import os import numpy as np from PIL import Image from skimage import io, img_as_float32, transform import torch import scipy.io as scio def get_facerender_data(coeff_path, pic_path, first_coeff_path, audio_path, batch_size, input_yaw_list=None, input_pitch_list=None, input_roll_list=None, expression_scale=1.0, still_mode = False): semantic_radius = 13 video_name = os.path.splitext(os.path.split(coeff_path)[-1])[0] txt_path = os.path.splitext(coeff_path)[0] data={} img1 = Image.open(pic_path) source_image = np.array(img1) source_image = img_as_float32(source_image) source_image = transform.resize(source_image, (256, 256, 3)) source_image = source_image.transpose((2, 0, 1)) source_image_ts = torch.FloatTensor(source_image).unsqueeze(0) source_image_ts = source_image_ts.repeat(batch_size, 1, 1, 1) data['source_image'] = source_image_ts source_semantics_dict = scio.loadmat(first_coeff_path) source_semantics = source_semantics_dict['coeff_3dmm'][:1,:70] #1 70 source_semantics_new = transform_semantic_1(source_semantics, semantic_radius) source_semantics_ts = torch.FloatTensor(source_semantics_new).unsqueeze(0) source_semantics_ts = source_semantics_ts.repeat(batch_size, 1, 1) data['source_semantics'] = source_semantics_ts # target generated_dict = scio.loadmat(coeff_path) generated_3dmm = generated_dict['coeff_3dmm'] generated_3dmm[:, :64] = generated_3dmm[:, :64] * expression_scale if still_mode: generated_3dmm[:, 64:] = np.repeat(source_semantics[:, 64:], generated_3dmm.shape[0], axis=0) with open(txt_path+'.txt', 'w') as f: for coeff in generated_3dmm: for i in coeff: f.write(str(i)[:7] + ' '+'\t') f.write('\n') target_semantics_list = [] frame_num = generated_3dmm.shape[0] data['frame_num'] = frame_num for frame_idx in range(frame_num): target_semantics = transform_semantic_target(generated_3dmm, frame_idx, semantic_radius) target_semantics_list.append(target_semantics) remainder = frame_num%batch_size if remainder!=0: for _ in range(batch_size-remainder): target_semantics_list.append(target_semantics) target_semantics_np = np.array(target_semantics_list) #frame_num 70 semantic_radius*2+1 target_semantics_np = target_semantics_np.reshape(batch_size, -1, target_semantics_np.shape[-2], target_semantics_np.shape[-1]) data['target_semantics_list'] = torch.FloatTensor(target_semantics_np) data['video_name'] = video_name data['audio_path'] = audio_path if input_yaw_list is not None: yaw_c_seq = gen_camera_pose(input_yaw_list, frame_num, batch_size) data['yaw_c_seq'] = torch.FloatTensor(yaw_c_seq) if input_pitch_list is not None: pitch_c_seq = gen_camera_pose(input_pitch_list, frame_num, batch_size) data['pitch_c_seq'] = torch.FloatTensor(pitch_c_seq) if input_roll_list is not None: roll_c_seq = gen_camera_pose(input_roll_list, frame_num, batch_size) data['roll_c_seq'] = torch.FloatTensor(roll_c_seq) return data def transform_semantic_1(semantic, semantic_radius): semantic_list = [semantic for i in range(0, semantic_radius*2+1)] coeff_3dmm = np.concatenate(semantic_list, 0) return coeff_3dmm.transpose(1,0) def transform_semantic_target(coeff_3dmm, frame_index, semantic_radius): num_frames = coeff_3dmm.shape[0] seq = list(range(frame_index- semantic_radius, frame_index+ semantic_radius+1)) index = [ min(max(item, 0), num_frames-1) for item in seq ] coeff_3dmm_g = coeff_3dmm[index, :] return coeff_3dmm_g.transpose(1,0) def gen_camera_pose(camera_degree_list, frame_num, batch_size): new_degree_list = [] if len(camera_degree_list) == 1: for _ in range(frame_num): new_degree_list.append(camera_degree_list[0]) remainder = frame_num%batch_size if remainder!=0: for _ in range(batch_size-remainder): new_degree_list.append(new_degree_list[-1]) new_degree_np = np.array(new_degree_list).reshape(batch_size, -1) return new_degree_np degree_sum = 0. for i, degree in enumerate(camera_degree_list[1:]): degree_sum += abs(degree-camera_degree_list[i]) degree_per_frame = degree_sum/(frame_num-1) for i, degree in enumerate(camera_degree_list[1:]): degree_last = camera_degree_list[i] degree_step = degree_per_frame * abs(degree-degree_last)/(degree-degree_last) new_degree_list = new_degree_list + list(np.arange(degree_last, degree, degree_step)) if len(new_degree_list) > frame_num: new_degree_list = new_degree_list[:frame_num] elif len(new_degree_list) < frame_num: for _ in range(frame_num-len(new_degree_list)): new_degree_list.append(new_degree_list[-1]) print(len(new_degree_list)) print(frame_num) remainder = frame_num%batch_size if remainder!=0: for _ in range(batch_size-remainder): new_degree_list.append(new_degree_list[-1]) new_degree_np = np.array(new_degree_list).reshape(batch_size, -1) return new_degree_np