| | import torch |
| | import numpy as np |
| | import trimesh |
| | from typing import Optional |
| | from yacs.config import CfgNode |
| |
|
| | from .geometry import perspective_projection |
| | from .render_openpose import render_openpose |
| |
|
| | class SkeletonRenderer: |
| |
|
| | def __init__(self, cfg: CfgNode): |
| | """ |
| | Object used to render 3D keypoints. Faster for use during training. |
| | Args: |
| | cfg (CfgNode): Model config file. |
| | """ |
| | self.cfg = cfg |
| |
|
| | def __call__(self, |
| | pred_keypoints_3d: torch.Tensor, |
| | gt_keypoints_3d: torch.Tensor, |
| | gt_keypoints_2d: torch.Tensor, |
| | images: Optional[np.array] = None, |
| | camera_translation: Optional[torch.Tensor] = None) -> np.array: |
| | """ |
| | Render batch of 3D keypoints. |
| | Args: |
| | pred_keypoints_3d (torch.Tensor): Tensor of shape (B, S, N, 3) containing a batch of predicted 3D keypoints, with S samples per image. |
| | gt_keypoints_3d (torch.Tensor): Tensor of shape (B, N, 4) containing corresponding ground truth 3D keypoints; last value is the confidence. |
| | gt_keypoints_2d (torch.Tensor): Tensor of shape (B, N, 3) containing corresponding ground truth 2D keypoints. |
| | images (torch.Tensor): Tensor of shape (B, H, W, 3) containing images with values in the [0,255] range. |
| | camera_translation (torch.Tensor): Tensor of shape (B, 3) containing the camera translation. |
| | Returns: |
| | np.array : Image with the following layout. Each row contains the a) input image, |
| | b) image with gt 2D keypoints, |
| | c) image with projected gt 3D keypoints, |
| | d_1, ... , d_S) image with projected predicted 3D keypoints, |
| | e) gt 3D keypoints rendered from a side view, |
| | f_1, ... , f_S) predicted 3D keypoints frorm a side view |
| | """ |
| | batch_size = pred_keypoints_3d.shape[0] |
| | |
| | pred_keypoints_3d = pred_keypoints_3d.clone().cpu().float() |
| | gt_keypoints_3d = gt_keypoints_3d.clone().cpu().float() |
| | gt_keypoints_3d[:, :, :-1] = gt_keypoints_3d[:, :, :-1] - gt_keypoints_3d[:, [25+14], :-1] + pred_keypoints_3d[:, [25+14]] |
| | gt_keypoints_2d = gt_keypoints_2d.clone().cpu().float().numpy() |
| | gt_keypoints_2d[:, :, :-1] = self.cfg.MODEL.IMAGE_SIZE * (gt_keypoints_2d[:, :, :-1] + 1.0) / 2.0 |
| |
|
| | openpose_indices = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] |
| | gt_indices = [12, 8, 7, 6, 9, 10, 11, 14, 2, 1, 0, 3, 4, 5] |
| | gt_indices = [25 + i for i in gt_indices] |
| | keypoints_to_render = torch.ones(batch_size, gt_keypoints_3d.shape[1], 1) |
| | rotation = torch.eye(3).unsqueeze(0) |
| | if camera_translation is None: |
| | camera_translation = torch.tensor([0.0, 0.0, 2 * self.cfg.EXTRA.FOCAL_LENGTH / (0.8 * self.cfg.MODEL.IMAGE_SIZE)]).unsqueeze(0).repeat(batch_size, 1) |
| | else: |
| | camera_translation = camera_translation.cpu() |
| |
|
| | if images is None: |
| | images = np.zeros((batch_size, self.cfg.MODEL.IMAGE_SIZE, self.cfg.MODEL.IMAGE_SIZE, 3)) |
| | focal_length = torch.tensor([self.cfg.EXTRA.FOCAL_LENGTH, self.cfg.EXTRA.FOCAL_LENGTH]).reshape(1, 2) |
| | camera_center = torch.tensor([self.cfg.MODEL.IMAGE_SIZE, self.cfg.MODEL.IMAGE_SIZE], dtype=torch.float).reshape(1, 2) / 2. |
| | gt_keypoints_3d_proj = perspective_projection(gt_keypoints_3d[:, :, :-1], rotation=rotation.repeat(batch_size, 1, 1), translation=camera_translation[:, :], focal_length=focal_length.repeat(batch_size, 1), camera_center=camera_center.repeat(batch_size, 1)) |
| | pred_keypoints_3d_proj = perspective_projection(pred_keypoints_3d.reshape(batch_size, -1, 3), rotation=rotation.repeat(batch_size, 1, 1), translation=camera_translation.reshape(batch_size, -1), focal_length=focal_length.repeat(batch_size, 1), camera_center=camera_center.repeat(batch_size, 1)).reshape(batch_size, -1, 2) |
| | gt_keypoints_3d_proj = torch.cat([gt_keypoints_3d_proj, gt_keypoints_3d[:, :, [-1]]], dim=-1).cpu().numpy() |
| | pred_keypoints_3d_proj = torch.cat([pred_keypoints_3d_proj, keypoints_to_render.reshape(batch_size, -1, 1)], dim=-1).cpu().numpy() |
| | rows = [] |
| | |
| | R = torch.tensor(trimesh.transformations.rotation_matrix(np.radians(90), [0, 1, 0])[:3, :3]).float() |
| | gt_keypoints_3d_side = gt_keypoints_3d.clone() |
| | gt_keypoints_3d_side[:, :, :-1] = torch.einsum('bni,ij->bnj', gt_keypoints_3d_side[:, :, :-1], R) |
| | pred_keypoints_3d_side = pred_keypoints_3d.clone() |
| | pred_keypoints_3d_side = torch.einsum('bni,ij->bnj', pred_keypoints_3d_side, R) |
| | gt_keypoints_3d_proj_side = perspective_projection(gt_keypoints_3d_side[:, :, :-1], rotation=rotation.repeat(batch_size, 1, 1), translation=camera_translation[:, :], focal_length=focal_length.repeat(batch_size, 1), camera_center=camera_center.repeat(batch_size, 1)) |
| | pred_keypoints_3d_proj_side = perspective_projection(pred_keypoints_3d_side.reshape(batch_size, -1, 3), rotation=rotation.repeat(batch_size, 1, 1), translation=camera_translation.reshape(batch_size, -1), focal_length=focal_length.repeat(batch_size, 1), camera_center=camera_center.repeat(batch_size, 1)).reshape(batch_size, -1, 2) |
| | gt_keypoints_3d_proj_side = torch.cat([gt_keypoints_3d_proj_side, gt_keypoints_3d_side[:, :, [-1]]], dim=-1).cpu().numpy() |
| | pred_keypoints_3d_proj_side = torch.cat([pred_keypoints_3d_proj_side, keypoints_to_render.reshape(batch_size, -1, 1)], dim=-1).cpu().numpy() |
| | for i in range(batch_size): |
| | img = images[i] |
| | side_img = np.zeros((self.cfg.MODEL.IMAGE_SIZE, self.cfg.MODEL.IMAGE_SIZE, 3)) |
| | |
| | body_keypoints_2d = gt_keypoints_2d[i, :25].copy() |
| | for op, gt in zip(openpose_indices, gt_indices): |
| | if gt_keypoints_2d[i, gt, -1] > body_keypoints_2d[op, -1]: |
| | body_keypoints_2d[op] = gt_keypoints_2d[i, gt] |
| | gt_keypoints_img = render_openpose(img, body_keypoints_2d) / 255. |
| | |
| | body_keypoints_3d_proj = gt_keypoints_3d_proj[i, :25].copy() |
| | for op, gt in zip(openpose_indices, gt_indices): |
| | if gt_keypoints_3d_proj[i, gt, -1] > body_keypoints_3d_proj[op, -1]: |
| | body_keypoints_3d_proj[op] = gt_keypoints_3d_proj[i, gt] |
| | gt_keypoints_3d_proj_img = render_openpose(img, body_keypoints_3d_proj) / 255. |
| | |
| | body_keypoints_3d_proj = gt_keypoints_3d_proj_side[i, :25].copy() |
| | for op, gt in zip(openpose_indices, gt_indices): |
| | if gt_keypoints_3d_proj_side[i, gt, -1] > body_keypoints_3d_proj[op, -1]: |
| | body_keypoints_3d_proj[op] = gt_keypoints_3d_proj_side[i, gt] |
| | gt_keypoints_3d_proj_img_side = render_openpose(side_img, body_keypoints_3d_proj) / 255. |
| | |
| | pred_keypoints_3d_proj_imgs = [] |
| | body_keypoints_3d_proj = pred_keypoints_3d_proj[i, :25].copy() |
| | for op, gt in zip(openpose_indices, gt_indices): |
| | if pred_keypoints_3d_proj[i, gt, -1] >= body_keypoints_3d_proj[op, -1]: |
| | body_keypoints_3d_proj[op] = pred_keypoints_3d_proj[i, gt] |
| | pred_keypoints_3d_proj_imgs.append(render_openpose(img, body_keypoints_3d_proj) / 255.) |
| | pred_keypoints_3d_proj_img = np.concatenate(pred_keypoints_3d_proj_imgs, axis=1) |
| | |
| | pred_keypoints_3d_proj_imgs_side = [] |
| | body_keypoints_3d_proj = pred_keypoints_3d_proj_side[i, :25].copy() |
| | for op, gt in zip(openpose_indices, gt_indices): |
| | if pred_keypoints_3d_proj_side[i, gt, -1] >= body_keypoints_3d_proj[op, -1]: |
| | body_keypoints_3d_proj[op] = pred_keypoints_3d_proj_side[i, gt] |
| | pred_keypoints_3d_proj_imgs_side.append(render_openpose(side_img, body_keypoints_3d_proj) / 255.) |
| | pred_keypoints_3d_proj_img_side = np.concatenate(pred_keypoints_3d_proj_imgs_side, axis=1) |
| | rows.append(np.concatenate((gt_keypoints_img, gt_keypoints_3d_proj_img, pred_keypoints_3d_proj_img, gt_keypoints_3d_proj_img_side, pred_keypoints_3d_proj_img_side), axis=1)) |
| | |
| | img = np.concatenate(rows, axis=0) |
| | img[:, ::self.cfg.MODEL.IMAGE_SIZE, :] = 1.0 |
| | img[::self.cfg.MODEL.IMAGE_SIZE, :, :] = 1.0 |
| | img[:, (1+1+1)*self.cfg.MODEL.IMAGE_SIZE, :] = 0.5 |
| | return img |
| |
|
