Added new content for g-api documentation. Removed obsolete links thr…

…ough all docs

docs/HOWTO/Custom_Layers_Guide.md

@@ -360,7 +360,6 @@ python3 $INTEL_OPENVINO_DIR/deployment_tools/tools/benchmark_tool/benchmark_app.
 - [Inference Engine Extensibility Mechanism](../IE_DG/Extensibility_DG/Intro.md)
 - [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
 - [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
-- [Inference Engine Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
 
 ## Converting Models:

docs/IE_DG/protecting_model_guide.md

@@ -59,5 +59,4 @@ should be called with `weights` passed as an empty `Blob`.
 - Inference Engine Developer Guide: [Inference Engine Developer Guide](Deep_Learning_Inference_Engine_DevGuide.md)
 - For more information on Sample Applications, see the [Inference Engine Samples Overview](Samples_Overview.md)
 - For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
-- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).

docs/doxygen/openvino_docs.xml

@@ -94,7 +94,17 @@
                 <!-- DL Streamer -->
                 <tab type="user" title="DL Streamer API Reference" url="https://openvinotoolkit.github.io/dlstreamer_gst/"/>
                 <!-- DL Streamer Examples -->
-                <tab type="usergroup" title="DL Streamer Examples" url="@ref gst_samples_README">
+                <tab type="usergroup" title="DL Streamer Examples" url="@ref gst_samples_README"></tab>
+                <!-- G-API -->
+                <tab type="usergroup" title="Graph API (G-API) Developer Guide" url="@ref openvino_docs_gapi_gapi_intro">
+                    <tab type="user" title="Introduction to G-API" url="@ref openvino_docs_gapi_gapi_intro"/>
+                    <tab type="user" title="G-API Kernel API" url="@ref openvino_docs_gapi_kernel_api"/>
+                    <tab type="usergroup" title="API Reference" url=""/>
+                        <tab type="user" title="G-API Core functionality" url="https://docs.opencv.org/4.2.0/df/d1f/group__gapi__core.html"/>
+                        <tab type="user" title="G-API Image processing functionality" url="https://docs.opencv.org/4.2.0/d2/d00/group__gapi__imgproc.html"/>
+                        <tab type="user" title="G-API Drawing and composition functionality" url="https://docs.opencv.org/4.2.0/df/de4/group__gapi__draw.html"/>
+                        <tab type="user" title="G-API framework" url="https://docs.opencv.org/4.2.0/d7/d0d/group__gapi.html"/>
+                    </tab>
                 </tab>    
                 <!-- OpenVX -->
                 <tab type="user" title="OpenVX Developer Guide" url="https://software.intel.com/en-us/openvino-ovx-guide"/>

docs/gapi/gapi_intro.md

@@ -1,4 +1,4 @@
-# Introduction to OpenCV Graph API (G-API)
+# Introduction to OpenCV Graph API (G-API) {#openvino_docs_gapi_gapi_intro}
 OpenCV Graph API (G-API) is an OpenCV module targeted to make regular image and video processing fast and portable. G-API is a special module in OpenCV – in contrast with the majority of other main modules, this one acts as a framework rather than some specific CV algorithm. 
 
 G-API is positioned as a next level optimization enabler for computer vision, focusing not on particular CV functions but on the whole algorithm optimization.

docs/get_started/get_started_dl_workbench.md

@@ -136,5 +136,4 @@ For detailed instructions to create a new project, visit the links below:
 * [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
 * [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
-* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
-* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)
+* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)

docs/get_started/get_started_linux.md

@@ -570,4 +570,3 @@ Use these resources to learn more about the OpenVINO™ toolkit:
 * [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
 * [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
-* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)

docs/get_started/get_started_macos.md

@@ -529,4 +529,3 @@ Use these resources to learn more about the OpenVINO™ toolkit:
 * [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
 * [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
-* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)

docs/get_started/get_started_raspbian.md

@@ -106,4 +106,3 @@ Use these resources to learn more about the OpenVINO™ toolkit:
 * [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
 * [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
-* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)

docs/get_started/get_started_windows.md

@@ -537,5 +537,4 @@ Use these resources to learn more about the OpenVINO™ toolkit:
 * [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md)
 * [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 * [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
-* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)
-* [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic)
+* [Overview of OpenVINO™ Toolkit Pre-Trained Models](https://software.intel.com/en-us/openvino-toolkit/documentation/pretrained-models)

docs/install_guides/PAC_Configure_2018R5.md

@@ -236,11 +236,7 @@ classification_sample_async -m squeezenet1.1.xml -i $IE_INSTALL/demo/car.png -d
 classification_sample_async -m squeezenet1.1.xml -i $IE_INSTALL/demo/car.png -d HETERO:FPGA,CPU -ni 100
 ```
 
-Congratulations, You are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and are other resources are provided below.
-
-## Hello World Face Detection Tutorial
-
-Use the  [Intel® Distribution of OpenVINO™ toolkit with FPGA Hello World Face Detection Exercise](https://github.com/fritzboyle/openvino-with-fpga-hello-world-face-detection) to learn more about how the software and hardware work together.
+Congratulations, You are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. 
 
 ## Additional Resources
 

docs/install_guides/PAC_Configure_2019RX.md

@@ -237,12 +237,7 @@ classification_sample_async -m squeezenet1.1.xml -i $IE_INSTALL/demo/car.png
 classification_sample_async -m squeezenet1.1.xml -i $IE_INSTALL/demo/car.png -d HETERO:FPGA,CPU
 ```
 
-Congratulations, You are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and are other resources are provided below.
-
-## Hello World Face Detection Tutorial
-
-Use the  [Intel® Distribution of OpenVINO™ toolkit with FPGA Hello World Face Detection Exercise](https://github.com/fritzboyle/openvino-with-fpga-hello-world-face-detection) to learn more about how the software and hardware work together.
-
+Congratulations, You are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. 
 ## Additional Resources
 
 Intel® Distribution of OpenVINO™ toolkit home page: [https://software.intel.com/en-us/openvino-toolkit](https://software.intel.com/en-us/openvino-toolkit)

docs/install_guides/VisionAcceleratorFPGA_Configure_2018R5.md

@@ -319,11 +319,7 @@ The throughput on FPGA is listed and may show a lower FPS. This is due to the in
 ./classification_sample_async -i car.png -m ~/squeezenet1.1_FP16/squeezenet1.1.xml -d HETERO:FPGA,CPU -ni 100
 ```
 
-Congratulations, you are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and are other resources are provided below.
-
-## Hello World Face Detection Tutorial
-
-Use the [Intel® Distribution of OpenVINO™ toolkit with FPGA Hello World Face Detection Exercise](https://github.com/fritzboyle/openvino-with-fpga-hello-world-face-detection) to learn more about how the software and hardware work together.
+Congratulations, you are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. 
 
 ## Additional Resources
 

docs/install_guides/VisionAcceleratorFPGA_Configure_2019R1.md

@@ -270,11 +270,7 @@ The throughput on FPGA is listed and may show a lower FPS. This is due to the in
 ./classification_sample_async -i car.png -m ~/squeezenet1.1_FP16/squeezenet1.1.xml -d HETERO:FPGA,CPU -ni 100
 ```
 
-Congratulations, you are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and are other resources are provided below.
-
-## Hello World Face Detection Tutorial
-
-Use the [Intel® Distribution of OpenVINO™ toolkit with FPGA Hello World Face Detection Exercise](https://github.com/fritzboyle/openvino-with-fpga-hello-world-face-detection) to learn more about how the software and hardware work together.
+Congratulations, you are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. 
 
 ## Additional Resources
 

docs/install_guides/VisionAcceleratorFPGA_Configure_2019R3.md

@@ -270,11 +270,7 @@ Note the CPU throughput in Frames Per Second (FPS). This tells you how quickly t
 ```
 The throughput on FPGA is listed and may show a lower FPS. This may be due to the initialization time. To account for that, increase the number of iterations or batch size when deploying to get a better sense of the speed the FPGA can run inference at.
 
-Congratulations, you are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and are other resources are provided below.
-
-## Hello World Face Detection Tutorial
-
-Use the [Intel® Distribution of OpenVINO™ toolkit with FPGA Hello World Face Detection Exercise](https://github.com/fritzboyle/openvino-with-fpga-hello-world-face-detection) to learn more about how the software and hardware work together.
+Congratulations, you are done with the Intel® Distribution of OpenVINO™ toolkit installation for FPGA. 
 
 ## Additional Resources
 

docs/install_guides/installing-openvino-apt.md

@@ -129,6 +129,5 @@ sudo apt autoremove intel-openvino-<PACKAGE_TYPE>-ubuntu<OS_VERSION>-<VERSION>.<
 - [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 - [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
 - For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
-- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic).
 - For IoT Libraries & Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
 

docs/install_guides/installing-openvino-conda.md

@@ -49,7 +49,7 @@ Now you can start to develop and run your application.
 ## Known Issues and Limitations
 
 - You cannot use Python bindings included in  Intel® Distribution of OpenVINO™ toolkit  with  [Anaconda* distribution](https://www.anaconda.com/products/individual/)
-- You cannot use Python OpenVINO™ bindings included in Anaconda* package with official  [Python distribution](https://https://www.python.org/).
+- You cannot use Python OpenVINO™ bindings included in Anaconda* package with official  [Python distribution](https://www.python.org/).
 
 
 ## Additional Resources
@@ -59,6 +59,5 @@ Now you can start to develop and run your application.
 - [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 - [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
 - For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
-- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic).
 - Intel® Distribution of OpenVINO™ toolkit Anaconda* home page: [https://anaconda.org/intel/openvino-ie4py](https://anaconda.org/intel/openvino-ie4py)
 

docs/install_guides/installing-openvino-linux.md

@@ -85,7 +85,6 @@ This guide provides step-by-step instructions on how to install the Intel® Dist
 After installing your Intel® Movidius™ VPU, you will return to this guide to complete OpenVINO™ installation.
 9. <a href="#run-a-sample">Run a Sample Application</a>
 10. <a href="#uninstall">Uninstall the Intel® Distribution of OpenVINO™ Toolkit.</a>
-11. <a href="#Hello-World-Face-Detection-Tutorial">Use the Face Detection Tutorial</a>
 
 ## <a name="install-openvino"></a>Install the Intel® Distribution of OpenVINO™ Toolkit Core Components
 
@@ -448,12 +447,6 @@ cd ~/inference_engine_samples_build/intel64/Release
 
 For information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
 
-Congratulations, you have finished the installation of the Intel® Distribution of OpenVINO™ toolkit for Linux*. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and other resources are provided below.
-
-## <a name="Hello-World-Face-Detection-Tutorial"></a>Hello World Face Detection Tutorial
-
-See the [OpenVINO™ Hello World Face Detection Exercise](https://github.com/intel-iot-devkit/inference-tutorials-generic).
-
 ## <a name="uninstall"></a>Uninstall the Intel® Distribution of OpenVINO™ Toolkit
 Choose one of the options provided below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system.
 
@@ -505,7 +498,6 @@ trusted-host = mirrors.aliyun.com
 - [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
 - For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
 - For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
-- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic)
 - For IoT Libraries and Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
 
 To learn more about converting models, go to:

docs/install_guides/installing-openvino-macos.md

@@ -304,10 +304,6 @@ For example, to install the `libusb` library using Homebrew\*, use the following
 brew install libusb
 ```
 
-## <a name="Hello-World-Tutorial"></a>Hello World Tutorials
-
-Visit the Intel Distribution of OpenVINO Toolkit [Inference Tutorials for Face Detection and Car Detection Exercises](https://github.com/intel-iot-devkit/inference-tutorials-generic/tree/openvino_toolkit_r3_0)
-
 ## <a name="uninstall"></a>Uninstall the Intel® Distribution of OpenVINO™ Toolkit
 
 Follow the steps below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system:

docs/install_guides/installing-openvino-windows.md

@@ -459,7 +459,7 @@ cd C:\Users\<username>\Documents\Intel\OpenVINO\inference_engine_samples_build\i
 
 For information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
 
-Congratulations, you have finished the installation of the Intel® Distribution of OpenVINO™ toolkit for Windows*. To learn more about how the Intel® Distribution of OpenVINO™ toolkit works, the Hello World tutorial and other resources are provided below.
+Congratulations, you have finished the installation of the Intel® Distribution of OpenVINO™ toolkit for Windows*.
 
 ## <a name="uninstall"></a>Uninstall the Intel® Distribution of OpenVINO™ Toolkit
 Follow the steps below to uninstall the Intel® Distribution of OpenVINO™ Toolkit from your system:
@@ -491,7 +491,6 @@ To learn more about converting deep learning models, go to:
 - [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md)
 - [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md)
 - [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_intel_index)
-- Intel Distribution of OpenVINO Toolkit Hello World Activities, see the [Inference Tutorials for Face Detection and Car Detection Exercises](https://github.com/intel-iot-devkit/inference-tutorials-generic/tree/openvino_toolkit_r3_0)
 - [Intel® Neural Compute Stick 2 Get Started](https://software.intel.com/en-us/neural-compute-stick/get-started)
 
 

docs/install_guides/installing-openvino-yum.md

@@ -106,6 +106,5 @@ sudo yum autoremove intel-openvino-runtime-centos<OS_VERSION>-<VERSION>.<UPDATE>
 - [Model Optimizer Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
 - [Inference Engine Developer Guide](../IE_DG/Deep_Learning_Inference_Engine_DevGuide.md).
 - For more information on Sample Applications, see the [Inference Engine Samples Overview](../IE_DG/Samples_Overview.md).
-- For information on Inference Engine Tutorials, see the [Inference Tutorials](https://github.com/intel-iot-devkit/inference-tutorials-generic).
 - For IoT Libraries & Code Samples see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).