junpter notebook_numpy_2

import matplotlib.pyplot as plt

import numpy as np

### 两个图片的列合并

o=plt.imread('onr.jpg')

plt.imshow(o)

o.shape

(456, 334, 3)

m=plt.imread('mj.jpg')

plt.imshow(m)

m.shape

(480, 640, 3)

plt.imshow(m[100:130,300:350][::2,::2])

mm = m.copy()

plt.imshow(mm)

<matplotlib.image.AxesImage at 0x1c84f866a58>

plt.imshow(np.concatenate([o[:,:],m[0:456,:]],axis=1)) 

### 注意：

### 图片二比图片一大，axis=1列对齐，行宽相等，取较小的图片的行数比例

### axis : 0 行合并（y） , 1 列合并（x） , -1 本维度的最后一个维度

### 马赛克

​

z = m[100:130,300:350][::5,::5]

plt.imshow(z)

z.shape

(6, 10, 3)

plt.imshow(m[100:130,300:350])

<matplotlib.image.AxesImage at 0x1c84f1bbb38>

### 图片据部马赛克

mm = m.copy()

for i in range(6):

    for j in range(10):

        mm[100+5*i:105+5*i,300+5*j:305+5*j]=m[100+5*i:105+5*i,300+5*j:305+5*j][::5,::5]

plt.imshow(mm)

<matplotlib.image.AxesImage at 0x1c850f56668>

plt.imshow(m[0:200,200:400][::10,::10])

<matplotlib.image.AxesImage at 0x1c84fc5c978>

z = m.copy()

for i in range(20):

    for j in range(20):

        z[0+10*i:10+10*i,200+10*j:210+10*j]=m[0+10*i:10+10*i,200+10*j:210+10*j][::10,::10]

plt.imshow(z)

<matplotlib.image.AxesImage at 0x1c84fd6f978>

### 图片的替换

plt.imshow(o)

plt.imshow(o[200:280,110:230])

<matplotlib.image.AxesImage at 0x1c851002cf8>

o[200:290,120:220].shape

(90, 100, 3)

v = m.copy()

mm = v[100:130,300:350]

plt.imshow(mm)

<matplotlib.image.AxesImage at 0x1c8536415c0>

o[200:290,120:220][::3,::2].shape

(30, 50, 3)

oo = o[200:290,120:220][::3,::2]

oo.shape

(30, 50, 3)

v[100:130,300:350]=o[200:290,120:220][::3,::2]

# mm = oo

plt.imshow(v)

<matplotlib.image.AxesImage at 0x1c853745eb8>

lj.shape

(483, 483, 3)

lj.size

699867

483*483*3

699867

lj.reshape((483*483*3,1))

array([[35],
       [36],
       [31],
       ...,
       [42],
       [43],
       [35]], dtype=uint8)

#矩阵的偏平化

lj.reshape(-1)

array([35, 36, 31, ..., 42, 43, 35], dtype=uint8)

A = np.array([1,2,3,4,5,6,7,8,9,10])

A.reshape(-1,1)

array([[ 1],
       [ 2],
       [ 3],
       [ 4],
       [ 5],
       [ 6],
       [ 7],
       [ 8],
       [ 9],
       [10]])

ndarray.ravel()  偏平化

lj.ravel()

array([35, 36, 31, ..., 42, 43, 35], dtype=uint8)