azure-percept 0.0.7

Unofficial Python package to control the Azure Percept SoM

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 an experimental privately written libray without any warranty, expect bugs. If you encounter them, please open an issue on Github.

Connect to your Percept

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

Prerequisites

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)
• Numpy: sudo pip3 install numpy

Install

The following guide assumes you run the code directly on the Percept board (CBL Mariner OS):

• 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 .
• To uninstall run sudo pip3 uninstall azure-percept

Note that the package includes pre-built libraries that will only run on an aarch64 architecture!

Azure Percept Audio sample

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 sudo python3 perceptaudio.py to run the script.

Azure Percept Vision samples

Run a machine learning model on the VPU

The following sample shows how to run a 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. 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", 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 sudo python3 perceptvision.py to run the script. Especially the model conversion can take several minutes. vision.start_inference(blob_model_path) will start 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.

During model conversion you might get an error like Cannot create X layer from unsupported opset. 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.

Process local image files on the VPU

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()

Take a picture and save it locally

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 as RGB
pil_img = Image.fromarray(img) # convert the numpy array to a Pillow image
pil_img.save("frame.jpg")
vision.close()

Record a video

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()

License

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.