text_files/001_quick_start.txt

### DeGirum PySDK Tutorial for Hailo8L/HAILO8
In this notebook, we illustrate the main features of PySDK and how it can be used to quickly develop edge AI applications using the `Hailo8L/HAILO8` accelerator. See [Setup Instructions for PySDK Notebooks](../README.md#setup-instructions-for-pysdk-notebooks). 

DeGirum's PySDK provides simple APIs to run AI model inference. In general, there are three steps in running an AI model:
1. Loading model using `degirum.load_model` method
2. Running inference on an input using the `model.predict` method 
3. Visualizing inference results using the `results.image_overlay` method

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# import degirum and degirum_tools
import degirum as dg, degirum_tools

inference_host_address = "@local"
zoo_url = 'degirum/hailo'
token=''
device_type='HAILORT/HAILO8L'

# set model name, and image source
model_name = "yolov8n_relu6_coco--640x640_quant_hailort_hailo8l_1"
image_source='../assets/ThreePersons.jpg'

# load AI model
model = dg.load_model(
    model_name=model_name,
    inference_host_address=inference_host_address,
    zoo_url=zoo_url,
    token=token,
    device_type=device_type,    
)

# perform AI model inference on given image source
print(f" Running inference using '{model_name}' on image source '{image_source}'")
inference_result = model(image_source)

# print('Inference Results \n', inference_result)  # numeric results
print(inference_result)
print("Press 'x' or 'q' to stop.")

# show results of inference
with degirum_tools.Display("AI Camera") as output_display:
    output_display.show_image(inference_result.image_overlay)

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#### Running Inference on Video Stream
- The `predict_stream` function in `degirum_tools` provides a powerful and efficient way to perform AI inference on video streams in real-time. It processes video frames sequentially and returns inference results frame by frame, enabling seamless integration with various video input sources.
- The code below shows how to use `predict_stream` on a video file.

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import degirum as dg, degirum_tools

inference_host_address = "@local"
zoo_url = 'degirum/hailo'
token=''
device_type='HAILORT/HAILO8L'

model_name = "yolov8n_relu6_coco--640x640_quant_hailort_hailo8l_1"
video_source = '../assets/Traffic.mp4'

# load AI model
model = dg.load_model(
    model_name=model_name,
    inference_host_address=inference_host_address,
    zoo_url=zoo_url,
    token=token,
    device_type=device_type
)

with degirum_tools.Display("AI Camera") as output_display:
    for inference_result in degirum_tools.predict_stream(model, video_source):
        output_display.show(inference_result)

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### Listing Models 
You can explore the models available using `degirum.list_models()` method.

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import degirum as dg, degirum_tools

inference_host_address = "@local"
zoo_url = 'degirum/hailo'
token=''
device_type=['HAILORT/HAILO8L']

model_list=dg.list_models(
    inference_host_address=inference_host_address,
    zoo_url=zoo_url,
    token=token,
    device_type=device_type
)
for index, model_name in enumerate(model_list):
    print(index, model_name)

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### Switch to local model zoo
In this repo, we provide a `models` folder with a couple of example models. You can use the code block as a reference to run models from a local folder.

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import degirum as dg

inference_host_address = "@local"
zoo_url = '../models'
token=''
device_type='HAILORT/HAILO8L'

model_list = dg.list_models(
    inference_host_address=inference_host_address,
    zoo_url=zoo_url,
    token=token,
    device_type=device_type 
)

for model_name in model_list.keys():
    print(model_name)

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import degirum as dg, degirum_tools

inference_host_address = "@local"
zoo_url = '../models'
token=''
device_type='HAILORT/HAILO8L'

# set model name, inference host address, zoo url, token, and image source
model_name = "yolov8n_relu6_coco--640x640_quant_hailort_hailo8l_1"
image_source='../assets/ThreePersons.jpg'

# load AI model
model = dg.load_model(
    model_name=model_name,
    inference_host_address=inference_host_address,
    zoo_url='../models',
    token=token,
    device_type=device_type
)

# perform AI model inference on given image source
print(f" Running inference using '{model_name}' on image source '{image_source}'")
inference_result = model(image_source)

# print('Inference Results \n', inference_result)  # numeric results
print(inference_result)
print("Press 'x' or 'q' to stop.")

# show results of inference
with degirum_tools.Display("AI Camera") as output_display:
    output_display.show_image(inference_result)

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