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xautodl/datasets/landmark_utils/__init__.py
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xautodl/datasets/landmark_utils/__init__.py
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from .point_meta import PointMeta2V, apply_affine2point, apply_boundary
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xautodl/datasets/landmark_utils/point_meta.py
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xautodl/datasets/landmark_utils/point_meta.py
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# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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#
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import copy, math, torch, numpy as np
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from xvision import normalize_points
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from xvision import denormalize_points
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class PointMeta:
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# points : 3 x num_pts (x, y, oculusion)
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# image_size: original [width, height]
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def __init__(self, num_point, points, box, image_path, dataset_name):
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self.num_point = num_point
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if box is not None:
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assert (isinstance(box, tuple) or isinstance(box, list)) and len(box) == 4
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self.box = torch.Tensor(box)
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else:
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self.box = None
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if points is None:
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self.points = points
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else:
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assert (
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len(points.shape) == 2
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and points.shape[0] == 3
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and points.shape[1] == self.num_point
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), "The shape of point is not right : {}".format(points)
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self.points = torch.Tensor(points.copy())
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self.image_path = image_path
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self.datasets = dataset_name
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def __repr__(self):
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if self.box is None:
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boxstr = "None"
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else:
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boxstr = "box=[{:.1f}, {:.1f}, {:.1f}, {:.1f}]".format(*self.box.tolist())
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return (
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"{name}(points={num_point}, ".format(
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name=self.__class__.__name__, **self.__dict__
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)
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+ boxstr
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+ ")"
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)
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def get_box(self, return_diagonal=False):
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if self.box is None:
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return None
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if not return_diagonal:
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return self.box.clone()
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else:
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W = (self.box[2] - self.box[0]).item()
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H = (self.box[3] - self.box[1]).item()
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return math.sqrt(H * H + W * W)
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def get_points(self, ignore_indicator=False):
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if ignore_indicator:
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last = 2
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else:
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last = 3
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if self.points is not None:
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return self.points.clone()[:last, :]
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else:
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return torch.zeros((last, self.num_point))
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def is_none(self):
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# assert self.box is not None, 'The box should not be None'
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return self.points is None
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# if self.box is None: return True
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# else : return self.points is None
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def copy(self):
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return copy.deepcopy(self)
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def visiable_pts_num(self):
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with torch.no_grad():
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ans = self.points[2, :] > 0
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ans = torch.sum(ans)
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ans = ans.item()
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return ans
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def special_fun(self, indicator):
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if (
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indicator == "68to49"
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): # For 300W or 300VW, convert the default 68 points to 49 points.
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assert self.num_point == 68, "num-point must be 68 vs. {:}".format(
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self.num_point
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)
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self.num_point = 49
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out = torch.ones((68), dtype=torch.uint8)
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out[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 60, 64]] = 0
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if self.points is not None:
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self.points = self.points.clone()[:, out]
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else:
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raise ValueError("Invalid indicator : {:}".format(indicator))
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def apply_horizontal_flip(self):
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# self.points[0, :] = width - self.points[0, :] - 1
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# Mugsy spefic or Synthetic
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if self.datasets.startswith("HandsyROT"):
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ori = np.array(list(range(0, 42)))
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pos = np.array(list(range(21, 42)) + list(range(0, 21)))
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self.points[:, pos] = self.points[:, ori]
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elif self.datasets.startswith("face68"):
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ori = np.array(list(range(0, 68)))
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pos = (
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np.array(
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[
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17,
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16,
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15,
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14,
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13,
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12,
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11,
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10,
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9,
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8,
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7,
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6,
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5,
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4,
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3,
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2,
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1,
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27,
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26,
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25,
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24,
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23,
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22,
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21,
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20,
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19,
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18,
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28,
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29,
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30,
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31,
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36,
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35,
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34,
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33,
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32,
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46,
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45,
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44,
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43,
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48,
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47,
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40,
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39,
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38,
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37,
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42,
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41,
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55,
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54,
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53,
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52,
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51,
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50,
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49,
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60,
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59,
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58,
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57,
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56,
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65,
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64,
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63,
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62,
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61,
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68,
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67,
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66,
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]
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)
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- 1
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)
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self.points[:, ori] = self.points[:, pos]
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else:
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raise ValueError("Does not support {:}".format(self.datasets))
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# shape = (H,W)
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def apply_affine2point(points, theta, shape):
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assert points.size(0) == 3, "invalid points shape : {:}".format(points.size())
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with torch.no_grad():
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ok_points = points[2, :] == 1
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assert torch.sum(ok_points).item() > 0, "there is no visiable point"
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points[:2, :] = normalize_points(shape, points[:2, :])
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norm_trans_points = ok_points.unsqueeze(0).repeat(3, 1).float()
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trans_points, ___ = torch.gesv(points[:, ok_points], theta)
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norm_trans_points[:, ok_points] = trans_points
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return norm_trans_points
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def apply_boundary(norm_trans_points):
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with torch.no_grad():
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norm_trans_points = norm_trans_points.clone()
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oks = torch.stack(
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(
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norm_trans_points[0] > -1,
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norm_trans_points[0] < 1,
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norm_trans_points[1] > -1,
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norm_trans_points[1] < 1,
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norm_trans_points[2] > 0,
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)
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)
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oks = torch.sum(oks, dim=0) == 5
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norm_trans_points[2, :] = oks
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return norm_trans_points
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