| import os
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| import numpy as np
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| from PIL import Image
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| from skimage import io, img_as_float32, transform
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| import torch
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| import scipy.io as scio
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|
|
| def get_facerender_data(coeff_path, pic_path, first_coeff_path, audio_path,
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| batch_size, input_yaw_list=None, input_pitch_list=None, input_roll_list=None,
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| expression_scale=1.0, still_mode = False, preprocess='crop'):
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|
|
| semantic_radius = 13
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| video_name = os.path.splitext(os.path.split(coeff_path)[-1])[0]
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| txt_path = os.path.splitext(coeff_path)[0]
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|
|
| data={}
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|
|
| img1 = Image.open(pic_path)
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| source_image = np.array(img1)
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| source_image = img_as_float32(source_image)
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| source_image = transform.resize(source_image, (256, 256, 3))
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| source_image = source_image.transpose((2, 0, 1))
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| source_image_ts = torch.FloatTensor(source_image).unsqueeze(0)
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| source_image_ts = source_image_ts.repeat(batch_size, 1, 1, 1)
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| data['source_image'] = source_image_ts
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|
|
| source_semantics_dict = scio.loadmat(first_coeff_path)
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|
|
| if preprocess.lower() != 'full':
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| source_semantics = source_semantics_dict['coeff_3dmm'][:1,:70]
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| else:
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| source_semantics = source_semantics_dict['coeff_3dmm'][:1,:73]
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|
|
| source_semantics_new = transform_semantic_1(source_semantics, semantic_radius)
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| source_semantics_ts = torch.FloatTensor(source_semantics_new).unsqueeze(0)
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| source_semantics_ts = source_semantics_ts.repeat(batch_size, 1, 1)
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| data['source_semantics'] = source_semantics_ts
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|
|
|
|
| generated_dict = scio.loadmat(coeff_path)
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| generated_3dmm = generated_dict['coeff_3dmm']
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| generated_3dmm[:, :64] = generated_3dmm[:, :64] * expression_scale
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|
|
| if preprocess.lower() == 'full':
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| generated_3dmm = np.concatenate([generated_3dmm, np.repeat(source_semantics[:,70:], generated_3dmm.shape[0], axis=0)], axis=1)
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|
|
| if still_mode:
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| generated_3dmm[:, 64:] = np.repeat(source_semantics[:, 64:], generated_3dmm.shape[0], axis=0)
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|
|
| with open(txt_path+'.txt', 'w') as f:
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| for coeff in generated_3dmm:
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| for i in coeff:
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| f.write(str(i)[:7] + ' '+'\t')
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| f.write('\n')
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|
|
| target_semantics_list = []
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| frame_num = generated_3dmm.shape[0]
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| data['frame_num'] = frame_num
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| for frame_idx in range(frame_num):
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| target_semantics = transform_semantic_target(generated_3dmm, frame_idx, semantic_radius)
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| target_semantics_list.append(target_semantics)
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|
|
| remainder = frame_num%batch_size
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| if remainder!=0:
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| for _ in range(batch_size-remainder):
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| target_semantics_list.append(target_semantics)
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|
|
| target_semantics_np = np.array(target_semantics_list)
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| target_semantics_np = target_semantics_np.reshape(batch_size, -1, target_semantics_np.shape[-2], target_semantics_np.shape[-1])
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| data['target_semantics_list'] = torch.FloatTensor(target_semantics_np)
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| data['video_name'] = video_name
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| data['audio_path'] = audio_path
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|
|
| if input_yaw_list is not None:
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| yaw_c_seq = gen_camera_pose(input_yaw_list, frame_num, batch_size)
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| data['yaw_c_seq'] = torch.FloatTensor(yaw_c_seq)
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| if input_pitch_list is not None:
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| pitch_c_seq = gen_camera_pose(input_pitch_list, frame_num, batch_size)
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| data['pitch_c_seq'] = torch.FloatTensor(pitch_c_seq)
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| if input_roll_list is not None:
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| roll_c_seq = gen_camera_pose(input_roll_list, frame_num, batch_size)
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| data['roll_c_seq'] = torch.FloatTensor(roll_c_seq)
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|
|
| return data
|
|
|
| def transform_semantic_1(semantic, semantic_radius):
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| semantic_list = [semantic for i in range(0, semantic_radius*2+1)]
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| coeff_3dmm = np.concatenate(semantic_list, 0)
|
| return coeff_3dmm.transpose(1,0)
|
|
|
| def transform_semantic_target(coeff_3dmm, frame_index, semantic_radius):
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| num_frames = coeff_3dmm.shape[0]
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| seq = list(range(frame_index- semantic_radius, frame_index + semantic_radius+1))
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| index = [ min(max(item, 0), num_frames-1) for item in seq ]
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| coeff_3dmm_g = coeff_3dmm[index, :]
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| return coeff_3dmm_g.transpose(1,0)
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|
|
| def gen_camera_pose(camera_degree_list, frame_num, batch_size):
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|
|
| new_degree_list = []
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| if len(camera_degree_list) == 1:
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| for _ in range(frame_num):
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| new_degree_list.append(camera_degree_list[0])
|
| remainder = frame_num%batch_size
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| if remainder!=0:
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| for _ in range(batch_size-remainder):
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| new_degree_list.append(new_degree_list[-1])
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| new_degree_np = np.array(new_degree_list).reshape(batch_size, -1)
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| return new_degree_np
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|
|
| degree_sum = 0.
|
| for i, degree in enumerate(camera_degree_list[1:]):
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| degree_sum += abs(degree-camera_degree_list[i])
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|
|
| degree_per_frame = degree_sum/(frame_num-1)
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| for i, degree in enumerate(camera_degree_list[1:]):
|
| degree_last = camera_degree_list[i]
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| 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))
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| if len(new_degree_list) > frame_num:
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| new_degree_list = new_degree_list[:frame_num]
|
| elif len(new_degree_list) < frame_num:
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| for _ in range(frame_num-len(new_degree_list)):
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| new_degree_list.append(new_degree_list[-1])
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| print(len(new_degree_list))
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| print(frame_num)
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|
|
| 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
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|
|
|
|
