File size: 8,285 Bytes
395d300
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
import albumentations as A

from torch.utils.data import Dataset, DataLoader
import pycocotools.mask as maskUtils
from pycocotools.coco import COCO
import random
import os.path as osp
import cv2
import numpy as np
from scipy.ndimage import distance_transform_bf, distance_transform_edt, distance_transform_cdt


def is_grey(img: np.ndarray):
    if len(img.shape) == 3 and img.shape[2] == 3:
        return False
    else:
        return True


def square_pad_resize(img: np.ndarray, tgt_size: int, pad_value = (0, 0, 0)):
    h, w = img.shape[:2]
    pad_h, pad_w = 0, 0
    
    # make square image
    if w < h:
        pad_w = h - w
        w += pad_w
    elif h < w:
        pad_h = w - h
        h += pad_h

    pad_size = tgt_size - h
    if pad_size > 0:
        pad_h += pad_size
        pad_w += pad_size

    if pad_h > 0 or pad_w > 0:    
        c = 1
        if is_grey(img):
            if isinstance(pad_value, tuple):
                pad_value = pad_value[0]
        else:
            if isinstance(pad_value, int):
                pad_value = (pad_value, pad_value, pad_value)

        img = cv2.copyMakeBorder(img, 0, pad_h, 0, pad_w, cv2.BORDER_CONSTANT, value=pad_value)

    resize_ratio = tgt_size / img.shape[0]
    if resize_ratio < 1:
        img = cv2.resize(img, (tgt_size, tgt_size), interpolation=cv2.INTER_AREA)
    elif resize_ratio > 1:
        img = cv2.resize(img, (tgt_size, tgt_size), interpolation=cv2.INTER_LINEAR)
        
    return img, resize_ratio, pad_h, pad_w


class MaskRefineDataset(Dataset):

    def __init__(self, 
                 refine_ann_path: str, 
                 data_root: str, 
                 load_instance_mask: bool = True, 
                 aug_ins_prob: float = 0.,
                 ins_rect_prob: float = 0.,
                 output_size: int = 720,
                 augmentation: bool = False,
                 with_distance: bool = False):
        self.load_instance_mask = load_instance_mask
        self.ann_util = COCO(refine_ann_path)
        self.img_ids = self.ann_util.getImgIds()
        self.set_load_method(load_instance_mask)
        self.data_root = data_root

        self.ins_rect_prob = ins_rect_prob
        self.aug_ins_prob = aug_ins_prob
        self.augmentation = augmentation
        if augmentation:
            transform = [
                A.OpticalDistortion(),
                A.HorizontalFlip(),
                A.CLAHE(),
                A.Posterize(),
                A.CropAndPad(percent=0.1, p=0.3, pad_mode=cv2.BORDER_CONSTANT, pad_cval=0, pad_cval_mask=0, keep_size=True),
                A.RandomContrast(), 
                A.Rotate(30, p=0.3, mask_value=0, border_mode=cv2.BORDER_CONSTANT)
            ]
            self._aug_transform = A.Compose(transform)
        else:
            self._aug_transform = None

        self.output_size = output_size
        self.with_distance = with_distance

    def set_output_size(self, size: int):
        self.output_size = size

    def set_load_method(self, load_instance_mask: bool):
        if load_instance_mask:
            self._load_mask = self._load_with_instance
        else:
            self._load_mask = self._load_without_instance

    def __getitem__(self, idx: int):
        img_id = self.img_ids[idx]
        img_meta = self.ann_util.imgs[img_id]
        img_path = osp.join(self.data_root, img_meta['file_name'])
        img = cv2.imread(img_path)

        annids = self.ann_util.getAnnIds([img_id])
        if len(annids) > 0:
            ann = random.choice(annids)
            ann = self.ann_util.anns[ann]
            assert ann['image_id'] == img_id
        else:
            ann = None
        
        return self._load_mask(img, ann)
    
    def transform(self, img: np.ndarray, mask: np.ndarray, ins_seg: np.ndarray = None) -> dict:
        if ins_seg is not None:
            use_seg = True
        else:
            use_seg = False

        if self.augmentation:
            masks = [mask]
            if use_seg:
                masks.append(ins_seg)
            data = self._aug_transform(image=img, masks=masks)
            img = data['image']
            masks = data['masks']
            mask = masks[0]
            if use_seg:
                ins_seg = masks[1]

        img = square_pad_resize(img, self.output_size, random.randint(0, 255))[0]
        mask = square_pad_resize(mask, self.output_size, 0)[0]
        if ins_seg is not None:
            ins_seg = square_pad_resize(ins_seg, self.output_size, 0)[0]

        img = (img.astype(np.float32) / 255.).transpose((2, 0, 1))
        mask = mask[None, ...]


        if use_seg:
            ins_seg = ins_seg[None, ...]
            img = np.concatenate((img, ins_seg), axis=0)

        data = {'img': img, 'mask': mask}
        if self.with_distance:
            dist = distance_transform_edt(mask[0])
            dist_max = dist.max()
            if dist_max != 0:
                dist = 1 - dist / dist_max
                # diff_mat = cv2.bitwise_xor(mask[0], ins_seg[0])
                # dist = dist + diff_mat + 0.2
                dist = dist + 0.2
                dist = dist.size / (dist.sum() + 1) * dist
                dist = np.clip(dist, 0, 20)
            else:
                dist = np.ones_like(dist)
                # print(dist.max(), dist.min())
            data['dist_weight'] = dist[None, ...]
        return data

    def _load_with_instance(self, img: np.ndarray, ann: dict):
        if ann is None:
            mask = np.zeros(img.shape[:2], dtype=np.float32)
            ins_seg = mask
        else:
            mask = maskUtils.decode(ann['segmentation']).astype(np.float32)
            if self.augmentation and random.random() < self.ins_rect_prob:
                ins_seg = np.zeros_like(mask)
                bbox = [int(b) for b in ann['bbox']]
                ins_seg[bbox[1]: bbox[1] + bbox[3], bbox[0]: bbox[0] + bbox[2]] = 1
            elif len(ann['pred_segmentations']) > 0:
                ins_seg = random.choice(ann['pred_segmentations'])
                ins_seg = maskUtils.decode(ins_seg).astype(np.float32)
            else:
                ins_seg = mask
            if self.augmentation and random.random() < self.aug_ins_prob:
                ksize = random.choice([1, 3, 5, 7])
                ksize = ksize * 2 + 1
                kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, ksize=(ksize, ksize))
                if random.random() < 0.5:
                    ins_seg = cv2.dilate(ins_seg, kernel)
                else:
                    ins_seg = cv2.erode(ins_seg, kernel)

        return self.transform(img, mask, ins_seg)

    def _load_without_instance(self, img: np.ndarray, ann: dict):
        if ann is None:
            mask = np.zeros(img.shape[:2], dtype=np.float32)
        else:
            mask = maskUtils.decode(ann['segmentation']).astype(np.float32)
        return self.transform(img, mask)

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


if __name__ == '__main__':
    ann_path = r'workspace/test_syndata/annotations/refine_train.json'
    data_root = r'workspace/test_syndata/train'

    ann_path = r'workspace/test_syndata/annotations/refine_train.json'
    data_root = r'workspace/test_syndata/train'
    aug_ins_prob = 0.5
    load_instance_mask = True
    ins_rect_prob = 0.25
    output_size = 640
    augmentation = True

    random.seed(0)

    md = MaskRefineDataset(ann_path, data_root, load_instance_mask, aug_ins_prob, ins_rect_prob, output_size, augmentation, with_distance=True)
    
    dl = DataLoader(md, batch_size=1, shuffle=False, persistent_workers=True,
                                  num_workers=1, pin_memory=True)
    for data in dl:
        img = data['img'].cpu().numpy()
        img = (img[0, :3].transpose((1, 2, 0)) * 255).astype(np.uint8)
        mask = (data['mask'].cpu().numpy()[0][0] * 255).astype(np.uint8)
        if load_instance_mask:
            ins = (data['img'].cpu().numpy()[0][3] * 255).astype(np.uint8)
            cv2.imshow('ins', ins)
        dist = data['dist_weight'].cpu().numpy()[0][0]
        dist = (dist / dist.max() * 255).astype(np.uint8)
        cv2.imshow('img', img)
        cv2.imshow('mask', mask)
        cv2.imshow('dist_weight', dist)
        cv2.waitKey(0)

        # cv2.imwrite('')