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| # -*- coding: utf-8 -*- | |
| # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is | |
| # holder of all proprietary rights on this computer program. | |
| # You can only use this computer program if you have closed | |
| # a license agreement with MPG or you get the right to use the computer | |
| # program from someone who is authorized to grant you that right. | |
| # Any use of the computer program without a valid license is prohibited and | |
| # liable to prosecution. | |
| # | |
| # Copyright©2020 Max-Planck-Gesellschaft zur Förderung | |
| # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute | |
| # for Intelligent Systems. All rights reserved. | |
| # | |
| # Contact: ps-license@tuebingen.mpg.de | |
| import torch | |
| import torch.nn.functional as F | |
| from mGPT.utils.joints import mmm_joints | |
| # Get the indexes of particular body part SMPLH case | |
| # Feet | |
| # LM, RM = smplh_joints.index("left_ankle"), smplh_joints.index("right_ankle") | |
| # LF, RF = smplh_joints.index("left_foot"), smplh_joints.index("right_foot") | |
| # # Shoulders | |
| # LS, RS = smplh_joints.index("left_shoulder"), smplh_joints.index("right_shoulder") | |
| # # Hips | |
| # LH, RH = smplh_joints.index("left_hip"), smplh_joints.index("right_hip") | |
| # Get the indexes of particular body part | |
| # Feet | |
| LM, RM = mmm_joints.index("LMrot"), mmm_joints.index("RMrot") | |
| LF, RF = mmm_joints.index("LF"), mmm_joints.index("RF") | |
| # Shoulders | |
| LS, RS = mmm_joints.index("LS"), mmm_joints.index("RS") | |
| # Hips | |
| LH, RH = mmm_joints.index("LH"), mmm_joints.index("RH") | |
| def get_forward_direction(poses, jointstype="mmm"): | |
| # assert jointstype == 'mmm' | |
| across = poses[..., RH, :] - poses[..., LH, :] + poses[..., RS, :] - poses[ | |
| ..., LS, :] | |
| forward = torch.stack((-across[..., 2], across[..., 0]), axis=-1) | |
| forward = torch.nn.functional.normalize(forward, dim=-1) | |
| return forward | |
| def get_floor(poses, jointstype="mmm"): | |
| # assert jointstype == 'mmm' | |
| ndim = len(poses.shape) | |
| foot_heights = poses[..., (LM, LF, RM, RF), 1].min(-1).values | |
| floor_height = softmin(foot_heights, softness=0.5, dim=-1) | |
| # changed this thing Mathis version 1.11 pytorch | |
| return floor_height[(ndim - 2) * [None]].transpose(0, -1) | |
| def softmax(x, softness=1.0, dim=None): | |
| maxi, mini = x.max(dim=dim).values, x.min(dim=dim).values | |
| return maxi + torch.log(softness + torch.exp(mini - maxi)) | |
| def softmin(x, softness=1.0, dim=0): | |
| return -softmax(-x, softness=softness, dim=dim) | |
| def gaussian_filter1d(_inputs, sigma, truncate=4.0): | |
| # Code adapted/mixed from scipy library into pytorch | |
| # https://github.com/scipy/scipy/blob/47bb6febaa10658c72962b9615d5d5aa2513fa3a/scipy/ndimage/filters.py#L211 | |
| # and gaussian kernel | |
| # https://github.com/scipy/scipy/blob/47bb6febaa10658c72962b9615d5d5aa2513fa3a/scipy/ndimage/filters.py#L179 | |
| # Correspond to mode="nearest" and order = 0 | |
| # But works batched | |
| if len(_inputs.shape) == 2: | |
| inputs = _inputs[None] | |
| else: | |
| inputs = _inputs | |
| sd = float(sigma) | |
| radius = int(truncate * sd + 0.5) | |
| sigma2 = sigma * sigma | |
| x = torch.arange(-radius, | |
| radius + 1, | |
| device=inputs.device, | |
| dtype=inputs.dtype) | |
| phi_x = torch.exp(-0.5 / sigma2 * x**2) | |
| phi_x = phi_x / phi_x.sum() | |
| # Conv1d weights | |
| groups = inputs.shape[-1] | |
| weights = torch.tile(phi_x, (groups, 1, 1)) | |
| inputs = inputs.transpose(-1, -2) | |
| outputs = F.conv1d(inputs, weights, padding="same", | |
| groups=groups).transpose(-1, -2) | |
| return outputs.reshape(_inputs.shape) | |