71 lines
2.0 KiB
Markdown
71 lines
2.0 KiB
Markdown
# OpenVINO™ MonoDepth Python Demo
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This tutorial describes the example from the following YouTube* video:
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///
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To learn more about how to run the MonoDepth Python* demo application, refer to the [documentation](https://docs.openvino.ai/latest/omz_demos_monodepth_demo_python.html).
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Tested on OpenVINO™ 2021, Ubuntu 18.04.
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## 1. Set Environment
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Define the OpenVINO™ install directory:
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```
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export OV=/opt/intel/openvino_2022/
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```
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Define the working directory. Make sure the directory exist:
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```
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export WD=~/MonoDepth_Python/
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```
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## 2. Install Prerequisits
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Initialize OpenVINO™:
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```
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source $OV/setupvars.sh
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```
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Install the Model Optimizer prerequisites:
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```
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cd $OV/tools/model_optimizer/install_prerequisites/
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sudo ./install_prerequisites.sh
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```
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Install the Model Downloader prerequisites:
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```
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cd $OV/extras/open_model_zoo/tools/downloader/
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python3 -mpip install --user -r ./requirements.in
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sudo python3 -mpip install --user -r ./requirements-pytorch.in
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sudo python3 -mpip install --user -r ./requirements-caffe2.in
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```
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## 3. Download Models
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Download all models from the Demo Models list:
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```
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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
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```
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## 4. Convert Models to Intermediate Representation (IR)
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Use the convert script to convert the models to ONNX*, and then to IR format:
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```
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cd $WD
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python3 $OV/extras/open_model_zoo/tools/downloader/converter.py --list $OV/deployment_tools/inference_engine/demos/python_demos/monodepth_demo/models.lst
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```
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## 5. Run Demo
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Install required Python modules, for example, kiwisolver or cycler, if you get missing module indication.
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Use your input image:
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```
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python3 $OV/inference_engine/demos/python_demos/monodepth_demo/monodepth_demo.py -m $WD/public/midasnet/FP32/midasnet.xml -i input-image.jpg
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```
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Check the result depth image:
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```
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eog disp.png &
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```
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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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