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/objdetect/misc/java/test/CascadeClassifierTest.java

@@ -0,0 +1,104 @@
+package org.opencv.test.objdetect;
+
+import org.opencv.core.Mat;
+import org.opencv.core.MatOfRect;
+import org.opencv.core.Size;
+import org.opencv.imgproc.Imgproc;
+import org.opencv.objdetect.CascadeClassifier;
+import org.opencv.objdetect.Objdetect;
+import org.opencv.test.OpenCVTestCase;
+import org.opencv.test.OpenCVTestRunner;
+
+public class CascadeClassifierTest extends OpenCVTestCase {
+
+    private CascadeClassifier cc;
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+
+        cc = null;
+    }
+
+    public void testCascadeClassifier() {
+        cc = new CascadeClassifier();
+        assertNotNull(cc);
+    }
+
+    public void testCascadeClassifierString() {
+        cc = new CascadeClassifier(OpenCVTestRunner.LBPCASCADE_FRONTALFACE_PATH);
+        assertNotNull(cc);
+    }
+
+    public void testDetectMultiScaleMatListOfRect() {
+        CascadeClassifier cc = new CascadeClassifier(OpenCVTestRunner.LBPCASCADE_FRONTALFACE_PATH);
+        MatOfRect faces = new MatOfRect();
+
+        Mat greyLena = new Mat();
+        Imgproc.cvtColor(rgbLena, greyLena, Imgproc.COLOR_RGB2GRAY);
+        Imgproc.equalizeHist(greyLena, greyLena);
+
+        cc.detectMultiScale(greyLena, faces, 1.1, 3, Objdetect.CASCADE_SCALE_IMAGE, new Size(30, 30), new Size());
+        assertEquals(1, faces.total());
+    }
+
+    public void testDetectMultiScaleMatListOfRectDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleIntInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleIntIntSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleIntIntSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleIntInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleIntIntSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleIntIntSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfIntegerListOfDoubleDoubleIntIntSizeSizeBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testEmpty() {
+        cc = new CascadeClassifier();
+        assertTrue(cc.empty());
+    }
+
+    public void testLoad() {
+        cc = new CascadeClassifier();
+        cc.load(OpenCVTestRunner.LBPCASCADE_FRONTALFACE_PATH);
+        assertFalse(cc.empty());
+    }
+
+}

3rdparty/opencv-4.5.4/modules/objdetect/misc/java/test/HOGDescriptorTest.java

@@ -0,0 +1,259 @@
+package org.opencv.test.objdetect;
+
+import org.opencv.objdetect.HOGDescriptor;
+import org.opencv.test.OpenCVTestCase;
+
+public class HOGDescriptorTest extends OpenCVTestCase {
+
+    public void testCheckDetectorSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeGradientMatMatMat() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeGradientMatMatMatSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeGradientMatMatMatSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfFloat() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfFloatSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfFloatSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testComputeMatListOfFloatSizeSizeListOfPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointDoubleSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointDoubleSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointDoubleSizeSizeListOfPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDoubleDoubleSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDoubleDoubleSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMatListOfPointListOfDoubleDoubleSizeSizeListOfPoint() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRect() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSizeSizeDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSizeSizeDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectDoubleSizeSizeDoubleDoubleBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSizeSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSizeSizeDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSizeSizeDoubleDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testDetectMultiScaleMatListOfRectListOfDoubleDoubleSizeSizeDoubleDoubleBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_blockSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_blockStride() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_cellSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_derivAperture() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_gammaCorrection() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_histogramNormType() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_L2HysThreshold() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_nbins() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_nlevels() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_svmDetector() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_winSigma() {
+        fail("Not yet implemented");
+    }
+
+    public void testGet_winSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testGetDaimlerPeopleDetector() {
+        fail("Not yet implemented");
+    }
+
+    public void testGetDefaultPeopleDetector() {
+        fail("Not yet implemented");
+    }
+
+    public void testGetDescriptorSize() {
+        fail("Not yet implemented");
+    }
+
+    public void testGetWinSigma() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptor() {
+        HOGDescriptor hog = new HOGDescriptor();
+
+        assertNotNull(hog);
+        assertEquals(HOGDescriptor.DEFAULT_NLEVELS, hog.get_nlevels());
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDoubleInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDoubleIntDouble() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDoubleIntDoubleBoolean() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorSizeSizeSizeSizeIntIntDoubleIntDoubleBooleanInt() {
+        fail("Not yet implemented");
+    }
+
+    public void testHOGDescriptorString() {
+        fail("Not yet implemented");
+    }
+
+    public void testLoadString() {
+        fail("Not yet implemented");
+    }
+
+    public void testLoadStringString() {
+        fail("Not yet implemented");
+    }
+
+    public void testSaveString() {
+        fail("Not yet implemented");
+    }
+
+    public void testSaveStringString() {
+        fail("Not yet implemented");
+    }
+
+    public void testSetSVMDetector() {
+        fail("Not yet implemented");
+    }
+
+}

3rdparty/opencv-4.5.4/modules/objdetect/misc/java/test/ObjdetectTest.java

@@ -0,0 +1,42 @@
+package org.opencv.test.objdetect;
+
+import org.opencv.test.OpenCVTestCase;
+
+public class ObjdetectTest extends OpenCVTestCase {
+
+    public void testGroupRectanglesListOfRectListOfIntegerInt() {
+        fail("Not yet implemented");
+        /*
+        final int NUM = 10;
+        MatOfRect rects = new MatOfRect();
+        rects.alloc(NUM);
+
+        for (int i = 0; i < NUM; i++)
+            rects.put(i, 0, 10, 10, 20, 20);
+
+        int groupThreshold = 1;
+        Objdetect.groupRectangles(rects, null, groupThreshold);//TODO: second parameter should not be null
+        assertEquals(1, rects.total());
+        */
+    }
+
+    public void testGroupRectanglesListOfRectListOfIntegerIntDouble() {
+        fail("Not yet implemented");
+        /*
+        final int NUM = 10;
+        MatOfRect rects = new MatOfRect();
+        rects.alloc(NUM);
+
+        for (int i = 0; i < NUM; i++)
+            rects.put(i, 0, 10, 10, 20, 20);
+
+        for (int i = 0; i < NUM; i++)
+            rects.put(i, 0, 10, 10, 25, 25);
+
+        int groupThreshold = 1;
+        double eps = 0.2;
+        Objdetect.groupRectangles(rects, null, groupThreshold, eps);//TODO: second parameter should not be null
+        assertEquals(2, rects.size());
+        */
+    }
+}

3rdparty/opencv-4.5.4/modules/objdetect/misc/java/test/QRCodeDetectorTest.java

@@ -0,0 +1,48 @@
+package org.opencv.test.objdetect;
+
+import java.util.List;
+import org.opencv.core.Mat;
+import org.opencv.objdetect.QRCodeDetector;
+import org.opencv.imgcodecs.Imgcodecs;
+import org.opencv.test.OpenCVTestCase;
+import java.util.Arrays;
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+
+public class QRCodeDetectorTest extends OpenCVTestCase {
+
+    private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";
+    private String testDataPath;
+
+    @Override
+    protected void setUp() throws Exception {
+        super.setUp();
+
+        testDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);
+        if (testDataPath == null)
+            throw new Exception(ENV_OPENCV_TEST_DATA_PATH + " has to be defined!");
+    }
+
+    public void testDetectAndDecode() {
+        Mat img = Imgcodecs.imread(testDataPath + "/cv/qrcode/link_ocv.jpg");
+        assertFalse(img.empty());
+        QRCodeDetector detector = new QRCodeDetector();
+        assertNotNull(detector);
+        String output = detector.detectAndDecode(img);
+        assertEquals(output, "https://opencv.org/");
+    }
+
+    public void testDetectAndDecodeMulti() {
+        Mat img = Imgcodecs.imread(testDataPath + "/cv/qrcode/multiple/6_qrcodes.png");
+        assertFalse(img.empty());
+        QRCodeDetector detector = new QRCodeDetector();
+        assertNotNull(detector);
+        List < String > output = new ArrayList< String >();
+        boolean result = detector.detectAndDecodeMulti(img, output);
+        assertTrue(result);
+        assertEquals(output.size(), 6);
+        List < String > expectedResults = Arrays.asList("SKIP", "EXTRA", "TWO STEPS FORWARD", "STEP BACK", "QUESTION", "STEP FORWARD");
+        assertEquals(new HashSet<String>(output), new HashSet<String>(expectedResults));
+    }
+}

3rdparty/opencv-4.5.4/modules/objdetect/misc/python/pyopencv_objdetect.hpp

@@ -0,0 +1,8 @@
+#ifdef HAVE_OPENCV_OBJDETECT
+
+#include "opencv2/objdetect.hpp"
+
+typedef HOGDescriptor::HistogramNormType HOGDescriptor_HistogramNormType;
+typedef HOGDescriptor::DescriptorStorageFormat HOGDescriptor_DescriptorStorageFormat;
+
+#endif

3rdparty/opencv-4.5.4/modules/objdetect/misc/python/test/test_facedetect.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python
+
+'''
+face detection using haar cascades
+'''
+
+# Python 2/3 compatibility
+from __future__ import print_function
+
+import numpy as np
+import cv2 as cv
+
+def detect(img, cascade):
+    rects = cascade.detectMultiScale(img, scaleFactor=1.275, minNeighbors=4, minSize=(30, 30),
+                                     flags=cv.CASCADE_SCALE_IMAGE)
+    if len(rects) == 0:
+        return []
+    rects[:,2:] += rects[:,:2]
+    return rects
+
+from tests_common import NewOpenCVTests, intersectionRate
+
+class facedetect_test(NewOpenCVTests):
+
+    def test_facedetect(self):
+        cascade_fn = self.repoPath + '/data/haarcascades/haarcascade_frontalface_alt.xml'
+        nested_fn  = self.repoPath + '/data/haarcascades/haarcascade_eye.xml'
+
+        cascade = cv.CascadeClassifier(cascade_fn)
+        nested = cv.CascadeClassifier(nested_fn)
+
+        samples = ['samples/data/lena.jpg', 'cv/cascadeandhog/images/mona-lisa.png']
+
+        faces = []
+        eyes = []
+
+        testFaces = [
+        #lena
+        [[218, 200, 389, 371],
+        [ 244, 240, 294, 290],
+        [ 309, 246, 352, 289]],
+
+        #lisa
+        [[167, 119, 307, 259],
+        [188, 153, 229, 194],
+        [236, 153, 277, 194]]
+        ]
+
+        for sample in samples:
+
+            img = self.get_sample(  sample)
+            gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+            gray = cv.GaussianBlur(gray, (5, 5), 0)
+
+            rects = detect(gray, cascade)
+            faces.append(rects)
+
+            if not nested.empty():
+                for x1, y1, x2, y2 in rects:
+                    roi = gray[y1:y2, x1:x2]
+                    subrects = detect(roi.copy(), nested)
+
+                    for rect in subrects:
+                        rect[0] += x1
+                        rect[2] += x1
+                        rect[1] += y1
+                        rect[3] += y1
+
+                    eyes.append(subrects)
+
+        faces_matches = 0
+        eyes_matches = 0
+
+        eps = 0.8
+
+        for i in range(len(faces)):
+            for j in range(len(testFaces)):
+                if intersectionRate(faces[i][0], testFaces[j][0]) > eps:
+                    faces_matches += 1
+                    #check eyes
+                    if len(eyes[i]) == 2:
+                        if intersectionRate(eyes[i][0], testFaces[j][1]) > eps and intersectionRate(eyes[i][1] , testFaces[j][2]) > eps:
+                            eyes_matches += 1
+                        elif intersectionRate(eyes[i][1], testFaces[j][1]) > eps and intersectionRate(eyes[i][0], testFaces[j][2]) > eps:
+                            eyes_matches += 1
+
+        self.assertEqual(faces_matches, 2)
+        self.assertEqual(eyes_matches, 2)
+
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

3rdparty/opencv-4.5.4/modules/objdetect/misc/python/test/test_peopledetect.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python
+
+'''
+example to detect upright people in images using HOG features
+'''
+
+# 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
+
+from tests_common import NewOpenCVTests, intersectionRate
+
+class peopledetect_test(NewOpenCVTests):
+    def test_peopledetect(self):
+
+        hog = cv.HOGDescriptor()
+        hog.setSVMDetector( cv.HOGDescriptor_getDefaultPeopleDetector() )
+
+        dirPath = 'samples/data/'
+        samples = ['basketball1.png', 'basketball2.png']
+
+        testPeople = [
+        [[23, 76, 164, 477], [440, 22, 637, 478]],
+        [[23, 76, 164, 477], [440, 22, 637, 478]]
+        ]
+
+        eps = 0.5
+
+        for sample in samples:
+
+            img = self.get_sample(dirPath + sample, 0)
+
+            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)
+
+            matches = 0
+
+            for i in range(len(found_filtered)):
+                for j in range(len(testPeople)):
+
+                    found_rect = (found_filtered[i][0], found_filtered[i][1],
+                        found_filtered[i][0] + found_filtered[i][2],
+                        found_filtered[i][1] + found_filtered[i][3])
+
+                    if intersectionRate(found_rect, testPeople[j][0]) > eps or intersectionRate(found_rect, testPeople[j][1]) > eps:
+                        matches += 1
+
+            self.assertGreater(matches, 0)
+
+if __name__ == '__main__':
+    NewOpenCVTests.bootstrap()

3rdparty/opencv-4.5.4/modules/objdetect/misc/python/test/test_qrcode_detect.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python
+'''
+===============================================================================
+QR code detect and decode pipeline.
+===============================================================================
+'''
+import os
+import numpy as np
+import cv2 as cv
+
+from tests_common import NewOpenCVTests
+
+class qrcode_detector_test(NewOpenCVTests):
+
+    def test_detect(self):
+        img = cv.imread(os.path.join(self.extraTestDataPath, 'cv/qrcode/link_ocv.jpg'))
+        self.assertFalse(img is None)
+        detector = cv.QRCodeDetector()
+        retval, points = detector.detect(img)
+        self.assertTrue(retval)
+        self.assertEqual(points.shape, (1, 4, 2))
+
+    def test_detect_and_decode(self):
+        img = cv.imread(os.path.join(self.extraTestDataPath, 'cv/qrcode/link_ocv.jpg'))
+        self.assertFalse(img is None)
+        detector = cv.QRCodeDetector()
+        retval, points, straight_qrcode = detector.detectAndDecode(img)
+        self.assertEqual(retval, "https://opencv.org/")
+        self.assertEqual(points.shape, (1, 4, 2))
+
+    def test_detect_multi(self):
+        img = cv.imread(os.path.join(self.extraTestDataPath, 'cv/qrcode/multiple/6_qrcodes.png'))
+        self.assertFalse(img is None)
+        detector = cv.QRCodeDetector()
+        retval, points = detector.detectMulti(img)
+        self.assertTrue(retval)
+        self.assertEqual(points.shape, (6, 4, 2))
+
+    def test_detect_and_decode_multi(self):
+        img = cv.imread(os.path.join(self.extraTestDataPath, 'cv/qrcode/multiple/6_qrcodes.png'))
+        self.assertFalse(img is None)
+        detector = cv.QRCodeDetector()
+        retval, decoded_data, points, straight_qrcode = detector.detectAndDecodeMulti(img)
+        self.assertTrue(retval)
+        self.assertEqual(len(decoded_data), 6)
+        self.assertEqual(decoded_data[0], "TWO STEPS FORWARD")
+        self.assertEqual(decoded_data[1], "EXTRA")
+        self.assertEqual(decoded_data[2], "SKIP")
+        self.assertEqual(decoded_data[3], "STEP FORWARD")
+        self.assertEqual(decoded_data[4], "STEP BACK")
+        self.assertEqual(decoded_data[5], "QUESTION")
+        self.assertEqual(points.shape, (6, 4, 2))