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/fast_neural_style.py

@@ -0,0 +1,53 @@
+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+
+parser = argparse.ArgumentParser(
+        description='This script is used to run style transfer models from '
+                    'https://github.com/jcjohnson/fast-neural-style using OpenCV')
+parser.add_argument('--input', help='Path to image or video. Skip to capture frames from camera')
+parser.add_argument('--model', help='Path to .t7 model')
+parser.add_argument('--width', default=-1, type=int, help='Resize input to specific width.')
+parser.add_argument('--height', default=-1, type=int, help='Resize input to specific height.')
+parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of postprocessing blurring.')
+args = parser.parse_args()
+
+net = cv.dnn.readNetFromTorch(cv.samples.findFile(args.model))
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
+
+if args.input:
+    cap = cv.VideoCapture(args.input)
+else:
+    cap = cv.VideoCapture(0)
+
+cv.namedWindow('Styled image', cv.WINDOW_NORMAL)
+while cv.waitKey(1) < 0:
+    hasFrame, frame = cap.read()
+    if not hasFrame:
+        cv.waitKey()
+        break
+
+    inWidth = args.width if args.width != -1 else frame.shape[1]
+    inHeight = args.height if args.height != -1 else frame.shape[0]
+    inp = cv.dnn.blobFromImage(frame, 1.0, (inWidth, inHeight),
+                              (103.939, 116.779, 123.68), swapRB=False, crop=False)
+
+    net.setInput(inp)
+    out = net.forward()
+
+    out = out.reshape(3, out.shape[2], out.shape[3])
+    out[0] += 103.939
+    out[1] += 116.779
+    out[2] += 123.68
+    out /= 255
+    out = out.transpose(1, 2, 0)
+
+    t, _ = net.getPerfProfile()
+    freq = cv.getTickFrequency() / 1000
+    print(t / freq, 'ms')
+
+    if args.median_filter:
+        out = cv.medianBlur(out, args.median_filter)
+
+    cv.imshow('Styled image', out)