308 lines
9.7 KiB
Python
308 lines
9.7 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import copy
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import numpy as np
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try:
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from collections.abc import Sequence
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except Exception:
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from collections import Sequence
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from paddle.io import Dataset
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from ppdet.core.workspace import register, serializable
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from ppdet.utils.download import get_dataset_path
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from ppdet.data import source
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from ppdet.utils.logger import setup_logger
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logger = setup_logger(__name__)
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@serializable
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class DetDataset(Dataset):
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"""
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Load detection dataset.
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Args:
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dataset_dir (str): root directory for dataset.
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image_dir (str): directory for images.
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anno_path (str): annotation file path.
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data_fields (list): key name of data dictionary, at least have 'image'.
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sample_num (int): number of samples to load, -1 means all.
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use_default_label (bool): whether to load default label list.
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repeat (int): repeat times for dataset, use in benchmark.
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"""
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def __init__(self,
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dataset_dir=None,
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image_dir=None,
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anno_path=None,
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data_fields=['image'],
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sample_num=-1,
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use_default_label=None,
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repeat=1,
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**kwargs):
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super(DetDataset, self).__init__()
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self.dataset_dir = dataset_dir if dataset_dir is not None else ''
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self.anno_path = anno_path
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self.image_dir = image_dir if image_dir is not None else ''
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self.data_fields = data_fields
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self.sample_num = sample_num
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self.use_default_label = use_default_label
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self.repeat = repeat
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self._epoch = 0
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self._curr_iter = 0
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def __len__(self, ):
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return len(self.roidbs) * self.repeat
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def __call__(self, *args, **kwargs):
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return self
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def __getitem__(self, idx):
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n = len(self.roidbs)
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if self.repeat > 1:
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idx %= n
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# data batch
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roidb = copy.deepcopy(self.roidbs[idx])
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if self.mixup_epoch == 0 or self._epoch < self.mixup_epoch:
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idx = np.random.randint(n)
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roidb = [roidb, copy.deepcopy(self.roidbs[idx])]
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elif self.cutmix_epoch == 0 or self._epoch < self.cutmix_epoch:
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idx = np.random.randint(n)
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roidb = [roidb, copy.deepcopy(self.roidbs[idx])]
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elif self.mosaic_epoch == 0 or self._epoch < self.mosaic_epoch:
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roidb = [roidb, ] + [
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copy.deepcopy(self.roidbs[np.random.randint(n)])
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for _ in range(4)
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]
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elif self.pre_img_epoch == 0 or self._epoch < self.pre_img_epoch:
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# Add previous image as input, only used in CenterTrack
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idx_pre_img = idx - 1
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if idx_pre_img < 0:
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idx_pre_img = idx + 1
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roidb = [roidb, ] + [copy.deepcopy(self.roidbs[idx_pre_img])]
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if isinstance(roidb, Sequence):
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for r in roidb:
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r['curr_iter'] = self._curr_iter
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else:
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roidb['curr_iter'] = self._curr_iter
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self._curr_iter += 1
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return self.transform(roidb)
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def check_or_download_dataset(self):
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self.dataset_dir = get_dataset_path(self.dataset_dir, self.anno_path,
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self.image_dir)
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def set_kwargs(self, **kwargs):
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self.mixup_epoch = kwargs.get('mixup_epoch', -1)
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self.cutmix_epoch = kwargs.get('cutmix_epoch', -1)
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self.mosaic_epoch = kwargs.get('mosaic_epoch', -1)
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self.pre_img_epoch = kwargs.get('pre_img_epoch', -1)
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def set_transform(self, transform):
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self.transform = transform
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def set_epoch(self, epoch_id):
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self._epoch = epoch_id
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def parse_dataset(self, ):
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raise NotImplementedError(
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"Need to implement parse_dataset method of Dataset")
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def get_anno(self):
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if self.anno_path is None:
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return
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return os.path.join(self.dataset_dir, self.anno_path)
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def _is_valid_file(f, extensions=('.jpg', '.jpeg', '.png', '.bmp')):
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return f.lower().endswith(extensions)
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def _make_dataset(dir):
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dir = os.path.expanduser(dir)
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if not os.path.isdir(dir):
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raise ('{} should be a dir'.format(dir))
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images = []
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for root, _, fnames in sorted(os.walk(dir, followlinks=True)):
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for fname in sorted(fnames):
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path = os.path.join(root, fname)
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if _is_valid_file(path):
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images.append(path)
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return images
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@register
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@serializable
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class ImageFolder(DetDataset):
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def __init__(self,
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dataset_dir=None,
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image_dir=None,
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anno_path=None,
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sample_num=-1,
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use_default_label=None,
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**kwargs):
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super(ImageFolder, self).__init__(
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dataset_dir,
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image_dir,
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anno_path,
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sample_num=sample_num,
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use_default_label=use_default_label)
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self._imid2path = {}
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self.roidbs = None
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self.sample_num = sample_num
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def check_or_download_dataset(self):
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return
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def get_anno(self):
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if self.anno_path is None:
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return
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if self.dataset_dir:
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return os.path.join(self.dataset_dir, self.anno_path)
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else:
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return self.anno_path
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def parse_dataset(self, ):
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if not self.roidbs:
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self.roidbs = self._load_images()
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def _parse(self):
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image_dir = self.image_dir
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if not isinstance(image_dir, Sequence):
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image_dir = [image_dir]
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images = []
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for im_dir in image_dir:
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if os.path.isdir(im_dir):
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im_dir = os.path.join(self.dataset_dir, im_dir)
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images.extend(_make_dataset(im_dir))
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elif os.path.isfile(im_dir) and _is_valid_file(im_dir):
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images.append(im_dir)
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return images
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def _load_images(self):
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images = self._parse()
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ct = 0
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records = []
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for image in images:
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assert image != '' and os.path.isfile(image), \
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"Image {} not found".format(image)
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if self.sample_num > 0 and ct >= self.sample_num:
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break
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rec = {'im_id': np.array([ct]), 'im_file': image}
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self._imid2path[ct] = image
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ct += 1
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records.append(rec)
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assert len(records) > 0, "No image file found"
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return records
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def get_imid2path(self):
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return self._imid2path
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def set_images(self, images):
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self.image_dir = images
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self.roidbs = self._load_images()
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def set_slice_images(self,
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images,
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slice_size=[640, 640],
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overlap_ratio=[0.25, 0.25]):
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self.image_dir = images
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ori_records = self._load_images()
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try:
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import sahi
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from sahi.slicing import slice_image
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except Exception as e:
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logger.error(
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'sahi not found, plaese install sahi. '
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'for example: `pip install sahi`, see https://github.com/obss/sahi.'
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)
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raise e
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sub_img_ids = 0
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ct = 0
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ct_sub = 0
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records = []
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for i, ori_rec in enumerate(ori_records):
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im_path = ori_rec['im_file']
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slice_image_result = sahi.slicing.slice_image(
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image=im_path,
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slice_height=slice_size[0],
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slice_width=slice_size[1],
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overlap_height_ratio=overlap_ratio[0],
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overlap_width_ratio=overlap_ratio[1])
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sub_img_num = len(slice_image_result)
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for _ind in range(sub_img_num):
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im = slice_image_result.images[_ind]
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rec = {
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'image': im,
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'im_id': np.array([sub_img_ids + _ind]),
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'h': im.shape[0],
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'w': im.shape[1],
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'ori_im_id': np.array([ori_rec['im_id'][0]]),
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'st_pix': np.array(
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slice_image_result.starting_pixels[_ind],
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dtype=np.float32),
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'is_last': 1 if _ind == sub_img_num - 1 else 0,
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} if 'image' in self.data_fields else {}
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records.append(rec)
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ct_sub += sub_img_num
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ct += 1
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logger.info('{} samples and slice to {} sub_samples.'.format(ct,
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ct_sub))
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self.roidbs = records
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def get_label_list(self):
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# Only VOC dataset needs label list in ImageFold
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return self.anno_path
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@register
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class CommonDataset(object):
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def __init__(self, **dataset_args):
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super(CommonDataset, self).__init__()
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dataset_args = copy.deepcopy(dataset_args)
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type = dataset_args.pop("name")
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self.dataset = getattr(source, type)(**dataset_args)
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def __call__(self):
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return self.dataset
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@register
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class TrainDataset(CommonDataset):
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pass
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@register
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class EvalMOTDataset(CommonDataset):
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pass
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@register
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class TestMOTDataset(CommonDataset):
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pass
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@register
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class EvalDataset(CommonDataset):
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pass
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@register
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class TestDataset(CommonDataset):
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pass
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