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Add MonoDepth Python Demo how-to (#5238)

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* Add POT how-to

* added new how-to and updated the link
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docs/how_tos/MonoDepth_how_to.md Normal file
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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](https://docs.openvinotoolkit.org/latest/omz_demos_monodepth_demo_python.html).
Tested on OpenVINO™ 2021, Ubuntu 18.04.
## 1. Set Environment
Define the OpenVINO™ install directory:
```
export OV=/opt/intel/openvino_2021/
```
Define the working directory. Make sure the directory exist:
```
export WD=~/MonoDepth_Python/
```
## 2. Install Prerequisits
Initialize OpenVINO™:
```
source $OV/bin/setupvars.sh
```
Install the Model Optimizer prerequisites:
```
cd $OV/deployment_tools/model_optimizer/install_prerequisites/
sudo ./install_prerequisites.sh
```
Install the Model Downloader prerequisites:
```
cd $OV/deployment_tools/tools/model_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/deployment_tools/tools/model_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/deployment_tools/tools/model_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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docs/how_tos/POT_how_to_example.md
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# Post-Training Optimization Tool - A real example
This tutorial describes the example from the following YouTube* video:
https://www.youtube.com/watch?v=cGQesbWuRhk&t=49s
http://XXXXX
Watch this video to learn the basics of Post-training Optimization Tool (POT):
https://www.youtube.com/watch?v=SvkI25Ca_SQ