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| import numpy as np | |
| from PIL import Image | |
| from skimage import img_as_float32, transform | |
| import torch | |
| import scipy.io as scio | |
| import os | |
| def get_facerender_data(coeff, 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, preprocess='crop', size = 256, facemodel='facevid2vid'): | |
| semantic_radius = 13 | |
| video_name = f"{os.path.basename(pic_path).split('.')[0]}_{os.path.basename(audio_path).split('.')[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, (size, size, 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) | |
| # generated_dict = scio.loadmat(coeff_path) | |
| if 'full' not in preprocess.lower() and facemodel != 'pirender': | |
| source_semantics = source_semantics_dict['coeff_3dmm'][:1,:70] #1 70 | |
| generated_3dmm = coeff[:,:70] | |
| else: | |
| source_semantics = source_semantics_dict['coeff_3dmm'][:1,:73] #1 70 | |
| generated_3dmm = coeff[:,: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_3dmm[:, :64] = generated_3dmm[:, :64] * expression_scale | |
| if 'full' in preprocess.lower() or facemodel == 'pirender': | |
| generated_3dmm = np.concatenate([generated_3dmm, np.repeat(source_semantics[:,70:], generated_3dmm.shape[0], axis=0)], axis=1) | |
| 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 | |