Python cv2.BORDER_REFLECT_101属性代码示例

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
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 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
def affine(img, angle, translate, scale, shear, interpolation=cv2.INTER_LINEAR, 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) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
def affine_transform(src, src_tri, dst_tri, size):
    warp_mat = cv2.getAffineTransform(np.float32(src_tri), np.float32(dst_tri))

    dst = cv2.warpAffine(src, warp_mat, (size[0], size[1]),
                         None,
                         flags=cv2.INTER_LINEAR,
                         borderMode=cv2.BORDER_REFLECT_101)

    return dst 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
def applyAffineTransform(src, srcTri, dstTri, size) :
    
    # Given a pair of triangles, find the affine transform.
    warpMat = cv2.getAffineTransform( np.float32(srcTri), np.float32(dstTri) )
    
    # Apply the Affine Transform just found to the src image
    dst = cv2.warpAffine( src, warpMat, (size[0], size[1]), None, flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101 )

    return dst


# Warps and alpha blends triangular regions from img1 and img2 to img 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
def affine(img, angle, translate, scale, shear, interpolation=cv2.INTER_LINEAR, 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_LINEAR``, for bilinear 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) 

# 需要导入模块: import cv2 [as 别名]
# 或者: from cv2 import BORDER_REFLECT_101 [as 别名]
def elastic_transform(image, severity=1):
    c = [(244 * 2, 244 * 0.7, 244 * 0.1),   # 244 should have been 224, but ultimately nothing is incorrect
         (244 * 2, 244 * 0.08, 244 * 0.2),
         (244 * 0.05, 244 * 0.01, 244 * 0.02),
         (244 * 0.07, 244 * 0.01, 244 * 0.02),
         (244 * 0.12, 244 * 0.01, 244 * 0.02)][severity - 1]

    image = np.array(image, dtype=np.float32) / 255.
    shape = image.shape
    shape_size = shape[:2]

    # random affine
    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 + np.random.uniform(-c[2], c[2], 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 = (gaussian(np.random.uniform(-1, 1, size=shape[:2]),
                   c[1], mode='reflect', truncate=3) * c[0]).astype(np.float32)
    dy = (gaussian(np.random.uniform(-1, 1, size=shape[:2]),
                   c[1], mode='reflect', truncate=3) * c[0]).astype(np.float32)
    dx, dy = dx[..., np.newaxis], dy[..., np.newaxis]

    x, y, z = np.meshgrid(np.arange(shape[1]), np.arange(shape[0]), np.arange(shape[2]))
    indices = np.reshape(y + dy, (-1, 1)), np.reshape(x + dx, (-1, 1)), np.reshape(z, (-1, 1))
    return np.clip(map_coordinates(image, indices, order=1, mode='reflect').reshape(shape), 0, 1) * 255


# /////////////// End Corruptions ///////////////