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/modules/python/test/test_kmeans.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python
+
+'''
+K-means clusterization test
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+from numpy import random
+import sys
+PY3 = sys.version_info[0] == 3
+if PY3:
+    xrange = range
+
+from tests_common import NewOpenCVTests
+
+def make_gaussians(cluster_n, img_size):
+    points = []
+    ref_distrs = []
+    sizes = []
+    for _ in xrange(cluster_n):
+        mean = (0.1 + 0.8*random.rand(2)) * img_size
+        a = (random.rand(2, 2)-0.5)*img_size*0.1
+        cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
+        n = 100 + random.randint(900)
+        pts = random.multivariate_normal(mean, cov, n)
+        points.append( pts )
+        ref_distrs.append( (mean, cov) )
+        sizes.append(n)
+    points = np.float32( np.vstack(points) )
+    return points, ref_distrs, sizes
+
+def getMainLabelConfidence(labels, nLabels):
+
+    n = len(labels)
+    labelsDict = dict.fromkeys(range(nLabels), 0)
+    labelsConfDict = dict.fromkeys(range(nLabels))
+
+    for i in range(n):
+        labelsDict[labels[i][0]] += 1
+
+    for i in range(nLabels):
+        labelsConfDict[i] = float(labelsDict[i]) / n
+
+    return max(labelsConfDict.values())
+
+class kmeans_test(NewOpenCVTests):
+
+    def test_kmeans(self):
+
+        np.random.seed(10)
+
+        cluster_n = 5
+        img_size = 512
+
+        points, _, clusterSizes = make_gaussians(cluster_n, img_size)
+
+        term_crit = (cv.TERM_CRITERIA_EPS, 30, 0.1)
+        _ret, labels, centers = cv.kmeans(points, cluster_n, None, term_crit, 10, 0)
+
+        self.assertEqual(len(centers), cluster_n)
+
+        offset = 0
+        for i in range(cluster_n):
+            confidence = getMainLabelConfidence(labels[offset : (offset + clusterSizes[i])], cluster_n)
+            offset += clusterSizes[i]
+            self.assertGreater(confidence, 0.9)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()