automatic insertion of htmlonly

docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md

@@ -111,10 +111,8 @@ The common workflow contains the following steps:
 8. **Get the output** - After inference is completed, get the output memory or read the memory you provided earlier. Do this with the `InferenceEngine::IInferRequest::GetBlob()` method.
 
 ## Video: Inference Engine Concept
-[![](https://img.youtube.com/vi/e6R13V8nbak/0.jpg)](https://www.youtube.com/watch?v=e6R13V8nbak)
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+
 <iframe width="560" height="315" src="https://www.youtube.com/embed/e6R13V8nbak" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-\endhtmlonly
 
 ## Further Reading
 

docs/IE_DG/supported_plugins/MULTI.md

@@ -96,10 +96,8 @@ Notice that you can use the FP16 IR to work with multi-device (as CPU automatica
 Also notice that no demos are (yet) fully optimized for the multi-device, by means of supporting the OPTIMAL_NUMBER_OF_INFER_REQUESTS metric, using the GPU streams/throttling, and so on.
 
 ## Video: MULTI Plugin
-[![](https://img.youtube.com/vi/xbORYFEmrqU/0.jpg)](https://www.youtube.com/watch?v=xbORYFEmrqU)
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+
 <iframe width="560" height="315" src="https://www.youtube.com/embed/xbORYFEmrqU" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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 ## See Also
 * [Supported Devices](Supported_Devices.md)

docs/MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md

@@ -36,9 +36,21 @@ To learn about all Model Optimizer parameters and conversion technics, see the [
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   <tr>
-    <td><iframe width="220" src="https://www.youtube.com/embed/Kl1ptVb7aI8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></td>
-    <td><iframe width="220" src="https://www.youtube.com/embed/BBt1rseDcy0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></td>
-    <td><iframe width="220" src="https://www.youtube.com/embed/RF8ypHyiKrY" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe></td>
+    <td>
+\endhtmlonly
+<iframe width="220" src="https://www.youtube.com/embed/Kl1ptVb7aI8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+\htmlonly
+</td>
+<td>
+\endhtmlonly
+    <iframe width="220" src="https://www.youtube.com/embed/BBt1rseDcy0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+\htmlonly
+</td>
+<td>
+\endhtmlonly
+<iframe width="220" src="https://www.youtube.com/embed/RF8ypHyiKrY" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+\htmlonly
+</td>
   </tr>
   <tr>
     <td><strong>Model Optimizer Concept</strong>. <br>Duration: 3:56</td>

docs/MO_DG/prepare_model/Config_Model_Optimizer.md

@@ -152,7 +152,7 @@ pip3 install -r requirements_onnx.txt
 ```
 
 ## Using the protobuf Library in the Model Optimizer for Caffe\*
-\htmlonly<details>\endhtmlonly
+<details>
     <summary>Click to expand</summary>
 
 
@@ -264,9 +264,7 @@ python3 -m easy_install dist/protobuf-3.6.1-py3.6-win-amd64.egg
 set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp
 ```
 
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 </details>
-\endhtmlonly
 
 ## See Also
 

docs/MO_DG/prepare_model/convert_model/Convert_Model_From_TensorFlow.md

@@ -405,10 +405,8 @@ Refer to [Supported Framework Layers ](../Supported_Frameworks_Layers.md) for th
 The Model Optimizer provides explanatory messages if it is unable to run to completion due to issues like typographical errors, incorrectly used options, or other issues. The message describes the potential cause of the problem and gives a link to the [Model Optimizer FAQ](../Model_Optimizer_FAQ.md). The FAQ has instructions on how to resolve most issues. The FAQ also includes links to relevant sections in the Model Optimizer Developer Guide to help you understand what went wrong.
 
 ## Video: Converting a TensorFlow Model
-[![](https://img.youtube.com/vi/QW6532LtiTc/0.jpg)](https://www.youtube.com/watch?v=QW6532LtiTc)
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 <iframe width="560" height="315" src="https://www.youtube.com/embed/QW6532LtiTc" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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 ## Summary
 In this document, you learned:

docs/doxygen/doxy_md_filter.py

@@ -51,6 +51,15 @@ def replace_links(content, items, folder, labels, docs_folder):
     return content
 
 
+def add_htmlonly(content):
+    content = content.replace('<details>', '\n\\htmlonly\n<details>')
+    content = content.replace('</summary>', '</summary>\n\\endhtmlonly')
+    content = content.replace('</details>', '\n\\htmlonly\n</details>\n\\endhtmlonly')
+    content = content.replace('<iframe', '\n\\htmlonly\n<iframe')
+    content = content.replace('</iframe>', '</iframe>\n\\endhtmlonly')
+    return content
+
+
 def process_github_md_links(content, items):
     """
     This is a workaround to support github markdown links in doxygen 1.8.12.
@@ -81,6 +90,7 @@ def process(docs_folder):
         content = replace_links(content, inline_links, md_folder, labels, docs_folder)
         content = replace_links(content, reference_links, md_folder, labels, docs_folder)
         content = process_github_md_links(content, github_md_links)
+        content = add_htmlonly(content)
         if inline_links or reference_links or github_md_links:
             with open(md_file, 'w', encoding='utf-8') as f:
                 f.write(content)

docs/get_started/get_started_dl_workbench.md

@@ -14,29 +14,23 @@ Start working with the OpenVINO™ toolkit right from your browser: import a mod
 * Analyze the quality of your model and visualize output.
 * Use preconfigured JupyterLab\* environment to learn OpenVINO™ workflow. 
 
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 <iframe width="560" height="315" src="https://www.youtube.com/embed/on8xSSTKCt8" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-\endhtmlonly
 
 ## Run DL Workbench 
 
 You can [run DL Workbench](@ref workbench_docs_Workbench_DG_Install) on your local system or in the Intel® DevCloud for the Edge. Ensure that you have met the [prerequisites](@ref workbench_docs_Workbench_DG_Prerequisites).
 
 Run DL Workbench on your local system by using the installation form. Select your options and run the commands on the local machine:
 
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 <iframe style="width: 100%; height: 620px;" src="https://openvinotoolkit.github.io/workbench_aux/"  frameborder="0" allow="clipboard-write;"></iframe>
-\endhtmlonly
 
 Once DL Workbench is set up, open the http://127.0.0.1:5665 link.
 
 ![](./dl_workbench_img/active_projects_page.png)
 
 Watch the video to learn more detailed information on how to run DL Workbench:
 
-\htmlonly
 <iframe width="560" height="315" src="https://www.youtube.com/embed/JBDG2g5hsoM"  frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
-\endhtmlonly
 
 Congratulations, you have installed DL Workbench. Your next step is to [Get Started with DL Workbench](@ref workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets) and create your first project. 
 

docs/how_tos/how-to-links.md

@@ -17,36 +17,30 @@ To learn about what is *custom operation* and how to work with them in the Deep
 
 ## Introducing OpenVINO™ and Computer Vision | IoT Developer Show Season 2 | Intel Software
 
-[![](https://img.youtube.com/vi/M6Nyh2JDLQs/0.jpg)](https://www.youtube.com/watch?v=M6Nyh2JDLQs)
 <iframe width="560" height="315" src="https://www.youtube.com/embed/M6Nyh2JDLQs" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 
 
 ## OpenVINO™ Toolkit and Two Hardware Development Kits | IoT Developer Show Season 2 | Intel Software
 
-[![](https://img.youtube.com/vi/GtJPBYjuyVU/0.jpg)](https://www.youtube.com/watch?v=GtJPBYjuyVU)
 <iframe width="560" height="315" src="https://www.youtube.com/embed/GtJPBYjuyVU" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 
 
 ## Intel Demonstration of High Performance Vision Deployment - The OpenVINO Toolkit in Action
 
-[![](https://img.youtube.com/vi/1_iI_4Zgufw/0.jpg)](https://www.youtube.com/watch?v=1_iI_4Zgufw)
 <iframe width="560" height="315" src="https://www.youtube.com/embed/1_iI_4Zgufw" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 
 
 ## Computer Vision at the Edge with OpenVINO by Krishnakumar Shetti at ODSC_India
 
-[![](https://img.youtube.com/vi/RfRCrq35LXg/0.jpg)](https://www.youtube.com/watch?v=RfRCrq35LXg)
 <iframe width="560" height="315" src="https://www.youtube.com/embed/RfRCrq35LXg" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 
 
 ## Model optimizer concept
 
-[![](https://img.youtube.com/vi/Kl1ptVb7aI8/0.jpg)](https://www.youtube.com/watch?v=Kl1ptVb7aI8)
 <iframe width="560" height="315" src="https://www.youtube.com/embed/Kl1ptVb7aI8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
 
 ## Computer Vision with Intel
 
-[![](https://img.youtube.com/vi/FZZD4FCvO9c/0.jpg)](https://www.youtube.com/watch?v=FZZD4FCvO9c)
 <iframe width="560" height="315" src="https://www.youtube.com/embed/FZZD4FCvO9c" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>