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| 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) | |
| break | |
| x_full_frames = [cv2.cvtColor(full_frames[0], cv2.COLOR_BGR2RGB) ] | |
| 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 | |
| original_size = (ox2 - ox1, oy2 - oy1) | |
| else: | |
| oy1, oy2, ox1, ox2 = 0, x_full_frames[0].shape[0], 0, x_full_frames[0].shape[1] | |
| original_size = (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, original_size |