TECH on Yaohong

How does warp Perspective work?

Sat, 23 Jan 2021 16:29:20 +0800

How does warp Perspective work?

1.warp perspective with cv2

if __name__ == "__main__":
 # coordinate: (y,x), left_top, right_rop, left_bottom, right_bottom
 src = np.float32([[20.0, 0.0], [20.0 ,315.0], [186.0, 17.2], [181.0, 299.0]])
 dst = np.float32([[0.0, 0.0], [0.0, 315.0], [202.0, 7.0], [200.0, 306.0]])

 # load image
 warp_img = cv2.imread("./my_wide_angle_orig.jpg")
 warp_img = cv2.cvtColor(warp_img, cv2.COLOR_BGR2RGB)
 print("warp_img: ",warp_img.shape) # (638, 958, 3)
 width = int(warp_img.shape[1]/3)
 height = int(warp_img.shape[0]/3)
 warp_img = cv2.resize(warp_img, (width,height), interpolation=cv2.INTER_LINEAR)
 print("warp_img.shape:",warp_img.shape) # (212, 319, 3)

 ## orig image
 plt.subplot(121),
 plt.title("warp_img")
 plt.imshow(warp_img)

 # cv2 warp perspective
 cv2_matrix = cv2.getPerspectiveTransform(src, dst)
 print("cv2_matrix:\n",cv2_matrix)
 cv2_fix_img = cv2.warpPerspective(warp_img, cv2_matrix, (width,height))
 plt.subplot(122),
 plt.title('cv2_fix_img')
 plt.imshow(cv2_fix_img) 
 plt.show()

2.Implement it in our way

Step1 calculate warp matrix:

my_warp_matrix reshape:
 [[ 1.13729359e+00 -8.24289989e-18 -2.27458717e+01]
 [-6.10786436e-02 9.69843448e-01 1.22157287e+00]
 [-3.44743207e-04 -7.38466280e-05 1.00000000e+00]]

Step2. use the warp matrix to warp perspective

codes are:

Differences On Numpyp.Floor And Python Int method.md

Thu, 31 Dec 2020 12:35:20 +0800

Differences On Numpyp.Floor And Python Int method.md

numpy.floor Return the floor of the input.

The floor of the scalar x is the largest integer i, such that i <= x;

np.floor() will not change its data type;

Example:

import numpy as np

tmp_list = list([-3.3, -2.22, -1.56, -0.56, 0.56, 1.56, 2.22, 3.33])

tmp_list = np.array(tmp_list)
print("type(tmp_list[0]):", type(tmp_list[0]) )
print("type(np.floor(tmp_list)[0]):", type(np.floor(tmp_list)[0]) )
print("np.floor(tmp_list):", np.floor(tmp_list))

# Output:
# type(tmp_list[0]): <class 'numpy.float64'>
# type(np.floor(tmp_list)[0]): <class 'numpy.float64'>
# np.floor(tmp_list): [-4. -3. -2. -1. 0. 1. 2. 3.]


for x in tmp_list:
	print("x:",x,",int:",int(x)) 

# output:
# x: -3.3 ,int: -3
# x: -2.22 ,int: -2
# x: -1.56 ,int: -1
# x: -0.56 ,int: 0
# x: 0.56 ,int: 0
# x: 1.56 ,int: 1
# x: 2.22 ,int: 2
# x: 3.33 ,int: 3

For floating point numbers, int() will truncates toward zero, so -0.1 will by truncate to 0, -1.8 to -1; And its data type will be changed to <class 'int'>.

How does numpy add two arrays with different shapes?

Sun, 27 Dec 2020 19:54:20 +0800

How does numpy add two arrays with different shapes?

Numpy has a add method which add two numpy array.

Arithmetic operation + does the same thing as Numpy.add;

1.Add a same shapes array

Let’s see a example.

import numpy as np

list1 = np.array([1, 2, 3]);
list2= np.array([10, 20, 30]);

print("list1:",list1,"list2:",list2);
# Print: list1: [1 2 3] list2: [10 20 30]

added_list = list1 + list2;
print("added_list.shape:",added_list.shape,"\nadded_list:",added_list);
# Print:
# added_list.shape: (3,) 
# added_list: [11 22 33]

added_list = np.add(list1, list2);
print("added_list.shape:",added_list.shape,"\nadded_list:",added_list);
# Print:
# added_list.shape: (3,) 
# added_list: [11 22 33]

2.Add a different shape array

But what happen if two array have different shapes?

Understanding Transpose

Tue, 22 Dec 2020 19:55:20 +0800

Understanding Numpy Transpose

1.Transpose is to switch the row and column indices of the matrix A;

x = np.arange(8).reshape((4,2))
print(x)
print(x.T)
# output:
# [[0 1] # x
# [2 3]
# [4 5]
# [6 7]]
# [[0 2 4 6] # x.T
# [1 3 5 7]]

x = np.arange(9).reshape((3,3))
print(x)
print(x.T)
print(x.shape, x.T.shape)
# output:
# [[0 1 2]
# [3 4 5]
# [6 7 8]]
# [[0 3 6]
# [1 4 7]
# [2 5 8]]

x = np.arange(8).reshape((2,4))
print(x)
print(x.T)
# output:
# [[0 1 2 3]
# [4 5 6 7]]
# [[0 4]
# [1 5]
# [2 6]
# [3 7]]

If the array has only one dimension, the transpose of the array will not change;

Understanding Numpy expand_dims

Mon, 30 Nov 2020 19:55:20 +0800

Understanding Numpy expand_dims

Shape (n,)(n is a number) means it has only one dimension.

The number of values is shape brackets represents the number of dimensions.

import numpy as np
arr = np.array([1,2,3,4,5]);
print("arr shape: ",arr.shape)
print("arr shape: ",arr)
arr2 = np.expand_dims(arr, 0);
print("expand_dims axis=0, shape:",arr2.shape)
print("expand_dims axis=0, arr2:",arr2)
arr2 = np.expand_dims(arr, 1);
print("expand_dims axis=1, shape:",arr2.shape)
print("expand_dims axis=1, arr2:",arr2)

output:

arr shape: (5,) 
arr shape: [1 2 3 4 5]
expand_dims axis=0, shape: (1, 5)
expand_dims axis=0, arr2: [[1 2 3 4 5]]
expand_dims axis=1, shape: (5, 1)
expand_dims axis=1, arr2: [[1]
 [2]
 [3]
 [4]
 [5]]

numpy.expand_dims

expand_dims looks like inserting 1 into the shape brackets base on the axis value;

Sed

Sat, 28 Nov 2020 21:42:20 +0800

Sed

sed is a steam editor. sed treats multiple input files as one long stream.

The full format for invoking sed is:

sed OPTIONS... [SCRIPT] [INPUTFILE...]

Some common OPTIONS:

-n to suppress output, sed -n '45p' file.txt this command prints only line 45 of input file;

-e options are used to specify a script expression, such as sed -e 's/hello/world/' input.txt > output.txt;

-f specify a script file, such as sed -f myscript.sed input.txt > output.txt

Simple Tree command

Sat, 28 Nov 2020 10:15:20 +0800

Simple Tree command

Using the follow command can view current folder tree:

find . -print| sed -e 's;[^/]*/;|____;g;s;____|; |;g'

output

$ find . -print| sed -e 's;[^/]*/;|____;g;s;____|; |;g'
.
|____composer.lock
|____LICENSE
|____README.md
|____.gitignore
|____build-phar.php
|____.git
| |____config
| |____objects
...

Below command only descend at most 2 directory levels:

find . -maxdepth 2 -e -print | sed -e 's;[^/]*/;|____;g;s;____|; |;g'

REFERENCE

Using a Mac Equivalent of Unix “tree” Command to View Folder Trees at Terminal

Quickly host your hugo web on Gitlab

Thu, 19 Nov 2020 13:41:20 +0800

Quickly host your hugo web on Gitlab

Quickly host your hugo web on gitlab.

1.Login gitlab

Gitlab

2.click `new project` after login

Click Create from template in Create new project and use “Hugo template”

In this example, my project name is: testPage

Now you can see your username on Project URL, for example, mine is https://gitlab.com/RhysYao/, and username is RhysYao which will be used later.

TECH on Yaohong

How does warp Perspective work?

How does warp Perspective work?

1.warp perspective with cv2

2.Implement it in our way

Step1 calculate warp matrix:

Step2. use the warp matrix to warp perspective

Differences On Numpyp.Floor And Python Int method.md

Differences On Numpyp.Floor And Python Int method.md

How does numpy add two arrays with different shapes?

How does numpy add two arrays with different shapes?

1.Add a same shapes array

2.Add a different shape array

Understanding Transpose

Understanding Numpy Transpose

Understanding Numpy expand_dims

Understanding Numpy expand_dims

numpy.expand_dims

Sed

Sed

Simple Tree command

Simple Tree command

Quickly host your hugo web on Gitlab

Quickly host your hugo web on Gitlab

1.Login gitlab

2.click new project after login

2.click `new project` after login