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/action_recognition.py

@@ -0,0 +1,82 @@
+import os
+import numpy as np
+import cv2 as cv
+import argparse
+from common import findFile
+
+parser = argparse.ArgumentParser(description='Use this script to run action recognition using 3D ResNet34',
+                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument('--input', '-i', help='Path to input video file. Skip this argument to capture frames from a camera.')
+parser.add_argument('--model', required=True, help='Path to model.')
+parser.add_argument('--classes', default=findFile('action_recongnition_kinetics.txt'), help='Path to classes list.')
+
+# To get net download original repository https://github.com/kenshohara/video-classification-3d-cnn-pytorch
+# For correct ONNX export modify file: video-classification-3d-cnn-pytorch/models/resnet.py
+# change
+# - def downsample_basic_block(x, planes, stride):
+# -     out = F.avg_pool3d(x, kernel_size=1, stride=stride)
+# -     zero_pads = torch.Tensor(out.size(0), planes - out.size(1),
+# -                              out.size(2), out.size(3),
+# -                              out.size(4)).zero_()
+# -     if isinstance(out.data, torch.cuda.FloatTensor):
+# -         zero_pads = zero_pads.cuda()
+# -
+# -     out = Variable(torch.cat([out.data, zero_pads], dim=1))
+# -     return out
+
+# To
+# + def downsample_basic_block(x, planes, stride):
+# +     out = F.avg_pool3d(x, kernel_size=1, stride=stride)
+# +     out = F.pad(out, (0, 0, 0, 0, 0, 0, 0, int(planes - out.size(1)), 0, 0), "constant", 0)
+# +     return out
+
+# To ONNX export use torch.onnx.export(model, inputs, model_name)
+
+def get_class_names(path):
+    class_names = []
+    with open(path) as f:
+        for row in f:
+            class_names.append(row[:-1])
+    return class_names
+
+def classify_video(video_path, net_path):
+    SAMPLE_DURATION = 16
+    SAMPLE_SIZE = 112
+    mean = (114.7748, 107.7354, 99.4750)
+    class_names = get_class_names(args.classes)
+
+    net = cv.dnn.readNet(net_path)
+    net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE)
+    net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
+
+    winName = 'Deep learning image classification in OpenCV'
+    cv.namedWindow(winName, cv.WINDOW_AUTOSIZE)
+    cap = cv.VideoCapture(video_path)
+    while cv.waitKey(1) < 0:
+        frames = []
+        for _ in range(SAMPLE_DURATION):
+            hasFrame, frame = cap.read()
+            if not hasFrame:
+                exit(0)
+            frames.append(frame)
+
+        inputs = cv.dnn.blobFromImages(frames, 1, (SAMPLE_SIZE, SAMPLE_SIZE), mean, True, crop=True)
+        inputs = np.transpose(inputs, (1, 0, 2, 3))
+        inputs = np.expand_dims(inputs, axis=0)
+        net.setInput(inputs)
+        outputs = net.forward()
+        class_pred = np.argmax(outputs)
+        label = class_names[class_pred]
+
+        for frame in frames:
+            labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+            cv.rectangle(frame, (0, 10 - labelSize[1]),
+                                (labelSize[0], 10 + baseLine), (255, 255, 255), cv.FILLED)
+            cv.putText(frame, label, (0, 10), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
+            cv.imshow(winName, frame)
+        if cv.waitKey(1) & 0xFF == ord('q'):
+            break
+
+if __name__ == "__main__":
+    args, _ = parser.parse_known_args()
+    classify_video(args.input if args.input else 0, args.model)