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/python/peopledetect.py

@@ -0,0 +1,76 @@
+#!/usr/bin/env python
+
+'''
+example to detect upright people in images using HOG features
+
+Usage:
+    peopledetect.py <image_names>
+
+Press any key to continue, ESC to stop.
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+
+
+def inside(r, q):
+    rx, ry, rw, rh = r
+    qx, qy, qw, qh = q
+    return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh
+
+
+def draw_detections(img, rects, thickness = 1):
+    for x, y, w, h in rects:
+        # the HOG detector returns slightly larger rectangles than the real objects.
+        # so we slightly shrink the rectangles to get a nicer output.
+        pad_w, pad_h = int(0.15*w), int(0.05*h)
+        cv.rectangle(img, (x+pad_w, y+pad_h), (x+w-pad_w, y+h-pad_h), (0, 255, 0), thickness)
+
+
+def main():
+    import sys
+    from glob import glob
+    import itertools as it
+
+    hog = cv.HOGDescriptor()
+    hog.setSVMDetector( cv.HOGDescriptor_getDefaultPeopleDetector() )
+
+    default = [cv.samples.findFile('basketball2.png')] if len(sys.argv[1:]) == 0 else []
+
+    for fn in it.chain(*map(glob, default + sys.argv[1:])):
+        print(fn, ' - ',)
+        try:
+            img = cv.imread(fn)
+            if img is None:
+                print('Failed to load image file:', fn)
+                continue
+        except:
+            print('loading error')
+            continue
+
+        found, _w = hog.detectMultiScale(img, winStride=(8,8), padding=(32,32), scale=1.05)
+        found_filtered = []
+        for ri, r in enumerate(found):
+            for qi, q in enumerate(found):
+                if ri != qi and inside(r, q):
+                    break
+            else:
+                found_filtered.append(r)
+        draw_detections(img, found)
+        draw_detections(img, found_filtered, 3)
+        print('%d (%d) found' % (len(found_filtered), len(found)))
+        cv.imshow('img', img)
+        ch = cv.waitKey()
+        if ch == 27:
+            break
+
+    print('Done')
+
+
+if __name__ == '__main__':
+    print(__doc__)
+    main()
+    cv.destroyAllWindows()