Spaces:
Running
Running
File size: 5,836 Bytes
95d308c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
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, preprocess='crop', size = 256, facemodel='facevid2vid'):
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, (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 = generated_dict['coeff_3dmm'][:,:70]
else:
source_semantics = source_semantics_dict['coeff_3dmm'][:1,:73] #1 70
generated_3dmm = generated_dict['coeff_3dmm'][:,: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
|