File size: 5,836 Bytes
8c9c9c7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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