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track.py
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track.py
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import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Optional, Tuple
import os
import hydra
import torch
import numpy as np
from hydra.core.config_store import ConfigStore
from omegaconf import DictConfig
from phalp.configs.base import FullConfig
from phalp.models.hmar.hmr import HMR2018Predictor
from phalp.trackers.PHALP import PHALP
from phalp.utils import get_pylogger
from phalp.configs.base import CACHE_DIR
from hmr2.datasets.utils import expand_bbox_to_aspect_ratio
warnings.filterwarnings('ignore')
log = get_pylogger(__name__)
class HMR2Predictor(HMR2018Predictor):
def __init__(self, cfg) -> None:
super().__init__(cfg)
# Setup our new model
from hmr2.models import download_models, load_hmr2
# Download and load checkpoints
download_models()
model, _ = load_hmr2()
self.model = model
self.model.eval()
def forward(self, x):
hmar_out = self.hmar_old(x)
batch = {
'img': x[:,:3,:,:],
'mask': (x[:,3,:,:]).clip(0,1),
}
model_out = self.model(batch)
out = hmar_out | {
'pose_smpl': model_out['pred_smpl_params'],
'pred_cam': model_out['pred_cam'],
}
return out
class HMR2023TextureSampler(HMR2Predictor):
def __init__(self, cfg) -> None:
super().__init__(cfg)
# Model's all set up. Now, load tex_bmap and tex_fmap
# Texture map atlas
bmap = np.load(os.path.join(CACHE_DIR, 'phalp/3D/bmap_256.npy'))
fmap = np.load(os.path.join(CACHE_DIR, 'phalp/3D/fmap_256.npy'))
self.register_buffer('tex_bmap', torch.tensor(bmap, dtype=torch.float))
self.register_buffer('tex_fmap', torch.tensor(fmap, dtype=torch.long))
self.img_size = 256 #self.cfg.MODEL.IMAGE_SIZE
self.focal_length = 5000. #self.cfg.EXTRA.FOCAL_LENGTH
import neural_renderer as nr
self.neural_renderer = nr.Renderer(dist_coeffs=None, orig_size=self.img_size,
image_size=self.img_size,
light_intensity_ambient=1,
light_intensity_directional=0,
anti_aliasing=False)
def forward(self, x):
batch = {
'img': x[:,:3,:,:],
'mask': (x[:,3,:,:]).clip(0,1),
}
model_out = self.model(batch)
# from hmr2.models.prohmr_texture import unproject_uvmap_to_mesh
def unproject_uvmap_to_mesh(bmap, fmap, verts, faces):
# bmap: 256,256,3
# fmap: 256,256
# verts: B,V,3
# faces: F,3
valid_mask = (fmap >= 0)
fmap_flat = fmap[valid_mask] # N
bmap_flat = bmap[valid_mask,:] # N,3
face_vids = faces[fmap_flat, :] # N,3
face_verts = verts[:, face_vids, :] # B,N,3,3
bs = face_verts.shape
map_verts = torch.einsum('bnij,ni->bnj', face_verts, bmap_flat) # B,N,3
return map_verts, valid_mask
pred_verts = model_out['pred_vertices'] + model_out['pred_cam_t'].unsqueeze(1)
device = pred_verts.device
face_tensor = torch.tensor(self.smpl.faces.astype(np.int64), dtype=torch.long, device=device)
map_verts, valid_mask = unproject_uvmap_to_mesh(self.tex_bmap, self.tex_fmap, pred_verts, face_tensor) # B,N,3
# Project map_verts to image using K,R,t
# map_verts_view = einsum('bij,bnj->bni', R, map_verts) + t # R=I t=0
focal = self.focal_length / (self.img_size / 2)
map_verts_proj = focal * map_verts[:, :, :2] / map_verts[:, :, 2:3] # B,N,2
map_verts_depth = map_verts[:, :, 2] # B,N
# Render Depth. Annoying but we need to create this
K = torch.eye(3, device=device)
K[0, 0] = K[1, 1] = self.focal_length
K[1, 2] = K[0, 2] = self.img_size / 2 # Because the neural renderer only support squared images
K = K.unsqueeze(0)
R = torch.eye(3, device=device).unsqueeze(0)
t = torch.zeros(3, device=device).unsqueeze(0)
rend_depth = self.neural_renderer(pred_verts,
face_tensor[None].expand(pred_verts.shape[0], -1, -1).int(),
# textures=texture_atlas_rgb,
mode='depth',
K=K, R=R, t=t)
rend_depth_at_proj = torch.nn.functional.grid_sample(rend_depth[:,None,:,:], map_verts_proj[:,None,:,:]) # B,1,1,N
rend_depth_at_proj = rend_depth_at_proj.squeeze(1).squeeze(1) # B,N
img_rgba = torch.cat([batch['img'], batch['mask'][:,None,:,:]], dim=1) # B,4,H,W
img_rgba_at_proj = torch.nn.functional.grid_sample(img_rgba, map_verts_proj[:,None,:,:]) # B,4,1,N
img_rgba_at_proj = img_rgba_at_proj.squeeze(2) # B,4,N
visibility_mask = map_verts_depth <= (rend_depth_at_proj + 1e-4) # B,N
img_rgba_at_proj[:,3,:][~visibility_mask] = 0
# Paste image back onto square uv_image
uv_image = torch.zeros((batch['img'].shape[0], 4, 256, 256), dtype=torch.float, device=device)
uv_image[:, :, valid_mask] = img_rgba_at_proj
out = {
'uv_image': uv_image,
'uv_vector' : self.hmar_old.process_uv_image(uv_image),
'pose_smpl': model_out['pred_smpl_params'],
'pred_cam': model_out['pred_cam'],
}
return out
class HMR2_4dhuman(PHALP):
def __init__(self, cfg):
super().__init__(cfg)
def setup_hmr(self):
self.HMAR = HMR2023TextureSampler(self.cfg)
def get_detections(self, image, frame_name, t_, additional_data=None, measurments=None):
(
pred_bbox, pred_bbox, pred_masks, pred_scores, pred_classes,
ground_truth_track_id, ground_truth_annotations
) = super().get_detections(image, frame_name, t_, additional_data, measurments)
# Pad bounding boxes
pred_bbox_padded = expand_bbox_to_aspect_ratio(pred_bbox, self.cfg.expand_bbox_shape)
return (
pred_bbox, pred_bbox_padded, pred_masks, pred_scores, pred_classes,
ground_truth_track_id, ground_truth_annotations
)
@dataclass
class Human4DConfig(FullConfig):
# override defaults if needed
expand_bbox_shape: Optional[Tuple[int]] = (192,256)
pass
cs = ConfigStore.instance()
cs.store(name="config", node=Human4DConfig)
@hydra.main(version_base="1.2", config_name="config")
def main(cfg: DictConfig) -> Optional[float]:
"""Main function for running the PHALP tracker."""
phalp_tracker = HMR2_4dhuman(cfg)
phalp_tracker.track()
if __name__ == "__main__":
main()