[Filter/Custom-Easy-C] Prototype of Custom-Easy Tensor Function

This allows to define a custom-filter (C) easily.
This will be especially helpful for C/C++ application writers.
The same approach may be implemented for C#/Java for Tizen.NET and Android app writers.

Change-Id: Id1ff43424283c217c0688e91b919ade99e3cc134
Signed-off-by: MyungJoo Ham <myungjoo.ham@samsung.com>

diff --git a/gst/nnstreamer/tensor_filter_custom_easy.h b/gst/nnstreamer/tensor_filter_custom_easy.h
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+/**
+ * GStreamer Tensor_Filter, Customized Module, Easy Mode
+ * Copyright (C) 2019 MyungJoo Ham <myungjoo.ham@samsung.com>
+ *
+ * This library is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Library General Public
+ * License as published by the Free Software Foundation; either
+ * version 2 of the License, or (at your option) any later version.
+ *
+ * This library is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Library General Public License for more details.
+ *
+ */
+/**
+ * @file       tensor_filter_custom_easy.h
+ * @date       24 Oct 2019
+ * @brief      Custom tensor processing interface for simple functions
+ * @see                http://github.com/nnsuite/nnstreamer
+ * @author     MyungJoo Ham <myungjoo.ham@samsung.com>
+ * @bug                No known bugs except for NYI items
+ *
+ * How To for NNdevelopers:
+ *
+ * Case 1. Provide the function as a shared object for other apps.
+ * 1. Define struct, "NNStreamer_custom_easy", with the functions defined.
+ * 2. Call NNS_custom_easy_register() at a global init/constructor function.
+ * 3. Compile as a shared object. (.so in Linux) And install at the custom-filter path.
+ * 4. Use NNStreamer (tensor_filter framework=custom_easy, model=${modelname}, custom=${_FILEPATH_OF_YOUR_SO} ...)
+ * 5. Note that you may register multiple models (functions) with a single .so.
+ *
+ * Case 2. Define the function in the app.
+ * 1. Define struct, "NNStreamer_custom_easy", with the functions defined.
+ * 2. Register the struct with "NNS_custom_easy_register" API.
+ * 3. Construct the nnstreamer pipeline and execute it in the app.
+ *
+ * Note that this does not support flexible dimensions.
+ *
+ * To Packagers:
+ *
+ * This file is to be packaged as "devel" package for NN developers.
+ */
+#ifndef __NNS_TENSOR_FILTER_CUSTOM_EASY_H__
+#define __NNS_TENSOR_FILTER_CUSTOM_EASY_H__
+
+#include <stdint.h>
+#include "tensor_typedef.h"
+#include "tensor_filter_custom.h"
+
+/**
+ * @brief Register the custom-easy tensor function.
+ * @param[in] modelname The name of custom-easy tensor function.
+ * @param[in] func The tensor function body
+ * @param[in/out] private_data The internal data for the function
+ * @param[in] in_info Input tensor metadata.
+ * @param[out] out_info Output tensor metadata
+ * @note NNS_custom_invoke defined in tensor_filter_custom.h
+ *       Output buffers for func are preallocated.
+ */
+int NNS_custom_easy_register (const char *modelname,
+    NNS_custom_invoke func, void *private_data,
+    const GstTensorsInfo *in_info, const GstTensorsInfo *out_info);
+
+#endif /*__NNS_TENSOR_FILTER_CUSTOM_EASY_H__*/