[OpenVINO-EP] V3.4 update samples (#59)

* Created symlink for synset.txt

* Added IO buffer sample for Linux and updated README

* Changed the font family to FreeMono

* Alignment correction

* Updated .net version to 5.0

* Updated README

* Use OS specific font in csharp sample

* Update the prerequisites for csharp sample

* Remove creating CPU folder part

* Added Requirements.txt file for tinyyolov2 sample

Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>

* Updated Readme

Signed-off-by: MaajidKhan <n.maajidkhan@gmail.com>

Co-authored-by: Aravind Gunda <aravindx.gunda@intel.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>

c_cxx/OpenVINO_EP/Linux/squeezenet_classification/README.md

@@ -4,16 +4,16 @@
 
 2. The sample involves presenting an image to the ONNX Runtime (RT), which uses the OpenVINO Execution Provider for ONNX RT to run inference on various Intel hardware devices like Intel CPU, GPU, VPU and more. The sample uses OpenCV for image processing and ONNX Runtime OpenVINO EP for inference. After the sample image is inferred, the terminal will output the predicted label classes in order of their confidence.
 
-The source code for this sample is available [here](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/c_cxx/OpenVINO_EP/squeezenet_classification).
+The source code for this sample is available [here](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/c_cxx/OpenVINO_EP/Linux/squeezenet_classification).
 
 # How to build
 
 ## Prerequisites
 1. [The Intel<sup>®</sup> Distribution of OpenVINO toolkit](https://docs.openvinotoolkit.org/latest/index.html)
-
 2. Use opencv (use the same opencv package that comes builtin with Intel<sup>®</sup> Distribution of OpenVINO toolkit)
-3. Use any sample image as input to the sample.
-4. Download the latest Squeezenet model from the ONNX Model Zoo.
+3. Use opencl for IO buffer sample (squeezenet_cpp_app_io.cpp).
+4. Use any sample image as input to the sample.
+5. Download the latest Squeezenet model from the ONNX Model Zoo.
    This example was adapted from [ONNX Model Zoo](https://github.com/onnx/models).Download the latest version of the [Squeezenet](https://github.com/onnx/models/tree/master/vision/classification/squeezenet) model from here.
 
 
@@ -25,7 +25,11 @@ The source code for this sample is available [here](https://github.com/microsoft
 ## Reference Documentation
 [Documentation](https://onnxruntime.ai/docs/execution-providers/OpenVINO-ExecutionProvider.html)
 
-If you build it by yourself, you must append the "--build_shared_lib" flag to your build command.
+If you build it by yourself, you must append the "--build_shared_lib" flag to your build command. Also for running the sample with IO Buffer optimization feature, make sure you set the OpenCL paths. For example if you are setting the path from openvino source build folder, the paths will be like:
+```
+export OPENCL_LIBS=path/to/your/directory/openvino/bin/intel64/Debug/lib/
+export OPENCL_INCS=path/to/your/directory/openvino/thirdparty/ocl/clhpp_headers/include/
+```
 
 ```
 ./build.sh --config Release --use_openvino CPU_FP32 --build_shared_lib
@@ -37,26 +41,43 @@ If you build it by yourself, you must append the "--build_shared_lib" flag to yo
 2. Now copy all the files required to run this sample at this same location (/onnxruntime/build/Linux/Release/)
 
 3. compile the sample
-```
-g++ -o run_squeezenet squeezenet_cpp_app.cpp -I ../../../include/onnxruntime/core/session/ -I /opt/intel/openvino_2021.4.689/opencv/include/ -I /opt/intel/openvino_2021.4.689/opencv/lib/ -L ./ -lonnxruntime_providers_openvino -lonnxruntime_providers_shared -lonnxruntime -L /opt/intel/openvino_2021.4.689/opencv/lib/ -lopencv_imgcodecs -lopencv_dnn -lopencv_core -lopencv_imgproc
-```
-Note: This build command is using the opencv location from OpenVINO 2021.3 Release Installation. You can use any version of OpenVINO and change the location path accordingly.
+
+   - For general sample
+      ```
+      g++ -o run_squeezenet squeezenet_cpp_app.cpp -I ../../../include/onnxruntime/core/session/ -I /opt/intel/openvino_2021.4.752/opencv/include/ -I /opt/intel/openvino_2021.4.752/opencv/lib/ -L ./ -lonnxruntime_providers_openvino -lonnxruntime_providers_shared -lonnxruntime -L /opt/intel/openvino_2021.4.752/opencv/lib/ -lopencv_imgcodecs -lopencv_dnn -lopencv_core -lopencv_imgproc
+      ```
+      Note: This build command is using the opencv location from OpenVINO 2021.4.2 Release Installation. You can use any version of OpenVINO and change the location path accordingly.
+
+   - For the sample using IO Buffer Optimization feature
+      Set the OpenCL lib and headers path. For example if you are setting the path from openvino source build folder, the paths will be like:
+      ```
+      export OPENCL_LIBS=path/to/your/directory/openvino/bin/intel64/Debug/lib/
+      export OPENCL_INCS=path/to/your/directory/openvino/thirdparty/ocl/clhpp_headers/include/
+      ```
+      Now based on the above path, compile command will be:
+      ```
+      g++ -o run_squeezenet squeezenet_cpp_app_io.cpp -I ../../../include/onnxruntime/core/session/ -I $OPENCL_INCS -I $OPENCL_INCS/../../cl_headers/ -I /opt/intel/openvino_2021.4.752/opencv/include/ -I /opt/intel/openvino_2021.4.752/opencv/lib/ -L ./ -lonnxruntime_providers_openvino -lonnxruntime_providers_shared -lonnxruntime -L /opt/intel/openvino_2021.4.752/opencv/lib/ -lopencv_imgcodecs -lopencv_dnn -lopencv_core -lopencv_imgproc -L $OPENCL_LIBS -lOpenCL
+      ```
 
 4. Run the sample
 
-To Run
-(using Intel OpenVINO-EP)
-```
-./run_squeezenet --use_openvino <path_to_onnx_model> <path_to_sample_image> <path_to_labels_file>
-```
-Example:
-```
-./run_squeezenet --use_openvino squeezenet1.1-7.onnx demo.jpeg synset.txt  (using Intel OpenVINO-EP)
-```
-(using Default CPU)
-```
-./run_squeezenet --use_cpu <path_to_onnx_model> <path_to_sample_image> <path_to_labels_file>
-```
+   - To Run the general sample
+      (using Intel OpenVINO-EP)
+      ```
+      ./run_squeezenet --use_openvino <path_to_onnx_model> <path_to_sample_image> <path_to_labels_file>
+      ```
+      Example:
+      ```
+      ./run_squeezenet --use_openvino squeezenet1.1-7.onnx demo.jpeg synset.txt  (using Intel OpenVINO-EP)
+      ```
+      (using Default CPU)
+      ```
+      ./run_squeezenet --use_cpu <path_to_onnx_model> <path_to_sample_image> <path_to_labels_file>
+      ```
+   - To Run the sample for IO Buffer Optimization feature
+      ```
+      ./run_squeezenet <path_to_onnx_model> <path_to_sample_image> <path_to_labels_file>
+      ```
 
 ## References: