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OpenVINO™ MonoDepth Python Demo

This tutorial describes the example from the following YouTube* video: ///

To learn more about how to run the MonoDepth Python* demo application, refer to the documentation.

Tested on OpenVINO™ 2021, Ubuntu 18.04.

1. Set Environment

Define the OpenVINO™ install directory:

export OV=/opt/intel/openvino_2022/

Define the working directory. Make sure the directory exist:

export WD=~/MonoDepth_Python/

2. Install Prerequisits

Initialize OpenVINO™:

source $OV/setupvars.sh

Install the Model Optimizer prerequisites:

cd $OV/tools/model_optimizer/install_prerequisites/
sudo ./install_prerequisites.sh

Install the Model Downloader prerequisites:

cd $OV/extras/open_model_zoo/tools/downloader/
python3 -mpip install --user -r ./requirements.in
sudo python3 -mpip install --user -r ./requirements-pytorch.in
sudo python3 -mpip install --user -r ./requirements-caffe2.in

3. Download Models

Download all models from the Demo Models list:

python3 $OV/extras/open_model_zoo/tools/downloader/downloader.py --list $OV/deployment_tools/inference_engine/demos/python_demos/monodepth_demo/models.lst -o $WD

4. Convert Models to Intermediate Representation (IR)

Use the convert script to convert the models to ONNX*, and then to IR format:

cd $WD
python3 $OV/extras/open_model_zoo/tools/downloader/converter.py --list $OV/deployment_tools/inference_engine/demos/python_demos/monodepth_demo/models.lst

5. Run Demo

Install required Python modules, for example, kiwisolver or cycler, if you get missing module indication.

Use your input image:

python3 $OV/inference_engine/demos/python_demos/monodepth_demo/monodepth_demo.py -m $WD/public/midasnet/FP32/midasnet.xml -i input-image.jpg

Check the result depth image:

eog disp.png &

You can also try to use another model. Note that the algorithm is the same, but the depth map will be different.

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