst[y,x] = src[y + random_num,x + random_num]

#显示图像
cv2.imshow('src',src)
cv2.imshow('dst',dst)

cv2.waitKey()
cv2.destroyAllWindows()

2 浮雕特效

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

#读取原始图像
img = cv2.imread('4.jpg', 1)

#获取图像的高度和宽度
height, width = img.shape[:2]

#图像灰度处理
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

#创建目标图像
dstImg = np.zeros((height,width,1),np.uint8)

#浮雕特效算法：newPixel = grayCurrentPixel - grayNextPixel + 150
for i in range(0,height):
    for j in range(0,width-1):
        grayCurrentPixel = int(gray[i,j])
        grayNextPixel = int(gray[i,j+1])
        newPixel = grayCurrentPixel - grayNextPixel + 150
        if newPixel > 255:
            newPixel = 255
        if newPixel < 0:
            newPixel = 0
        dstImg[i,j] = newPixel
        
#显示图像
cv2.imshow('src', img)
cv2.imshow('dst',dstImg)

#等待显示
cv2.waitKey()
cv2.destroyAllWindows()

3 油漆特效

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

#读取原始图像
src = cv2.imread('4.jpg')

#图像灰度处理
gray = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)

#自定义卷积核
kernel = np.array([[-1,-1,-1],[-1,10,-1],[-1,-1,-1]])

#图像浮雕效果
output = cv2.filter2D(gray, -1, kernel)

#显示图像
cv2.imshow('Original Image', src)
cv2.imshow('Emboss_1',output)

#等待显示
cv2.waitKey()
cv2.destroyAllWindows()

4 素描

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

#读取原始图像
img = cv2.imread('4.jpg')

#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()

5 怀旧特效

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

#读取原始图像
img = cv2.imread('4.jpg')

#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()

6 光影特效

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

#读取原始图像
img = cv2.imread('4.jpg')

#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()

7 流年

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

#读取原始图像
img = cv2.imread('4.jpg')

#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()

8 滤镜

#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('img.jpg')
lj_map = cv2.imread('5.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()