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Running
on
A10G
import numpy as np | |
import cv2, os, sys, torch | |
from tqdm import tqdm | |
from PIL import Image | |
# 3dmm extraction | |
from src.face3d.util.preprocess import align_img | |
from src.face3d.util.load_mats import load_lm3d | |
from src.face3d.models import networks | |
from src.face3d.extract_kp_videos import KeypointExtractor | |
from scipy.io import loadmat, savemat | |
from src.utils.croper import Croper | |
import warnings | |
warnings.filterwarnings("ignore") | |
def split_coeff(coeffs): | |
""" | |
Return: | |
coeffs_dict -- a dict of torch.tensors | |
Parameters: | |
coeffs -- torch.tensor, size (B, 256) | |
""" | |
id_coeffs = coeffs[:, :80] | |
exp_coeffs = coeffs[:, 80: 144] | |
tex_coeffs = coeffs[:, 144: 224] | |
angles = coeffs[:, 224: 227] | |
gammas = coeffs[:, 227: 254] | |
translations = coeffs[:, 254:] | |
return { | |
'id': id_coeffs, | |
'exp': exp_coeffs, | |
'tex': tex_coeffs, | |
'angle': angles, | |
'gamma': gammas, | |
'trans': translations | |
} | |
class CropAndExtract(): | |
def __init__(self, path_of_lm_croper, path_of_net_recon_model, dir_of_BFM_fitting, device): | |
self.croper = Croper(path_of_lm_croper) | |
self.kp_extractor = KeypointExtractor(device) | |
self.net_recon = networks.define_net_recon(net_recon='resnet50', use_last_fc=False, init_path='').to(device) | |
checkpoint = torch.load(path_of_net_recon_model, map_location=torch.device(device)) | |
self.net_recon.load_state_dict(checkpoint['net_recon']) | |
self.net_recon.eval() | |
self.lm3d_std = load_lm3d(dir_of_BFM_fitting) | |
self.device = device | |
def generate(self, input_path, save_dir, crop_or_resize='crop'): | |
pic_size = 256 | |
pic_name = os.path.splitext(os.path.split(input_path)[-1])[0] | |
landmarks_path = os.path.join(save_dir, pic_name+'_landmarks.txt') | |
coeff_path = os.path.join(save_dir, pic_name+'.mat') | |
png_path = os.path.join(save_dir, pic_name+'.png') | |
#load input | |
if not os.path.isfile(input_path): | |
raise ValueError('input_path must be a valid path to video/image file') | |
elif input_path.split('.')[1] in ['jpg', 'png', 'jpeg']: | |
# loader for first frame | |
full_frames = [cv2.imread(input_path)] | |
fps = 25 | |
else: | |
# loader for videos | |
video_stream = cv2.VideoCapture(input_path) | |
fps = video_stream.get(cv2.CAP_PROP_FPS) | |
full_frames = [] | |
while 1: | |
still_reading, frame = video_stream.read() | |
if not still_reading: | |
video_stream.release() | |
break | |
full_frames.append(frame) | |
x_full_frames = [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) for frame in full_frames] | |
if crop_or_resize.lower() == 'crop': # default crop | |
x_full_frames, crop, quad = self.croper.crop(x_full_frames, xsize=pic_size) | |
clx, cly, crx, cry = crop | |
lx, ly, rx, ry = quad | |
lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry) | |
oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx | |
crop_info = ((ox2 - ox1, oy2 - oy1), crop, quad) | |
else: | |
oy1, oy2, ox1, ox2 = 0, x_full_frames[0].shape[0], 0, x_full_frames[0].shape[1] | |
crop_info = ((ox2 - ox1, oy2 - oy1)) | |
frames_pil = [Image.fromarray(cv2.resize(frame,(pic_size, pic_size))) for frame in x_full_frames] | |
if len(frames_pil) == 0: | |
print('No face is detected in the input file') | |
return None, None | |
# save crop info | |
for frame in frames_pil: | |
cv2.imwrite(png_path, cv2.cvtColor(np.array(frame), cv2.COLOR_RGB2BGR)) | |
# 2. get the landmark according to the detected face. | |
if not os.path.isfile(landmarks_path): | |
lm = self.kp_extractor.extract_keypoint(frames_pil, landmarks_path) | |
else: | |
print(' Using saved landmarks.') | |
lm = np.loadtxt(landmarks_path).astype(np.float32) | |
lm = lm.reshape([len(x_full_frames), -1, 2]) | |
if not os.path.isfile(coeff_path): | |
# load 3dmm paramter generator from Deep3DFaceRecon_pytorch | |
video_coeffs, full_coeffs = [], [] | |
for idx in tqdm(range(len(frames_pil)), desc='3DMM Extraction In Video:'): | |
frame = frames_pil[idx] | |
W,H = frame.size | |
lm1 = lm[idx].reshape([-1, 2]) | |
if np.mean(lm1) == -1: | |
lm1 = (self.lm3d_std[:, :2]+1)/2. | |
lm1 = np.concatenate( | |
[lm1[:, :1]*W, lm1[:, 1:2]*H], 1 | |
) | |
else: | |
lm1[:, -1] = H - 1 - lm1[:, -1] | |
trans_params, im1, lm1, _ = align_img(frame, lm1, self.lm3d_std) | |
trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32) | |
im_t = torch.tensor(np.array(im1)/255., dtype=torch.float32).permute(2, 0, 1).to(self.device).unsqueeze(0) | |
with torch.no_grad(): | |
full_coeff = self.net_recon(im_t) | |
coeffs = split_coeff(full_coeff) | |
pred_coeff = {key:coeffs[key].cpu().numpy() for key in coeffs} | |
pred_coeff = np.concatenate([ | |
pred_coeff['exp'], | |
pred_coeff['angle'], | |
pred_coeff['trans'], | |
trans_params[2:][None], | |
], 1) | |
video_coeffs.append(pred_coeff) | |
full_coeffs.append(full_coeff.cpu().numpy()) | |
semantic_npy = np.array(video_coeffs)[:,0] | |
savemat(coeff_path, {'coeff_3dmm': semantic_npy, 'full_3dmm': np.array(full_coeffs)[0]}) | |
return coeff_path, png_path, crop_info |