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/face_match.py

@@ -0,0 +1,57 @@
+import argparse
+
+import numpy as np
+import cv2 as cv
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input1', '-i1', type=str, help='Path to the input image1.')
+parser.add_argument('--input2', '-i2', type=str, help='Path to the input image2.')
+parser.add_argument('--face_detection_model', '-fd', type=str, help='Path to the face detection model. Download the model at https://github.com/ShiqiYu/libfacedetection.train/tree/master/tasks/task1/onnx.')
+parser.add_argument('--face_recognition_model', '-fr', type=str, help='Path to the face recognition model. Download the model at https://drive.google.com/file/d/1ClK9WiB492c5OZFKveF3XiHCejoOxINW/view.')
+args = parser.parse_args()
+
+# Read the input image
+img1 = cv.imread(args.input1)
+img2 = cv.imread(args.input2)
+
+# Instantiate face detector and recognizer
+detector = cv.FaceDetectorYN.create(
+    args.face_detection_model,
+    "",
+    (img1.shape[1], img1.shape[0])
+)
+recognizer = cv.FaceRecognizerSF.create(
+    args.face_recognition_model,
+    ""
+)
+
+# Detect face
+detector.setInputSize((img1.shape[1], img1.shape[0]))
+face1 = detector.detect(img1)
+detector.setInputSize((img2.shape[1], img2.shape[0]))
+face2 = detector.detect(img2)
+assert face1[1].shape[0] > 0, 'Cannot find a face in {}'.format(args.input1)
+assert face2[1].shape[0] > 0, 'Cannot find a face in {}'.format(args.input2)
+
+# Align faces
+face1_align = recognizer.alignCrop(img1, face1[1][0])
+face2_align = recognizer.alignCrop(img2, face2[1][0])
+
+# Extract features
+face1_feature = recognizer.faceFeature(face1_align)
+face2_feature = recognizer.faceFeature(face2_align)
+
+# Calculate distance (0: cosine, 1: L2)
+cosine_similarity_threshold = 0.363
+cosine_score = recognizer.faceMatch(face1_feature, face2_feature, 0)
+msg = 'different identities'
+if cosine_score >= cosine_similarity_threshold:
+    msg = 'the same identity'
+print('They have {}. Cosine Similarity: {}, threshold: {} (higher value means higher similarity, max 1.0).'.format(msg, cosine_score, cosine_similarity_threshold))
+
+l2_similarity_threshold = 1.128
+l2_score = recognizer.faceMatch(face1_feature, face2_feature, 1)
+msg = 'different identities'
+if l2_score <= l2_similarity_threshold:
+    msg = 'the same identity'
+print('They have {}. NormL2 Distance: {}, threshold: {} (lower value means higher similarity, min 0.0).'.format(msg, l2_score, l2_similarity_threshold))