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/python/tutorial_code/imgProc/BasicGeometricDrawing/basic_geometric_drawing.py

@@ -0,0 +1,115 @@
+import cv2 as cv
+import numpy as np
+
+W = 400
+## [my_ellipse]
+def my_ellipse(img, angle):
+    thickness = 2
+    line_type = 8
+
+    cv.ellipse(img,
+                (W // 2, W // 2),
+                (W // 4, W // 16),
+                angle,
+                0,
+                360,
+                (255, 0, 0),
+                thickness,
+                line_type)
+## [my_ellipse]
+## [my_filled_circle]
+def my_filled_circle(img, center):
+    thickness = -1
+    line_type = 8
+
+    cv.circle(img,
+               center,
+               W // 32,
+               (0, 0, 255),
+               thickness,
+               line_type)
+## [my_filled_circle]
+## [my_polygon]
+def my_polygon(img):
+    line_type = 8
+
+    # Create some points
+    ppt = np.array([[W / 4, 7 * W / 8], [3 * W / 4, 7 * W / 8],
+                    [3 * W / 4, 13 * W / 16], [11 * W / 16, 13 * W / 16],
+                    [19 * W / 32, 3 * W / 8], [3 * W / 4, 3 * W / 8],
+                    [3 * W / 4, W / 8], [26 * W / 40, W / 8],
+                    [26 * W / 40, W / 4], [22 * W / 40, W / 4],
+                    [22 * W / 40, W / 8], [18 * W / 40, W / 8],
+                    [18 * W / 40, W / 4], [14 * W / 40, W / 4],
+                    [14 * W / 40, W / 8], [W / 4, W / 8],
+                    [W / 4, 3 * W / 8], [13 * W / 32, 3 * W / 8],
+                    [5 * W / 16, 13 * W / 16], [W / 4, 13 * W / 16]], np.int32)
+    ppt = ppt.reshape((-1, 1, 2))
+    cv.fillPoly(img, [ppt], (255, 255, 255), line_type)
+    # Only drawind the lines would be:
+    # cv.polylines(img, [ppt], True, (255, 0, 255), line_type)
+## [my_polygon]
+## [my_line]
+def my_line(img, start, end):
+    thickness = 2
+    line_type = 8
+
+    cv.line(img,
+             start,
+             end,
+             (0, 0, 0),
+             thickness,
+             line_type)
+## [my_line]
+## [create_images]
+# Windows names
+atom_window = "Drawing 1: Atom"
+rook_window = "Drawing 2: Rook"
+
+# Create black empty images
+size = W, W, 3
+atom_image = np.zeros(size, dtype=np.uint8)
+rook_image = np.zeros(size, dtype=np.uint8)
+## [create_images]
+## [draw_atom]
+# 1. Draw a simple atom:
+# -----------------------
+
+# 1.a. Creating ellipses
+my_ellipse(atom_image, 90)
+my_ellipse(atom_image, 0)
+my_ellipse(atom_image, 45)
+my_ellipse(atom_image, -45)
+
+# 1.b. Creating circles
+my_filled_circle(atom_image, (W // 2, W // 2))
+## [draw_atom]
+## [draw_rook]
+
+# 2. Draw a rook
+# ------------------
+# 2.a. Create a convex polygon
+my_polygon(rook_image)
+## [rectangle]
+# 2.b. Creating rectangles
+cv.rectangle(rook_image,
+              (0, 7 * W // 8),
+              (W, W),
+              (0, 255, 255),
+              -1,
+              8)
+## [rectangle]
+
+#  2.c. Create a few lines
+my_line(rook_image, (0, 15 * W // 16), (W, 15 * W // 16))
+my_line(rook_image, (W // 4, 7 * W // 8), (W // 4, W))
+my_line(rook_image, (W // 2, 7 * W // 8), (W // 2, W))
+my_line(rook_image, (3 * W // 4, 7 * W // 8), (3 * W // 4, W))
+## [draw_rook]
+cv.imshow(atom_window, atom_image)
+cv.moveWindow(atom_window, 0, 200)
+cv.imshow(rook_window, rook_image)
+cv.moveWindow(rook_window, W, 200)
+
+cv.waitKey(0)
+cv.destroyAllWindows()

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/HitMiss/hit_miss.py

@@ -0,0 +1,38 @@
+import cv2 as cv
+import numpy as np
+
+input_image = np.array((
+    [0, 0, 0, 0, 0, 0, 0, 0],
+    [0, 255, 255, 255, 0, 0, 0, 255],
+    [0, 255, 255, 255, 0, 0, 0, 0],
+    [0, 255, 255, 255, 0, 255, 0, 0],
+    [0, 0, 255, 0, 0, 0, 0, 0],
+    [0, 0, 255, 0, 0, 255, 255, 0],
+    [0,255, 0, 255, 0, 0, 255, 0],
+    [0, 255, 255, 255, 0, 0, 0, 0]), dtype="uint8")
+
+kernel = np.array((
+        [0, 1, 0],
+        [1, -1, 1],
+        [0, 1, 0]), dtype="int")
+
+output_image = cv.morphologyEx(input_image, cv.MORPH_HITMISS, kernel)
+
+rate = 50
+kernel = (kernel + 1) * 127
+kernel = np.uint8(kernel)
+
+kernel = cv.resize(kernel, None, fx = rate, fy = rate, interpolation = cv.INTER_NEAREST)
+cv.imshow("kernel", kernel)
+cv.moveWindow("kernel", 0, 0)
+
+input_image = cv.resize(input_image, None, fx = rate, fy = rate, interpolation = cv.INTER_NEAREST)
+cv.imshow("Original", input_image)
+cv.moveWindow("Original", 0, 200)
+
+output_image = cv.resize(output_image, None , fx = rate, fy = rate, interpolation = cv.INTER_NEAREST)
+cv.imshow("Hit or Miss", output_image)
+cv.moveWindow("Hit or Miss", 500, 200)
+
+cv.waitKey(0)
+cv.destroyAllWindows()

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/Pyramids/pyramids.py

@@ -0,0 +1,51 @@
+import sys
+import cv2 as cv
+
+
+def main(argv):
+    print("""
+    Zoom In-Out demo
+    ------------------
+    * [i] -> Zoom [i]n
+    * [o] -> Zoom [o]ut
+    * [ESC] -> Close program
+    """)
+    ## [load]
+    filename = argv[0] if len(argv) > 0 else 'chicky_512.png'
+
+    # Load the image
+    src = cv.imread(cv.samples.findFile(filename))
+
+    # Check if image is loaded fine
+    if src is None:
+        print ('Error opening image!')
+        print ('Usage: pyramids.py [image_name -- default ../data/chicky_512.png] \n')
+        return -1
+    ## [load]
+    ## [loop]
+    while 1:
+        rows, cols, _channels = map(int, src.shape)
+        ## [show_image]
+        cv.imshow('Pyramids Demo', src)
+        ## [show_image]
+        k = cv.waitKey(0)
+
+        if k == 27:
+            break
+            ## [pyrup]
+        elif chr(k) == 'i':
+            src = cv.pyrUp(src, dstsize=(2 * cols, 2 * rows))
+            print ('** Zoom In: Image x 2')
+            ## [pyrup]
+            ## [pyrdown]
+        elif chr(k) == 'o':
+            src = cv.pyrDown(src, dstsize=(cols // 2, rows // 2))
+            print ('** Zoom Out: Image / 2')
+            ## [pyrdown]
+    ## [loop]
+
+    cv.destroyAllWindows()
+    return 0
+
+if __name__ == "__main__":
+    main(sys.argv[1:])

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/Smoothing/smoothing.py

@@ -0,0 +1,107 @@
+import sys
+import cv2 as cv
+import numpy as np
+
+#  Global Variables
+
+DELAY_CAPTION = 1500
+DELAY_BLUR = 100
+MAX_KERNEL_LENGTH = 31
+
+src = None
+dst = None
+window_name = 'Smoothing Demo'
+
+
+def main(argv):
+    cv.namedWindow(window_name, cv.WINDOW_AUTOSIZE)
+
+    # Load the source image
+    imageName = argv[0] if len(argv) > 0 else 'lena.jpg'
+
+    global src
+    src = cv.imread(cv.samples.findFile(imageName))
+    if src is None:
+        print ('Error opening image')
+        print ('Usage: smoothing.py [image_name -- default ../data/lena.jpg] \n')
+        return -1
+
+    if display_caption('Original Image') != 0:
+        return 0
+
+    global dst
+    dst = np.copy(src)
+    if display_dst(DELAY_CAPTION) != 0:
+        return 0
+
+    # Applying Homogeneous blur
+    if display_caption('Homogeneous Blur') != 0:
+        return 0
+
+    ## [blur]
+    for i in range(1, MAX_KERNEL_LENGTH, 2):
+        dst = cv.blur(src, (i, i))
+        if display_dst(DELAY_BLUR) != 0:
+            return 0
+    ## [blur]
+
+    # Applying Gaussian blur
+    if display_caption('Gaussian Blur') != 0:
+        return 0
+
+    ## [gaussianblur]
+    for i in range(1, MAX_KERNEL_LENGTH, 2):
+        dst = cv.GaussianBlur(src, (i, i), 0)
+        if display_dst(DELAY_BLUR) != 0:
+            return 0
+    ## [gaussianblur]
+
+    # Applying Median blur
+    if display_caption('Median Blur') != 0:
+        return 0
+
+    ## [medianblur]
+    for i in range(1, MAX_KERNEL_LENGTH, 2):
+        dst = cv.medianBlur(src, i)
+        if display_dst(DELAY_BLUR) != 0:
+            return 0
+    ## [medianblur]
+
+    # Applying Bilateral Filter
+    if display_caption('Bilateral Blur') != 0:
+        return 0
+
+    ## [bilateralfilter]
+    # Remember, bilateral is a bit slow, so as value go higher, it takes long time
+    for i in range(1, MAX_KERNEL_LENGTH, 2):
+        dst = cv.bilateralFilter(src, i, i * 2, i / 2)
+        if display_dst(DELAY_BLUR) != 0:
+            return 0
+    ## [bilateralfilter]
+
+    #  Done
+    display_caption('Done!')
+
+    return 0
+
+
+def display_caption(caption):
+    global dst
+    dst = np.zeros(src.shape, src.dtype)
+    rows, cols, _ch = src.shape
+    cv.putText(dst, caption,
+                (int(cols / 4), int(rows / 2)),
+                cv.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255))
+
+    return display_dst(DELAY_CAPTION)
+
+
+def display_dst(delay):
+    cv.imshow(window_name, dst)
+    c = cv.waitKey(delay)
+    if c >= 0 : return -1
+    return 0
+
+
+if __name__ == "__main__":
+    main(sys.argv[1:])

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/anisotropic_image_segmentation/anisotropic_image_segmentation.py

@@ -0,0 +1,92 @@
+
+import cv2 as cv
+import numpy as np
+import argparse
+
+W = 52          # window size is WxW
+C_Thr = 0.43    # threshold for coherency
+LowThr = 35     # threshold1 for orientation, it ranges from 0 to 180
+HighThr = 57    # threshold2 for orientation, it ranges from 0 to 180
+
+## [calcGST]
+## [calcJ_header]
+## [calcGST_proto]
+def calcGST(inputIMG, w):
+## [calcGST_proto]
+    img = inputIMG.astype(np.float32)
+
+    # GST components calculation (start)
+    # J =  (J11 J12; J12 J22) - GST
+    imgDiffX = cv.Sobel(img, cv.CV_32F, 1, 0, 3)
+    imgDiffY = cv.Sobel(img, cv.CV_32F, 0, 1, 3)
+    imgDiffXY = cv.multiply(imgDiffX, imgDiffY)
+    ## [calcJ_header]
+
+    imgDiffXX = cv.multiply(imgDiffX, imgDiffX)
+    imgDiffYY = cv.multiply(imgDiffY, imgDiffY)
+
+    J11 = cv.boxFilter(imgDiffXX, cv.CV_32F, (w,w))
+    J22 = cv.boxFilter(imgDiffYY, cv.CV_32F, (w,w))
+    J12 = cv.boxFilter(imgDiffXY, cv.CV_32F, (w,w))
+    # GST components calculations (stop)
+
+    # eigenvalue calculation (start)
+    # lambda1 = 0.5*(J11 + J22 + sqrt((J11-J22)^2 + 4*J12^2))
+    # lambda2 = 0.5*(J11 + J22 - sqrt((J11-J22)^2 + 4*J12^2))
+    tmp1 = J11 + J22
+    tmp2 = J11 - J22
+    tmp2 = cv.multiply(tmp2, tmp2)
+    tmp3 = cv.multiply(J12, J12)
+    tmp4 = np.sqrt(tmp2 + 4.0 * tmp3)
+
+    lambda1 = 0.5*(tmp1 + tmp4)    # biggest eigenvalue
+    lambda2 = 0.5*(tmp1 - tmp4)    # smallest eigenvalue
+    # eigenvalue calculation (stop)
+
+    # Coherency calculation (start)
+    # Coherency = (lambda1 - lambda2)/(lambda1 + lambda2)) - measure of anisotropism
+    # Coherency is anisotropy degree (consistency of local orientation)
+    imgCoherencyOut = cv.divide(lambda1 - lambda2, lambda1 + lambda2)
+    # Coherency calculation (stop)
+
+    # orientation angle calculation (start)
+    # tan(2*Alpha) = 2*J12/(J22 - J11)
+    # Alpha = 0.5 atan2(2*J12/(J22 - J11))
+    imgOrientationOut = cv.phase(J22 - J11, 2.0 * J12, angleInDegrees = True)
+    imgOrientationOut = 0.5 * imgOrientationOut
+    # orientation angle calculation (stop)
+
+    return imgCoherencyOut, imgOrientationOut
+## [calcGST]
+
+parser = argparse.ArgumentParser(description='Code for Anisotropic image segmentation tutorial.')
+parser.add_argument('-i', '--input', help='Path to input image.', required=True)
+args = parser.parse_args()
+
+imgIn = cv.imread(args.input, cv.IMREAD_GRAYSCALE)
+if imgIn is None:
+    print('Could not open or find the image: {}'.format(args.input))
+    exit(0)
+
+## [main_extra]
+## [main]
+imgCoherency, imgOrientation = calcGST(imgIn, W)
+
+## [thresholding]
+_, imgCoherencyBin = cv.threshold(imgCoherency, C_Thr, 255, cv.THRESH_BINARY)
+_, imgOrientationBin = cv.threshold(imgOrientation, LowThr, HighThr, cv.THRESH_BINARY)
+## [thresholding]
+
+## [combining]
+imgBin = cv.bitwise_and(imgCoherencyBin, imgOrientationBin)
+## [combining]
+## [main]
+
+imgCoherency = cv.normalize(imgCoherency, None, alpha=0, beta=1, norm_type=cv.NORM_MINMAX, dtype=cv.CV_32F)
+imgOrientation = cv.normalize(imgOrientation, None, alpha=0, beta=1, norm_type=cv.NORM_MINMAX, dtype=cv.CV_32F)
+
+cv.imshow('result.jpg', np.uint8(0.5*(imgIn + imgBin)))
+cv.imshow('Coherency.jpg', imgCoherency)
+cv.imshow('Orientation.jpg', imgOrientation)
+cv.waitKey(0)
+## [main_extra]

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/BasicLinearTransforms.py

@@ -0,0 +1,55 @@
+from __future__ import print_function
+from builtins import input
+import cv2 as cv
+import numpy as np
+import argparse
+
+# Read image given by user
+## [basic-linear-transform-load]
+parser = argparse.ArgumentParser(description='Code for Changing the contrast and brightness of an image! tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
+args = parser.parse_args()
+
+image = cv.imread(cv.samples.findFile(args.input))
+if image is None:
+    print('Could not open or find the image: ', args.input)
+    exit(0)
+## [basic-linear-transform-load]
+
+## [basic-linear-transform-output]
+new_image = np.zeros(image.shape, image.dtype)
+## [basic-linear-transform-output]
+
+## [basic-linear-transform-parameters]
+alpha = 1.0 # Simple contrast control
+beta = 0    # Simple brightness control
+
+# Initialize values
+print(' Basic Linear Transforms ')
+print('-------------------------')
+try:
+    alpha = float(input('* Enter the alpha value [1.0-3.0]: '))
+    beta = int(input('* Enter the beta value [0-100]: '))
+except ValueError:
+    print('Error, not a number')
+## [basic-linear-transform-parameters]
+
+# Do the operation new_image(i,j) = alpha*image(i,j) + beta
+# Instead of these 'for' loops we could have used simply:
+# new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta)
+# but we wanted to show you how to access the pixels :)
+## [basic-linear-transform-operation]
+for y in range(image.shape[0]):
+    for x in range(image.shape[1]):
+        for c in range(image.shape[2]):
+            new_image[y,x,c] = np.clip(alpha*image[y,x,c] + beta, 0, 255)
+## [basic-linear-transform-operation]
+
+## [basic-linear-transform-display]
+# Show stuff
+cv.imshow('Original Image', image)
+cv.imshow('New Image', new_image)
+
+# Wait until user press some key
+cv.waitKey()
+## [basic-linear-transform-display]

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/changing_contrast_brightness_image/changing_contrast_brightness_image.py

@@ -0,0 +1,74 @@
+from __future__ import print_function
+from __future__ import division
+import cv2 as cv
+import numpy as np
+import argparse
+
+alpha = 1.0
+alpha_max = 500
+beta = 0
+beta_max = 200
+gamma = 1.0
+gamma_max = 200
+
+def basicLinearTransform():
+    res = cv.convertScaleAbs(img_original, alpha=alpha, beta=beta)
+    img_corrected = cv.hconcat([img_original, res])
+    cv.imshow("Brightness and contrast adjustments", img_corrected)
+
+def gammaCorrection():
+    ## [changing-contrast-brightness-gamma-correction]
+    lookUpTable = np.empty((1,256), np.uint8)
+    for i in range(256):
+        lookUpTable[0,i] = np.clip(pow(i / 255.0, gamma) * 255.0, 0, 255)
+
+    res = cv.LUT(img_original, lookUpTable)
+    ## [changing-contrast-brightness-gamma-correction]
+
+    img_gamma_corrected = cv.hconcat([img_original, res])
+    cv.imshow("Gamma correction", img_gamma_corrected)
+
+def on_linear_transform_alpha_trackbar(val):
+    global alpha
+    alpha = val / 100
+    basicLinearTransform()
+
+def on_linear_transform_beta_trackbar(val):
+    global beta
+    beta = val - 100
+    basicLinearTransform()
+
+def on_gamma_correction_trackbar(val):
+    global gamma
+    gamma = val / 100
+    gammaCorrection()
+
+parser = argparse.ArgumentParser(description='Code for Changing the contrast and brightness of an image! tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='lena.jpg')
+args = parser.parse_args()
+
+img_original = cv.imread(cv.samples.findFile(args.input))
+if img_original is None:
+    print('Could not open or find the image: ', args.input)
+    exit(0)
+
+img_corrected = np.empty((img_original.shape[0], img_original.shape[1]*2, img_original.shape[2]), img_original.dtype)
+img_gamma_corrected = np.empty((img_original.shape[0], img_original.shape[1]*2, img_original.shape[2]), img_original.dtype)
+
+img_corrected = cv.hconcat([img_original, img_original])
+img_gamma_corrected = cv.hconcat([img_original, img_original])
+
+cv.namedWindow('Brightness and contrast adjustments')
+cv.namedWindow('Gamma correction')
+
+alpha_init = int(alpha *100)
+cv.createTrackbar('Alpha gain (contrast)', 'Brightness and contrast adjustments', alpha_init, alpha_max, on_linear_transform_alpha_trackbar)
+beta_init = beta + 100
+cv.createTrackbar('Beta bias (brightness)', 'Brightness and contrast adjustments', beta_init, beta_max, on_linear_transform_beta_trackbar)
+gamma_init = int(gamma * 100)
+cv.createTrackbar('Gamma correction', 'Gamma correction', gamma_init, gamma_max, on_gamma_correction_trackbar)
+
+on_linear_transform_alpha_trackbar(alpha_init)
+on_gamma_correction_trackbar(gamma_init)
+
+cv.waitKey()

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/erosion_dilatation/morphology_1.py

@@ -0,0 +1,78 @@
+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+
+src = None
+erosion_size = 0
+max_elem = 2
+max_kernel_size = 21
+title_trackbar_element_shape = 'Element:\n 0: Rect \n 1: Cross \n 2: Ellipse'
+title_trackbar_kernel_size = 'Kernel size:\n 2n +1'
+title_erosion_window = 'Erosion Demo'
+title_dilation_window = 'Dilation Demo'
+
+
+## [main]
+def main(image):
+    global src
+    src = cv.imread(cv.samples.findFile(image))
+    if src is None:
+        print('Could not open or find the image: ', image)
+        exit(0)
+
+    cv.namedWindow(title_erosion_window)
+    cv.createTrackbar(title_trackbar_element_shape, title_erosion_window, 0, max_elem, erosion)
+    cv.createTrackbar(title_trackbar_kernel_size, title_erosion_window, 0, max_kernel_size, erosion)
+
+    cv.namedWindow(title_dilation_window)
+    cv.createTrackbar(title_trackbar_element_shape, title_dilation_window, 0, max_elem, dilatation)
+    cv.createTrackbar(title_trackbar_kernel_size, title_dilation_window, 0, max_kernel_size, dilatation)
+
+    erosion(0)
+    dilatation(0)
+    cv.waitKey()
+## [main]
+
+# optional mapping of values with morphological shapes
+def morph_shape(val):
+    if val == 0:
+        return cv.MORPH_RECT
+    elif val == 1:
+        return cv.MORPH_CROSS
+    elif val == 2:
+        return cv.MORPH_ELLIPSE
+
+
+## [erosion]
+def erosion(val):
+    erosion_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_erosion_window)
+    erosion_shape = morph_shape(cv.getTrackbarPos(title_trackbar_element_shape, title_erosion_window))
+
+    ## [kernel]
+    element = cv.getStructuringElement(erosion_shape, (2 * erosion_size + 1, 2 * erosion_size + 1),
+                                       (erosion_size, erosion_size))
+    ## [kernel]
+    erosion_dst = cv.erode(src, element)
+    cv.imshow(title_erosion_window, erosion_dst)
+## [erosion]
+
+
+## [dilation]
+def dilatation(val):
+    dilatation_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_dilation_window)
+    dilation_shape = morph_shape(cv.getTrackbarPos(title_trackbar_element_shape, title_dilation_window))
+
+    element = cv.getStructuringElement(dilation_shape, (2 * dilatation_size + 1, 2 * dilatation_size + 1),
+                                       (dilatation_size, dilatation_size))
+    dilatation_dst = cv.dilate(src, element)
+    cv.imshow(title_dilation_window, dilatation_dst)
+## [dilation]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Code for Eroding and Dilating tutorial.')
+    parser.add_argument('--input', help='Path to input image.', default='LinuxLogo.jpg')
+    args = parser.parse_args()
+
+    main(args.input)

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/hough_line_transform/hough_line_transform.py

@@ -0,0 +1,22 @@
+import cv2 as cv
+import numpy as np
+
+img = cv.imread(cv.samples.findFile('sudoku.png'))
+gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
+edges = cv.Canny(gray,50,150,apertureSize = 3)
+
+lines = cv.HoughLines(edges,1,np.pi/180,200)
+for line in lines:
+    rho,theta = line[0]
+    a = np.cos(theta)
+    b = np.sin(theta)
+    x0 = a*rho
+    y0 = b*rho
+    x1 = int(x0 + 1000*(-b))
+    y1 = int(y0 + 1000*(a))
+    x2 = int(x0 - 1000*(-b))
+    y2 = int(y0 - 1000*(a))
+
+    cv.line(img,(x1,y1),(x2,y2),(0,0,255),2)
+
+cv.imwrite('houghlines3.jpg',img)

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/hough_line_transform/probabilistic_hough_line_transform.py

@@ -0,0 +1,12 @@
+import cv2 as cv
+import numpy as np
+
+img = cv.imread(cv.samples.findFile('sudoku.png'))
+gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
+edges = cv.Canny(gray,50,150,apertureSize = 3)
+lines = cv.HoughLinesP(edges,1,np.pi/180,100,minLineLength=100,maxLineGap=10)
+for line in lines:
+    x1,y1,x2,y2 = line[0]
+    cv.line(img,(x1,y1),(x2,y2),(0,255,0),2)
+
+cv.imwrite('houghlines5.jpg',img)

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/match_template/match_template.py

@@ -0,0 +1,97 @@
+from __future__ import print_function
+import sys
+import cv2 as cv
+
+## [global_variables]
+use_mask = False
+img = None
+templ = None
+mask = None
+image_window = "Source Image"
+result_window = "Result window"
+
+match_method = 0
+max_Trackbar = 5
+## [global_variables]
+
+def main(argv):
+
+    if (len(sys.argv) < 3):
+        print('Not enough parameters')
+        print('Usage:\nmatch_template_demo.py <image_name> <template_name> [<mask_name>]')
+        return -1
+
+    ## [load_image]
+    global img
+    global templ
+    img = cv.imread(sys.argv[1], cv.IMREAD_COLOR)
+    templ = cv.imread(sys.argv[2], cv.IMREAD_COLOR)
+
+    if (len(sys.argv) > 3):
+        global use_mask
+        use_mask = True
+        global mask
+        mask = cv.imread( sys.argv[3], cv.IMREAD_COLOR )
+
+    if ((img is None) or (templ is None) or (use_mask and (mask is None))):
+        print('Can\'t read one of the images')
+        return -1
+    ## [load_image]
+
+    ## [create_windows]
+    cv.namedWindow( image_window, cv.WINDOW_AUTOSIZE )
+    cv.namedWindow( result_window, cv.WINDOW_AUTOSIZE )
+    ## [create_windows]
+
+    ## [create_trackbar]
+    trackbar_label = 'Method: \n 0: SQDIFF \n 1: SQDIFF NORMED \n 2: TM CCORR \n 3: TM CCORR NORMED \n 4: TM COEFF \n 5: TM COEFF NORMED'
+    cv.createTrackbar( trackbar_label, image_window, match_method, max_Trackbar, MatchingMethod )
+    ## [create_trackbar]
+
+    MatchingMethod(match_method)
+
+    ## [wait_key]
+    cv.waitKey(0)
+    return 0
+    ## [wait_key]
+
+def MatchingMethod(param):
+
+    global match_method
+    match_method = param
+
+    ## [copy_source]
+    img_display = img.copy()
+    ## [copy_source]
+    ## [match_template]
+    method_accepts_mask = (cv.TM_SQDIFF == match_method or match_method == cv.TM_CCORR_NORMED)
+    if (use_mask and method_accepts_mask):
+        result = cv.matchTemplate(img, templ, match_method, None, mask)
+    else:
+        result = cv.matchTemplate(img, templ, match_method)
+    ## [match_template]
+
+    ## [normalize]
+    cv.normalize( result, result, 0, 1, cv.NORM_MINMAX, -1 )
+    ## [normalize]
+    ## [best_match]
+    _minVal, _maxVal, minLoc, maxLoc = cv.minMaxLoc(result, None)
+    ## [best_match]
+
+    ## [match_loc]
+    if (match_method == cv.TM_SQDIFF or match_method == cv.TM_SQDIFF_NORMED):
+        matchLoc = minLoc
+    else:
+        matchLoc = maxLoc
+    ## [match_loc]
+
+    ## [imshow]
+    cv.rectangle(img_display, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )
+    cv.rectangle(result, matchLoc, (matchLoc[0] + templ.shape[0], matchLoc[1] + templ.shape[1]), (0,0,0), 2, 8, 0 )
+    cv.imshow(image_window, img_display)
+    cv.imshow(result_window, result)
+    ## [imshow]
+    pass
+
+if __name__ == "__main__":
+    main(sys.argv[1:])

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/morph_lines_detection/morph_lines_detection.py

@@ -0,0 +1,136 @@
+"""
+@file morph_lines_detection.py
+@brief Use morphology transformations for extracting horizontal and vertical lines sample code
+"""
+import numpy as np
+import sys
+import cv2 as cv
+
+
+def show_wait_destroy(winname, img):
+    cv.imshow(winname, img)
+    cv.moveWindow(winname, 500, 0)
+    cv.waitKey(0)
+    cv.destroyWindow(winname)
+
+
+def main(argv):
+    # [load_image]
+    # Check number of arguments
+    if len(argv) < 1:
+        print ('Not enough parameters')
+        print ('Usage:\nmorph_lines_detection.py < path_to_image >')
+        return -1
+
+    # Load the image
+    src = cv.imread(argv[0], cv.IMREAD_COLOR)
+
+    # Check if image is loaded fine
+    if src is None:
+        print ('Error opening image: ' + argv[0])
+        return -1
+
+    # Show source image
+    cv.imshow("src", src)
+    # [load_image]
+
+    # [gray]
+    # Transform source image to gray if it is not already
+    if len(src.shape) != 2:
+        gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+    else:
+        gray = src
+
+    # Show gray image
+    show_wait_destroy("gray", gray)
+    # [gray]
+
+    # [bin]
+    # Apply adaptiveThreshold at the bitwise_not of gray, notice the ~ symbol
+    gray = cv.bitwise_not(gray)
+    bw = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, \
+                                cv.THRESH_BINARY, 15, -2)
+    # Show binary image
+    show_wait_destroy("binary", bw)
+    # [bin]
+
+    # [init]
+    # Create the images that will use to extract the horizontal and vertical lines
+    horizontal = np.copy(bw)
+    vertical = np.copy(bw)
+    # [init]
+
+    # [horiz]
+    # Specify size on horizontal axis
+    cols = horizontal.shape[1]
+    horizontal_size = cols // 30
+
+    # Create structure element for extracting horizontal lines through morphology operations
+    horizontalStructure = cv.getStructuringElement(cv.MORPH_RECT, (horizontal_size, 1))
+
+    # Apply morphology operations
+    horizontal = cv.erode(horizontal, horizontalStructure)
+    horizontal = cv.dilate(horizontal, horizontalStructure)
+
+    # Show extracted horizontal lines
+    show_wait_destroy("horizontal", horizontal)
+    # [horiz]
+
+    # [vert]
+    # Specify size on vertical axis
+    rows = vertical.shape[0]
+    verticalsize = rows // 30
+
+    # Create structure element for extracting vertical lines through morphology operations
+    verticalStructure = cv.getStructuringElement(cv.MORPH_RECT, (1, verticalsize))
+
+    # Apply morphology operations
+    vertical = cv.erode(vertical, verticalStructure)
+    vertical = cv.dilate(vertical, verticalStructure)
+
+    # Show extracted vertical lines
+    show_wait_destroy("vertical", vertical)
+    # [vert]
+
+    # [smooth]
+    # Inverse vertical image
+    vertical = cv.bitwise_not(vertical)
+    show_wait_destroy("vertical_bit", vertical)
+
+    '''
+    Extract edges and smooth image according to the logic
+    1. extract edges
+    2. dilate(edges)
+    3. src.copyTo(smooth)
+    4. blur smooth img
+    5. smooth.copyTo(src, edges)
+    '''
+
+    # Step 1
+    edges = cv.adaptiveThreshold(vertical, 255, cv.ADAPTIVE_THRESH_MEAN_C, \
+                                cv.THRESH_BINARY, 3, -2)
+    show_wait_destroy("edges", edges)
+
+    # Step 2
+    kernel = np.ones((2, 2), np.uint8)
+    edges = cv.dilate(edges, kernel)
+    show_wait_destroy("dilate", edges)
+
+    # Step 3
+    smooth = np.copy(vertical)
+
+    # Step 4
+    smooth = cv.blur(smooth, (2, 2))
+
+    # Step 5
+    (rows, cols) = np.where(edges != 0)
+    vertical[rows, cols] = smooth[rows, cols]
+
+    # Show final result
+    show_wait_destroy("smooth - final", vertical)
+    # [smooth]
+
+    return 0
+
+if __name__ == "__main__":
+    main(sys.argv[1:])

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/opening_closing_hats/morphology_2.py

@@ -0,0 +1,48 @@
+from __future__ import print_function
+import cv2 as cv
+import numpy as np
+import argparse
+
+morph_size = 0
+max_operator = 4
+max_elem = 2
+max_kernel_size = 21
+title_trackbar_operator_type = 'Operator:\n 0: Opening - 1: Closing  \n 2: Gradient - 3: Top Hat \n 4: Black Hat'
+title_trackbar_element_type = 'Element:\n 0: Rect - 1: Cross - 2: Ellipse'
+title_trackbar_kernel_size = 'Kernel size:\n 2n + 1'
+title_window = 'Morphology Transformations Demo'
+morph_op_dic = {0: cv.MORPH_OPEN, 1: cv.MORPH_CLOSE, 2: cv.MORPH_GRADIENT, 3: cv.MORPH_TOPHAT, 4: cv.MORPH_BLACKHAT}
+
+def morphology_operations(val):
+    morph_operator = cv.getTrackbarPos(title_trackbar_operator_type, title_window)
+    morph_size = cv.getTrackbarPos(title_trackbar_kernel_size, title_window)
+    morph_elem = 0
+    val_type = cv.getTrackbarPos(title_trackbar_element_type, title_window)
+    if val_type == 0:
+        morph_elem = cv.MORPH_RECT
+    elif val_type == 1:
+        morph_elem = cv.MORPH_CROSS
+    elif val_type == 2:
+        morph_elem = cv.MORPH_ELLIPSE
+
+    element = cv.getStructuringElement(morph_elem, (2*morph_size + 1, 2*morph_size+1), (morph_size, morph_size))
+    operation = morph_op_dic[morph_operator]
+    dst = cv.morphologyEx(src, operation, element)
+    cv.imshow(title_window, dst)
+
+parser = argparse.ArgumentParser(description='Code for More Morphology Transformations tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='LinuxLogo.jpg')
+args = parser.parse_args()
+
+src = cv.imread(cv.samples.findFile(args.input))
+if src is None:
+    print('Could not open or find the image: ', args.input)
+    exit(0)
+
+cv.namedWindow(title_window)
+cv.createTrackbar(title_trackbar_operator_type, title_window , 0, max_operator, morphology_operations)
+cv.createTrackbar(title_trackbar_element_type, title_window , 0, max_elem, morphology_operations)
+cv.createTrackbar(title_trackbar_kernel_size, title_window , 0, max_kernel_size, morphology_operations)
+
+morphology_operations(0)
+cv.waitKey()

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/threshold/threshold.py

@@ -0,0 +1,54 @@
+from __future__ import print_function
+import cv2 as cv
+import argparse
+
+max_value = 255
+max_type = 4
+max_binary_value = 255
+trackbar_type = 'Type: \n 0: Binary \n 1: Binary Inverted \n 2: Truncate \n 3: To Zero \n 4: To Zero Inverted'
+trackbar_value = 'Value'
+window_name = 'Threshold Demo'
+
+## [Threshold_Demo]
+def Threshold_Demo(val):
+    #0: Binary
+    #1: Binary Inverted
+    #2: Threshold Truncated
+    #3: Threshold to Zero
+    #4: Threshold to Zero Inverted
+    threshold_type = cv.getTrackbarPos(trackbar_type, window_name)
+    threshold_value = cv.getTrackbarPos(trackbar_value, window_name)
+    _, dst = cv.threshold(src_gray, threshold_value, max_binary_value, threshold_type )
+    cv.imshow(window_name, dst)
+## [Threshold_Demo]
+
+parser = argparse.ArgumentParser(description='Code for Basic Thresholding Operations tutorial.')
+parser.add_argument('--input', help='Path to input image.', default='stuff.jpg')
+args = parser.parse_args()
+
+## [load]
+# Load an image
+src = cv.imread(cv.samples.findFile(args.input))
+if src is None:
+    print('Could not open or find the image: ', args.input)
+    exit(0)
+# Convert the image to Gray
+src_gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+## [load]
+
+## [window]
+# Create a window to display results
+cv.namedWindow(window_name)
+## [window]
+
+## [trackbar]
+# Create Trackbar to choose type of Threshold
+cv.createTrackbar(trackbar_type, window_name , 3, max_type, Threshold_Demo)
+# Create Trackbar to choose Threshold value
+cv.createTrackbar(trackbar_value, window_name , 0, max_value, Threshold_Demo)
+## [trackbar]
+
+# Call the function to initialize
+Threshold_Demo(0)
+# Wait until user finishes program
+cv.waitKey()

3rdparty/opencv-4.5.4/samples/python/tutorial_code/imgProc/threshold_inRange/threshold_inRange.py

@@ -0,0 +1,107 @@
+from __future__ import print_function
+import cv2 as cv
+import argparse
+
+max_value = 255
+max_value_H = 360//2
+low_H = 0
+low_S = 0
+low_V = 0
+high_H = max_value_H
+high_S = max_value
+high_V = max_value
+window_capture_name = 'Video Capture'
+window_detection_name = 'Object Detection'
+low_H_name = 'Low H'
+low_S_name = 'Low S'
+low_V_name = 'Low V'
+high_H_name = 'High H'
+high_S_name = 'High S'
+high_V_name = 'High V'
+
+## [low]
+def on_low_H_thresh_trackbar(val):
+    global low_H
+    global high_H
+    low_H = val
+    low_H = min(high_H-1, low_H)
+    cv.setTrackbarPos(low_H_name, window_detection_name, low_H)
+## [low]
+
+## [high]
+def on_high_H_thresh_trackbar(val):
+    global low_H
+    global high_H
+    high_H = val
+    high_H = max(high_H, low_H+1)
+    cv.setTrackbarPos(high_H_name, window_detection_name, high_H)
+## [high]
+
+def on_low_S_thresh_trackbar(val):
+    global low_S
+    global high_S
+    low_S = val
+    low_S = min(high_S-1, low_S)
+    cv.setTrackbarPos(low_S_name, window_detection_name, low_S)
+
+def on_high_S_thresh_trackbar(val):
+    global low_S
+    global high_S
+    high_S = val
+    high_S = max(high_S, low_S+1)
+    cv.setTrackbarPos(high_S_name, window_detection_name, high_S)
+
+def on_low_V_thresh_trackbar(val):
+    global low_V
+    global high_V
+    low_V = val
+    low_V = min(high_V-1, low_V)
+    cv.setTrackbarPos(low_V_name, window_detection_name, low_V)
+
+def on_high_V_thresh_trackbar(val):
+    global low_V
+    global high_V
+    high_V = val
+    high_V = max(high_V, low_V+1)
+    cv.setTrackbarPos(high_V_name, window_detection_name, high_V)
+
+parser = argparse.ArgumentParser(description='Code for Thresholding Operations using inRange tutorial.')
+parser.add_argument('--camera', help='Camera divide number.', default=0, type=int)
+args = parser.parse_args()
+
+## [cap]
+cap = cv.VideoCapture(args.camera)
+## [cap]
+
+## [window]
+cv.namedWindow(window_capture_name)
+cv.namedWindow(window_detection_name)
+## [window]
+
+## [trackbar]
+cv.createTrackbar(low_H_name, window_detection_name , low_H, max_value_H, on_low_H_thresh_trackbar)
+cv.createTrackbar(high_H_name, window_detection_name , high_H, max_value_H, on_high_H_thresh_trackbar)
+cv.createTrackbar(low_S_name, window_detection_name , low_S, max_value, on_low_S_thresh_trackbar)
+cv.createTrackbar(high_S_name, window_detection_name , high_S, max_value, on_high_S_thresh_trackbar)
+cv.createTrackbar(low_V_name, window_detection_name , low_V, max_value, on_low_V_thresh_trackbar)
+cv.createTrackbar(high_V_name, window_detection_name , high_V, max_value, on_high_V_thresh_trackbar)
+## [trackbar]
+
+while True:
+    ## [while]
+    ret, frame = cap.read()
+    if frame is None:
+        break
+
+    frame_HSV = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
+    frame_threshold = cv.inRange(frame_HSV, (low_H, low_S, low_V), (high_H, high_S, high_V))
+    ## [while]
+
+    ## [show]
+    cv.imshow(window_capture_name, frame)
+    cv.imshow(window_detection_name, frame_threshold)
+    ## [show]
+
+    key = cv.waitKey(30)
+    if key == ord('q') or key == 27:
+        break