From 127f931a5e27b7b8db56b5e2cc445a59079ebb52 Mon Sep 17 00:00:00 2001
From: Tatiana Savina <tatiana.savina@intel.com>
Date: Wed, 14 Apr 2021 22:33:04 +0300
Subject: [PATCH] Add MonoDepth Python Demo how-to (#5238)

* Add POT how-to

* added new how-to and updated the link
---
 docs/how_tos/MonoDepth_how_to.md   | 70 ++++++++++++++++++++++++++++++
 docs/how_tos/POT_how_to_example.md |  2 +-
 2 files changed, 71 insertions(+), 1 deletion(-)
 create mode 100644 docs/how_tos/MonoDepth_how_to.md

diff --git a/docs/how_tos/MonoDepth_how_to.md b/docs/how_tos/MonoDepth_how_to.md
new file mode 100644
index 00000000000..329eac9e063
--- /dev/null
+++ b/docs/how_tos/MonoDepth_how_to.md
@@ -0,0 +1,70 @@
+#  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. 
diff --git a/docs/how_tos/POT_how_to_example.md b/docs/how_tos/POT_how_to_example.md
index 571269a92ff..28adc19062b 100644
--- a/docs/how_tos/POT_how_to_example.md
+++ b/docs/how_tos/POT_how_to_example.md
@@ -1,8 +1,8 @@
 # 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