Utils Module API Manual#
Overview#
This manual is intended to provide developers with some common processing methods used when developing AI Demo with MicroPython.
API Introduction#
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, __enter__ is called when entering the with block, and __exit__ is called when leaving.
from libs.Utils import ScopedTiming
def test_time():
with ScopedTiming("test",1):
#####code#####
# ...
##############
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
info |
Description of the timed section |
Input |
Default “” |
enable_profile |
Whether to enable debug printing of timing information |
Input |
Default True |
Return Value
Return Value |
Description |
|---|---|
- |
No return value |
read_json#
Description
Reads fields from a JSON file based on the file path, mainly used for reading configuration files of scripts deployed on the online training platform.
Syntax
from libs.Utils import read_json
json_path="/sdcard/examples/test.json"
json_data=read_json(json_path)
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
json_path |
Path of the JSON file on the development board |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
dict |
JSON field content |
read_image#
Description
Reads image data based on the image path, used during AI inference to read static images and feed them to the model to complete inference.
Syntax
from libs.Utils import read_image
img_path="/sdcard/examples/test.jpg"
img_chw,img_rgb888=read_image(img_path)
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
img_path |
Path of the image file on the development board |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
img_chw |
Image data in CHW layout in |
img_rgb888 |
Image instance in rgb888 format, which can be used for drawing results |
get_colors#
Description
Gets an ARGB color array based on the number of labels, e.g., [255,191,162,208]. 80 colors are preset; when the number exceeds 80, the modulo operation is applied.
Syntax
from libs.Utils import get_colors
classes_num=30
colors=get_colors(classes_num)
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
classes_num |
Number of labels |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
list[tuple] |
A list of ARGB colors, where each element is an |
center_crop_param#
Description
center_crop_param is a square center-cropping function that takes an input parameter input_size representing the image size, which is usually a list containing two elements representing the image width and height respectively. The function calculates the coordinates of the top-left corner (top and left) of the cropping area based on the image center, as well as the side length m of the cropping area, and ultimately returns 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 |
Notes |
|---|---|---|---|
input_size |
Input width and height list |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
top |
Distance from the top-left corner of the crop to the top boundary |
left |
Distance from the top-left corner of the crop to the left boundary |
m |
Crop side length |
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, and adds padding around it. It is commonly used in computer vision tasks such as object detection to ensure that the input image fits the model’s input size. The function performs single-side padding, i.e., only padding on the bottom and right sides, and returns the top, bottom, left, and right boundary widths of the padding area, as well as the scale ratio. The result calculated by this method is generally used together with resize.
Syntax
from libs.Utils import letterbox_pad_param
input_size=[1920,1080]
output_size=[640,640]
top,bottom,left,right,scale=letterbox_pad_param(input_size,output_size)
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
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 |
|---|---|
top |
Top padding value |
bottom |
Bottom padding value |
left |
Left padding value |
right |
Right padding value |
scale |
Scale ratio |
center_pad_param#
Description
The main purpose of the center_pad_param function is to calculate the parameters required for padding the input image in a centered manner and scaling it to the specified output size. When processing images, in order to fit a specific output size while maintaining the original aspect ratio, a scaling operation is usually performed, and then padding is added around the image so that the image is centered in the output area. The function returns the top, bottom, left, and right boundary widths of the padding area, as well as the scale ratio. The result calculated by this method is generally used together with resize.
Syntax
from libs.Utils import center_pad_param
input_size=[1920,1080]
output_size=[640,640]
top,bottom,left,right,scale=center_pad_param(input_size,output_size)
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
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 |
|---|---|
top |
Top padding value |
bottom |
Bottom padding value |
left |
Left padding value |
right |
Right padding value |
scale |
Scale ratio |
softmax#
Description
The softmax function is widely used in classification problems. Its main purpose is to convert a real-valued vector into a probability distribution, making each element in the vector fall within the [0, 1] interval and the sum of all elements equal to 1. In this way, the model’s output can be interpreted as the probability corresponding to each category.
Syntax
from libs.Utils import softmax
input_data=[1,2,3]
output_data=softmax(input_data)
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
x |
One-dimensional data returned by the classification model |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
softmax_res |
Probability distribution of categories |
sigmoid#
Description
The sigmoid function is widely used in binary classification models. Its purpose is to map any real number to the (0, 1) interval, which is commonly used to convert model output into probability values for probability judgment or classification decisions.
Syntax
from libs.Utils import sigmoid
input_data = 0.5
output_data = sigmoid(input_data)
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
input_data |
Any real number |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
sigmoid_res |
A value or array mapped to the (0, 1) interval |
chw2hwc#
Description
Converts ulab.numpy.ndarray data in CHW layout to HWC layout. Only applicable to 3-dimensional data.
Syntax
from libs.Utils import chw2hwc
# chw_np is ulab.numpy.ndarray data in CHW layout
hwc_np = chw2hwc(chw_np)
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
chw_np |
3-dimensional |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
hwc_np |
3-dimensional |
hwc2chw#
Description
Converts ulab.numpy.ndarray data in HWC layout to CHW layout. Only applicable to 3-dimensional data, commonly used to convert Image instance data before feeding it to AI models.
Syntax
from libs.Utils import hwc2chw
# hwc_np is ulab.numpy.ndarray data in CHW layout
chw_np = hwc2chw(hwc_np)
Parameters
Parameter Name |
Description |
Input / Output |
Notes |
|---|---|---|---|
hwc_np |
3-dimensional |
Input |
Required |
Return Value
Return Value |
Description |
|---|---|
chw_np |
3-dimensional |