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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import numbers | |
| import cv2 | |
| import numpy as np | |
| from ..utils import to_2tuple | |
| from .io import imread_backend | |
| try: | |
| from PIL import Image | |
| except ImportError: | |
| Image = None | |
| def _scale_size(size, scale): | |
| """Rescale a size by a ratio. | |
| Args: | |
| size (tuple[int]): (w, h). | |
| scale (float | tuple(float)): Scaling factor. | |
| Returns: | |
| tuple[int]: scaled size. | |
| """ | |
| if isinstance(scale, (float, int)): | |
| scale = (scale, scale) | |
| w, h = size | |
| return int(w * float(scale[0]) + 0.5), int(h * float(scale[1]) + 0.5) | |
| cv2_interp_codes = { | |
| 'nearest': cv2.INTER_NEAREST, | |
| 'bilinear': cv2.INTER_LINEAR, | |
| 'bicubic': cv2.INTER_CUBIC, | |
| 'area': cv2.INTER_AREA, | |
| 'lanczos': cv2.INTER_LANCZOS4 | |
| } | |
| if Image is not None: | |
| pillow_interp_codes = { | |
| 'nearest': Image.NEAREST, | |
| 'bilinear': Image.BILINEAR, | |
| 'bicubic': Image.BICUBIC, | |
| 'box': Image.BOX, | |
| 'lanczos': Image.LANCZOS, | |
| 'hamming': Image.HAMMING | |
| } | |
| def imresize(img, | |
| size, | |
| return_scale=False, | |
| interpolation='bilinear', | |
| out=None, | |
| backend=None): | |
| """Resize image to a given size. | |
| Args: | |
| img (ndarray): The input image. | |
| size (tuple[int]): Target size (w, h). | |
| return_scale (bool): Whether to return `w_scale` and `h_scale`. | |
| interpolation (str): Interpolation method, accepted values are | |
| "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2' | |
| backend, "nearest", "bilinear" for 'pillow' backend. | |
| out (ndarray): The output destination. | |
| backend (str | None): The image resize backend type. Options are `cv2`, | |
| `pillow`, `None`. If backend is None, the global imread_backend | |
| specified by ``mmcv.use_backend()`` will be used. Default: None. | |
| Returns: | |
| tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or | |
| `resized_img`. | |
| """ | |
| h, w = img.shape[:2] | |
| if backend is None: | |
| backend = imread_backend | |
| if backend not in ['cv2', 'pillow']: | |
| raise ValueError(f'backend: {backend} is not supported for resize.' | |
| f"Supported backends are 'cv2', 'pillow'") | |
| if backend == 'pillow': | |
| assert img.dtype == np.uint8, 'Pillow backend only support uint8 type' | |
| pil_image = Image.fromarray(img) | |
| pil_image = pil_image.resize(size, pillow_interp_codes[interpolation]) | |
| resized_img = np.array(pil_image) | |
| else: | |
| resized_img = cv2.resize( | |
| img, size, dst=out, interpolation=cv2_interp_codes[interpolation]) | |
| if not return_scale: | |
| return resized_img | |
| else: | |
| w_scale = size[0] / w | |
| h_scale = size[1] / h | |
| return resized_img, w_scale, h_scale | |
| def imresize_to_multiple(img, | |
| divisor, | |
| size=None, | |
| scale_factor=None, | |
| keep_ratio=False, | |
| return_scale=False, | |
| interpolation='bilinear', | |
| out=None, | |
| backend=None): | |
| """Resize image according to a given size or scale factor and then rounds | |
| up the the resized or rescaled image size to the nearest value that can be | |
| divided by the divisor. | |
| Args: | |
| img (ndarray): The input image. | |
| divisor (int | tuple): Resized image size will be a multiple of | |
| divisor. If divisor is a tuple, divisor should be | |
| (w_divisor, h_divisor). | |
| size (None | int | tuple[int]): Target size (w, h). Default: None. | |
| scale_factor (None | float | tuple[float]): Multiplier for spatial | |
| size. Should match input size if it is a tuple and the 2D style is | |
| (w_scale_factor, h_scale_factor). Default: None. | |
| keep_ratio (bool): Whether to keep the aspect ratio when resizing the | |
| image. Default: False. | |
| return_scale (bool): Whether to return `w_scale` and `h_scale`. | |
| interpolation (str): Interpolation method, accepted values are | |
| "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2' | |
| backend, "nearest", "bilinear" for 'pillow' backend. | |
| out (ndarray): The output destination. | |
| backend (str | None): The image resize backend type. Options are `cv2`, | |
| `pillow`, `None`. If backend is None, the global imread_backend | |
| specified by ``mmcv.use_backend()`` will be used. Default: None. | |
| Returns: | |
| tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or | |
| `resized_img`. | |
| """ | |
| h, w = img.shape[:2] | |
| if size is not None and scale_factor is not None: | |
| raise ValueError('only one of size or scale_factor should be defined') | |
| elif size is None and scale_factor is None: | |
| raise ValueError('one of size or scale_factor should be defined') | |
| elif size is not None: | |
| size = to_2tuple(size) | |
| if keep_ratio: | |
| size = rescale_size((w, h), size, return_scale=False) | |
| else: | |
| size = _scale_size((w, h), scale_factor) | |
| divisor = to_2tuple(divisor) | |
| size = tuple([int(np.ceil(s / d)) * d for s, d in zip(size, divisor)]) | |
| resized_img, w_scale, h_scale = imresize( | |
| img, | |
| size, | |
| return_scale=True, | |
| interpolation=interpolation, | |
| out=out, | |
| backend=backend) | |
| if return_scale: | |
| return resized_img, w_scale, h_scale | |
| else: | |
| return resized_img | |
| def imresize_like(img, | |
| dst_img, | |
| return_scale=False, | |
| interpolation='bilinear', | |
| backend=None): | |
| """Resize image to the same size of a given image. | |
| Args: | |
| img (ndarray): The input image. | |
| dst_img (ndarray): The target image. | |
| return_scale (bool): Whether to return `w_scale` and `h_scale`. | |
| interpolation (str): Same as :func:`resize`. | |
| backend (str | None): Same as :func:`resize`. | |
| Returns: | |
| tuple or ndarray: (`resized_img`, `w_scale`, `h_scale`) or | |
| `resized_img`. | |
| """ | |
| h, w = dst_img.shape[:2] | |
| return imresize(img, (w, h), return_scale, interpolation, backend=backend) | |
| def rescale_size(old_size, scale, return_scale=False): | |
| """Calculate the new size to be rescaled to. | |
| Args: | |
| old_size (tuple[int]): The old size (w, h) of image. | |
| scale (float | tuple[int]): The scaling factor or maximum size. | |
| If it is a float number, then the image will be rescaled by this | |
| factor, else if it is a tuple of 2 integers, then the image will | |
| be rescaled as large as possible within the scale. | |
| return_scale (bool): Whether to return the scaling factor besides the | |
| rescaled image size. | |
| Returns: | |
| tuple[int]: The new rescaled image size. | |
| """ | |
| w, h = old_size | |
| if isinstance(scale, (float, int)): | |
| if scale <= 0: | |
| raise ValueError(f'Invalid scale {scale}, must be positive.') | |
| scale_factor = scale | |
| elif isinstance(scale, tuple): | |
| max_long_edge = max(scale) | |
| max_short_edge = min(scale) | |
| scale_factor = min(max_long_edge / max(h, w), | |
| max_short_edge / min(h, w)) | |
| else: | |
| raise TypeError( | |
| f'Scale must be a number or tuple of int, but got {type(scale)}') | |
| new_size = _scale_size((w, h), scale_factor) | |
| if return_scale: | |
| return new_size, scale_factor | |
| else: | |
| return new_size | |
| def imrescale(img, | |
| scale, | |
| return_scale=False, | |
| interpolation='bilinear', | |
| backend=None): | |
| """Resize image while keeping the aspect ratio. | |
| Args: | |
| img (ndarray): The input image. | |
| scale (float | tuple[int]): The scaling factor or maximum size. | |
| If it is a float number, then the image will be rescaled by this | |
| factor, else if it is a tuple of 2 integers, then the image will | |
| be rescaled as large as possible within the scale. | |
| return_scale (bool): Whether to return the scaling factor besides the | |
| rescaled image. | |
| interpolation (str): Same as :func:`resize`. | |
| backend (str | None): Same as :func:`resize`. | |
| Returns: | |
| ndarray: The rescaled image. | |
| """ | |
| h, w = img.shape[:2] | |
| new_size, scale_factor = rescale_size((w, h), scale, return_scale=True) | |
| rescaled_img = imresize( | |
| img, new_size, interpolation=interpolation, backend=backend) | |
| if return_scale: | |
| return rescaled_img, scale_factor | |
| else: | |
| return rescaled_img | |
| def imflip(img, direction='horizontal'): | |
| """Flip an image horizontally or vertically. | |
| Args: | |
| img (ndarray): Image to be flipped. | |
| direction (str): The flip direction, either "horizontal" or | |
| "vertical" or "diagonal". | |
| Returns: | |
| ndarray: The flipped image. | |
| """ | |
| assert direction in ['horizontal', 'vertical', 'diagonal'] | |
| if direction == 'horizontal': | |
| return np.flip(img, axis=1) | |
| elif direction == 'vertical': | |
| return np.flip(img, axis=0) | |
| else: | |
| return np.flip(img, axis=(0, 1)) | |
| def imflip_(img, direction='horizontal'): | |
| """Inplace flip an image horizontally or vertically. | |
| Args: | |
| img (ndarray): Image to be flipped. | |
| direction (str): The flip direction, either "horizontal" or | |
| "vertical" or "diagonal". | |
| Returns: | |
| ndarray: The flipped image (inplace). | |
| """ | |
| assert direction in ['horizontal', 'vertical', 'diagonal'] | |
| if direction == 'horizontal': | |
| return cv2.flip(img, 1, img) | |
| elif direction == 'vertical': | |
| return cv2.flip(img, 0, img) | |
| else: | |
| return cv2.flip(img, -1, img) | |
| def imrotate(img, | |
| angle, | |
| center=None, | |
| scale=1.0, | |
| border_value=0, | |
| interpolation='bilinear', | |
| auto_bound=False): | |
| """Rotate an image. | |
| Args: | |
| img (ndarray): Image to be rotated. | |
| angle (float): Rotation angle in degrees, positive values mean | |
| clockwise rotation. | |
| center (tuple[float], optional): Center point (w, h) of the rotation in | |
| the source image. If not specified, the center of the image will be | |
| used. | |
| scale (float): Isotropic scale factor. | |
| border_value (int): Border value. | |
| interpolation (str): Same as :func:`resize`. | |
| auto_bound (bool): Whether to adjust the image size to cover the whole | |
| rotated image. | |
| Returns: | |
| ndarray: The rotated image. | |
| """ | |
| if center is not None and auto_bound: | |
| raise ValueError('`auto_bound` conflicts with `center`') | |
| h, w = img.shape[:2] | |
| if center is None: | |
| center = ((w - 1) * 0.5, (h - 1) * 0.5) | |
| assert isinstance(center, tuple) | |
| matrix = cv2.getRotationMatrix2D(center, -angle, scale) | |
| if auto_bound: | |
| cos = np.abs(matrix[0, 0]) | |
| sin = np.abs(matrix[0, 1]) | |
| new_w = h * sin + w * cos | |
| new_h = h * cos + w * sin | |
| matrix[0, 2] += (new_w - w) * 0.5 | |
| matrix[1, 2] += (new_h - h) * 0.5 | |
| w = int(np.round(new_w)) | |
| h = int(np.round(new_h)) | |
| rotated = cv2.warpAffine( | |
| img, | |
| matrix, (w, h), | |
| flags=cv2_interp_codes[interpolation], | |
| borderValue=border_value) | |
| return rotated | |
| def bbox_clip(bboxes, img_shape): | |
| """Clip bboxes to fit the image shape. | |
| Args: | |
| bboxes (ndarray): Shape (..., 4*k) | |
| img_shape (tuple[int]): (height, width) of the image. | |
| Returns: | |
| ndarray: Clipped bboxes. | |
| """ | |
| assert bboxes.shape[-1] % 4 == 0 | |
| cmin = np.empty(bboxes.shape[-1], dtype=bboxes.dtype) | |
| cmin[0::2] = img_shape[1] - 1 | |
| cmin[1::2] = img_shape[0] - 1 | |
| clipped_bboxes = np.maximum(np.minimum(bboxes, cmin), 0) | |
| return clipped_bboxes | |
| def bbox_scaling(bboxes, scale, clip_shape=None): | |
| """Scaling bboxes w.r.t the box center. | |
| Args: | |
| bboxes (ndarray): Shape(..., 4). | |
| scale (float): Scaling factor. | |
| clip_shape (tuple[int], optional): If specified, bboxes that exceed the | |
| boundary will be clipped according to the given shape (h, w). | |
| Returns: | |
| ndarray: Scaled bboxes. | |
| """ | |
| if float(scale) == 1.0: | |
| scaled_bboxes = bboxes.copy() | |
| else: | |
| w = bboxes[..., 2] - bboxes[..., 0] + 1 | |
| h = bboxes[..., 3] - bboxes[..., 1] + 1 | |
| dw = (w * (scale - 1)) * 0.5 | |
| dh = (h * (scale - 1)) * 0.5 | |
| scaled_bboxes = bboxes + np.stack((-dw, -dh, dw, dh), axis=-1) | |
| if clip_shape is not None: | |
| return bbox_clip(scaled_bboxes, clip_shape) | |
| else: | |
| return scaled_bboxes | |
| def imcrop(img, bboxes, scale=1.0, pad_fill=None): | |
| """Crop image patches. | |
| 3 steps: scale the bboxes -> clip bboxes -> crop and pad. | |
| Args: | |
| img (ndarray): Image to be cropped. | |
| bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes. | |
| scale (float, optional): Scale ratio of bboxes, the default value | |
| 1.0 means no padding. | |
| pad_fill (Number | list[Number]): Value to be filled for padding. | |
| Default: None, which means no padding. | |
| Returns: | |
| list[ndarray] | ndarray: The cropped image patches. | |
| """ | |
| chn = 1 if img.ndim == 2 else img.shape[2] | |
| if pad_fill is not None: | |
| if isinstance(pad_fill, (int, float)): | |
| pad_fill = [pad_fill for _ in range(chn)] | |
| assert len(pad_fill) == chn | |
| _bboxes = bboxes[None, ...] if bboxes.ndim == 1 else bboxes | |
| scaled_bboxes = bbox_scaling(_bboxes, scale).astype(np.int32) | |
| clipped_bbox = bbox_clip(scaled_bboxes, img.shape) | |
| patches = [] | |
| for i in range(clipped_bbox.shape[0]): | |
| x1, y1, x2, y2 = tuple(clipped_bbox[i, :]) | |
| if pad_fill is None: | |
| patch = img[y1:y2 + 1, x1:x2 + 1, ...] | |
| else: | |
| _x1, _y1, _x2, _y2 = tuple(scaled_bboxes[i, :]) | |
| if chn == 1: | |
| patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1) | |
| else: | |
| patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1, chn) | |
| patch = np.array( | |
| pad_fill, dtype=img.dtype) * np.ones( | |
| patch_shape, dtype=img.dtype) | |
| x_start = 0 if _x1 >= 0 else -_x1 | |
| y_start = 0 if _y1 >= 0 else -_y1 | |
| w = x2 - x1 + 1 | |
| h = y2 - y1 + 1 | |
| patch[y_start:y_start + h, x_start:x_start + w, | |
| ...] = img[y1:y1 + h, x1:x1 + w, ...] | |
| patches.append(patch) | |
| if bboxes.ndim == 1: | |
| return patches[0] | |
| else: | |
| return patches | |
| def impad(img, | |
| *, | |
| shape=None, | |
| padding=None, | |
| pad_val=0, | |
| padding_mode='constant'): | |
| """Pad the given image to a certain shape or pad on all sides with | |
| specified padding mode and padding value. | |
| Args: | |
| img (ndarray): Image to be padded. | |
| shape (tuple[int]): Expected padding shape (h, w). Default: None. | |
| padding (int or tuple[int]): Padding on each border. If a single int is | |
| provided this is used to pad all borders. If tuple of length 2 is | |
| provided this is the padding on left/right and top/bottom | |
| respectively. If a tuple of length 4 is provided this is the | |
| padding for the left, top, right and bottom borders respectively. | |
| Default: None. Note that `shape` and `padding` can not be both | |
| set. | |
| pad_val (Number | Sequence[Number]): Values to be filled in padding | |
| areas when padding_mode is 'constant'. Default: 0. | |
| padding_mode (str): Type of padding. Should be: constant, edge, | |
| reflect or symmetric. Default: constant. | |
| - constant: pads with a constant value, this value is specified | |
| with pad_val. | |
| - edge: pads with the last value at the edge of the image. | |
| - reflect: pads with reflection of image without repeating the | |
| last value on the edge. For example, padding [1, 2, 3, 4] | |
| with 2 elements on both sides in reflect mode will result | |
| in [3, 2, 1, 2, 3, 4, 3, 2]. | |
| - symmetric: pads with reflection of image repeating the last | |
| value on the edge. For example, padding [1, 2, 3, 4] with | |
| 2 elements on both sides in symmetric mode will result in | |
| [2, 1, 1, 2, 3, 4, 4, 3] | |
| Returns: | |
| ndarray: The padded image. | |
| """ | |
| assert (shape is not None) ^ (padding is not None) | |
| if shape is not None: | |
| padding = (0, 0, shape[1] - img.shape[1], shape[0] - img.shape[0]) | |
| # check pad_val | |
| if isinstance(pad_val, tuple): | |
| assert len(pad_val) == img.shape[-1] | |
| elif not isinstance(pad_val, numbers.Number): | |
| raise TypeError('pad_val must be a int or a tuple. ' | |
| f'But received {type(pad_val)}') | |
| # check padding | |
| if isinstance(padding, tuple) and len(padding) in [2, 4]: | |
| if len(padding) == 2: | |
| padding = (padding[0], padding[1], padding[0], padding[1]) | |
| elif isinstance(padding, numbers.Number): | |
| padding = (padding, padding, padding, padding) | |
| else: | |
| raise ValueError('Padding must be a int or a 2, or 4 element tuple.' | |
| f'But received {padding}') | |
| # check padding mode | |
| assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'] | |
| border_type = { | |
| 'constant': cv2.BORDER_CONSTANT, | |
| 'edge': cv2.BORDER_REPLICATE, | |
| 'reflect': cv2.BORDER_REFLECT_101, | |
| 'symmetric': cv2.BORDER_REFLECT | |
| } | |
| img = cv2.copyMakeBorder( | |
| img, | |
| padding[1], | |
| padding[3], | |
| padding[0], | |
| padding[2], | |
| border_type[padding_mode], | |
| value=pad_val) | |
| return img | |
| def impad_to_multiple(img, divisor, pad_val=0): | |
| """Pad an image to ensure each edge to be multiple to some number. | |
| Args: | |
| img (ndarray): Image to be padded. | |
| divisor (int): Padded image edges will be multiple to divisor. | |
| pad_val (Number | Sequence[Number]): Same as :func:`impad`. | |
| Returns: | |
| ndarray: The padded image. | |
| """ | |
| pad_h = int(np.ceil(img.shape[0] / divisor)) * divisor | |
| pad_w = int(np.ceil(img.shape[1] / divisor)) * divisor | |
| return impad(img, shape=(pad_h, pad_w), pad_val=pad_val) | |
| def cutout(img, shape, pad_val=0): | |
| """Randomly cut out a rectangle from the original img. | |
| Args: | |
| img (ndarray): Image to be cutout. | |
| shape (int | tuple[int]): Expected cutout shape (h, w). If given as a | |
| int, the value will be used for both h and w. | |
| pad_val (int | float | tuple[int | float]): Values to be filled in the | |
| cut area. Defaults to 0. | |
| Returns: | |
| ndarray: The cutout image. | |
| """ | |
| channels = 1 if img.ndim == 2 else img.shape[2] | |
| if isinstance(shape, int): | |
| cut_h, cut_w = shape, shape | |
| else: | |
| assert isinstance(shape, tuple) and len(shape) == 2, \ | |
| f'shape must be a int or a tuple with length 2, but got type ' \ | |
| f'{type(shape)} instead.' | |
| cut_h, cut_w = shape | |
| if isinstance(pad_val, (int, float)): | |
| pad_val = tuple([pad_val] * channels) | |
| elif isinstance(pad_val, tuple): | |
| assert len(pad_val) == channels, \ | |
| 'Expected the num of elements in tuple equals the channels' \ | |
| 'of input image. Found {} vs {}'.format( | |
| len(pad_val), channels) | |
| else: | |
| raise TypeError(f'Invalid type {type(pad_val)} for `pad_val`') | |
| img_h, img_w = img.shape[:2] | |
| y0 = np.random.uniform(img_h) | |
| x0 = np.random.uniform(img_w) | |
| y1 = int(max(0, y0 - cut_h / 2.)) | |
| x1 = int(max(0, x0 - cut_w / 2.)) | |
| y2 = min(img_h, y1 + cut_h) | |
| x2 = min(img_w, x1 + cut_w) | |
| if img.ndim == 2: | |
| patch_shape = (y2 - y1, x2 - x1) | |
| else: | |
| patch_shape = (y2 - y1, x2 - x1, channels) | |
| img_cutout = img.copy() | |
| patch = np.array( | |
| pad_val, dtype=img.dtype) * np.ones( | |
| patch_shape, dtype=img.dtype) | |
| img_cutout[y1:y2, x1:x2, ...] = patch | |
| return img_cutout | |
| def _get_shear_matrix(magnitude, direction='horizontal'): | |
| """Generate the shear matrix for transformation. | |
| Args: | |
| magnitude (int | float): The magnitude used for shear. | |
| direction (str): The flip direction, either "horizontal" | |
| or "vertical". | |
| Returns: | |
| ndarray: The shear matrix with dtype float32. | |
| """ | |
| if direction == 'horizontal': | |
| shear_matrix = np.float32([[1, magnitude, 0], [0, 1, 0]]) | |
| elif direction == 'vertical': | |
| shear_matrix = np.float32([[1, 0, 0], [magnitude, 1, 0]]) | |
| return shear_matrix | |
| def imshear(img, | |
| magnitude, | |
| direction='horizontal', | |
| border_value=0, | |
| interpolation='bilinear'): | |
| """Shear an image. | |
| Args: | |
| img (ndarray): Image to be sheared with format (h, w) | |
| or (h, w, c). | |
| magnitude (int | float): The magnitude used for shear. | |
| direction (str): The flip direction, either "horizontal" | |
| or "vertical". | |
| border_value (int | tuple[int]): Value used in case of a | |
| constant border. | |
| interpolation (str): Same as :func:`resize`. | |
| Returns: | |
| ndarray: The sheared image. | |
| """ | |
| assert direction in ['horizontal', | |
| 'vertical'], f'Invalid direction: {direction}' | |
| height, width = img.shape[:2] | |
| if img.ndim == 2: | |
| channels = 1 | |
| elif img.ndim == 3: | |
| channels = img.shape[-1] | |
| if isinstance(border_value, int): | |
| border_value = tuple([border_value] * channels) | |
| elif isinstance(border_value, tuple): | |
| assert len(border_value) == channels, \ | |
| 'Expected the num of elements in tuple equals the channels' \ | |
| 'of input image. Found {} vs {}'.format( | |
| len(border_value), channels) | |
| else: | |
| raise ValueError( | |
| f'Invalid type {type(border_value)} for `border_value`') | |
| shear_matrix = _get_shear_matrix(magnitude, direction) | |
| sheared = cv2.warpAffine( | |
| img, | |
| shear_matrix, | |
| (width, height), | |
| # Note case when the number elements in `border_value` | |
| # greater than 3 (e.g. shearing masks whose channels large | |
| # than 3) will raise TypeError in `cv2.warpAffine`. | |
| # Here simply slice the first 3 values in `border_value`. | |
| borderValue=border_value[:3], | |
| flags=cv2_interp_codes[interpolation]) | |
| return sheared | |
| def _get_translate_matrix(offset, direction='horizontal'): | |
| """Generate the translate matrix. | |
| Args: | |
| offset (int | float): The offset used for translate. | |
| direction (str): The translate direction, either | |
| "horizontal" or "vertical". | |
| Returns: | |
| ndarray: The translate matrix with dtype float32. | |
| """ | |
| if direction == 'horizontal': | |
| translate_matrix = np.float32([[1, 0, offset], [0, 1, 0]]) | |
| elif direction == 'vertical': | |
| translate_matrix = np.float32([[1, 0, 0], [0, 1, offset]]) | |
| return translate_matrix | |
| def imtranslate(img, | |
| offset, | |
| direction='horizontal', | |
| border_value=0, | |
| interpolation='bilinear'): | |
| """Translate an image. | |
| Args: | |
| img (ndarray): Image to be translated with format | |
| (h, w) or (h, w, c). | |
| offset (int | float): The offset used for translate. | |
| direction (str): The translate direction, either "horizontal" | |
| or "vertical". | |
| border_value (int | tuple[int]): Value used in case of a | |
| constant border. | |
| interpolation (str): Same as :func:`resize`. | |
| Returns: | |
| ndarray: The translated image. | |
| """ | |
| assert direction in ['horizontal', | |
| 'vertical'], f'Invalid direction: {direction}' | |
| height, width = img.shape[:2] | |
| if img.ndim == 2: | |
| channels = 1 | |
| elif img.ndim == 3: | |
| channels = img.shape[-1] | |
| if isinstance(border_value, int): | |
| border_value = tuple([border_value] * channels) | |
| elif isinstance(border_value, tuple): | |
| assert len(border_value) == channels, \ | |
| 'Expected the num of elements in tuple equals the channels' \ | |
| 'of input image. Found {} vs {}'.format( | |
| len(border_value), channels) | |
| else: | |
| raise ValueError( | |
| f'Invalid type {type(border_value)} for `border_value`.') | |
| translate_matrix = _get_translate_matrix(offset, direction) | |
| translated = cv2.warpAffine( | |
| img, | |
| translate_matrix, | |
| (width, height), | |
| # Note case when the number elements in `border_value` | |
| # greater than 3 (e.g. translating masks whose channels | |
| # large than 3) will raise TypeError in `cv2.warpAffine`. | |
| # Here simply slice the first 3 values in `border_value`. | |
| borderValue=border_value[:3], | |
| flags=cv2_interp_codes[interpolation]) | |
| return translated | |