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import os
import cv2
import glob
import numpy as np
from PIL import Image
from tqdm import tqdm
from scipy.io import savemat
import torch
from models import create_model
from options.inference_options import InferenceOptions
from util.preprocess import align_img
from util.load_mats import load_lm3d
from util.util import mkdirs, tensor2im, save_image
def get_data_path(root, keypoint_root):
filenames = list()
keypoint_filenames = list()
VIDEO_EXTENSIONS_LOWERCASE = {'mp4'}
VIDEO_EXTENSIONS = VIDEO_EXTENSIONS_LOWERCASE.union({f.upper() for f in VIDEO_EXTENSIONS_LOWERCASE})
extensions = VIDEO_EXTENSIONS
for ext in extensions:
filenames += glob.glob(f'{root}/**/*.{ext}', recursive=True)
filenames = sorted(filenames)
keypoint_filenames = sorted(glob.glob(f'{keypoint_root}/**/*.txt', recursive=True))
assert len(filenames) == len(keypoint_filenames)
return filenames, keypoint_filenames
class VideoPathDataset(torch.utils.data.Dataset):
def __init__(self, filenames, txt_filenames, bfm_folder):
self.filenames = filenames
self.txt_filenames = txt_filenames
self.lm3d_std = load_lm3d(bfm_folder)
def __len__(self):
return len(self.filenames)
def __getitem__(self, index):
filename = self.filenames[index]
txt_filename = self.txt_filenames[index]
frames = self.read_video(filename)
lm = np.loadtxt(txt_filename).astype(np.float32)
lm = lm.reshape([len(frames), -1, 2])
out_images, out_trans_params = list(), list()
for i in range(len(frames)):
out_img, _, out_trans_param \
= self.image_transform(frames[i], lm[i])
out_images.append(out_img[None])
out_trans_params.append(out_trans_param[None])
return {
'imgs': torch.cat(out_images, 0),
'trans_param':torch.cat(out_trans_params, 0),
'filename': filename
}
def read_video(self, filename):
frames = list()
cap = cv2.VideoCapture(filename)
while cap.isOpened():
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = Image.fromarray(frame)
frames.append(frame)
else:
break
cap.release()
return frames
def image_transform(self, images, lm):
W,H = images.size
if np.mean(lm) == -1:
lm = (self.lm3d_std[:, :2]+1)/2.
lm = np.concatenate(
[lm[:, :1]*W, lm[:, 1:2]*H], 1
)
else:
lm[:, -1] = H - 1 - lm[:, -1]
trans_params, img, lm, _ = align_img(images, lm, self.lm3d_std)
img = torch.tensor(np.array(img)/255., dtype=torch.float32).permute(2, 0, 1)
lm = torch.tensor(lm)
trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)])
trans_params = torch.tensor(trans_params.astype(np.float32))
return img, lm, trans_params
def main(opt, model):
# import torch.multiprocessing
# torch.multiprocessing.set_sharing_strategy('file_system')
filenames, keypoint_filenames = get_data_path(opt.input_dir, opt.keypoint_dir)
dataset = VideoPathDataset(filenames, keypoint_filenames, opt.bfm_folder)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=1, # can noly set to one here!
shuffle=False,
drop_last=False,
num_workers=0,
)
batch_size = opt.inference_batch_size
for data in tqdm(dataloader):
num_batch = data['imgs'][0].shape[0] // batch_size + 1
pred_coeffs = list()
for index in range(num_batch):
data_input = {
'imgs': data['imgs'][0,index*batch_size:(index+1)*batch_size],
}
model.set_input(data_input)
model.test()
pred_coeff = {key:model.pred_coeffs_dict[key].cpu().numpy() for key in model.pred_coeffs_dict}
pred_coeff = np.concatenate([
pred_coeff['id'],
pred_coeff['exp'],
pred_coeff['tex'],
pred_coeff['angle'],
pred_coeff['gamma'],
pred_coeff['trans']], 1)
pred_coeffs.append(pred_coeff)
visuals = model.get_current_visuals() # get image results
if False: # debug
for name in visuals:
images = visuals[name]
for i in range(images.shape[0]):
image_numpy = tensor2im(images[i])
save_image(
image_numpy,
os.path.join(
opt.output_dir,
os.path.basename(data['filename'][0])+str(i).zfill(5)+'.jpg')
)
exit()
pred_coeffs = np.concatenate(pred_coeffs, 0)
pred_trans_params = data['trans_param'][0].cpu().numpy()
name = data['filename'][0].split('/')[-2:]
name[-1] = os.path.splitext(name[-1])[0] + '.mat'
os.makedirs(os.path.join(opt.output_dir, name[-2]), exist_ok=True)
savemat(
os.path.join(opt.output_dir, name[-2], name[-1]),
{'coeff':pred_coeffs, 'transform_params':pred_trans_params}
)
if __name__ == '__main__':
opt = InferenceOptions().parse() # get test options
model = create_model(opt)
model.setup(opt)
model.device = 'cuda:0'
model.parallelize()
model.eval()
main(opt, model)
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