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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
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