File size: 6,881 Bytes
3d49622
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
#!/usr/bin/env python
# -*- encoding: utf-8 -*-

"""
@Author  :   Peike Li
@Contact :   peike.li@yahoo.com
@File    :   datasets.py
@Time    :   8/4/19 3:35 PM
@Desc    :
@License :   This source code is licensed under the license found in the
             LICENSE file in the root directory of this source tree.
"""

import os
import numpy as np
import random
import torch
import cv2
from torch.utils import data
from utils.transforms import get_affine_transform


class LIPDataSet(data.Dataset):
    def __init__(self, root, dataset, crop_size=[473, 473], scale_factor=0.25,
                 rotation_factor=30, ignore_label=255, transform=None):
        self.root = root
        self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0]
        self.crop_size = np.asarray(crop_size)
        self.ignore_label = ignore_label
        self.scale_factor = scale_factor
        self.rotation_factor = rotation_factor
        self.flip_prob = 0.5
        self.transform = transform
        self.dataset = dataset

        list_path = os.path.join(self.root, self.dataset + '_id.txt')
        train_list = [i_id.strip() for i_id in open(list_path)]

        self.train_list = train_list
        self.number_samples = len(self.train_list)

    def __len__(self):
        return self.number_samples

    def _box2cs(self, box):
        x, y, w, h = box[:4]
        return self._xywh2cs(x, y, w, h)

    def _xywh2cs(self, x, y, w, h):
        center = np.zeros((2), dtype=np.float32)
        center[0] = x + w * 0.5
        center[1] = y + h * 0.5
        if w > self.aspect_ratio * h:
            h = w * 1.0 / self.aspect_ratio
        elif w < self.aspect_ratio * h:
            w = h * self.aspect_ratio
        scale = np.array([w * 1.0, h * 1.0], dtype=np.float32)
        return center, scale

    def __getitem__(self, index):
        train_item = self.train_list[index]

        im_path = os.path.join(self.root, self.dataset + '_images', train_item + '.jpg')
        parsing_anno_path = os.path.join(self.root, self.dataset + '_segmentations', train_item + '.png')

        im = cv2.imread(im_path, cv2.IMREAD_COLOR)
        h, w, _ = im.shape
        parsing_anno = np.zeros((h, w), dtype=np.long)

        # Get person center and scale
        person_center, s = self._box2cs([0, 0, w - 1, h - 1])
        r = 0

        if self.dataset != 'test':
            # Get pose annotation
            parsing_anno = cv2.imread(parsing_anno_path, cv2.IMREAD_GRAYSCALE)
            if self.dataset == 'train' or self.dataset == 'trainval':
                sf = self.scale_factor
                rf = self.rotation_factor
                s = s * np.clip(np.random.randn() * sf + 1, 1 - sf, 1 + sf)
                r = np.clip(np.random.randn() * rf, -rf * 2, rf * 2) if random.random() <= 0.6 else 0

                if random.random() <= self.flip_prob:
                    im = im[:, ::-1, :]
                    parsing_anno = parsing_anno[:, ::-1]
                    person_center[0] = im.shape[1] - person_center[0] - 1
                    right_idx = [15, 17, 19]
                    left_idx = [14, 16, 18]
                    for i in range(0, 3):
                        right_pos = np.where(parsing_anno == right_idx[i])
                        left_pos = np.where(parsing_anno == left_idx[i])
                        parsing_anno[right_pos[0], right_pos[1]] = left_idx[i]
                        parsing_anno[left_pos[0], left_pos[1]] = right_idx[i]

        trans = get_affine_transform(person_center, s, r, self.crop_size)
        input = cv2.warpAffine(
            im,
            trans,
            (int(self.crop_size[1]), int(self.crop_size[0])),
            flags=cv2.INTER_LINEAR,
            borderMode=cv2.BORDER_CONSTANT,
            borderValue=(0, 0, 0))

        if self.transform:
            input = self.transform(input)

        meta = {
            'name': train_item,
            'center': person_center,
            'height': h,
            'width': w,
            'scale': s,
            'rotation': r
        }

        if self.dataset == 'val' or self.dataset == 'test':
            return input, meta
        else:
            label_parsing = cv2.warpAffine(
                parsing_anno,
                trans,
                (int(self.crop_size[1]), int(self.crop_size[0])),
                flags=cv2.INTER_NEAREST,
                borderMode=cv2.BORDER_CONSTANT,
                borderValue=(255))

            label_parsing = torch.from_numpy(label_parsing)

            return input, label_parsing, meta


class LIPDataValSet(data.Dataset):
    def __init__(self, root, dataset='val', crop_size=[473, 473], transform=None, flip=False):
        self.root = root
        self.crop_size = crop_size
        self.transform = transform
        self.flip = flip
        self.dataset = dataset
        self.root = root
        self.aspect_ratio = crop_size[1] * 1.0 / crop_size[0]
        self.crop_size = np.asarray(crop_size)

        list_path = os.path.join(self.root, self.dataset + '_id.txt')
        val_list = [i_id.strip() for i_id in open(list_path)]

        self.val_list = val_list
        self.number_samples = len(self.val_list)

    def __len__(self):
        return len(self.val_list)

    def _box2cs(self, box):
        x, y, w, h = box[:4]
        return self._xywh2cs(x, y, w, h)

    def _xywh2cs(self, x, y, w, h):
        center = np.zeros((2), dtype=np.float32)
        center[0] = x + w * 0.5
        center[1] = y + h * 0.5
        if w > self.aspect_ratio * h:
            h = w * 1.0 / self.aspect_ratio
        elif w < self.aspect_ratio * h:
            w = h * self.aspect_ratio
        scale = np.array([w * 1.0, h * 1.0], dtype=np.float32)

        return center, scale

    def __getitem__(self, index):
        val_item = self.val_list[index]
        # Load training image
        im_path = os.path.join(self.root, self.dataset + '_images', val_item + '.jpg')
        im = cv2.imread(im_path, cv2.IMREAD_COLOR)
        h, w, _ = im.shape
        # Get person center and scale
        person_center, s = self._box2cs([0, 0, w - 1, h - 1])
        r = 0
        trans = get_affine_transform(person_center, s, r, self.crop_size)
        input = cv2.warpAffine(
            im,
            trans,
            (int(self.crop_size[1]), int(self.crop_size[0])),
            flags=cv2.INTER_LINEAR,
            borderMode=cv2.BORDER_CONSTANT,
            borderValue=(0, 0, 0))
        input = self.transform(input)
        flip_input = input.flip(dims=[-1])
        if self.flip:
            batch_input_im = torch.stack([input, flip_input])
        else:
            batch_input_im = input

        meta = {
            'name': val_item,
            'center': person_center,
            'height': h,
            'width': w,
            'scale': s,
            'rotation': r
        }

        return batch_input_im, meta