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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[:, [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 = []
# 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, :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.
# gt 3D keypoints
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.
# gt 3D keypoints from the side
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 3D keypoints
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)
# gt 3D keypoints from the side
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))
# 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
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