Python cv2.BORDER_REFLECT_101 Examples

def __getitem__(self, index):
        x, _ = self.dataset[index // num_perts]
        pert = pert_configs[index % num_perts]

        x = np.asarray(resize_and_crop(x))

        if np.random.uniform() < 0.5:
            x = x[:, ::-1]
        x = cv2f.affine(np.asarray(x), 0, (pert[0], pert[1]), 1, 0,
                        interpolation=cv2.INTER_LINEAR, mode=cv2.BORDER_REFLECT_101)

        label = [expanded_params[i].index(pert[i]) for i in range(len(expanded_params))]
        label = np.vstack((label + [0], label + [1], label + [2], label + [3]))

        x = trnF.to_tensor(x.copy()).unsqueeze(0).numpy()
        x = np.concatenate((x, np.rot90(x, 1, axes=(2, 3)),
                            np.rot90(x, 2, axes=(2, 3)), np.rot90(x, 3, axes=(2, 3))), 0)

        return torch.FloatTensor(x), label 

def cv_preprocess_image(img, output_height, output_width, is_training):
        assert output_height == output_width
        img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
        img[:, :, 0] = np.uint8((np.int32(img[:, :, 0]) + (180 + random.randrange(-9, 10))) % 180)
        img = cv2.cvtColor(img, cv2.COLOR_HSV2RGB)
        rows, cols, ch = img.shape
        output_size = output_width

        def r():
            return (random.random() - 0.5) * 0.1 * output_size
        pts1 = np.float32([[0, 0], [cols, rows], [0, rows]])
        pts2 = np.float32([[r(), r()], [output_size + r(), output_size + r()], [r(), output_size + r()]])
        M = cv2.getAffineTransform(pts1, pts2)
        noize = np.random.normal(0, random.random() * (0.05 * 255), size=img.shape)
        img = np.array(img, dtype=np.float32) + noize
        img = cv2.warpAffine(img, M, (output_size, output_size), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
        return img 

def shift_scale_rotate(img, angle, scale, dx, dy):
    height, width = img.shape[:2]

    cc = math.cos(angle/180*math.pi) * scale
    ss = math.sin(angle/180*math.pi) * scale
    rotate_matrix = np.array([[cc, -ss], [ss, cc]])

    box0 = np.array([[0, 0], [width, 0],  [width, height], [0, height], ])
    box1 = box0 - np.array([width/2, height/2])
    box1 = np.dot(box1, rotate_matrix.T) + np.array([width/2+dx*width, height/2+dy*height])

    box0 = box0.astype(np.float32)
    box1 = box1.astype(np.float32)
    mat = cv2.getPerspectiveTransform(box0, box1)
    img = cv2.warpPerspective(img, mat, (width, height),
                              flags=cv2.INTER_LINEAR,
                              borderMode=cv2.BORDER_REFLECT_101)

    return img 

def distort_affine_cv2(image, alpha_affine=10, random_state=None):
    if random_state is None:
        random_state = np.random.RandomState(None)

    shape = image.shape
    shape_size = shape[:2]

    center_square = np.float32(shape_size) // 2
    square_size = min(shape_size) // 3
    pts1 = np.float32([
        center_square + square_size,
        [center_square[0] + square_size, center_square[1] - square_size],
        center_square - square_size])
    pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)

    M = cv2.getAffineTransform(pts1, pts2)
    distorted_image = cv2.warpAffine(
        image, M, shape_size[::-1], borderMode=cv2.BORDER_REPLICATE) #cv2.BORDER_REFLECT_101)

    return distorted_image 

def do_rotation_transform(image, mask, angle=0):
    height, width = image.shape[:2]
    cc = np.cos(angle / 180 * np.pi)
    ss = np.sin(angle / 180 * np.pi)
    rotate_matrix = np.array([[cc, -ss], [ss, cc]])

    box0 = np.array([[0, 0], [width, 0], [width, height], [0, height], ], np.float32)
    box1 = box0 - np.array([width / 2, height / 2])
    box1 = np.dot(box1, rotate_matrix.T) + np.array([width / 2, height / 2])

    box0 = box0.astype(np.float32)
    box1 = box1.astype(np.float32)
    mat = cv2.getPerspectiveTransform(box0, box1)

    image = cv2.warpPerspective(image, mat, (width, height), flags=cv2.INTER_LINEAR,
                                borderMode=cv2.BORDER_REFLECT_101,
                                borderValue=(0, 0, 0,))
    mask = cv2.warpPerspective(mask, mat, (width, height), flags=cv2.INTER_NEAREST,
                               borderMode=cv2.BORDER_REFLECT_101,
                               borderValue=(0, 0, 0,))
    # mask = (mask > 0.5).astype(np.float32)
    return image, mask 

def warpTriangle(img1, img2, t1, t2):
        def applyAffineTransform(src, srcTri, dstTri, size) :
            warpMat = cv2.getAffineTransform(np.float32(srcTri), np.float32(dstTri))
            dst = cv2.warpAffine(src, warpMat, (size[0], size[1]), None, 
                                 flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
            return dst
        r1 = cv2.boundingRect(np.float32([t1]))
        r2 = cv2.boundingRect(np.float32([t2]))
        t1Rect = [] 
        t2Rect = []
        t2RectInt = []
        for i in range(0, 3):
            t1Rect.append(((t1[i][0] - r1[0]),(t1[i][1] - r1[1])))
            t2Rect.append(((t2[i][0] - r2[0]),(t2[i][1] - r2[1])))
            t2RectInt.append(((t2[i][0] - r2[0]),(t2[i][1] - r2[1])))
        mask = np.zeros((r2[3], r2[2], 3), dtype = np.float32)
        cv2.fillConvexPoly(mask, np.int32(t2RectInt), (1, 1, 1));
        img1Rect = img1[r1[1]:r1[1] + r1[3], r1[0]:r1[0] + r1[2]]
        size = (r2[2], r2[3])
        img2Rect = applyAffineTransform(img1Rect, t1Rect, t2Rect, size)
        img2Rect = img2Rect * mask
        img2[r2[1]: r2[1] + r2[3], r2[0]: r2[0] + r2[2]] = img2[r2[1]: r2[1] + r2[3], 
                                                                r2[0]: r2[0] + r2[2]] * ((1.0, 1.0, 1.0) - mask)
        img2[r2[1]: r2[1] + r2[3], r2[0]: r2[0] + r2[2]] = img2[r2[1]: r2[1] + r2[3],
                                                                 r2[0]: r2[0] + r2[2]] + img2Rect 

def __call__(self, img, mask=None):
        if random.random() < self.prob:
            limit = self.limit
            dx = round(random.uniform(-limit, limit))
            dy = round(random.uniform(-limit, limit))

            height, width, channel = img.shape
            y1 = limit+1+dy
            y2 = y1 + height
            x1 = limit+1+dx
            x2 = x1 + width

            img1 = cv2.copyMakeBorder(img, limit+1, limit+1, limit+1, limit+1, borderType=cv2.BORDER_REFLECT_101)
            img = img1[y1:y2, x1:x2, :]
            if mask is not None:
                msk1 = cv2.copyMakeBorder(mask, limit+1, limit+1, limit+1, limit+1, borderType=cv2.BORDER_REFLECT_101)
                mask = msk1[y1:y2, x1:x2, :]

        return img, mask 

def __call__(self, img, mask=None):
        if random.random() < self.prob:
            limit = self.limit
            dx = round(random.uniform(-limit, limit))
            dy = round(random.uniform(-limit, limit))

            height, width, channel = img.shape
            y1 = limit + 1 + dy
            y2 = y1 + height
            x1 = limit + 1 + dx
            x2 = x1 + width

            img1 = cv2.copyMakeBorder(img, limit + 1, limit + 1, limit + 1, limit + 1, borderType=cv2.BORDER_REFLECT_101)
            img = img1[y1:y2, x1:x2, :].copy()
            if mask is not None:
                msk1 = cv2.copyMakeBorder(mask, limit + 1, limit + 1, limit + 1, limit + 1, borderType=cv2.BORDER_REFLECT_101)
                mask = msk1[y1:y2, x1:x2, :].copy()

        return img, mask 

def do_horizontal_shear(image, mask, scale=0):
    height, width = image.shape[:2]
    dx = int(scale * width)

    box0 = np.array([[0, 0], [width, 0], [width, height], [0, height], ], np.float32)
    box1 = np.array([[+dx, 0], [width + dx, 0], [width - dx, height], [-dx, height], ], np.float32)

    box0 = box0.astype(np.float32)
    box1 = box1.astype(np.float32)
    mat = cv2.getPerspectiveTransform(box0, box1)

    image = cv2.warpPerspective(image, mat, (width, height), flags=cv2.INTER_LINEAR,
                                borderMode=cv2.BORDER_REFLECT_101, borderValue=(0, 0, 0,))
    mask = cv2.warpPerspective(mask, mat, (width, height), flags=cv2.INTER_NEAREST,
                               borderMode=cv2.BORDER_REFLECT_101, borderValue=(0, 0, 0,))
    # mask = (mask > 0.5).astype(np.float32)
    return image, mask 

def PreprocessImage(name, args):
    """Preprocess according to the original author's code."""

    image = cv2.imread(name, 1).astype(np.float32) - args.mean

    if args.resize_dims is not None:
        image = cv2.resize(image, dsize = (args.resize_dims[0], args.resize_dims[1]))

    im_height = image.shape[0]
    im_width = image.shape[1]

    label_margin = 186
    input_image = cv2.copyMakeBorder(image, label_margin, label_margin,
                                     label_margin, label_margin, cv2.BORDER_REFLECT_101)
    input_size = [args.pad_size[1], args.pad_size[0]] # Order is H x W
    margin = [0, input_size[0] - input_image.shape[0],
              0, input_size[1] - input_image.shape[1]]

    input_image = cv2.copyMakeBorder(input_image, margin[0], margin[1], margin[2],
                                     margin[3], cv2.BORDER_REFLECT_101)

    input_image = input_image.transpose([2,0,1]) # To make it C x H x W
    return input_image, im_height, im_width, image 

def shift_scale_rotate(img, angle, scale, dx, dy):
    height, width = img.shape[:2]

    cc = math.cos(angle/180*math.pi) * scale
    ss = math.sin(angle/180*math.pi) * scale
    rotate_matrix = np.array([[cc, -ss], [ss, cc]])

    box0 = np.array([[0, 0], [width, 0],  [width, height], [0, height], ])
    box1 = box0 - np.array([width/2, height/2])
    box1 = np.dot(box1, rotate_matrix.T) + np.array([width/2+dx*width, height/2+dy*height])

    box0 = box0.astype(np.float32)
    box1 = box1.astype(np.float32)
    mat = cv2.getPerspectiveTransform(box0, box1)
    img = cv2.warpPerspective(img, mat, (width, height),
                              flags=cv2.INTER_LINEAR,
                              borderMode=cv2.BORDER_REFLECT_101)

    return img 

def rotate(img, angle):
    height, width = img.shape[0:2]
    mat = cv2.getRotationMatrix2D((width/2, height/2), angle, 1.0)
    img = cv2.warpAffine(img, mat, (width, height),
                         flags=cv2.INTER_LINEAR,
                         borderMode=cv2.BORDER_REFLECT_101)
    return img 

def elastic_transform_fast(image, alpha, sigma, alpha_affine, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_ (with modifications).
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
         Convolutional Neural Networks applied to Visual Document Analysis", in
         Proc. of the International Conference on Document Analysis and
         Recognition, 2003.

     Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5
    """
    if random_state is None:
        random_state = np.random.RandomState(1234)

    shape = image.shape
    shape_size = shape[:2]


    # Random affine
    center_square = np.float32(shape_size) // 2
    square_size = min(shape_size) // 3
    alpha = float(alpha)
    sigma = float(sigma)
    alpha_affine = float(alpha_affine)

    pts1 = np.float32([center_square + square_size, [center_square[0] + square_size, center_square[1] - square_size],
                       center_square - square_size])
    pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)
    M = cv2.getAffineTransform(pts1, pts2)

    image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)

    dx = np.float32(gaussian_filter((random_state.rand(*shape_size) * 2 - 1), sigma) * alpha)
    dy = np.float32(gaussian_filter((random_state.rand(*shape_size) * 2 - 1), sigma) * alpha)

    x, y = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]))

    mapx = np.float32(x + dx)
    mapy = np.float32(y + dy)

    return cv2.remap(image, mapx, mapy, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101) 

def rotate(img, angle, resample=False, expand=False, center=None):
    """Rotate the image by angle.
    Args:
        img (numpy ndarray): numpy ndarray to be rotated.
        angle (float or int): In degrees degrees counter clockwise order.
        resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or ``PIL.Image.BICUBIC``, optional):
            An optional resampling filter. See `filters`_ for more information.
            If omitted, or if the image has mode "1" or "P", it is set to ``PIL.Image.NEAREST``.
        expand (bool, optional): Optional expansion flag.
            If true, expands the output image to make it large enough to hold the entire rotated image.
            If false or omitted, make the output image the same size as the input image.
            Note that the expand flag assumes rotation around the center and no translation.
        center (2-tuple, optional): Optional center of rotation.
            Origin is the upper left corner.
            Default is the center of the image.
    .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be numpy Image. Got {}'.format(type(img)))
    rows,cols = img.shape[0:2]
    if center is None:
        center = (cols/2-0.5, rows/2-0.5)
    M = cv2.getRotationMatrix2D(center,angle,1)
    # if img.shape[2]==1:
    #     return cv2.warpAffine(img,M,(cols,rows), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)[:,:,np.newaxis]
    # else:
    #     return cv2.warpAffine(img,M,(cols,rows), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
    if img.shape[2]==1:
        return cv2.warpAffine(img,M,(cols,rows), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)[:,:,np.newaxis]
    else:
        return cv2.warpAffine(img,M,(cols,rows), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101) 

def affine(img, angle, translate, scale, shear, interpolation=cv2.INTER_CUBIC, mode=cv2.BORDER_CONSTANT, fillcolor=0):
    """Apply affine transformation on the image keeping image center invariant
    Args:
        img (numpy ndarray): numpy ndarray to be transformed.
        angle (float or int): rotation angle in degrees between -180 and 180, clockwise direction.
        translate (list or tuple of integers): horizontal and vertical translations (post-rotation translation)
        scale (float): overall scale
        shear (float): shear angle value in degrees between -180 to 180, clockwise direction.
        interpolation (``cv2.INTER_NEAREST` or ``cv2.INTER_LINEAR`` or ``cv2.INTER_AREA``, ``cv2.INTER_CUBIC``):
            An optional resampling filter.
            See `filters`_ for more information.
            If omitted, it is set to ``cv2.INTER_CUBIC``, for bicubic interpolation.
        mode (``cv2.BORDER_CONSTANT`` or ``cv2.BORDER_REPLICATE`` or ``cv2.BORDER_REFLECT`` or ``cv2.BORDER_REFLECT_101``)
            Method for filling in border regions. 
            Defaults to cv2.BORDER_CONSTANT, meaning areas outside the image are filled with a value (val, default 0)
        val (int): Optional fill color for the area outside the transform in the output image. Default: 0
    """
    if not _is_numpy_image(img):
        raise TypeError('img should be numpy Image. Got {}'.format(type(img)))

    assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
        "Argument translate should be a list or tuple of length 2"

    assert scale > 0.0, "Argument scale should be positive"

    output_size = img.shape[0:2]
    center = (img.shape[1] * 0.5 + 0.5, img.shape[0] * 0.5 + 0.5)
    matrix = _get_affine_matrix(center, angle, translate, scale, shear)
    
    if img.shape[2]==1:
        return cv2.warpAffine(img, matrix, output_size[::-1],interpolation, borderMode=mode, borderValue=fillcolor)[:,:,np.newaxis]
    else:
        return cv2.warpAffine(img, matrix, output_size[::-1],interpolation, borderMode=mode, borderValue=fillcolor) 

def applyAffineTransform(src, srcTri, dstTri, size) :
    warpMat = cv2.getAffineTransform( np.float32(srcTri), np.float32(dstTri) )
    return cv2.warpAffine( src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101 ) 

def __init__(self, limit=90, interpolation=cv2.INTER_LINEAR,
                 border_mode=cv2.BORDER_REFLECT_101, border_value=255, always_apply=False, p=.5):
        super().__init__(always_apply, p)
        self.limit = to_tuple(limit)
        self.interpolation = interpolation
        self.border_mode = border_mode
        self.border_value = border_value 

def __getitem__(self, index):
        x_orig, classifier_target = self.dataset[index]

        if self.train_mode == True and np.random.uniform() < 0.5:
            x_orig = np.copy(x_orig)[:, ::-1]
        else:
            x_orig =  np.copy(x_orig)

        if self.train_mode == True:
            x_orig = Image.fromarray(x_orig)
            x_orig = randomly_crop(x_orig)
            x_orig = np.asarray(x_orig)

        x_tf_0 = np.copy(x_orig)
        x_tf_90 = np.rot90(x_orig.copy(), k=1).copy()
        x_tf_180 = np.rot90(x_orig.copy(), k=2).copy()
        x_tf_270 = np.rot90(x_orig.copy(), k=3).copy()

        possible_translations = list(itertools.product([0, 8, -8], [0, 8, -8]))
        num_possible_translations = len(possible_translations)
        tx, ty = possible_translations[random.randint(0, num_possible_translations - 1)]
        tx_target = {0: 0, 8: 1, -8: 2}[tx]
        ty_target = {0: 0, 8: 1, -8: 2}[ty]
        x_tf_trans = cv2f.affine(np.asarray(x_orig).copy(), 0, (tx, ty), 1, 0, interpolation=cv2.INTER_CUBIC, mode=cv2.BORDER_REFLECT_101)

        return \
            normalize(trnF.to_tensor(x_tf_0)), \
            normalize(trnF.to_tensor(x_tf_90)), \
            normalize(trnF.to_tensor(x_tf_180)), \
            normalize(trnF.to_tensor(x_tf_270)), \
            normalize(trnF.to_tensor(x_tf_trans)), \
            torch.tensor(tx_target), \
            torch.tensor(ty_target), \
            torch.tensor(classifier_target) 

def __getitem__(self, index):
        x_orig, classifier_target = self.dataset[index]
        x_orig = x_orig.numpy()
        classifier_target = classifier_target.numpy()

        x_tf_0 = np.copy(x_orig)
        x_tf_90 = np.rot90(x_orig.copy(), k=1).copy()
        x_tf_180 = np.rot90(x_orig.copy(), k=2).copy()
        x_tf_270 = np.rot90(x_orig.copy(), k=3).copy()

        possible_translations = list(itertools.product([0, 8, -8], [0, 8, -8]))
        num_possible_translations = len(possible_translations)
        tx, ty = possible_translations[random.randint(0, num_possible_translations - 1)]
        tx_target = {0: 0, 8: 1, -8: 2}[tx]
        ty_target = {0: 0, 8: 1, -8: 2}[ty]
        x_tf_trans = cv2f.affine(np.asarray(x_orig).copy(), 0, (tx, ty), 1, 0, interpolation=cv2.INTER_CUBIC, mode=cv2.BORDER_REFLECT_101)

        return \
            normalize(trnF.to_tensor(x_tf_0)), \
            normalize(trnF.to_tensor(x_tf_90)), \
            normalize(trnF.to_tensor(x_tf_180)), \
            normalize(trnF.to_tensor(x_tf_270)), \
            normalize(trnF.to_tensor(x_tf_trans)), \
            torch.tensor(tx_target), \
            torch.tensor(ty_target), \
            torch.tensor(classifier_target) 

def cvBorderMode(self):
        if self.fill_mode == 'constant':
            return cv2.BORDER_CONSTANT
        if self.fill_mode == 'nearest':
            return cv2.BORDER_REPLICATE
        if self.fill_mode == 'reflect':
            return cv2.BORDER_REFLECT_101
        if self.fill_mode == 'wrap':
            return cv2.BORDER_WRAP 

def distort1(img, k=0, dx=0, dy=0):
    """"
    ## unconverntional augmnet ################################################################################3
    ## https://stackoverflow.com/questions/6199636/formulas-for-barrel-pincushion-distortion

    ## https://stackoverflow.com/questions/10364201/image-transformation-in-opencv
    ## https://stackoverflow.com/questions/2477774/correcting-fisheye-distortion-programmatically
    ## http://www.coldvision.io/2017/03/02/advanced-lane-finding-using-opencv/

    ## barrel\pincushion distortion
    """
    height, width = img.shape[:2]
    #  map_x, map_y =
    # cv2.initUndistortRectifyMap(intrinsics, dist_coeffs, None, None, (width,height),cv2.CV_32FC1)
    # https://stackoverflow.com/questions/6199636/formulas-for-barrel-pincushion-distortion
    # https://stackoverflow.com/questions/10364201/image-transformation-in-opencv
    k = k * 0.00001
    dx = dx * width
    dy = dy * height
    x, y = np.mgrid[0:width:1, 0:height:1]
    x = x.astype(np.float32) - width/2 - dx
    y = y.astype(np.float32) - height/2 - dy
    theta = np.arctan2(y, x)
    d = (x*x + y*y)**0.5
    r = d*(1+k*d*d)
    map_x = r*np.cos(theta) + width/2 + dx
    map_y = r*np.sin(theta) + height/2 + dy

    img = cv2.remap(img, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
    return img 

def elastic_transform_fast(image, alpha, sigma, alpha_affine, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_ (with modifications).
    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
         Convolutional Neural Networks applied to Visual Document Analysis", in
         Proc. of the International Conference on Document Analysis and
         Recognition, 2003.

     Based on https://gist.github.com/erniejunior/601cdf56d2b424757de5
    """
    if random_state is None:
        random_state = np.random.RandomState(1234)

    shape = image.shape
    shape_size = shape[:2]


    # Random affine
    center_square = np.float32(shape_size) // 2
    square_size = min(shape_size) // 3
    alpha = float(alpha)
    sigma = float(sigma)
    alpha_affine = float(alpha_affine)

    pts1 = np.float32([center_square + square_size, [center_square[0] + square_size, center_square[1] - square_size],
                       center_square - square_size])
    pts2 = pts1 + random_state.uniform(-alpha_affine, alpha_affine, size=pts1.shape).astype(np.float32)
    M = cv2.getAffineTransform(pts1, pts2)

    image = cv2.warpAffine(image, M, shape_size[::-1], borderMode=cv2.BORDER_REFLECT_101)

    dx = np.float32(gaussian_filter((random_state.rand(*shape_size) * 2 - 1), sigma) * alpha)
    dy = np.float32(gaussian_filter((random_state.rand(*shape_size) * 2 - 1), sigma) * alpha)

    x, y = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]))

    mapx = np.float32(x + dx)
    mapy = np.float32(y + dy)

    return cv2.remap(image, mapx, mapy, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101) 

def distort1(img, k=0, dx=0, dy=0):
    """"
    ## unconverntional augmnet ################################################################################3
    ## https://stackoverflow.com/questions/6199636/formulas-for-barrel-pincushion-distortion

    ## https://stackoverflow.com/questions/10364201/image-transformation-in-opencv
    ## https://stackoverflow.com/questions/2477774/correcting-fisheye-distortion-programmatically
    ## http://www.coldvision.io/2017/03/02/advanced-lane-finding-using-opencv/

    ## barrel\pincushion distortion
    """
    height, width = img.shape[:2]
    #  map_x, map_y =
    # cv2.initUndistortRectifyMap(intrinsics, dist_coeffs, None, None, (width,height),cv2.CV_32FC1)
    # https://stackoverflow.com/questions/6199636/formulas-for-barrel-pincushion-distortion
    # https://stackoverflow.com/questions/10364201/image-transformation-in-opencv
    k = k * 0.00001
    dx = dx * width
    dy = dy * height
    x, y = np.mgrid[0:width:1, 0:height:1]
    x = x.astype(np.float32) - width/2 - dx
    y = y.astype(np.float32) - height/2 - dy
    theta = np.arctan2(y, x)
    d = (x*x + y*y)**0.5
    r = d*(1+k*d*d)
    map_x = r*np.cos(theta) + width/2 + dx
    map_y = r*np.sin(theta) + height/2 + dy

    img = cv2.remap(img, map_x, map_y, interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
    return img 

def rotate(img, angle):
    height, width = img.shape[0:2]
    mat = cv2.getRotationMatrix2D((width/2, height/2), angle, 1.0)
    img = cv2.warpAffine(img, mat, (width, height),
                         flags=cv2.INTER_LINEAR,
                         borderMode=cv2.BORDER_REFLECT_101)
    return img 

def cvBorderMode(self):
        if self.fill_mode == 'constant':
            return cv2.BORDER_CONSTANT
        if self.fill_mode == 'nearest':
            return cv2.BORDER_REPLICATE
        if self.fill_mode == 'reflect':
            return cv2.BORDER_REFLECT_101
        if self.fill_mode == 'wrap':
            return cv2.BORDER_WRAP 

def rotate_image(image, angle, scale, rot_pnt):
    rot_mat = cv2.getRotationMatrix2D(rot_pnt, angle, scale)
    result = cv2.warpAffine(image, rot_mat, image.shape[:2], flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
    return result 

def shift_image(img, shift_pnt):
    M = np.float32([[1, 0, shift_pnt[0]], [0, 1, shift_pnt[1]]])
    res = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]), borderMode=cv2.BORDER_REFLECT_101)
    return res 

def shift_image(img, shift_pnt):
    M = np.float32([[1, 0, shift_pnt[0]], [0, 1, shift_pnt[1]]])
    res = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]), borderMode=cv2.BORDER_REFLECT_101)
    return res 

def rotate_image(image, angle, scale, rot_pnt):
    rot_mat = cv2.getRotationMatrix2D(rot_pnt, angle, scale)
    result = cv2.warpAffine(image, rot_mat, image.shape[:2], flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)
    return result 

def shift_image(img, shift_pnt):
    M = np.float32([[1, 0, shift_pnt[0]], [0, 1, shift_pnt[1]]])
    res = cv2.warpAffine(img, M, (img.shape[1], img.shape[0]), borderMode=cv2.BORDER_REFLECT_101)
    return res