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/mask_rcnn.py

@@ -0,0 +1,143 @@
+import cv2 as cv
+import argparse
+import numpy as np
+
+parser = argparse.ArgumentParser(description=
+        'Use this script to run Mask-RCNN object detection and semantic '
+        'segmentation network from TensorFlow Object Detection API.')
+parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera.')
+parser.add_argument('--model', required=True, help='Path to a .pb file with weights.')
+parser.add_argument('--config', required=True, help='Path to a .pxtxt file contains network configuration.')
+parser.add_argument('--classes', help='Optional path to a text file with names of classes.')
+parser.add_argument('--colors', help='Optional path to a text file with colors for an every class. '
+                                     'An every color is represented with three values from 0 to 255 in BGR channels order.')
+parser.add_argument('--width', type=int, default=800,
+                    help='Preprocess input image by resizing to a specific width.')
+parser.add_argument('--height', type=int, default=800,
+                    help='Preprocess input image by resizing to a specific height.')
+parser.add_argument('--thr', type=float, default=0.5, help='Confidence threshold')
+args = parser.parse_args()
+
+np.random.seed(324)
+
+# Load names of classes
+classes = None
+if args.classes:
+    with open(args.classes, 'rt') as f:
+        classes = f.read().rstrip('\n').split('\n')
+
+# Load colors
+colors = None
+if args.colors:
+    with open(args.colors, 'rt') as f:
+        colors = [np.array(color.split(' '), np.uint8) for color in f.read().rstrip('\n').split('\n')]
+
+legend = None
+def showLegend(classes):
+    global legend
+    if not classes is None and legend is None:
+        blockHeight = 30
+        assert(len(classes) == len(colors))
+
+        legend = np.zeros((blockHeight * len(colors), 200, 3), np.uint8)
+        for i in range(len(classes)):
+            block = legend[i * blockHeight:(i + 1) * blockHeight]
+            block[:,:] = colors[i]
+            cv.putText(block, classes[i], (0, blockHeight//2), cv.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255))
+
+        cv.namedWindow('Legend', cv.WINDOW_NORMAL)
+        cv.imshow('Legend', legend)
+        classes = None
+
+
+def drawBox(frame, classId, conf, left, top, right, bottom):
+    # Draw a bounding box.
+    cv.rectangle(frame, (left, top), (right, bottom), (0, 255, 0))
+
+    label = '%.2f' % conf
+
+    # Print a label of class.
+    if classes:
+        assert(classId < len(classes))
+        label = '%s: %s' % (classes[classId], label)
+
+    labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+    top = max(top, labelSize[1])
+    cv.rectangle(frame, (left, top - labelSize[1]), (left + labelSize[0], top + baseLine), (255, 255, 255), cv.FILLED)
+    cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))
+
+
+# Load a network
+net = cv.dnn.readNet(cv.samples.findFile(args.model), cv.samples.findFile(args.config))
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
+
+winName = 'Mask-RCNN in OpenCV'
+cv.namedWindow(winName, cv.WINDOW_NORMAL)
+
+cap = cv.VideoCapture(cv.samples.findFileOrKeep(args.input) if args.input else 0)
+legend = None
+while cv.waitKey(1) < 0:
+    hasFrame, frame = cap.read()
+    if not hasFrame:
+        cv.waitKey()
+        break
+
+    frameH = frame.shape[0]
+    frameW = frame.shape[1]
+
+    # Create a 4D blob from a frame.
+    blob = cv.dnn.blobFromImage(frame, size=(args.width, args.height), swapRB=True, crop=False)
+
+    # Run a model
+    net.setInput(blob)
+
+    boxes, masks = net.forward(['detection_out_final', 'detection_masks'])
+
+    numClasses = masks.shape[1]
+    numDetections = boxes.shape[2]
+
+    # Draw segmentation
+    if not colors:
+        # Generate colors
+        colors = [np.array([0, 0, 0], np.uint8)]
+        for i in range(1, numClasses + 1):
+            colors.append((colors[i - 1] + np.random.randint(0, 256, [3], np.uint8)) / 2)
+        del colors[0]
+
+    boxesToDraw = []
+    for i in range(numDetections):
+        box = boxes[0, 0, i]
+        mask = masks[i]
+        score = box[2]
+        if score > args.thr:
+            classId = int(box[1])
+            left = int(frameW * box[3])
+            top = int(frameH * box[4])
+            right = int(frameW * box[5])
+            bottom = int(frameH * box[6])
+
+            left = max(0, min(left, frameW - 1))
+            top = max(0, min(top, frameH - 1))
+            right = max(0, min(right, frameW - 1))
+            bottom = max(0, min(bottom, frameH - 1))
+
+            boxesToDraw.append([frame, classId, score, left, top, right, bottom])
+
+            classMask = mask[classId]
+            classMask = cv.resize(classMask, (right - left + 1, bottom - top + 1))
+            mask = (classMask > 0.5)
+
+            roi = frame[top:bottom+1, left:right+1][mask]
+            frame[top:bottom+1, left:right+1][mask] = (0.7 * colors[classId] + 0.3 * roi).astype(np.uint8)
+
+    for box in boxesToDraw:
+        drawBox(*box)
+
+    # Put efficiency information.
+    t, _ = net.getPerfProfile()
+    label = 'Inference time: %.2f ms' % (t * 1000.0 / cv.getTickFrequency())
+    cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
+
+    showLegend(classes)
+
+    cv.imshow(winName, frame)