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Source code for mmedit.datasets.pipelines.generate_assistant

# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch

from ..registry import PIPELINES
from .utils import make_coord


[docs]@PIPELINES.register_module() class GenerateHeatmap: """Generate heatmap from keypoint. Args: keypoint (str): Key of keypoint in dict. ori_size (int | Tuple[int]): Original image size of keypoint. target_size (int | Tuple[int]): Target size of heatmap. sigma (float): Sigma parameter of heatmap. Default: 1.0 """ def __init__(self, keypoint, ori_size, target_size, sigma=1.0): if isinstance(ori_size, int): ori_size = (ori_size, ori_size) else: ori_size = ori_size[:2] if isinstance(target_size, int): target_size = (target_size, target_size) else: target_size = target_size[:2] self.size_ratio = (target_size[0] / ori_size[0], target_size[1] / ori_size[1]) self.keypoint = keypoint self.sigma = sigma self.target_size = target_size self.ori_size = ori_size def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Require keypoint. Returns: dict: A dict containing the processed data and information. Add 'heatmap'. """ keypoint_list = [(keypoint[0] * self.size_ratio[0], keypoint[1] * self.size_ratio[1]) for keypoint in results[self.keypoint]] heatmap_list = [ self._generate_one_heatmap(keypoint) for keypoint in keypoint_list ] results['heatmap'] = np.stack(heatmap_list, axis=2) return results def _generate_one_heatmap(self, keypoint): """Generate One Heatmap. Args: landmark (Tuple[float]): Location of a landmark. results: heatmap (np.ndarray): A heatmap of landmark. """ w, h = self.target_size x_range = np.arange(start=0, stop=w, dtype=int) y_range = np.arange(start=0, stop=h, dtype=int) grid_x, grid_y = np.meshgrid(x_range, y_range) dist2 = (grid_x - keypoint[0])**2 + (grid_y - keypoint[1])**2 exponent = dist2 / 2.0 / self.sigma / self.sigma heatmap = np.exp(-exponent) return heatmap def __repr__(self): return (f'{self.__class__.__name__}, ' f'keypoint={self.keypoint}, ' f'ori_size={self.ori_size}, ' f'target_size={self.target_size}, ' f'sigma={self.sigma}')
[docs]@PIPELINES.register_module() class GenerateCoordinateAndCell: """Generate coordinate and cell. Generate coordinate from the desired size of SR image. Train or val: 1. Generate coordinate from GT. 2. Reshape GT image to (HgWg, 3) and transpose to (3, HgWg). where `Hg` and `Wg` represent the height and width of GT. Test: Generate coordinate from LQ and scale or target_size. Then generate cell from coordinate. Args: sample_quantity (int): The quantity of samples in coordinates. To ensure that the GT tensors in a batch have the same dimensions. Default: None. scale (float): Scale of upsampling. Default: None. target_size (tuple[int]): Size of target image. Default: None. The priority of getting 'size of target image' is: 1, results['gt'].shape[-2:] 2, results['lq'].shape[-2:] * scale 3, target_size """ def __init__(self, sample_quantity=None, scale=None, target_size=None): self.sample_quantity = sample_quantity self.scale = scale self.target_size = target_size def __call__(self, results): """Call function. Args: results (dict): A dict containing the necessary information and data for augmentation. Require either in results: 1. 'lq' (tensor), whose shape is similar as (3, H, W). 2. 'gt' (tensor), whose shape is similar as (3, H, W). 3. None, the premise is self.target_size and len(self.target_size) >= 2. Returns: dict: A dict containing the processed data and information. Reshape 'gt' to (-1, 3) and transpose to (3, -1) if 'gt' in results. Add 'coord' and 'cell'. """ # generate hr_coord (and hr_rgb) if 'gt' in results: crop_hr = results['gt'] self.target_size = crop_hr.shape hr_rgb = crop_hr.contiguous().view(3, -1).permute(1, 0) results['gt'] = hr_rgb elif self.scale is not None and 'lq' in results: _, h_lr, w_lr = results['lq'].shape self.target_size = (round(h_lr * self.scale), round(w_lr * self.scale)) else: assert self.target_size is not None assert len(self.target_size) >= 2 hr_coord = make_coord(self.target_size[-2:]) if self.sample_quantity is not None and 'gt' in results: sample_lst = np.random.choice( len(hr_coord), self.sample_quantity, replace=False) hr_coord = hr_coord[sample_lst] results['gt'] = results['gt'][sample_lst] # Preparations for cell decoding cell = torch.ones_like(hr_coord) cell[:, 0] *= 2 / self.target_size[-2] cell[:, 1] *= 2 / self.target_size[-1] results['coord'] = hr_coord results['cell'] = cell return results def __repr__(self): repr_str = self.__class__.__name__ repr_str += (f'sample_quantity={self.sample_quantity}, ' f'scale={self.scale}, target_size={self.target_size}') return repr_str
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