mmedit.datasets.transforms.matlab_like_resize 源代码

# This code is referenced from matlab_imresize with modifications
# Reference:
# Original licence: Copyright (c) 2020 fatheral, under the MIT License.
import numpy as np
from mmcv.transforms import BaseTransform

from mmedit.registry import TRANSFORMS

[文档]def get_size_from_scale(input_size, scale_factor): """Get the output size given input size and scale factor. Args: input_size (tuple): The size of the input image. scale_factor (float): The resize factor. Returns: output_shape (list[int]): The size of the output image. """ output_shape = [ int(np.ceil(scale * shape)) for (scale, shape) in zip(scale_factor, input_size) ] return output_shape
[文档]def get_scale_from_size(input_size, output_size): """Get the scale factor given input size and output size. Args: input_size (tuple(int)): The size of the input image. output_size (tuple(int)): The size of the output image. Returns: scale (list[float]): The scale factor of each dimension. """ scale = [ 1.0 * output_shape / input_shape for (input_shape, output_shape) in zip(input_size, output_size) ] return scale
[文档]def _cubic(x): """Cubic function. Args: x (np.ndarray): The distance from the center position. Returns: np.ndarray: The weight corresponding to a particular distance. """ x = np.array(x, dtype=np.float32) x_abs = np.abs(x) x_abs_sq = x_abs**2 x_abs_cu = x_abs_sq * x_abs # if |x| <= 1: y = 1.5|x|^3 - 2.5|x|^2 + 1 # if 1 < |x| <= 2: -0.5|x|^3 + 2.5|x|^2 - 4|x| + 2 f = (1.5 * x_abs_cu - 2.5 * x_abs_sq + 1) * (x_abs <= 1) + ( -0.5 * x_abs_cu + 2.5 * x_abs_sq - 4 * x_abs + 2) * ((1 < x_abs) & (x_abs <= 2)) return f
[文档]def get_weights_indices(input_length, output_length, scale, kernel, kernel_width): """Get weights and indices for interpolation. Args: input_length (int): Length of the input sequence. output_length (int): Length of the output sequence. scale (float): Scale factor. kernel (func): The kernel used for resizing. kernel_width (int): The width of the kernel. Returns: tuple(list[np.ndarray], list[np.ndarray]): The weights and the indices for interpolation. """ if scale < 1: # modified kernel for antialiasing def h(x): return scale * kernel(scale * x) kernel_width = 1.0 * kernel_width / scale else: h = kernel kernel_width = kernel_width # coordinates of output x = np.arange(1, output_length + 1).astype(np.float32) # coordinates of input u = x / scale + 0.5 * (1 - 1 / scale) left = np.floor(u - kernel_width / 2) # leftmost pixel p = int(np.ceil(kernel_width)) + 2 # maximum number of pixels # indices of input pixels ind = left[:, np.newaxis, ...] + np.arange(p) indices = ind.astype(np.int32) # weights of input pixels weights = h(u[:, np.newaxis, ...] - indices - 1) weights = weights / np.sum(weights, axis=1)[:, np.newaxis, ...] # remove all-zero columns aux = np.concatenate( (np.arange(input_length), np.arange(input_length - 1, -1, step=-1))).astype(np.int32) indices = aux[np.mod(indices, aux.size)] ind2store = np.nonzero(np.any(weights, axis=0)) weights = weights[:, ind2store] indices = indices[:, ind2store] return weights, indices
[文档]def resize_along_dim(img_in, weights, indices, dim): """Resize along a specific dimension. Args: img_in (np.ndarray): The input image. weights (ndarray): The weights used for interpolation, computed from [get_weights_indices]. indices (ndarray): The indices used for interpolation, computed from [get_weights_indices]. dim (int): Which dimension to undergo interpolation. Returns: np.ndarray: Interpolated (along one dimension) image. """ img_in = img_in.astype(np.float32) w_shape = weights.shape output_shape = list(img_in.shape) output_shape[dim] = w_shape[0] img_out = np.zeros(output_shape) if dim == 0: for i in range(w_shape[0]): w = weights[i, :][np.newaxis, ...] ind = indices[i, :] img_slice = img_in[ind, :] img_out[i] = np.sum(np.squeeze(img_slice, axis=0) * w.T, axis=0) elif dim == 1: for i in range(w_shape[0]): w = weights[i, :][:, :, np.newaxis] ind = indices[i, :] img_slice = img_in[:, ind] img_out[:, i] = np.sum(np.squeeze(img_slice, axis=1) * w.T, axis=1) if img_in.dtype == np.uint8: img_out = np.clip(img_out, 0, 255) return np.around(img_out).astype(np.uint8) else: return img_out
[文档]class MATLABLikeResize(BaseTransform): """Resize the input image using MATLAB-like downsampling. Currently support bicubic interpolation only. Note that the output of this function is slightly different from the official MATLAB function. Required keys are the keys in attribute "keys". Added or modified keys are "scale" and "output_shape", and the keys in attribute "keys". Args: keys (list[str]): A list of keys whose values are modified. scale (float | None, optional): The scale factor of the resize operation. If None, it will be determined by output_shape. Default: None. output_shape (tuple(int) | None, optional): The size of the output image. If None, it will be determined by scale. Note that if scale is provided, output_shape will not be used. Default: None. kernel (str, optional): The kernel for the resize operation. Currently support 'bicubic' only. Default: 'bicubic'. kernel_width (float): The kernel width. Currently support 4.0 only. Default: 4.0. """ def __init__(self, keys, scale=None, output_shape=None, kernel='bicubic', kernel_width=4.0): if kernel.lower() != 'bicubic': raise ValueError('Currently support bicubic kernel only.') if float(kernel_width) != 4.0: raise ValueError('Current support only width=4 only.') if scale is None and output_shape is None: raise ValueError('"scale" and "output_shape" cannot be both None') self.kernel_func = _cubic self.keys = keys self.scale = scale self.output_shape = output_shape self.kernel = kernel self.kernel_width = kernel_width
[文档] def _resize(self, img): """resize an image to the require size. Args: img (np.ndarray): The original image. Returns: output (np.ndarray): The resized image. """ weights = {} indices = {} # compute scale and output_size if self.scale is not None: scale = float(self.scale) scale = [scale, scale] output_size = get_size_from_scale(img.shape, scale) else: scale = get_scale_from_size(img.shape, self.output_shape) output_size = list(self.output_shape) # apply cubic interpolation along two dimensions order = np.argsort(np.array(scale)) for k in range(2): key = (img.shape[k], output_size[k], scale[k], self.kernel_func, self.kernel_width) weight, index = get_weights_indices(img.shape[k], output_size[k], scale[k], self.kernel_func, self.kernel_width) weights[key] = weight indices[key] = index output = np.copy(img) if output.ndim == 2: # grayscale image output = output[:, :, np.newaxis] for k in range(2): dim = order[k] key = (img.shape[dim], output_size[dim], scale[dim], self.kernel_func, self.kernel_width) output = resize_along_dim(output, weights[key], indices[key], dim) return output
[文档] def transform(self, results): """transform function. Args: results (dict): A dict containing the necessary information and data for augmentation. Returns: dict: A dict containing the processed data and information. """ for key in self.keys: is_single_image = False if isinstance(results[key], np.ndarray): is_single_image = True results[key] = [results[key]] results[key] = [self._resize(img) for img in results[key]] if is_single_image: results[key] = results[key][0] results['scale'] = self.scale results['output_shape'] = self.output_shape return results
[文档] def __repr__(self): repr_str = self.__class__.__name__ repr_str += ( f'(keys={self.keys}, scale={self.scale}, ' f'output_shape={self.output_shape}, ' f'kernel={self.kernel}, kernel_width={self.kernel_width})') return repr_str
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