195 lines
6.7 KiB
Python
195 lines
6.7 KiB
Python
# Copyright (c) 2021 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 cv2
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import json
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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 ppdet.core.workspace import register, serializable
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from ppdet.data.crop_utils.annotation_cropper import AnnoCropper
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from .coco import COCODataSet
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from .dataset import _make_dataset, _is_valid_file
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from ppdet.utils.logger import setup_logger
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logger = setup_logger('sniper_coco_dataset')
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@register
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@serializable
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class SniperCOCODataSet(COCODataSet):
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"""SniperCOCODataSet"""
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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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proposals_file=None,
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data_fields=['image'],
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sample_num=-1,
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load_crowd=False,
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allow_empty=True,
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empty_ratio=1.,
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is_trainset=True,
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image_target_sizes=[2000, 1000],
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valid_box_ratio_ranges=[[-1, 0.1],[0.08, -1]],
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chip_target_size=500,
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chip_target_stride=200,
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use_neg_chip=False,
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max_neg_num_per_im=8,
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max_per_img=-1,
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nms_thresh=0.5):
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super(SniperCOCODataSet, self).__init__(
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dataset_dir=dataset_dir,
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image_dir=image_dir,
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anno_path=anno_path,
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data_fields=data_fields,
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sample_num=sample_num,
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load_crowd=load_crowd,
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allow_empty=allow_empty,
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empty_ratio=empty_ratio
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)
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self.proposals_file = proposals_file
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self.proposals = None
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self.anno_cropper = None
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self.is_trainset = is_trainset
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self.image_target_sizes = image_target_sizes
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self.valid_box_ratio_ranges = valid_box_ratio_ranges
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self.chip_target_size = chip_target_size
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self.chip_target_stride = chip_target_stride
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self.use_neg_chip = use_neg_chip
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self.max_neg_num_per_im = max_neg_num_per_im
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self.max_per_img = max_per_img
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self.nms_thresh = nms_thresh
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def parse_dataset(self):
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if not hasattr(self, "roidbs"):
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super(SniperCOCODataSet, self).parse_dataset()
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if self.is_trainset:
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self._parse_proposals()
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self._merge_anno_proposals()
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self.ori_roidbs = copy.deepcopy(self.roidbs)
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self.init_anno_cropper()
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self.roidbs = self.generate_chips_roidbs(self.roidbs, self.is_trainset)
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def set_proposals_file(self, file_path):
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self.proposals_file = file_path
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def init_anno_cropper(self):
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logger.info("Init AnnoCropper...")
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self.anno_cropper = AnnoCropper(
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image_target_sizes=self.image_target_sizes,
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valid_box_ratio_ranges=self.valid_box_ratio_ranges,
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chip_target_size=self.chip_target_size,
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chip_target_stride=self.chip_target_stride,
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use_neg_chip=self.use_neg_chip,
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max_neg_num_per_im=self.max_neg_num_per_im,
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max_per_img=self.max_per_img,
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nms_thresh=self.nms_thresh
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)
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def generate_chips_roidbs(self, roidbs, is_trainset):
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if is_trainset:
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roidbs = self.anno_cropper.crop_anno_records(roidbs)
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else:
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roidbs = self.anno_cropper.crop_infer_anno_records(roidbs)
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return roidbs
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def _parse_proposals(self):
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if self.proposals_file:
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self.proposals = {}
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logger.info("Parse proposals file:{}".format(self.proposals_file))
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with open(self.proposals_file, 'r') as f:
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proposals = json.load(f)
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for prop in proposals:
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image_id = prop["image_id"]
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if image_id not in self.proposals:
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self.proposals[image_id] = []
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x, y, w, h = prop["bbox"]
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self.proposals[image_id].append([x, y, x + w, y + h])
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def _merge_anno_proposals(self):
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assert self.roidbs
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if self.proposals and len(self.proposals.keys()) > 0:
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logger.info("merge proposals to annos")
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for id, record in enumerate(self.roidbs):
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image_id = int(record["im_id"])
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if image_id not in self.proposals.keys():
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logger.info("image id :{} no proposals".format(image_id))
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record["proposals"] = np.array(self.proposals.get(image_id, []), dtype=np.float32)
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self.roidbs[id] = record
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def get_ori_roidbs(self):
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if not hasattr(self, "ori_roidbs"):
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return None
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return self.ori_roidbs
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def get_roidbs(self):
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if not hasattr(self, "roidbs"):
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self.parse_dataset()
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return self.roidbs
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def set_roidbs(self, roidbs):
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self.roidbs = roidbs
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def check_or_download_dataset(self):
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return
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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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im = cv2.imread(image)
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h, w, c = im.shape
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rec = {'im_id': np.array([ct]), 'im_file': image, "h": h, "w": w}
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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._imid2path = {}
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self.image_dir = images
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self.roidbs = self._load_images()
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