2022-02-16

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补充一些Numpy的用法 *

复习之前写过的Numpy数组操作内容：Numpy数组操作(reshape, concatenate, vstack 等) 🔗 [2021-11-02 - Truxton's blog] https://truxton2blog.com/2021-11-02/

Numpy hstack

（本程序由上次写的程序继承拓展而来）

import numpy as np

a = np.arange(1, 9, 1).reshape(4, 2)
print('a:')
print(a)
print('\n')

b = np.arange(-1, -9, -1)
b = b.reshape(4, 2)
print('b:')
print(b)
print('\n')

print('np.vstack((a,b))')
print(np.vstack((a, b)))
print('\n')
print('np.concatenate((a,b),axis=0)')
print(np.concatenate((a, b), axis=0))
print('\n')
print('np.concatenate((a,b),axis=1)')
print(np.concatenate((a, b), axis=1))
print('\n')
print('np.hstack(a,b)')
print(np.hstack((a, b)))

结果：

a:
[[1 2]
 [3 4]
 [5 6]
 [7 8]]


b:
[[-1 -2]
 [-3 -4]
 [-5 -6]
 [-7 -8]]


np.vstack((a,b))
[[ 1  2]
 [ 3  4]
 [ 5  6]
 [ 7  8]
 [-1 -2]
 [-3 -4]
 [-5 -6]
 [-7 -8]]


np.concatenate((a,b),axis=0)
[[ 1  2]
 [ 3  4]
 [ 5  6]
 [ 7  8]
 [-1 -2]
 [-3 -4]
 [-5 -6]
 [-7 -8]]


np.concatenate((a,b),axis=1)
[[ 1  2 -1 -2]
 [ 3  4 -3 -4]
 [ 5  6 -5 -6]
 [ 7  8 -7 -8]]


np.hstack(a,b)
[[ 1  2 -1 -2]
 [ 3  4 -3 -4]
 [ 5  6 -5 -6]
 [ 7  8 -7 -8]]

Numpy reshape

import numpy as np

a = np.arange(1, 9, 1).reshape(4, 2)
print('a:')
print(a)
print('\n')
print('a.reshape(-1, 1)')
print(a.reshape(-1, 1))
print('\n')
print('a.reshape(1,-1)')
print(a.reshape(1, -1))
print('\n')
print('a.reshape(-1)')
print(a.reshape(-1))
# 下面的代码会报错：ValueError: cannot reshape array of size 8 into shape (1,)
# print('\n')
# print('a.reshape(1)')
# print(a.reshape(1))
print('\n')
print('a.reshape(8)')
print(a.reshape(8))

结果：

a:
[[1 2]
 [3 4]
 [5 6]
 [7 8]]


a.reshape(-1, 1)
[[1]
 [2]
 [3]
 [4]
 [5]
 [6]
 [7]
 [8]]


a.reshape(1,-1)
[[1 2 3 4 5 6 7 8]]


a.reshape(-1)
[1 2 3 4 5 6 7 8]


a.reshape(8)
[1 2 3 4 5 6 7 8]

Numpy flattern

import numpy as np

a = np.arange(1, 21, 1).reshape(4, 5)
print('a:')
print(a)
print('\n')
print('a.flatten()')
print(a.flatten())

结果：

a:
[[ 1  2  3  4  5]
 [ 6  7  8  9 10]
 [11 12 13 14 15]
 [16 17 18 19 20]]


a.flatten()
[[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20]]

np.where()

In [1]: a = np.arange(1, 21, 1).reshape(4, 5)

In [2]: a
Out[2]:
array([[ 1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10],
       [11, 12, 13, 14, 15],
       [16, 17, 18, 19, 20]])

In [3]: b = a.copy()

In [4]: b[np.where(b < 10)] = -1

In [5]: b
Out[5]:
array([[-1, -1, -1, -1, -1],
       [-1, -1, -1, -1, 10],
       [11, 12, 13, 14, 15],
       [16, 17, 18, 19, 20]])

In [6]: np.where(a < 10, 999, a)
Out[6]:
array([[999, 999, 999, 999, 999],
       [999, 999, 999, 999,  10],
       [ 11,  12,  13,  14,  15],
       [ 16,  17,  18,  19,  20]])

In [7]: np.where(1, 999, a)
Out[7]:
array([[999, 999, 999, 999, 999],
       [999, 999, 999, 999, 999],
       [999, 999, 999, 999, 999],
       [999, 999, 999, 999, 999]])

In [8]: np.where(-1, 999, a)
Out[8]:
array([[999, 999, 999, 999, 999],
       [999, 999, 999, 999, 999],
       [999, 999, 999, 999, 999],
       [999, 999, 999, 999, 999]])

In [9]: np.where(0.1, 999, a)
Out[9]:
array([[999, 999, 999, 999, 999],
       [999, 999, 999, 999, 999],
       [999, 999, 999, 999, 999],
       [999, 999, 999, 999, 999]])

In [10]: np.where(0, 999, a)
Out[10]:
array([[ 1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10],
       [11, 12, 13, 14, 15],
       [16, 17, 18, 19, 20]])

复习或学习一些数字图像处理方法

Thresholding

🔗 [OpenCV: Image Thresholding] https://docs.opencv.org/4.x/d7/d4d/tutorial_py_thresholding.html

Filtering

比如高斯模糊

🔗 [OpenCV: Smoothing Images] https://docs.opencv.org/3.4/d4/d13/tutorial_py_filtering.html

轮廓提取算法（参考cv2.findContours）

🔗 [python - Detect white rectangle on black & white image and crop (OpenCV) - Stack Overflow] https://stackoverflow.com/questions/60794387/detect-white-rectangle-on-black-white-image-and-crop-opencv

🔗 [OpenCV轮廓提取算法详解findContours() - 知乎] https://zhuanlan.zhihu.com/p/107257870

🔗 [图像的分割之轮廓提取算法ReWz的博客-CSDN博客轮廓提取算法] https://blog.csdn.net/qq_43409114/article/details/104673115