5.5 Utils Module API Manual#
1. Overview#
This manual is intended to provide developers with some common processing methods for developing AI demos using MicroPython.
2. API Introduction#
2.1 Timing Module ScopedTiming#
Description
The ScopedTiming class is a context manager used to measure the execution time of a code block. A context manager is created by defining a class that contains the __enter__ and __exit__ methods. When an instance of this class is used in a with statement, the __enter__ method is called when entering the with block, and the __exit__ method is called when leaving.
from libs.Utils import ScopedTiming
def test_time():
with ScopedTiming("test",1):
##### Code #####
# ...
##############
Parameters
Parameter Name |
Description |
Input / Output |
Remarks |
|---|---|---|---|
info |
Description of the timed part |
Input |
Default is “” |
enable_profile |
Whether to debug and print timing information |
Input |
Default is True |
Return Value
Return Value |
Description |
|---|---|
- |
No return value |
2.2 get_colors#
Description
Get the ARGB color array according to the number of labels, such as [255,191,162,208]. There are 80 preset colors. When the number is greater than 80, the remainder is taken.
Syntax
from libs.Utils import get_colors
classes_num = 30
colors = get_colors(classes_num)
Parameters
Parameter Name |
Description |
Input / Output |
Remarks |
|---|---|---|---|
classes_num |
Number of labels |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
list[list] |
ARGB color array |
2.3 center_crop_param#
Description
The center_crop_param is a square center cropping function that receives an input parameter input_size representing the image size. It is usually a list containing two elements, representing the width and height of the image respectively. The function will calculate the top-left coordinates (top and left) of the cropping area and the side length m of the cropping area based on the center of the image, and finally return these parameters.
Syntax
from libs.Utils import center_crop_param
input_size = [640,320]
top, left, m = center_crop_param(input_size)
Parameters
Parameter Name |
Description |
Input / Output |
Remarks |
|---|---|---|---|
input_size |
Input width and height list |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
left |
Distance from the top-left corner of the cropping area to the left border |
top |
Distance from the top-left corner of the cropping area to the top border |
m |
Side length of the cropping area |
2.4 letterbox_pad_param#
Description
The letterbox_pad_param function is used to calculate the parameters required for padding the input image. This padding is a technique that scales the image to the specified output size while maintaining the aspect ratio of the image and adds padding around it. It is commonly used in computer vision tasks such as object detection to ensure that the input image can fit the input size of the model. This function performs one-sided padding, that is, only padding on the bottom and right sides, and returns the widths of the top, bottom, left, and right padding areas and the scaling ratio. The calculation results of this method are generally used together with resize.
Syntax
from libs.Utils import letterbox_pad_param
input_size = [1920,1080]
output_size = [640,640]
left, right, top, bottom, scale = letterbox_pad_param(input_size, output_size)
Parameters
Parameter Name |
Description |
Input / Output |
Remarks |
|---|---|---|---|
input_size |
Input resolution before scaling and padding |
Input |
Required |
output_size |
Output resolution after scaling and padding |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
left |
Left padding value |
right |
Right padding value |
top |
Top padding value |
bottom |
Bottom padding value |
scale |
Scaling ratio |
2.5 center_pad_param#
Description
The main purpose of the center_pad_param function is to calculate the parameters required to center-pad the input image and scale it to the specified output size. When processing an image, in order to make the image fit a specific output size while maintaining its original aspect ratio, a scaling operation is usually performed, and then padding is added around the image to center the image in the output area. This function will return the widths of the top, bottom, left, and right padding areas and the scaling ratio. The calculation results of this method are generally used together with resize.
Syntax
from libs.Utils import center_pad_param
input_size = [1920,1080]
output_size = [640,640]
left, right, top, bottom, scale = center_pad_param(input_size, output_size)
Parameters
Parameter Name |
Description |
Input / Output |
Remarks |
|---|---|---|---|
input_size |
Input resolution before scaling and padding |
Input |
Required |
output_size |
Output resolution after scaling and padding |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
left |
Left padding value |
right |
Right padding value |
top |
Top padding value |
bottom |
Bottom padding value |
scale |
Scaling ratio |
2.6 softmax#
Description
The softmax function is widely used in classification problems. Its main function is to convert a real-valued vector into a probability distribution, so that each element in the vector is in the interval [0, 1], and the sum of all elements is 1. In this way, the output of the model can be interpreted as the probability corresponding to each class.
Syntax
from libs.Utils import softmax
input_data = [1,2,3]
output_data = softmax(input_data)
Parameters
Parameter Name |
Description |
Input / Output |
Remarks |
|---|---|---|---|
x |
One-dimensional data returned by the classification model |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
softmax_res |
Probability distribution of classes |