DeGirum PySDK Examples

Quick Start

1. Install DeGirum PySDK. Refer to DeGirum Developers Center for instructions.

2. The following script will download MobileNetv2+SSD CPU model from DeGirum public mode zoo and perform ML inference of a test image with two cats. The inference result will be displayed in both text and graphical form.

   import degirum as dg         # import DeGirum PySDK package
   zoo = dg.connect_model_zoo() # connect to DeGirum public model zoo
   print(zoo.list_models())     # print all available models in the model zoo
   
   # load mobilenet_ssd model for CPU;
   # model_name should be one returned by zoo.list_models()
   model_name = "mobilenet_v2_ssd_coco--300x300_quant_n2x_cpu_1"     
   model = zoo.load_model(model_name)
   
   # perform AI inference of an image specified by URL
   image_url = "https://degirum.github.io/images/samples/TwoCats.jpg"
   result = model(image_url)
   
   print(result)                # print numeric results
   result.image_overlay.show()  # show graphical results

Resources

1. DeGirum Developers Center: place to look for PySDK documentation
2. DeGirum Cloud Platform: place to manage your cloud API access tokens
3. DeGirum public mode zoo GitHub repo

Running PySDK Examples

This repository provides PySDK example scripts that can perform ML inferences on the following hosting options:

1. Using DeGirum Cloud Platform,
2. On DeGirum-hosted AI server node shared via Peer-to-Peer VPN,
3. On AI server node hosted by you in your local network,
4. On AI server running on your local machine,
5. On DeGirum ORCA accelerator directly installed on your local machine.

To try different options, you need to just change the inference_option variable in the script code.

To run the examples, clone this repo:

git clone https://github.com/DeGirum/PySDKExamples.git

Inside the repo, create an .env file and fill the required authentication details by assigning the following variables (you may use alternative name env.ini if you work under Jupyter Lab GUI, which does not allow you to create hidden files):

Variable Name	Description
DEGIRUM_CLOUD_TOKEN	DeGirum cloud platform API access token, obtained on DeGirum Cloud Platform site.
P2P_VPN_SERVER_ADDRESS	IP address of DeGirum-hosted AI server node shared via Peer-to-Peer VPN; please contact [email protected] to obtain one.
LOCAL_NETWORK_SERVER_ADDRESS	IP address of AI server node hosted by you in your local network; refer to DeGirum Developers Center for AI server installation details.
GITHUB_TOKEN	GitHub personal access token to access DeGirum public mode zoo repo; any valid GitHub token will work.
CAMERA_ID	Local camera index or web camera URL in the format rtsp://<user>:<password>@<ip or hostname>

This will allow loading the required information from the .env file instead of hard-coding the values in the script. You can copy the below lines and fill in the missing information.

DEGIRUM_CLOUD_TOKEN = 'Enter your DeGirum cloud platform token'
P2P_VPN_SERVER_ADDRESS = 'Enter IP address of the DeGirum P2P AI server'
LOCAL_NETWORK_SERVER_ADDRESS = 'Enter IP address of the AI server in your local network'
GITHUB_TOKEN = 'Enter your GitHub personal access token'
CAMERA_ID = 'rtsp://<user>:<password>@<ip or hostname>'

The .env file is added to .gitignore and will not be checked in. This will ensure that your token information is not leaked.

Examples Directory

Example	Description
ObjectDetectionImage	One of the most simplest examples how to do AI inference of a graphical file using object detection model.
ObjectDetectionVideoFile	How to do AI inference of a video stream from a video file, show annotated video, and save it to another video file.
ObjectDetectionCameraStream	How to do AI inference of a video stream from a video camera and show annotated video in real-time.
ObjectDetectionDataset	How to do AI inference on an image dataset and calculate performance metrics. An image dataset is retrieved from the cloud using fiftyone API.
ObjectDetectionDatasetMultithreaded	How to do multi-threaded AI inference on an image dataset. An image dataset is retrieved from the cloud using fiftyone API.
ObjectDetectionVideoFile2Images	How to do AI inference of a video stream from a video file and save annotated frame images into a directory.
FaceHandDetectionParallelCameraStream	How to run two models side-by-side and combine results of both models. A video stream from a video camera is processed simultaneously by the hand and face detection models. Combined result is then displayed.
PersonPoseDetectionPipelinedImage	How to do AI inference of a graphical file using two AI models: person detection and pose detection. The person detection model is run on the image and the results are then processed by the pose detection model, one person bounding box at a time. Combined result is then displayed.
PersonPoseDetectionPipelinedCameraStream	A video stream from a video camera is processed by the person detection model. The person detection results are then processed by the pose detection model, one person bounding box at a time. Combined results are then displayed as an annotated video in real-time.
SoundClassificationAudioStream	How to do sound classification AI inference of an audio stream from a local microphone in real time. The result label with highest probability is displayed for each inference while keeping history few steps back.
SoundClassificationAndObjectDetectionAsynchronous	How to perform parallel inferences on two asynchronous data streams with different frame rates. To achieve maximum performance this example uses non-blocking batch prediction mode.