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tracking_online.py
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tracking_online.py
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import cv2
import os
import numpy as np
import time
import torch
import argparse
from model import get_faceverse
import model.losses as losses
from data_reader import OnlineReader
from util_functions import get_length, ply_from_array_color
def init_optim_with_id(args, faceverse_model):
rigid_optimizer = torch.optim.Adam([faceverse_model.get_rot_tensor(),
faceverse_model.get_trans_tensor(),
faceverse_model.get_id_tensor(),
faceverse_model.get_exp_tensor()],
lr=args.rf_lr)
nonrigid_optimizer = torch.optim.Adam(
[faceverse_model.get_id_tensor(), faceverse_model.get_exp_tensor(),
faceverse_model.get_gamma_tensor(), faceverse_model.get_tex_tensor(),
faceverse_model.get_rot_tensor(), faceverse_model.get_trans_tensor()], lr=args.nrf_lr)
return rigid_optimizer, nonrigid_optimizer
def tracking(args, device):
faceverse_model, faceverse_dict = get_faceverse(version=args.version, batch_size=1, focal=1315, img_size=args.tar_size, device=device)
lm_weights = losses.get_lm_weights(device)
onreader = OnlineReader(camera_id=0, width=1920, height=1080)
onreader.start()
frame_ind = 0
while not onreader.thread_exit:
# load data
onreader.thread_lock.acquire()
frame, lms, frame_num = onreader.get_data()
onreader.thread_lock.release()
if lms[29, 0] == 0:
cv2.waitKey(100)
continue
# init crop parameters and optimizer
if frame_ind == 0:
border = 500
half_length = int(get_length(lms))
crop_center = lms[29].copy() + border
print('First frame:', half_length, crop_center)
rigid_optimizer, nonrigid_optimizer = init_optim_with_id(args, faceverse_model)
frame_b = cv2.copyMakeBorder(frame, border, border, border, border, cv2.BORDER_CONSTANT, value=0)
align = cv2.resize(frame_b[crop_center[1] - half_length:crop_center[1] + half_length, crop_center[0] - half_length:crop_center[0] + half_length],
(args.tar_size, args.tar_size), cv2.INTER_AREA)
resized_lms = (lms - (crop_center - half_length - border)[np.newaxis, :]) / half_length / 2 * args.tar_size
resized_lms = resized_lms.astype(np.int64)
lms = torch.from_numpy(resized_lms[np.newaxis, :, :]).type(torch.float32).to(device)
img_tensor = torch.from_numpy(align[np.newaxis, ...]).type(torch.float32).to(device)
if frame_ind == 0:
num_iters_rf = args.first_rf_iters
num_iters_nrf = args.first_nrf_iters
else:
num_iters_rf = args.rest_rf_iters
num_iters_nrf = args.rest_nrf_iters
# fitting using only landmarks
for i in range(num_iters_rf):
rigid_optimizer.zero_grad()
pred_dict = faceverse_model(faceverse_model.get_packed_tensors(), render=False, texture=False)
lm_loss_val = losses.lm_loss(pred_dict['lms_proj'], lms, lm_weights, img_size=args.tar_size)
exp_reg_loss = losses.get_l2(faceverse_model.get_exp_tensor())
id_reg_loss = losses.get_l2(faceverse_model.get_id_tensor())
total_loss = args.lm_loss_w * lm_loss_val + id_reg_loss*args.id_reg_w + exp_reg_loss*args.exp_reg_w
total_loss.backward()
rigid_optimizer.step()
# fitting with differentiable rendering
for i in range(num_iters_nrf):
nonrigid_optimizer.zero_grad()
pred_dict = faceverse_model(faceverse_model.get_packed_tensors(), render=True, texture=True)
rendered_img = pred_dict['rendered_img']
lms_proj = pred_dict['lms_proj']
face_texture = pred_dict['face_texture']
mask = rendered_img[:, :, :, 3].detach()
lm_loss_val = losses.lm_loss(lms_proj, lms, lm_weights,img_size=args.tar_size)
photo_loss_val = losses.photo_loss(rendered_img[:, :, :, :3], img_tensor, mask > 0)
exp_reg_loss = losses.get_l2(faceverse_model.get_exp_tensor())
id_reg_loss = losses.get_l2(faceverse_model.get_id_tensor())
tex_reg_loss = losses.get_l2(faceverse_model.get_tex_tensor())
tex_loss_val = losses.reflectance_loss(face_texture, faceverse_model.get_skinmask())
loss = lm_loss_val*args.lm_loss_w + id_reg_loss*args.id_reg_w + exp_reg_loss*args.exp_reg_w + \
tex_reg_loss*args.tex_reg_w + tex_loss_val*args.tex_w + photo_loss_val*args.rgb_loss_w
loss.backward()
nonrigid_optimizer.step()
# show data
with torch.no_grad():
pred_dict = faceverse_model(faceverse_model.get_packed_tensors(), render=True, texture=True)
rendered_img_c = pred_dict['rendered_img']
rendered_img_c = np.clip(rendered_img_c.cpu().squeeze().numpy(), 0, 255)
pred_dict = faceverse_model(faceverse_model.get_packed_tensors(), render=True, texture=False)
rendered_img_r = pred_dict['rendered_img']
rendered_img_r = np.clip(rendered_img_r.cpu().squeeze().numpy(), 0, 255)
mask_img_c = (rendered_img_c[:, :, 3:4] > 0).astype(np.uint8)
drive_img_c = rendered_img_c[:, :, :3].astype(np.uint8) * mask_img_c + align * (1 - mask_img_c)
mask_img_r = (rendered_img_r[:, :, 3:4] > 0).astype(np.uint8)
drive_img_r = rendered_img_r[:, :, :3].astype(np.uint8) * mask_img_r + align * (1 - mask_img_r)
drive_img = np.concatenate([align, drive_img_c, drive_img_r], axis=1)
if frame_ind == 0:
start_t = time.time()
frame_ind += 1
cv2.imshow('FaceVerse Tracking', drive_img[:, :, ::-1])
print(f'Speed:{(time.time() - start_t) / frame_ind:.4f}, {frame_ind:4} / {frame_num:4}, {total_loss.item():.4f}')
# keyboard reaction
keyc = cv2.waitKey(1) & 0xFF
if keyc == ord('q'):
onreader.thread_exit = True
elif keyc == ord('z'):
half_length = int(half_length * 1.01)
elif keyc == ord('x'):
half_length = int(half_length / 1.01)
elif keyc == ord('a'):
crop_center[0] += 1
elif keyc == ord('d'):
crop_center[0] -= 1
elif keyc == ord('w'):
crop_center[1] += 1
elif keyc == ord('s'):
crop_center[1] -= 1
elif keyc == ord('f'):
vertices = pred_dict['vs'].detach().cpu().squeeze().numpy()
colors = pred_dict['face_texture'].detach().cpu().squeeze().numpy()
colors = np.clip(colors, 0, 255).astype(np.uint8)
ply_from_array_color(vertices, colors, faceverse_dict['tri'], 'test.ply')
cv2.destroyAllWindows()
onreader.join()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="FaceVerse online tracker")
parser.add_argument('--version', type=int, default=1,
help='FaceVerse model version.')
parser.add_argument('--tar_size', type=int, default=512,
help='size for rendering window. We use a square window.')
parser.add_argument('--padding_ratio', type=float, default=1.0,
help='enlarge the face detection bbox by a margin.')
parser.add_argument('--recon_model', type=str, default='faceverse',
help='choose a 3dmm model, default: faceverse')
parser.add_argument('--first_rf_iters', type=int, default=500,
help='iteration number of landmark fitting for the first frame in video fitting.')
parser.add_argument('--first_nrf_iters', type=int, default=300,
help='iteration number of differentiable fitting for the first frame in video fitting.')
parser.add_argument('--rest_rf_iters', type=int, default=15,
help='iteration number of landmark fitting for the remaining frames in video fitting.')
parser.add_argument('--rest_nrf_iters', type=int, default=15,
help='iteration number of differentiable fitting for the remaining frames in video fitting.')
parser.add_argument('--rf_lr', type=float, default=1e-2,
help='learning rate for landmark fitting')
parser.add_argument('--nrf_lr', type=float, default=1e-2,
help='learning rate for differentiable fitting')
parser.add_argument('--lm_loss_w', type=float, default=3e3,
help='weight for landmark loss')
parser.add_argument('--rgb_loss_w', type=float, default=1.6,
help='weight for rgb loss')
parser.add_argument('--id_reg_w', type=float, default=1e-3,
help='weight for id coefficient regularizer')
parser.add_argument('--exp_reg_w', type=float, default=1.5e-4,
help='weight for expression coefficient regularizer')
parser.add_argument('--tex_reg_w', type=float, default=3e-4,
help='weight for texture coefficient regularizer')
parser.add_argument('--tex_w', type=float, default=1,
help='weight for texture reflectance loss.')
args = parser.parse_args()
device = 'cuda'
tracking(args, device)