This is the source code for the azure-percept package - an unofficial Python library to access the sensors of Azure Percept in Python.
IMPORTANT: This is a community-driven open source library without any warranty, expect bugs. If you encounter them, please open an issue on Github.
Please refer to the official documentation to learn how to connect to the device: https://docs.microsoft.com/en-us/azure/azure-percept/how-to-ssh-into-percept-dk
This package is intended to run on an Azure Percept device (or a container hosted on Azure Percept). Make sure you use one of the following Python versions: 3.6, 3.7, 3.8, 3.9, 3.10. Then install the package with:
sudo yum install python3-pip
python3 -m venv ~/.venv
source ~/.venv/bin/activate
pip3 install --upgrade pip
pip3 install azure-percept
sudo usermod -aG apdk_accessories,audio $(whoami)
After running these commands, log out and log in again so the group membership changes take effect.
Make sure the following is installed on your Percept device or the container you want to use:
- libalsa, libusb, gcc, binutils, Python headers, setuptools and pip (run
sudo yum install -y git alsa-lib-devel libusb-devel gcc glibc-devel kernel-devel kernel-headers binutils python3-devel python3-setuptools python3-pip) - pthreads (libpthread should be available on most OS by default, check your library path - for example /usr/lib/ - to be sure)
- Clone the source code on your Percept device
git clone https://github.com/christian-vorhemus/azure-percept-py.git - Open a terminal and cd into
azure-percept-py - Run
sudo pip3 install .In case you get an error message like "module_info.ld: No such file or directory", runsudo /usr/lib/rpm/mariner/gen-ld-script.shto create the necessary scripts. - Run
sudo usermod -aG apdk_accessories,audio $(whoami) - Log out and log in again
- To uninstall run
sudo pip3 uninstall azure-percept
Note that the package includes pre-built libraries that will only run on an aarch64 architecture!
The following sample authenticates the Azure Percept Audio sensor, records audio for 5 seconds and saves the result locally as a WAV file. Create a new file perceptaudio.py with the following content
from azure.iot.percept import AudioDevice
import time
audio = AudioDevice()
print("Authenticating sensor...")
while True:
if audio.is_ready() is True:
break
else:
time.sleep(1)
print("Authentication successful!")
print("Recording...")
audio.start_recording("./sample.wav")
time.sleep(5)
audio.stop_recording()
print("Recording stopped")
audio.close()Type python3 perceptaudio.py to run the script.
The following sample shows how to run a machine learning model on the Azure Vision Myriad VPU. It assumes we have a .onnx model ready for inference. If not, download a model from the ONNX Model Zoo, for example ResNet-18 or browse the sample models which are already converted to the .blob format. Create a new file perceptvision.py with the following content
from azure.iot.percept import VisionDevice, InferenceResult
import time
import numpy
vision = VisionDevice()
print("Authenticating sensor...")
while True:
if vision.is_ready() is True:
break
else:
time.sleep(1)
print("Authentication successful!")
# this will convert a ONNX model to a model file with the same name
# and a .blob suffix to the output directory "/path/to"
vision.convert_model("/path/to/resnet18-v1-7.onnx",
scale_values=[58.395, 57.120, 57.375], mean_values=[123.675, 116.28, 103.53],
reverse_input_channels=True, output_dir="/path/to")
vision.start_inference("/path/to/resnet18-v1-7.blob")
res: InferenceResult = vision.get_inference(return_image=True)
print(res.inference)
print(res.image)
vision.stop_inference()
vision.close()Type python3 perceptvision.py to run the script. Especially the model conversion can take several minutes. The preprocessing of images (if needed) is baked into the model itself and applied by first converting the BGR camera frame to RGB (if specified with reverse_input_channels=True), then subtracting the mean_values from the input and finally dividing all tensor elements per channel by scale_values. vision.start_inference(blob_model_path) will initialize the Azure Percept Vision camera as well as the VPU. To specify the input camera sources, pass the input_src argument, for example vision.start_inference(blob_model_path, input_src=["/dev/video0", "/dev/video2"]) whereas /camera1 would identify the Percept module camera and /dev/video0, /dev/video2 are conventional USB cameras plugged into the Percept DK. With vision.get_inference() the prediction results are returned as an InferenceResult object or as a list of InferenceResult objects in case of multiple input sources. The prediction is stored as a numpy array in res.inference.
It's also possible to use a local image file instead of reading from a camera device. To do so, convert the image into a BGR sequence of bytes and pass them in the input argument of get_inference():
from azure.iot.percept import VisionDevice, InferenceResult
import time
from PIL import Image
import numpy as np
vision = VisionDevice()
print("Authenticating sensor...")
while True:
if vision.is_ready() is True:
break
else:
time.sleep(1)
print("Authentication successful!")
image = Image.open("./<yourfile>.jpg")
image_np = np.array(image)
image_np = np.moveaxis(image_np, -1, 0)
r = image_np[0].tobytes()
g = image_np[1].tobytes()
b = image_np[2].tobytes()
img = b+g+r
vision.start_inference("<model>.blob")
res: InferenceResult = vision.get_inference(input=img, input_shape=(image.height, image.width))
print(res.inference)
vision.stop_inference()
vision.close()The following sample gets an image (as a numpy array) from the Azure Percept Vision device in BGR format with shape (height, width, channels) and saves it as a JPG file (you need Pillow for this sample to work: pip3 install Pillow)
from azure.iot.percept import VisionDevice
import time
import numpy as np
from PIL import Image
vision = VisionDevice()
print("Authenticating sensor...")
while True:
if vision.is_ready() is True:
break
else:
time.sleep(1)
print("Authentication successful!")
img = vision.get_frame() # get a camera frame from the Azure Vision device
img = img[...,::-1].copy() # copy the BGR image to RGB
pil_img = Image.fromarray(img) # convert the numpy array to a Pillow image object
pil_img.save("frame.jpg")
vision.close()The following sample records a video for 5 seconds and saves it locally as a MP4 file.
from azure.iot.percept import VisionDevice
import time
vision = VisionDevice()
print("Authenticating sensor...")
while True:
if vision.is_ready() is True:
break
else:
time.sleep(1)
print("Authentication successful!")
print("Recording...")
vision.start_recording("./sample.mp4")
time.sleep(5)
vision.stop_recording()
print("Recording stopped")
vision.close()This indicates that the model contains a layer that can't be converted to a model definition the VPU can process. For a list of supported layers see here.
Reading audio data fails with "ValueError: Device not found" or "Exception: Azure Ear could not authenticate".
Type in "lsusb". You should see a list of several devices, try to find ID "045e:0673". If this device is not present, unplug and plug in your Azure Audio device again and restart the device. Additionally make sure your device has Internet connectivity during the authentication process. It's also possible that the user you run the command with has no rights to access soundcards. You can check this if you install alsa utils (sudo yum install alsa-utils) and then run aplay -l. If you see an output like "no soundcards found", add the user you run the script with (e..g the current user sudo usermod -aG audio $(whoami)) to the audio group, log out and log in and test again.
When running the package in a docker container I get "Exception: Azure Eye could not authenticate" or "Failed to find MX booted device. Retrying..." is running in a loop.
Type "lsusb" and check if "045e:066f" is present. If not, unplug the vision device and plug it in again. If it's visible, check if "03e7:2485" is in the list. If not, the authentication process is not finished. Check if your device has Internet connectivity. Additionally, make sure the container is using the host's network to receive udev events and, mount the /dev path to the container and run in privileged mode (e.g., docker run --net=host -v /dev:/dev --privileged <imagename>)
When trying to install azure-percept I get "Could not find a version that satisfies the requirement azure-percept".
Make sure that you install the package on the Azure Percept DK directly and you use a supported Python version (check with python3 --version). Also make sure you use a recent version of pip (python3 -m pip install --upgrade pip)
This library is licensed under Apache License Version 2.0 and uses binaries and scripts from the OpenVINO toolkit which is as well licensed under Apache License Version 2.0.