feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake

1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试
2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程
3.重整权利声明文件，重整代码工程，确保最小化侵权风险

Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake
Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c

3rdparty/opencv-4.5.4/samples/dnn/shrink_tf_graph_weights.py

@@ -0,0 +1,62 @@
+# This file is part of OpenCV project.
+# It is subject to the license terms in the LICENSE file found in the top-level directory
+# of this distribution and at http://opencv.org/license.html.
+#
+# Copyright (C) 2017, Intel Corporation, all rights reserved.
+# Third party copyrights are property of their respective owners.
+import tensorflow as tf
+import struct
+import argparse
+import numpy as np
+
+parser = argparse.ArgumentParser(description='Convert weights of a frozen TensorFlow graph to fp16.')
+parser.add_argument('--input', required=True, help='Path to frozen graph.')
+parser.add_argument('--output', required=True, help='Path to output graph.')
+parser.add_argument('--ops', default=['Conv2D', 'MatMul'], nargs='+',
+                    help='List of ops which weights are converted.')
+args = parser.parse_args()
+
+DT_FLOAT = 1
+DT_HALF = 19
+
+# For the frozen graphs, an every node that uses weights connected to Const nodes
+# through an Identity node. Usually they're called in the same way with '/read' suffix.
+# We'll replace all of them to Cast nodes.
+
+# Load the model
+with tf.gfile.FastGFile(args.input) as f:
+    graph_def = tf.GraphDef()
+    graph_def.ParseFromString(f.read())
+
+# Set of all inputs from desired nodes.
+inputs = []
+for node in graph_def.node:
+    if node.op in args.ops:
+        inputs += node.input
+
+weightsNodes = []
+for node in graph_def.node:
+    # From the whole inputs we need to keep only an Identity nodes.
+    if node.name in inputs and node.op == 'Identity' and node.attr['T'].type == DT_FLOAT:
+        weightsNodes.append(node.input[0])
+
+        # Replace Identity to Cast.
+        node.op = 'Cast'
+        node.attr['DstT'].type = DT_FLOAT
+        node.attr['SrcT'].type = DT_HALF
+        del node.attr['T']
+        del node.attr['_class']
+
+# Convert weights to halfs.
+for node in graph_def.node:
+    if node.name in weightsNodes:
+        node.attr['dtype'].type = DT_HALF
+        node.attr['value'].tensor.dtype = DT_HALF
+
+        floats = node.attr['value'].tensor.tensor_content
+
+        floats = struct.unpack('f' * (len(floats) / 4), floats)
+        halfs = np.array(floats).astype(np.float16).view(np.uint16)
+        node.attr['value'].tensor.tensor_content = struct.pack('H' * len(halfs), *halfs)
+
+tf.train.write_graph(graph_def, "", args.output, as_text=False)