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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import xml.etree.ElementTree as ET
from ppdet.core.workspace import register, serializable
from .dataset import DetDataset
from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
@register
@serializable
class VOCDataSet(DetDataset):
"""
Load dataset with PascalVOC format.
Notes:
`anno_path` must contains xml file and image file path for annotations.
Args:
dataset_dir (str): root directory for dataset.
image_dir (str): directory for images.
anno_path (str): voc annotation file path.
data_fields (list): key name of data dictionary, at least have 'image'.
sample_num (int): number of samples to load, -1 means all.
label_list (str): if use_default_label is False, will load
mapping between category and class index.
allow_empty (bool): whether to load empty entry. False as default
empty_ratio (float): the ratio of empty record number to total
record's, if empty_ratio is out of [0. ,1.), do not sample the
records and use all the empty entries. 1. as default
repeat (int): repeat times for dataset, use in benchmark.
"""
def __init__(self,
dataset_dir=None,
image_dir=None,
anno_path=None,
data_fields=['image'],
sample_num=-1,
label_list=None,
allow_empty=False,
empty_ratio=1.,
repeat=1):
super(VOCDataSet, self).__init__(
dataset_dir=dataset_dir,
image_dir=image_dir,
anno_path=anno_path,
data_fields=data_fields,
sample_num=sample_num,
repeat=repeat)
self.label_list = label_list
self.allow_empty = allow_empty
self.empty_ratio = empty_ratio
def _sample_empty(self, records, num):
# if empty_ratio is out of [0. ,1.), do not sample the records
if self.empty_ratio < 0. or self.empty_ratio >= 1.:
return records
import random
sample_num = min(
int(num * self.empty_ratio / (1 - self.empty_ratio)), len(records))
records = random.sample(records, sample_num)
return records
def parse_dataset(self, ):
anno_path = os.path.join(self.dataset_dir, self.anno_path)
image_dir = os.path.join(self.dataset_dir, self.image_dir)
# mapping category name to class id
# first_class:0, second_class:1, ...
records = []
empty_records = []
ct = 0
cname2cid = {}
if self.label_list:
label_path = os.path.join(self.dataset_dir, self.label_list)
if not os.path.exists(label_path):
raise ValueError("label_list {} does not exists".format(
label_path))
with open(label_path, 'r') as fr:
label_id = 0
for line in fr.readlines():
cname2cid[line.strip()] = label_id
label_id += 1
else:
cname2cid = pascalvoc_label()
with open(anno_path, 'r') as fr:
while True:
line = fr.readline()
if not line:
break
img_file, xml_file = [os.path.join(image_dir, x) \
for x in line.strip().split()[:2]]
if not os.path.exists(img_file):
logger.warning(
'Illegal image file: {}, and it will be ignored'.format(
img_file))
continue
if not os.path.isfile(xml_file):
logger.warning(
'Illegal xml file: {}, and it will be ignored'.format(
xml_file))
continue
tree = ET.parse(xml_file)
if tree.find('id') is None:
im_id = np.array([ct])
else:
im_id = np.array([int(tree.find('id').text)])
objs = tree.findall('object')
im_w = float(tree.find('size').find('width').text)
im_h = float(tree.find('size').find('height').text)
if im_w < 0 or im_h < 0:
logger.warning(
'Illegal width: {} or height: {} in annotation, '
'and {} will be ignored'.format(im_w, im_h, xml_file))
continue
num_bbox, i = len(objs), 0
gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
gt_class = np.zeros((num_bbox, 1), dtype=np.int32)
gt_score = np.zeros((num_bbox, 1), dtype=np.float32)
difficult = np.zeros((num_bbox, 1), dtype=np.int32)
for obj in objs:
cname = obj.find('name').text
# user dataset may not contain difficult field
_difficult = obj.find('difficult')
_difficult = int(
_difficult.text) if _difficult is not None else 0
x1 = float(obj.find('bndbox').find('xmin').text)
y1 = float(obj.find('bndbox').find('ymin').text)
x2 = float(obj.find('bndbox').find('xmax').text)
y2 = float(obj.find('bndbox').find('ymax').text)
x1 = max(0, x1)
y1 = max(0, y1)
x2 = min(im_w - 1, x2)
y2 = min(im_h - 1, y2)
if x2 > x1 and y2 > y1:
gt_bbox[i, :] = [x1, y1, x2, y2]
gt_class[i, 0] = cname2cid[cname]
gt_score[i, 0] = 1.
difficult[i, 0] = _difficult
i += 1
else:
logger.warning(
'Found an invalid bbox in annotations: xml_file: {}'
', x1: {}, y1: {}, x2: {}, y2: {}.'.format(
xml_file, x1, y1, x2, y2))
gt_bbox = gt_bbox[:i, :]
gt_class = gt_class[:i, :]
gt_score = gt_score[:i, :]
difficult = difficult[:i, :]
voc_rec = {
'im_file': img_file,
'im_id': im_id,
'h': im_h,
'w': im_w
} if 'image' in self.data_fields else {}
gt_rec = {
'gt_class': gt_class,
'gt_score': gt_score,
'gt_bbox': gt_bbox,
'difficult': difficult
}
for k, v in gt_rec.items():
if k in self.data_fields:
voc_rec[k] = v
if len(objs) == 0:
empty_records.append(voc_rec)
else:
records.append(voc_rec)
ct += 1
if self.sample_num > 0 and ct >= self.sample_num:
break
assert ct > 0, 'not found any voc record in %s' % (self.anno_path)
logger.debug('{} samples in file {}'.format(ct, anno_path))
if self.allow_empty and len(empty_records) > 0:
empty_records = self._sample_empty(empty_records, len(records))
records += empty_records
self.roidbs, self.cname2cid = records, cname2cid
def get_label_list(self):
return os.path.join(self.dataset_dir, self.label_list)
def pascalvoc_label():
labels_map = {
'aeroplane': 0,
'bicycle': 1,
'bird': 2,
'boat': 3,
'bottle': 4,
'bus': 5,
'car': 6,
'cat': 7,
'chair': 8,
'cow': 9,
'diningtable': 10,
'dog': 11,
'horse': 12,
'motorbike': 13,
'person': 14,
'pottedplant': 15,
'sheep': 16,
'sofa': 17,
'train': 18,
'tvmonitor': 19
}
return labels_map
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