Text , Human Pose , Vehicle detector using pre trained models of openvino toolkit
So far, you've downloaded some pre-trained models, handled their inputs, and learned how to handle outputs. In this exercise, you'll implement the handling of the outputs of our three models from before, and get to see inference actually performed by adding these models to some example edge applications.
There's a lot of code still involved behind the scenes here. With the Pre-Trained Models available with the OpenVINO toolkit, you don't need to worry about the Model Optimizer, but there is still work done to load the model into the Inference Engine. We won't learn about this code until later, so in this case, you'll just need to call your functions to handle the input and output of the model within the app.
If you do want a sneak preview of some of the code that interfaces with the Inference Engine,
you can check it out in inference.py. You'll work out of the handle_models.py file, as
well as adding functions calls within the edge app in app.py.
To test your implementations, you can use app.py to run each edge application, with
the following arguments:
-t: The model type, which should be one of"POSE","TEXT", or"CAR_META"-m: The location of the model .xml file-i: The location of the input image used for testing-c: A CPU extension file, if applicable. See below for what this is for the workspace. The results of your output will be saved down for viewing in theoutputsdirectory.
As an example, here is an example of running the app with related arguments:
python app.py -i "images/blue-car.jpg" -t "CAR_META" -m "/home/workspace/models/vehicle-attributes-recognition-barrier-0039.xml" -c "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so"
Once again, here are the links to the models, so you can use the Output section to help you get started (there are additional comments in the code to assist):
- Human Pose Estimation: human-pose-estimation-0001
- Text Detection: text-detection-0004
- Determining Car Type & Color: vehicle-attributes-recognition-barrier-0039