计算机视觉之图像美化（实现图片直方图、直方图均衡化、图片修补、亮度增强、磨皮美白、高斯均值滤波、中值滤波等功能）

1.彩色图片直方图

测试代码如下：

 1 import cv2
 2 import numpy as np
 3 def ImageHist(image,type):
 4     color = (255,255,255)
 5     windowName = 'Gray'
 6     if type == 31:
 7         color = (255,0,0)
 8         windowName = 'B Hist'
 9     elif type == 32:
10         color = (0,255,0)
11         windowName = 'G Hist'
12     elif type == 33:
13         color = (0,0,255)
14         windowName = 'R Hist'
15     # 参数 1 image 2 第一个通道  3模板 4 分成多少份 5各像素的值
16     hist = cv2.calcHist([image],[0],None,[256],[0.0,255.0])
17     minV,maxV,minL,maxL = cv2.minMaxLoc(hist)
18     histImg = np.zeros([256,256,3],np.uint8)
19     for h in range(256):
20         intenNormal = int(hist[h]*256/maxV)
21         cv2.line(histImg,(h,256),(h,256-intenNormal),color)
22     cv2.imshow(windowName,histImg)
23     return histImg
24 img = cv2.imread('image0.jpg', 1)
25 channels = cv2.split(img)  # 分解成三个通道
26 print(channels)
27 for i in range(0,3):
28     ImageHist(channels[i],31+i)
29 cv2.waitKey(0)

运行结果如下：

蓝色通道下的直方图：

绿色通道下的直方图：

红色通道下的直方图：

灰度直方图源码实现：

 1 # encoding:utf-8
 2 
 3 import cv2
 4 import numpy as np
 5 import matplotlib.pyplot as plt
 6 
 7 # 本质 统计每个像素灰度 出现的概率 0-255 p
 8 img = cv2.imread('image0.jpg',1)
 9 imgInfo = img.shape
10 height = imgInfo[0]
11 width = imgInfo[1]
12 gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
13 cnt = np.zeros(256,np.float)
14 for i in range(0,height):
15     for j in range(0,width):
16         pixel = gray[i,j]
17         index = int(pixel)
18         cnt[index] = cnt[index]+1
19 for i in range(0,256):
20     cnt[i] = cnt[i]/(height*width)
21 # x轴
22 x = np.linspace(0,255,256)
23 # y轴
24 y = cnt
25 plt.bar(x,y,0.9,alpha=1,color='b')
26 plt.show()
27 cv2.waitKey(0)

运行结果如下：

彩色直方图源码实现：

 1 # encoding:utf-8
 2 
 3 import cv2
 4 import numpy as np
 5 import matplotlib.pyplot as plt
 6 
 7 # 本质 统计每个像素灰度 出现的概率 0-255 p
 8 img = cv2.imread('image0.jpg',1)
 9 imgInfo = img.shape
10 height = imgInfo[0]
11 width = imgInfo[1]
12 gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
13 cnt_b = np.zeros(256,np.float)
14 cnt_g = np.zeros(256,np.float)
15 cnt_r = np.zeros(256,np.float)
16 for i in range(0,height):
17     for j in range(0,width):
18         (b,g,r) = img[i,j]
19         index_b = int(b)
20         index_g = int(g)
21         index_r = int(r)
22         cnt_b[index_b] = cnt_b[index_b]+1
23         cnt_r[index_r] = cnt_r[index_r]+1
24         cnt_g[index_g] = cnt_g[index_g]+1
25 for i in range(0,256):
26     cnt_b[i] = cnt_b[i]/(height*width)
27     cnt_r[i] = cnt_r[i]/(height*width)
28     cnt_r[i] = cnt_r[i]/(height*width)
29 # x轴
30 x = np.linspace(0,255,256)
31 # y轴
32 y_b = cnt_b
33 plt.figure()
34 plt.bar(x,y_b,0.9,alpha=1,color='b')
35 y_r = cnt_r
36 plt.figure()
37 plt.bar(x,y_r,0.9,alpha=1,color='r')
38 y_g = cnt_g
39 plt.figure()
40 plt.bar(x,y_g,0.9,alpha=1,color='g')
41 plt.show()
42 cv2.waitKey(0)

蓝色通道下：

绿色通道下：

红色通道下：

2.直方图均衡化

灰度图均衡化：

测试代码如下：

1 import cv2
2 import numpy as np
3 img = cv2.imread('image0.jpg', 1)
4 gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
5 cv2.imshow('src',gray)
6 dst = cv2.equalizeHist(gray)
7 cv2.imshow('dst',dst)
8 cv2.waitKey(0)

运行结果如下：

src初始图像：

均衡化后的图像：

BGR彩色图均衡化：

测试代码如下：

 1 import cv2
 2 import numpy as np
 3 img = cv2.imread('image0.jpg', 1)
 4 cv2.imshow('src',img)
 5 # 通道分解分割成三个分量
 6 (b,g,r) = cv2.split(img)
 7 bH = cv2.equalizeHist(b)
 8 gH = cv2.equalizeHist(g)
 9 rH = cv2.equalizeHist(r)
10 # 通道合成
11 result = cv2.merge((bH,gH,rH))
12 cv2.imshow('dst',result)
13 cv2.waitKey(0)

运行结果如下：

src为初始图像：

dst为均衡化之后的图像：

其他的均衡化：（待补充。。。）

测试代码如下：

 1 import cv2
 2 import numpy as np
 3 img = cv2.imread('image0.jpg', 1)
 4 imgYUV = cv2.cvtColor(img,cv2.COLOR_BGR2YCrCb)
 5 cv2.imshow('src',img)
 6 channelYUV = cv2.split(imgYUV)
 7 # for i in range(0,3):
 8 #     channelYUV[i] = cv2.equalizeHist(channelYUV[i])
 9 # channelYUV[0] = cv2.equalizeHist(channelYUV[0])
10 channelYUV[1] = cv2.equalizeHist(channelYUV[1])
11 # channelYUV[2] = cv2.equalizeHist(channelYUV[2])
12 channels = cv2.merge(channelYUV)
13 result = cv2.cvtColor(channels,cv2.COLOR_YCrCb2BGR)
14 cv2.imshow('dst',result)
15 cv2.waitKey(0)

运行结果如下：

src原图像：

绿色通道下：

红色通道下：

蓝色通道下：

三个通道一起：

灰度直方图均衡化源码实现：

 1 # 本质 统计每个像素灰度 出现的概率 0-255 p
 2 # 累积概率
 3 # 1 0.2 0.2
 4 # 2 0.3 0.5
 5 # 3 0.1 0.6
 6 # 256个累积概率
 7 import cv2
 8 import numpy as np
 9 import matplotlib.pyplot as plt
10 img = cv2.imread('image0.jpg',1)
11 imgInfo = img.shape
12 height = imgInfo[0]
13 width = imgInfo[1]
14 gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
15 cv2.imshow('src',gray)
16 count = np.zeros(256,np.float)
17 for i in range(0,height):
18     for j in range(0,width):
19         pixel = gray[i,j]
20         index = int(pixel)
21         count[index] = count[index]+1
22 for i in range(0,255):
23     count[i] = count[i]/(height*width)
24 # 计算累计概率
25 sum1 = float(0)
26 for i in range(0,256):
27     sum1 = sum1+count[i]
28     count[i] = sum1
29 # print(count)
30 # 计算映射表
31 map1 = np.zeros(256,np.uint16)
32 for i in range(0,256):
33     map1[i] = np.uint16(count[i]*255)
34 # 映射
35 for i in range(0,height):
36     for j in range(0,width):
37         pixel = gray[i,j]
38         gray[i,j] = map1[pixel]
39 cv2.imshow('dst',gray)
40 cv2.waitKey(0)

运行结果：

彩色直方图均衡化源码实现：

 1 # encoding:utf-8
 2 
 3 import cv2
 4 import numpy as np
 5 import matplotlib.pyplot as plt
 6 
 7 # 本质 统计每个像素灰度 出现的概率 0-255 p
 8 img = cv2.imread('image0.jpg',1)
 9 cv2.imshow('src',img)
10 imgInfo = img.shape
11 height = imgInfo[0]
12 width = imgInfo[1]
13 # gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
14 cnt_b = np.zeros(256,np.float)
15 cnt_g = np.zeros(256,np.float)
16 cnt_r = np.zeros(256,np.float)
17 for i in range(0,height):
18     for j in range(0,width):
19         (b,g,r) = img[i,j]
20         index_b = int(b)
21         index_g = int(g)
22         index_r = int(r)
23         cnt_b[index_b] = cnt_b[index_b]+1
24         cnt_r[index_r] = cnt_r[index_r]+1
25         cnt_g[index_g] = cnt_g[index_g]+1
26 for i in range(0,256):
27     cnt_b[i] = cnt_b[i]/(height*width)
28     cnt_r[i] = cnt_r[i]/(height*width)
29     cnt_g[i] = cnt_g[i]/(height*width)
30 # 计算机累积概率
31 sum_b = float(0)
32 sum_g = float(0)
33 sum_r = float(0)
34 for i in range(0,256):
35     sum_b = sum_b+cnt_b[i]
36     sum_g = sum_g+cnt_g[i]
37     sum_r = sum_r+cnt_r[i]
38     cnt_b[i] = sum_b
39     cnt_g[i] = sum_g
40     cnt_r[i] = sum_r
41 # 生成映射表
42 map_b = np.zeros(256,np.uint16)
43 map_g = np.zeros(256,np.uint16)
44 map_r = np.zeros(256,np.uint16)
45 for i in range(0,256):
46     map_b[i] = np.uint16(cnt_b[i]*255)
47     map_g[i] = np.uint16(cnt_g[i]*255)
48     map_r[i] = np.uint16(cnt_r[i]*255)
49 # 映射
50 dst = np.zeros((height,width,3),np.uint8)
51 for i in range(0,height):
52     for j in range(0,width):
53         (n,g,r) = img[i,j]
54         b = map_b[b]
55         g = map_g[g]
56         r = map_r[r]
57         dst[i,j]= (b,g,r)
58 cv2.imshow('dst',dst)
59 cv2.waitKey(0)

运行结果：

3.图片修补

首先构建一副画图：

 1 import cv2
 2 import numpy as np
 3 
 4 img = cv2.imread('image0.jpg',1)
 5 for i in range(200,300):
 6     img[i,200] = (255,255,255)
 7     img[i,200+1] = (255,255,255)
 8     img[i,200-1] = (255,255,255)
 9 for i in range(150,250):
10     img[250,i] = (255,255,255)
11     img[250+1,i] = (255,255,255)
12     img[250-1,i] = (255,255,255)
13 cv2.imwrite('damaged.jpg',img)
14 cv2.imshow('damaged.jpg',img)
15 cv2.waitKey(0)

构建的坏图为：（多了一个白十字）

修补坏图，测试代码如下：

 1 # 1 坏图 2 修补那一部分 3 调用api实现修补
 2 import cv2
 3 import numpy as np
 4 img = cv2.imread('damaged.jpg',1)
 5 cv2.imshow('damaged',img)
 6 imgInfo = img.shape
 7 height = imgInfo[0]
 8 width = imgInfo[1]
 9 paint = np.zeros((height,width,1),np.uint8)
10 for i in range(200,300):
11     paint[i,200] = 255
12     paint[i,200+1] = 255
13     paint[i,200-1] = 255
14 for i in range(150,250):
15     paint[250,i] = 255
16     paint[250+1,i] = 255
17     paint[250-1,i] = 255
18 cv2.imshow('paint',paint)
19 # 完成图片修补 1 src 2 mask
20 imgDst = cv2.inpaint(img,paint,3,cv2.INPAINT_TELEA)
21 cv2.imshow('image',imgDst)
22 cv2.waitKey(0)

画图的特征图：（即需要修补的部分）

修补后的图像：

修补成功！

4.亮度增强

测试代码如下：

 1 # encoding:utf-8
 2 import cv2
 3 import numpy as np
 4 img = cv2.imread('image0.jpg',1)
 5 imgInfo = img.shape
 6 height = imgInfo[0]
 7 width = imgInfo[1]
 8 cv2.imshow('src',img)
 9 dst = np.zeros((height,width,3),np.uint8)
10 for i in range(0,height):
11     for j in range(0,width):
12         (b,g,r) = img[i,j]
13         bb = int(b)+40
14         rr = int(r)+40
15         gg = int(g)+40
16         if bb>255:
17             bb = 255
18         if rr>255:
19             rr = 255
20         if gg>255:
21             gg = 255
22         dst[i,j] = (bb,gg,rr)
23 cv2.imshow('dst',dst)
24 cv2.waitKey(0)

运行结果如下：

src为原图像：

dst为亮度增强后的图像：

另一种方式实现亮度增强，测试代码如下：

 1 import cv2
 2 import numpy as np
 3 img = cv2.imread('image0.jpg',1)
 4 imgInfo = img.shape
 5 height = imgInfo[0]
 6 width = imgInfo[1]
 7 cv2.imshow('src',img)
 8 dst = np.zeros((height,width,3),np.uint8)
 9 for i in range(0,height):
10     for j in range(0,width):
11         (b,g,r) = img[i,j]
12         bb = int(b*1.3)+10
13         gg = int(g*1.2)+15
14         if bb>255:
15             bb = 255
16         if gg>255:
17             gg = 255
18         dst[i,j] = (bb,gg,r)
19 cv2.imshow('dst',dst)
20 cv2.waitKey(0)

亮度增强后的图像为：

5.磨皮美白

测试代码如下：

 1 # 双边滤波 磨皮美白
 2 import cv2
 3 import numpy as np
 4 img = cv2.imread('1.jpg',1)
 5 cv2.imshow('src',img)
 6 imgInfo = img.shape
 7 height = imgInfo[0]
 8 width = imgInfo[1]
 9 dst = cv2.bilateralFilter(img,15,35,35)
10 cv2.imshow('dst',dst)
11 cv2.waitKey(0)

测试结果如下：

src为原图像：

dst为处理后的图像：

6.高斯均值滤波

调用api实现，测试代码如下：

1 import cv2
2 import numpy as np
3 img = cv2.imread('image11.jpg',1)  # 含有椒盐噪声的发发
4 cv2.imshow('src',img)
5 dst = cv2.GaussianBlur(img,(5,5),1.5)
6 cv2.imshow('dst',dst)
7 cv2.waitKey(0)

运行结果如下：

src为添加了噪声的图像：

dst为高斯均值滤波后的图像：

自己实现均值滤波，测试代码如下：

 1 # 均值 模板6*6 用模板6*6求得的均值替代中心值
 2 import cv2
 3 import numpy as np
 4 img = cv2.imread('image11.jpg',1)  # 含有椒盐噪声的发发
 5 cv2.imshow('src',img)
 6 imgInfo = img.shape
 7 height = imgInfo[0]
 8 width = imgInfo[1]
 9 dst = np.zeros((height,width,3),np.uint8)
10 b = np.zeros(1,np.uint8)
11 g = np.zeros(1,np.uint8)
12 r = np.zeros(1,np.uint8)
13 for i in range(3,height-3):
14     for j in range(3,width-3):
15         sum_b = int(0)
16         sum_g = int(0)
17         sum_r = int(0)
18         for m in range(-3,3):
19             for n in range(-3,3):
20                 (b,g,r) = img[i+m,j+n]
21                 sum_b = sum_b+int(b)
22                 sum_g = sum_g+int(g)
23                 sum_r = sum_r+int(r)
24         b = np.uint8(sum_b/36)
25         g = np.uint8(sum_g/36)
26         r = np.uint8(sum_r/36)
27         dst[i,j] = (b,g,r)
28 cv2.imshow('dst',dst)
29 cv2.waitKey(0)

运行结果如下：（处理后的图像）

7.中值滤波

测试代码如下：

 1 # 中值滤波 用模板6*6求得的中值替代中心值
 2 import cv2
 3 import numpy as np
 4 img = cv2.imread('image11.jpg',1)  # 含有椒盐噪声的发发
 5 imgInfo = img.shape
 6 height = imgInfo[0]
 7 width = imgInfo[1]
 8 img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
 9 cv2.imshow('src',img)
10 dst = np.zeros((height,width,3),np.uint8)
11 collect = np.zeros(9,np.uint8)
12 for i in range(1,height-2):
13     for j in range(1,width-2):
14         k = 0
15         for m in range(-1,2):
16             for n in range(-1,2):
17                 gray = img[i+m,j+n]
18                 collect[k] = gray
19                 k = k+1
20         # 排序
21         for k in range(0,9):
22             p1 = collect[k]
23             for t in range(k+1,9):
24                 if p1<collect[t]:
25                     mid = collect[t]
26                     collect[t] = p1
27                     p1 = mid
28         dst[i,j] = collect[4]
29 cv2.imshow('dst',dst)
30 cv2.waitKey(0)

运行结果如下：

src初始图像：

dst处理后的图像：