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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('') |