【Python】Scipy处理图片（ndimage shift rotate zoom）

例图

一、灰度图处理（二维）

可采用Scipy自带图片，亦可读入本地图片。misc.imread()方式读入失败，故这里采用cv2.imread()读入。

#图片处理
import numpy as np
import scipy.misc as misc
import scipy.ndimage as ndimage

#黑白图
#face =misc.face(gray =True)
#face = misc.imread('handsome.jpg')
import cv2
face = cv2.imread('handsome.jpg',cv2.IMREAD_GRAYSCALE)  #读入灰度图

import matplotlib.pyplot as plt
#misc.imshow(face)
#plt.imshow(face, cmp = 'gray')  # 'AxesImage' object has no property 'cmp'
plt.imshow(face,cmap =plt.cm.gray)

#图片移动
face2 = ndimage.shift(face,shift = [300,150])# mode = {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}
#plt.imshow(face2，cmap='gray') #invalid character in identifier
plt.imshow(face2,cmap =plt.cm.gray)

#图片移动
face2 = ndimage.shift(face,shift = [100,100],mode = 'mirror')# mode = {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}
#plt.imshow(face2，cmap='gray') #invalid character in identifier
plt.imshow(face2,cmap =plt.cm.gray)

#图片旋转
face3 = ndimage.rotate(face,angle = 180)
#plt.imshow(face3)
#plt.imshow(face3,cmap ='gray')
plt.imshow(face3,cmap =plt.cm.gray) 

#图片缩放
face4 = ndimage.zoom(face,zoom = 0.5)
#plt.imshow(face4,cmap =plt.cm.gray) 
plt.imshow(face4,cmap ='gray') 

face4 = ndimage.zoom(face,zoom = 0.2)
plt.imshow(face4,cmap =plt.cm.gray) 

#图片切割
face5 = face[:512,-512:]
plt.imshow(face5, cmap= 'gray')

或许我不应该选用带人的图片？

二、彩图处理（三维）

步骤和上面的类似，就是看着需求操作罢了

import numpy as np
import scipy.misc as misc
import scipy.ndimage as ndimage
import matplotlib.pyplot as plt

import cv2
face = cv2.imread('handsome.jpg')  #读入彩图
#face =misc.face(gray =False)
#plt.imshow(face,cmap ='gray')
plt.imshow(face)

#图片移动（三维）
face2 = ndimage.shift(face,shift = [0,500,0],mode = 'mirror')# mode = {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}
#plt.imshow(face2，cmap='gray') #invalid character in identifier
plt.imshow(face2)

#图片移动（三维）
face2 = ndimage.shift(face,shift = [400,-625,2],mode = 'mirror')# mode = {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}
#plt.imshow(face2，cmap='gray') #invalid character in identifier
#plt.imshow(face2,cmap =plt.cm.gray)
cv2.imwrite('mirror.png',face2)

#图片移动（三维）
face_wrap = ndimage.shift(face,shift = [400,-625,1],mode = 'wrap')# mode = {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}
#plt.imshow(face2，cmap='gray') #invalid character in identifier
cv2.imwrite('wrap.png',face_wrap)

#图片旋转,三维二维都一样
face3 = ndimage.rotate(face,angle = 180)
plt.imshow(face3)
#cv2.imwrite('rotate.png',face3)

#图片缩放（三维）
face4 = ndimage.zoom(face,zoom = [0.1,0.1,1])
plt.imshow(face4,cmap =plt.cm.gray) 

#图片缩放（三维）
face4 = ndimage.zoom(face,zoom = [2,2,1])
plt.imshow(face4,cmap ='gray') 

#plt.imsave('zoom_face.jpg',face4)
#plt.imsave('face.jpg',face)


plt.imshow(face+36,cmap = 'gray')

face = misc.face(gray = True)
face = face[:512,-512:]
plt.imshow(face,cmap ='gray')

三、滤波处理

import numpy as np
import scipy.misc as misc
import scipy.ndimage as ndimage
import matplotlib.pyplot as plt

#添加噪声
face = misc.face(gray = True)
face = face+face.std()*0.8*np.random.random(face.shape)

#高斯滤波
face_g = ndimage.gaussian_filter(face,sigma = 1)#sigma越大越糊  
plt.imshow(face_g,cmap ='gray')

moon = plt.imread(r'E:\Document transfer\Tencent Files\1005634200\FileRecv\数据分析方法 周一56\第15周\moonlanding.png')
moon_g = ndimage.gaussian_filter(moon,sigma = 5)
plt.figure(figsize =(9,8))
plt.imshow(moon_g,cmap ='gray')


#中值滤波
face_m = ndimage.median_filter(face,size=8)
#plt.figure(figsize=(10,6))
plt.imshow(face_m,cmap = 'gray')


#维纳滤波(可处理老照片)
import scipy.signal as signal
face_w = signal.wiener(face,mysize =2)
plt.imshow(face_w,cmap = 'gray')

参考资料

python scipy库基础学习(图片消噪, 求解圆周率, Scipy文件输入/输出, scipy矩阵)

千锋教育