中小企业排行榜优化大师好用吗

1 图像加减乘除位运算

1.1 加法 img = cv2.add(img1, img2)

import cv2
import numpy as np
import matplotlib.pyplot as pltlena = cv2.imread('lenacolor.png',-1)
noise = np.random.randint(0,255,lena.shape,dtype=np.uint8)
img_add = lena+noise
img_cv_add = cv2.add(lena,noise)plt.subplot(221)
plt.title('lena')
plt.imshow(lena[...,::-1])
plt.subplot(222)
plt.title('noise')
plt.imshow(noise[...,::-1])
plt.subplot(223)
plt.title('img_add')
plt.imshow(img_add[...,::-1])
plt.subplot(224)
plt.title('img_cv_add')
plt.imshow(img_cv_add[...,::-1])
plt.show()

1.2 减法 img = cv2.subtract(img1, img2)

import cv2
import numpy as np
import matplotlib.pyplot as pltimg_0 = cv2.imread('34.jpeg',-1)
img_1 = cv2.imread('35.jpeg',-1)
img_sub = cv2.subtract(img_0, img_1)plt.subplot(131)
plt.title('img_0')
plt.imshow(img_0[...,::-1])
plt.subplot(132)
plt.title('img_1')
plt.imshow(img_1[...,::-1])
plt.subplot(133)
plt.title('img_sub')
plt.imshow(img_sub[...,::-1])
plt.show()

import cv2
import numpy as np
import matplotlib.pyplot as pltimg_0 = cv2.imread('img_no.png',0)
img_1 = cv2.imread('sub.png',0)
img_sub = cv2.subtract(img_0, img_1)plt.subplot(131)
plt.title('img_0')
plt.imshow(img_0,cmap='gray')
plt.subplot(132)
plt.title('img_1')
plt.imshow(img_1,cmap='gray')
plt.subplot(133)
plt.title('img_sub')
plt.imshow(img_sub,cmap='gray')
plt.show()

1.3 乘法 img = cv2.multiply(img1, img2)

import cv2
import numpy as np
import matplotlib.pyplot as pltlena = cv2.imread('lenacolor.png',-1)
mask = np.zeros_like(lena,np.uint8)
mask[204:392,213:354] = 1
img_mul = cv2.multiply(lena, mask)plt.subplot(131)
plt.title('lena')
plt.imshow(lena[...,::-1])
plt.subplot(132)
plt.title('mask')
plt.imshow(mask[...,::-1])
plt.subplot(133)
plt.title('img_mul')
plt.imshow(img_mul[...,::-1])
plt.show()

1.4 除法 img = cv2.divide(img1, img2)

import cv2
import numpy as np
import matplotlib.pyplot as pltlena = cv2.imread('lenacolor.png',0)
img_noise = cv2.circle(lena.copy(),(280,300),150,(0,255,0),10)
img_div = cv2.divide(img_noise,lena)plt.subplot(131)
plt.title('lena')
plt.imshow(lena,cmap='gray')
plt.subplot(132)
plt.title('img_noise')
plt.imshow(img_noise,cmap='gray')
plt.subplot(133)
plt.title('img_div')
plt.imshow(img_div,cmap='gray')
plt.show()

1.5 位运算 cv2.bitwise_and()

import cv2
import numpy as np
import matplotlib.pyplot as pltlena = cv2.imread('lenacolor.png',1)
mask = np.zeros_like(lena,dtype=np.uint8)
mask = cv2.circle(mask,(280,280),111,(255,255,255),-1)
re = cv2.bitwise_and(lena,mask)plt.subplot(131)
plt.title('lena')
plt.imshow(lena[...,::-1])
plt.subplot(132)
plt.title('mask')
plt.imshow(mask[...,::-1])
plt.subplot(133)
plt.title('re')
plt.imshow(re[...,::-1])
plt.show()

import cv2
import numpy as np
import matplotlib.pyplot as pltlena = cv2.imread('lenacolor.png',1)
mask = np.zeros(lena.shape[:2],dtype=np.uint8)
mask = cv2.circle(mask,(280,280),111,(255,255,255),-1)
re = cv2.bitwise_and(lena,lena,mask=mask)plt.subplot(131)
plt.title('lena')
plt.imshow(lena[...,::-1])
plt.subplot(132)
plt.title('mask')
plt.imshow(mask,'gray')
plt.subplot(133)
plt.title('re')
plt.imshow(re[...,::-1])
plt.show()

2 图像增强

2.1 线性变换

import cv2
import numpy as np
import matplotlib.pyplot as pltimg = cv2.imread('lianhua.png',1)
re = img*2+10
re = re.astype(np.uint8)
re1 = cv2.convertScaleAbs(img, alpha=2, beta=10)plt.subplot(131)
plt.title('img')
plt.imshow(img[...,::-1])
plt.subplot(132)
plt.title('re0')
plt.imshow(re0[...,::-1])
plt.subplot(133)
plt.title('re1')
plt.imshow(re1[...,::-1])
plt.show()

2.2 非线性变换

import cv2
import numpy as np
import matplotlib.pyplot as plt## 1 gamma
def gamma_aug(img,c,gamma):gamma_table=[c*np.power(x/255.0,gamma)*255.0 for x in range(256)]gamma_table=np.round(np.array(gamma_table)).astype(np.uint8)return cv2.LUT(img,gamma_table)## 2 log
def log_aug(img,c,r):gamma_table=[c*np.log10(1+x/255.0*r)*255.0 for x in range(256)]gamma_table=np.round(np.array(gamma_table)).astype(np.uint8)return cv2.LUT(img,gamma_table)if __name__ == '__main__':img = cv2.imread('lianhua.png',1)img11 =  gamma_aug(img,c=1,gamma=0.1)img12 = gamma_aug(img, c=1, gamma=0.8)img21 = log_aug(img, c=1, r=10)img22 = log_aug(img, c=2, r=10)plt.subplot(231)plt.title('img')plt.imshow(img[...,::-1])plt.subplot(232)plt.title('img11')plt.imshow(img11[..., ::-1])plt.subplot(233)plt.title('img12')plt.imshow(img12[..., ::-1])plt.subplot(234)plt.title('img')plt.imshow(img[...,::-1])plt.subplot(235)plt.title('img21')plt.imshow(img21[..., ::-1])plt.subplot(236)plt.title('img22')plt.imshow(img22[..., ::-1])plt.show()

3 图像几何变换

3.1 裁剪、放大、缩小

3.2 平移变换

3.3 错切变换

3.4 镜像变换

3.5 旋转变换

3.6 透视变换

3.7 最近邻插值、双线性插值