Python on Maix-III AXera-Pi

2023-06-15

Date	Author	Change
2023.02.04	wonder	Create file
2023.03.01	wonder	Add Yolo8 model

Finish reading AXera-Pi Guide, we can start Python on MAIX-III AXera-Pi.

Preface

From MAIX-I MCU, MAIX-II SOC to MAIX-III Linux board, SIPEED is devoted for easy-use development board, and created many documents on how to use them. If you are not good at Linux or C++, let's use python to play MAIX-III AXera-Pi.

What is Python

Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation.It is dynamically typed and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described as a "batteries included" language due to its comprehensive standard library. wikipedia

Difference between Python and C++

Python is a kind of Scripting language, this means it is interpreted at runtime, users run the code files named with extension name py on Python interpreter, instaed of compiling them then run it. C++ is compiling language, it needs to be compiled before running, which takes a long time to compile. Python is easier for users to understand and start, while C++ is normally complex and difficult for users to start.
Python tutorial

Python website provides many documents, which is enough for studying python. Besides, use Jupyter Notebook to start python about AI is a good idea, which you have to learn and try.

What is Jupyter Notebook

The Jupyter Notebook is the original web application for creating and sharing computational documents. It offers a simple, streamlined, document-centric experience. This document is created by Jupyter Notebook, saved all results of example codes and explanation for users to read.

We have set Python3、Jupyter Notebook、Pinpong、Pillow in AXera-Pi for users easy-use with python, here we take Jupyter Notebook as the python program example.

Run Jupyter Notebook

To run Jupyter Notebook on AXera-Pi, we need to get the IP address of AXera-Pi, both RNDIS IP address or network IP address are OK, with this we can open the Jupyter Notebook website application running on AXera-Pi. Run ifconfig to get the IP address of AXera-Pi, and run command jupyter notebook on AXera-Pi via a terminal, then visit the IP:8888 in your computer browser (Attention：ifconfig command result lo:127.0.0.1 is inaccessible), like 192.168.0.125:8888 (192.168.0.125 is a example IP address of AXera-Pi, see the following picture), then you can run your python code on Jupyter Notebook.

Note: Pay attention to the communication between computer and AXera-Pi, operator may fail due to bad communication quality.

Visit the IP:8888, enter root to login if required password.

Click New and choose Python3 to new a file with python kernel.

Python3：A file with python3 kernel
Text File：A test file
Folder：New a folder
Terminal：New a terminal

Jupyter Notebook basic usage

In Jupyter Notebook, we edit by cell table, there are blue cell table(Command mode) and green cell table(Editing mode) two versions.

Blue cell table(Command mode)

Green cell table(Editing mode)

Common usage：

Shift+ Enter： Run cell table, move to next cell table and enter into command mode.
Ctrl + Enter： Run cell table, and enter into command mode.

Edit mode：

Esc：Enter Command mode

Command mode：

h： Open help
Enter： Enter Editing mode
x： Cut cell table
c： Copy cell table
v： Paste cell table
dd： Delete this cell table
ii： Stop running task
a： Add cell table above
b： Add cell table below
m： Change cell table into markdown format

Run code

All examples in this document are run on GC4653 camera sensor, visit Maix-III AXera-Pi (FAQ) to see how to switch camera sensor if you use OS04A10 night enhanced camera sensor.

Here are examples running code or command on Jupyter Notebook, the result is shown in Jupyter Notebook or onboard screen.

Use command ! + cmd to run command or script

In [5]:

!ls home/images # Run command

air.jpg        carvana02.jpg	 face5.jpg	   o2_resize.jpg  ssd_car.jpg
aoa-2.jpeg     carvana03.jpg	 grace_hopper.jpg  pineapple.jpg  ssd_dog.jpg
aoa.jpeg       carvana04.jpg	 mobileface01.jpg  pose-1.jpeg	  ssd_horse.jpg
bike.jpg       cat.jpg		 mobileface02.jpg  pose-2.jpeg
bike2.jpg      cityscape.png	 mtcnn_face4.jpg   pose-3.jpeg
cable.jpg      dog.jpg		 mtcnn_face6.jpg   pose.jpg
carvana01.jpg  efficientdet.png  mv2seg.png	   selfie.jpg

In [13]:

!/home/ax-samples/build/install/bin/ax_yolov5s -m /home/models/yolov5s.joint -i /home/images/cat.jpg -r 10 # Run script

--------------------------------------
model file : /home/models/yolov5s.joint
image file : /home/images/cat.jpg
img_h, img_w : 640 640
[AX_SYS_LOG] AX_SYS_Log2ConsoleThread_Start
Run-Joint Runtime version: 0.5.10
--------------------------------------
[INFO]: Virtual npu mode is 1_1

Tools version: d696ee2f
run over: output len 3
--------------------------------------
Create handle took 487.99 ms (neu 22.29 ms, axe 0.00 ms, overhead 465.70 ms)
--------------------------------------
Repeat 10 times, avg time 22.57 ms, max_time 22.88 ms, min_time 22.46 ms
--------------------------------------
detection num: 1
15:  89%, [ 167,   28,  356,  353], cat
[AX_SYS_LOG] Waiting thread(2867848448) to exit
[AX_SYS_LOG] AX_Log2ConsoleRoutine terminated!!!
exit[AX_SYS_LOG] join thread(2867848448) ret:0

In [15]:

from IPython.display import Image # Run python code
Image("yolov5s_out.jpg")

Out[15]:

No description has been provided for this image

Use %run to run script file, here we use hello.py as example.

In [18]:

%run hello.py

hello world！

New markdown

Add markdown cell table in Jupyter Notebook to make it easy to write the document explanation.

Click the Edit table and choose Markdown to switch cell table format，and in command mode click M to switch the cell table into markdown cell table format.

Transfer file

With Jyputer notebook, transfer file is also OK.

Click Upload to transfer file from computer to AXera-Pi.

Download file from AXera-Pi to computer

Document edit on Jupyter Notebook can be downloaded to computer, the downloaded file format is .ipynb, click File->Download as->your target format if you want other file format.

ax-pipeline-api

ax-pipeline-api： is built on ax-pipeline, and it's suitable for pybind11 and ctypes, which is used for Python API programming, with which users can load many built-in AI models by Python, or use python library like pinpong、opencv、numpy、pillow. These make AXera-Pi easier to use.

What is the difference between ctypes and pybind11

ctypes is what we first used on AXera-Pi, so it's more stable and has more interface than pybind11, while pybind11 is easier to see the result from screen or website and it's easier for people to understand. Because of limited performance, they should not be used at the same time.

Before running ax-pipeline-api, make sure you have installed it. And if you have trouble running this application, updating this application may be a good solution.

In [2]:

!pip3 install ax-pipeline-api -U

Requirement already satisfied: ax-pipeline-api in /usr/local/lib/python3.9/dist-packages (1.0.7)
Collecting ax-pipeline-api
  Using cached ax-pipeline-api-1.0.7.tar.gz (15.5 MB)
  Using cached ax-pipeline-api-1.0.6.tar.gz (19.5 MB)

Finishing installing or updating ax-pipeline-api, run yolov8 model to have a try.

Object detection with Yolov8 (ctypes)

In [10]:

import time
from ax import pipeline

pipeline.load([
    'libsample_vin_ivps_joint_vo_sipy.so',
    '-p', '/home/config/yolov8.json',
    '-c', '2',
])

while pipeline.work():
    time.sleep(0.001)
    tmp = pipeline.result()
    if tmp and tmp['nObjSize']:
        for i in tmp['mObjects']:
            x, y, w, h = i['bbox']['x'], i['bbox']['y'], i['bbox']['w'], i['bbox']['h']
            objname, objprob = i['objname'], i['prob']
            print(objname, objprob, x, y, w, h)
        # if tmp['nObjSize'] > 10: # try exit
        #     pipeline.drop()
pipeline.drop()

b'toilet' 0.4541160762310028 0.602770209312439 0.9111631512641907 0.16810722649097443 0.08513855934143066
b'toilet' 0.6902503967285156 0.606963574886322 0.9117961525917053 0.16024480760097504 0.08727789670228958
b'toilet' 0.6852353811264038 0.6020327210426331 0.9118891358375549 0.16942621767520905 0.08718493580818176
b'toilet' 0.7014157176017761 0.6041151881217957 0.9120386242866516 0.16582755744457245 0.0863698348402977
b'cup' 0.46080872416496277 0.6049922108650208 0.9143685698509216 0.1643451750278473 0.08425315469503403

As the log of running yolov8 above, object detection result parameters are printed out. And the detection picture is shown onboard screen.

All examples in this document are run on GC4653 camera sensor, change code '-c', '2' into '-c', '0' if you use OS04A10 night enhanced camera sensor.

pipeline.load([
        'libsample_vin_ivps_joint_venc_rtsp_vo_sipy.so',
        '-p', '/home/config/yolov5s.json',
        '-c', '2', # GC4653 camera sensor
        # '-c', '0', # OS04A10 camera sensor
    ])

We can also using different .so library or other AI models to do more AI usages. There are also useful information on ax-pipeline-api.

Here are the ibxxx*.so library in AXera-Pi:

libsample_h264_ivps_joint_vo_sipy.so                # input h264 video to ivps joint output screen vo
libsample_v4l2_user_ivps_joint_vo_sipy.so           # input v4l2 /dev/videoX to ivps joint output screen vo
libsample_rtsp_ivps_joint_rtsp_vo_sipy.so           # input video from rtsp to ivps joint output rtsp and screen vo
libsample_vin_ivps_joint_vo_sipy.so                 # input mipi sensor to ivps joint output screen vo
libsample_vin_ivps_joint_venc_rtsp_sipy.so          # input mipi sensor to ivps joint output rtsp
libsample_vin_ivps_joint_venc_rtsp_vo_sipy.so       # input mipi sensor to ivps joint output rtsp and screen vo
libsample_vin_ivps_joint_vo_h265_sipy.so            # input mipi sensor to ivps joint output screen vo and save h265 video file
libsample_multi_rtsp_ivps_joint_multi_rtsp_sipy.so  # input multi rtsp video to ivps joint output multi rtsp video\n",
libsample_rtsp_ivps_joint_rtsp_sipy.so              # input video from rtsp to ivps joint output rtsp\n",
libsample_rtsp_ivps_joint_rtsp_vo_sipy.so           # input video from rtsp to ivps joint output rtsp and screen vo\n",
libsample_rtsp_ivps_joint_vo_sipy.so                # input video from rtsp to ivps joint output screen vo

This is the main code of changing ibxxx*.so.

pipeline.load([
        'libsample_vin_ivps_joint_venc_rtsp_vo_sipy.so',
        '-p', '/home/config/yolov5s.json',
        '-c', '2',
    ])

Here are the AI models (which are in the folder /home/config) in AXera-Pi:

ax_bvc_det.json              hrnet_pose_yolov8.json           yolov5s_face_recognition.json
ax_person_det.json           license_plate_recognition.json   yolov5s_license_plate.json
ax_pose.json		          nanodet.json		                yolov6.json
ax_pose_yolov5s.json	      palm_hand_detection.json	     yolov7.json
ax_pose_yolov8.json	      pp_human_seg.json		         yolov7_face.json
crowdcount.json		      scrfd.json			             yolov7_palm_hand.json
hand_pose.json		          yolo_fastbody.json		         yolov8.json
hand_pose_yolov7_palm.json   yolopv2.json		                 yolov8_seg.json
hrnet_animal_pose.json	      yolov5_seg.json		             yolox.json
hrnet_pose.json		      yolov5s.json
hrnet_pose_ax_det.json	      yolov5s_face.json

This is the main code of changing AI models.

pipeline.load([
    'libsample_vin_ivps_joint_vo_sipy.so',
    '-p', '/home/config/yolov5s_face.json',
    '-c', '2',
])

Yolov8 segmentation

In [17]:

import time
from ax import pipeline

pipeline.load([
    'libsample_vin_ivps_joint_vo_sipy.so',
    '-p', '/home/config/yolov8_seg.json',
    '-c', '2',
])

while pipeline.work():
    time.sleep(0.001)
    tmp = pipeline.result()
    if tmp and tmp['nObjSize']:
        for i in tmp['mObjects']:
            print(i)
        # if tmp['nObjSize'] > 10: # try exit
        #     pipeline.drop()
pipeline.drop()

{'label': 39, 'prob': 0.41857901215553284, 'objname': b'bottle', 'bbox': {'x': 0.02848125249147415, 'y': 0.03796946257352829, 'w': 0.03146517649292946, 'h': 0.15615946054458618}, 'bHasMask': 1, 'mYolov5Mask': {'w': 6, 'h': 15, 'data': b'\x00\x00\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00'}}
{'label': 39, 'prob': 0.4042961299419403, 'objname': b'bottle', 'bbox': {'x': 0.027379659935832024, 'y': 0.037133704870939255, 'w': 0.033295709639787674, 'h': 0.15926814079284668}, 'bHasMask': 1, 'mYolov5Mask': {'w': 6, 'h': 15, 'data': b'\x00\x00\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00'}}
{'label': 39, 'prob': 0.4118087887763977, 'objname': b'bottle', 'bbox': {'x': 0.028065890073776245, 'y': 0.03647643327713013, 'w': 0.0326821468770504, 'h': 0.15858806669712067}, 'bHasMask': 1, 'mYolov5Mask': {'w': 6, 'h': 15, 'data': b'\x00\x00\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\xff\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00\xff\xff\xff\xff\x00\x00'}}

Body, vehicle and non-motor vehicle detection

This code is used for detecting people, vehicle and non-motor vehicle.

In [2]:

import time
from ax import pipeline

pipeline.load([
    'libsample_vin_ivps_joint_vo_sipy.so',
    '-p', '/home/config/ax_bvc_det.json',
    '-c', '2',
])

while pipeline.work():
    time.sleep(0.001)
    tmp = pipeline.result()
    if tmp and tmp['nObjSize']:
        for i in tmp['mObjects']:
            x, y, w, h = i['bbox']['x'], i['bbox']['y'], i['bbox']['w'], i['bbox']['h']
            objname, objprob = i['objname'], i['prob']
            print(objname, objprob, x, y, w, h)
        # if tmp['nObjSize'] > 10: # try exit
        #     pipeline.drop()
pipeline.drop()

b'vehicle' 0.9299032092094421 0.3565574288368225 0.44399410486221313 0.23071418702602386 0.2580929398536682
b'vehicle' 0.9225113391876221 0.357175350189209 0.44230249524116516 0.23054184019565582 0.2606807053089142
b'vehicle' 0.9186123609542847 0.3581112325191498 0.44336238503456116 0.22992925345897675 0.26163965463638306
b'vehicle' 0.5208129286766052 0.3618425130844116 0.4461480975151062 0.23065532743930817 0.2652992308139801
b'vehicle' 0.7194858193397522 0.3608142137527466 0.45302334427833557 0.23270295560359955 0.2703518867492676
b'vehicle' 0.8540934324264526 0.3617907166481018 0.4548843204975128 0.23152287304401398 0.27814221382141113
b'vehicle' 0.8912967443466187 0.3607788681983948 0.4583750367164612 0.2328854203224182 0.27056747674942017
b'vehicle' 0.8969211578369141 0.3522576689720154 0.45107507705688477 0.22706037759780884 0.26078158617019653
b'vehicle' 0.7722539901733398 0.32488399744033813 0.4447280168533325 0.23074783384799957 0.26997989416122437
b'vehicle' 0.8158320784568787 0.31976011395454407 0.46247294545173645 0.23671922087669373 0.28281715512275696
b'vehicle' 0.9343507289886475 0.332416832447052 0.5008321404457092 0.2320593148469925 0.26590874791145325
b'vehicle' 0.896917462348938 0.3266463875770569 0.48811814188957214 0.23444382846355438 0.266757994890213
b'vehicle' 0.7356315851211548 0.31006965041160583 0.45962005853652954 0.23436705768108368 0.26961490511894226

Crowdcount

In [ ]:

import time
from ax import pipeline

pipeline.load([
    'libsample_vin_ivps_joint_vo_sipy.so',
    '-p', '/home/config/crowdcount.json',
    '-c', '2',
])

while pipeline.work():
    time.sleep(0.001)
    tmp = pipeline.result()
    if tmp and tmp['nObjSize']:
        for i in tmp['mObjects']:
            x, y, w, h = i['bbox']['x'], i['bbox']['y'], i['bbox']['w'], i['bbox']['h']
            objname, objprob = i['objname'], i['prob']
            print(objname, objprob, x, y, w, h)
        # if tmp['nObjSize'] > 10: # try exit
        #     pipeline.drop()
pipeline.drop()

Human posture detection on ax-pose-ppl

These are the detection points.

0: Nose 1: left eye 2: right eye 3: left ear 4: right ear 5: left shoulder 6: right shoulder 7: left elbow 8: right elbow 9: left wrist 10: right wrist 11: left hip 12: right hip 13: left knee 14: right knee 15: left ankle 16: right ankle

In [26]:

import time
from ax import pipeline

pipeline.load([
    'libsample_vin_ivps_joint_vo_sipy.so',
    '-p', '/home/config/ax_pose.json',
    '-c', '2',
])

while pipeline.work():
    time.sleep(0.001)
    tmp = pipeline.result()
    if tmp and tmp['nObjSize']:
        for i in tmp['mObjects']:
            print(i)
        # if tmp['nObjSize'] > 10: # try exit
        #     pipeline.drop()
pipeline.drop()

{'label': 0, 'prob': 0.41659796237945557, 'objname': b'person', 'bbox': {'x': 0.01200273260474205, 'y': 0.0, 'w': 0.9315435290336609, 'h': 0.9421796798706055}, 'bHasBoxVertices': 0, 'bHasLandmark': 17, 'landmark': [{'x': 0.6708333492279053, 'y': 0.23333333432674408}, {'x': 0.6427083611488342, 'y': 0.16851851344108582}, {'x': 0.6520833373069763, 'y': 0.14629629254341125}, {'x': 0.7322916388511658, 'y': 0.5055555701255798}, {'x': 0.7614583373069763, 'y': 0.06481481343507767}, {'x': 0.7541666626930237, 'y': 0.09444444626569748}, {'x': 0.7541666626930237, 'y': 0.1518518477678299}, {'x': 0.7124999761581421, 'y': 0.15925925970077515}, {'x': 0.5041666626930237, 'y': 0.08703703433275223}, {'x': 0.6739583611488342, 'y': 0.07407407462596893}, {'x': 0.690625011920929, 'y': 0.6814814805984497}, {'x': 0.7833333611488342, 'y': 0.25}, {'x': 0.7614583373069763, 'y': 0.25}, {'x': 0.35104167461395264, 'y': 0.6074073910713196}, {'x': 0.3489583432674408, 'y': 0.5777778029441833}, {'x': 0.0572916679084301, 'y': 0.5185185074806213}, {'x': 0.0677083358168602, 'y': 0.5185185074806213}]}

Pillow Yolov5s object detection

In [ ]:

import time
from ax import pipeline
from PIL import Image, ImageDraw

# ready sipeed logo canvas
lcd_width, lcd_height = 854, 480

img = Image.new('RGBA', (lcd_width, lcd_height), (255,0,0,200))
ui = ImageDraw.ImageDraw(img)
ui.rectangle((20,20,lcd_width-20,lcd_height-20), fill=(0,0,0,0), outline=(0,0,255,100), width=20)

logo = Image.open("/home/res/logo.png")
img.paste(logo, box=(lcd_width-logo.size[0], lcd_height-logo.size[1]), mask=None)

def rgba2argb(rgba):
    r,g,b,a = rgba.split()
    return Image.merge("RGBA", (a,b,g,r))
canvas_argb = rgba2argb(img)

pipeline.load([
    'libsample_vin_ivps_joint_vo_sipy.so',
    '-p', '/home/config/yolov5s.json',
    # '-p', '/home/config/yolov8.json',
    '-c', '2',
])

while pipeline.work():
    time.sleep(0.001)
    argb = canvas_argb.copy()
    tmp = pipeline.result()
    if tmp and tmp['nObjSize']:
        ui = ImageDraw.ImageDraw(argb)
        for i in tmp['mObjects']:
            x = i['bbox']['x'] * lcd_width
            y = i['bbox']['y'] * lcd_height
            w = i['bbox']['w'] * lcd_width
            h = i['bbox']['h'] * lcd_height
            objlabel = i['label']
            objprob = i['prob']
            ui.rectangle((x,y,x+w,y+h), fill=(100,0,0,255), outline=(255,0,0,255))
            ui.text((x,y), str(objlabel))
            ui.text((x,y+20), str(objprob))
    pipeline.config("ui_image", (lcd_width, lcd_height, "ARGB", argb.tobytes()))

pipeline.free()

Pillow Yolov8 object detection (pybind11)

Have a try on Yolov8 object detection via pybind11 API, the pybind11 API supports save camera image and the reference result or display them on screen or website.

It's not stable enough, rerun jupyter notebook if there is someting error.

In [ ]:


import m3axpi

# m3axpi.camera(SysCase=0) # switch os04a10
# m3axpi.camera(SysCase=2) # default gc4653
# m3axpi.load("/home/config/yolov8.json")

from PIL import Image, ImageDraw, ImageFont

lcd_width, lcd_height, lcd_channel = 854, 480, 4

fnt = ImageFont.truetype("/home/res/sans.ttf", 20)
img = Image.new('RGBA', (lcd_width, lcd_height), (255,0,0,200))
ui = ImageDraw.ImageDraw(img)
ui.rectangle((20, 20, lcd_width-20, lcd_height-20), fill=(0,0,0,0), outline=(0,0,255,100), width=20)

logo = Image.open("/home/res/logo.png")
img.paste(logo, box=(lcd_width-logo.size[0], lcd_height-logo.size[1]), mask=None)

while True:
    rgba = img.copy()

    tmp = m3axpi.capture()
    rgb = Image.frombuffer("RGB", (tmp[1], tmp[0]), tmp[3])
    rgba.paste(rgb, box=(0, 0), mask=None) ## camera 320x180 paste 854x480

    res = m3axpi.forward()
    if 'nObjSize' in res:
        ui = ImageDraw.ImageDraw(rgba)
        ui.text((0, 0), "fps:%02d" % (res['niFps']), font=fnt)
        for obj in res['mObjects']:
            x, y, w, h = int(obj['bbox'][0]*lcd_width), int(obj['bbox'][1]*lcd_height), int(obj['bbox'][2]*lcd_width), int(obj['bbox'][3]*lcd_height)
            ui.rectangle((x,y,x+w,y+h), fill=(255,0,0,100), outline=(255,0,0,255))
            ui.text((x, y), "%s:%02d" % (obj['objname'], obj['prob']*100), font=fnt)
            rgba.paste(logo, box=(x+w-logo.size[1], y+h-logo.size[1]), mask=None)

    m3axpi.display([lcd_height, lcd_width, lcd_channel, rgba.tobytes()])
#     display(rgb)显示到网页

Image processing and displaying based on Pillow

Pillow（PIL） is the Python Imaging Library, is used for image processing purposes like image store, image display and format conversion.

Here is a example to use Python Pillow:

In [4]:

!ls home/images

air.jpg        carvana02.jpg	 face5.jpg	   o2_resize.jpg  ssd_car.jpg
aoa-2.jpeg     carvana03.jpg	 grace_hopper.jpg  pineapple.jpg  ssd_dog.jpg
aoa.jpeg       carvana04.jpg	 mobileface01.jpg  pose-1.jpeg	  ssd_horse.jpg
bike.jpg       cat.jpg		 mobileface02.jpg  pose-2.jpeg
bike2.jpg      cityscape.png	 mtcnn_face4.jpg   pose-3.jpeg
cable.jpg      dog.jpg		 mtcnn_face6.jpg   pose.jpg
carvana01.jpg  efficientdet.png  mv2seg.png	   selfie.jpg

In [8]:

from PIL import Image, ImageDraw
pil_im = Image.open('home/images/bike2.jpg', 'r')
draw = ImageDraw.Draw(pil_im)
draw.arc((0, 0,400,400) , start=0, end=300, fill='red',width=3)
draw.rectangle((20, 20, 200, 100), fill=(100, 20, 60), outline="#FF0000", width=3)
pil_im.show() # display(pil_im)

Click me to know more usage about Pillow

Calculation on Numpy

NumPy (Numerical Python) is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

We can use Numpy on AXera-Pi：

In [11]:

import numpy as np
# 'i1', 'i2','i4','i8' are same as int8, int16, int32, int64
dt = np.dtype('i4')
print(dt)

int32

Visit Numpy for more information

Use USB camera with Opencv

Connect USB camera with USB-OTG port of your AXera-Pi first.

This is the Opencv example, take and display the photo via USB camera.

In [ ]:

import os
import cv2
video = cv2.VideoCapture(0)
for i in range(30):
    ret, frame = video.read()
    if ret:
        cv2.imwrite("/tmp/capture.jpg", frame)
        os.system("fbon && fbv /tmp/capture.jpg")

Visit Maix-III AXera-Pi Q&A if failing running

Play media

Play media by Python

Use Http or MQTT

Click me

Use Microbit with PinPong on AXera-Pi

PinPong is an open source hardware control python library, is based on Firmata protocol and support MicroPython language.

Connect Microbit with USB-OTG port of your AXera-Pi first.

Run following example code on AXera-Pi, there is hello world scroll text on microbit.

In [ ]:

import time
from pinpong.board import Board,Pin
from pinpong.extension.microbit import *
Board("microbit","/dev/ttyACM0").begin()
display.show(Image.HEART)
while True:    
    display.scroll("hello world")


  __________________________________________
 |    ____  _       ____                    |
 |   / __ \(_)___  / __ \____  ____  ____ _ |
 |  / /_/ / / __ \/ /_/ / __ \/ __ \/ __ `/ |
 | / ____/ / / / / ____/ /_/ / / / / /_/ /  |
 |/_/   /_/_/ /_/_/    \____/_/ /_/\__, /   |
 |   v0.4.9  Designed by DFRobot  /____/    |
 |__________________________________________|
 
[01] Python3.9.2 Linux-4.19.125-armv7l-with-glibc2.31 Board: MICROBIT
selected -> board: MICROBIT serial: /dev/ttyACM0
[10] Opening /dev/ttyACM0
[32] Firmata ID: 2.7
[22] Arduino compatible device found and connected to /dev/ttyACM0
[40] Retrieving analog map...
[42] Auto-discovery complete. Found 26 Digital Pins and 6 Analog Pins
------------------------------
All right. PinPong go...
------------------------------

Visit PINPONG for more information

Control Arduino UNO with PinPong on AXera-Pi

Connect Arduino UNO with USB-OTG port of your AXera-Pi first.

Run following example code on AXera-Pi, LED on Arduino UNO blinks.

In [5]:

import time
from pinpong.board import Board,Pin
Board("uno","/dev/ttyUSB0").begin()               
led = Pin(Pin.D13, Pin.OUT) # Init pin direction

while True:
  led.value(1) # High voltage output
  print("1") # Print information
  time.sleep(1) # Wait 1 second

  led.value(0) # Low voltage output 
  print("0") # Print information
  time.sleep(1) # Wait 1 second


  __________________________________________
 |    ____  _       ____                    |
 |   / __ \(_)___  / __ \____  ____  ____ _ |
 |  / /_/ / / __ \/ /_/ / __ \/ __ \/ __ `/ |
 | / ____/ / / / / ____/ /_/ / / / / /_/ /  |
 |/_/   /_/_/ /_/_/    \____/_/ /_/\__, /   |
 |   v0.4.9  Designed by DFRobot  /____/    |
 |__________________________________________|
 
[01] Python3.9.2 Linux-4.19.125-armv7l-with-glibc2.31 Board: UNO
selected -> board: UNO serial: /dev/ttyUSB0
[10] Opening /dev/ttyUSB0
[32] Firmata ID: 2.7
[22] Arduino compatible device found and connected to /dev/ttyUSB0
[40] Retrieving analog map...
[42] Auto-discovery complete. Found 20 Digital Pins and 6 Analog Pins
------------------------------
All right. PinPong go...
------------------------------

1
0
1
0
1
user quit process

An exception has occurred, use %tb to see the full traceback.

SystemExit: 0

Click me to see more information.

AXera-Pi manual

C/C++ programming