# Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # import copy, math, torch, numpy as np from xvision import normalize_points from xvision import denormalize_points class PointMeta(): # points : 3 x num_pts (x, y, oculusion) # image_size: original [width, height] def __init__(self, num_point, points, box, image_path, dataset_name): self.num_point = num_point if box is not None: assert (isinstance(box, tuple) or isinstance(box, list)) and len(box) == 4 self.box = torch.Tensor(box) else: self.box = None if points is None: self.points = points else: assert len(points.shape) == 2 and points.shape[0] == 3 and points.shape[1] == self.num_point, 'The shape of point is not right : {}'.format( points ) self.points = torch.Tensor(points.copy()) self.image_path = image_path self.datasets = dataset_name def __repr__(self): if self.box is None: boxstr = 'None' else : boxstr = 'box=[{:.1f}, {:.1f}, {:.1f}, {:.1f}]'.format(*self.box.tolist()) return ('{name}(points={num_point}, '.format(name=self.__class__.__name__, **self.__dict__) + boxstr + ')') def get_box(self, return_diagonal=False): if self.box is None: return None if not return_diagonal: return self.box.clone() else: W = (self.box[2]-self.box[0]).item() H = (self.box[3]-self.box[1]).item() return math.sqrt(H*H+W*W) def get_points(self, ignore_indicator=False): if ignore_indicator: last = 2 else : last = 3 if self.points is not None: return self.points.clone()[:last, :] else : return torch.zeros((last, self.num_point)) def is_none(self): #assert self.box is not None, 'The box should not be None' return self.points is None #if self.box is None: return True #else : return self.points is None def copy(self): return copy.deepcopy(self) def visiable_pts_num(self): with torch.no_grad(): ans = self.points[2,:] > 0 ans = torch.sum(ans) ans = ans.item() return ans def special_fun(self, indicator): if indicator == '68to49': # For 300W or 300VW, convert the default 68 points to 49 points. assert self.num_point == 68, 'num-point must be 68 vs. {:}'.format(self.num_point) self.num_point = 49 out = torch.ones((68), dtype=torch.uint8) out[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,60,64]] = 0 if self.points is not None: self.points = self.points.clone()[:, out] else: raise ValueError('Invalid indicator : {:}'.format( indicator )) def apply_horizontal_flip(self): #self.points[0, :] = width - self.points[0, :] - 1 # Mugsy spefic or Synthetic if self.datasets.startswith('HandsyROT'): ori = np.array(list(range(0, 42))) pos = np.array(list(range(21,42)) + list(range(0,21))) self.points[:, pos] = self.points[:, ori] elif self.datasets.startswith('face68'): ori = np.array(list(range(0, 68))) pos = np.array([17,16,15,14,13,12,11,10, 9, 8,7,6,5,4,3,2,1, 27,26,25,24,23,22,21,20,19,18, 28,29,30,31, 36,35,34,33,32, 46,45,44,43,48,47, 40,39,38,37,42,41, 55,54,53,52,51,50,49,60,59,58,57,56,65,64,63,62,61,68,67,66])-1 self.points[:, ori] = self.points[:, pos] else: raise ValueError('Does not support {:}'.format(self.datasets)) # shape = (H,W) def apply_affine2point(points, theta, shape): assert points.size(0) == 3, 'invalid points shape : {:}'.format(points.size()) with torch.no_grad(): ok_points = points[2,:] == 1 assert torch.sum(ok_points).item() > 0, 'there is no visiable point' points[:2,:] = normalize_points(shape, points[:2,:]) norm_trans_points = ok_points.unsqueeze(0).repeat(3, 1).float() trans_points, ___ = torch.gesv(points[:, ok_points], theta) norm_trans_points[:, ok_points] = trans_points return norm_trans_points def apply_boundary(norm_trans_points): with torch.no_grad(): norm_trans_points = norm_trans_points.clone() oks = torch.stack((norm_trans_points[0]>-1, norm_trans_points[0]<1, norm_trans_points[1]>-1, norm_trans_points[1]<1, norm_trans_points[2]>0)) oks = torch.sum(oks, dim=0) == 5 norm_trans_points[2, :] = oks return norm_trans_points