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Zero
Running
on
Zero
| 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] | |
| # num_samples = pred_keypoints_3d.shape[1] | |
| 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[:, [0], :-1] + pred_keypoints_3d[:, [0]] | |
| 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] | |
| openpose_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20] | |
| gt_indices = openpose_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 = [] | |
| # Rotate keypoints to visualize side view | |
| 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)) | |
| # gt 2D keypoints | |
| body_keypoints_2d = gt_keypoints_2d[i, :21].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. | |
| # gt 3D keypoints | |
| body_keypoints_3d_proj = gt_keypoints_3d_proj[i, :21].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. | |
| # gt 3D keypoints from the side | |
| body_keypoints_3d_proj = gt_keypoints_3d_proj_side[i, :21].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 3D keypoints | |
| pred_keypoints_3d_proj_imgs = [] | |
| body_keypoints_3d_proj = pred_keypoints_3d_proj[i, :21].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) | |
| # gt 3D keypoints from the side | |
| pred_keypoints_3d_proj_imgs_side = [] | |
| body_keypoints_3d_proj = pred_keypoints_3d_proj_side[i, :21].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)) | |
| # Concatenate images | |
| 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 | |