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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import absolute_import
import copy
import os
import os.path as osp
import random
from collections import OrderedDict
import numpy as np
from .base import BaseDataset
from paddlers.utils import logging, get_encoding, norm_path, is_pic
from paddlers.transforms import DecodeImg, MixupImage
from paddlers.tools import YOLOAnchorCluster
class COCODetection(BaseDataset):
"""读取COCO格式的检测数据集,并对样本进行相应的处理。
Args:
data_dir (str): 数据集所在的目录路径。
image_dir (str): 描述数据集图片文件路径。
anno_path (str): COCO标注文件路径。
label_list (str): 描述数据集包含的类别信息文件路径。
transforms (paddlers.transforms.Compose): 数据集中每个样本的预处理/增强算子。
num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。当设为'auto'时,根据
系统的实际CPU核数设置`num_workers`: 如果CPU核数的一半大于8,则`num_workers`为8,否则为CPU核数的
一半。
shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
allow_empty (bool): 是否加载负样本。默认为False。
empty_ratio (float): 用于指定负样本占总样本数的比例。如果小于0或大于等于1,则保留全部的负样本。默认为1。
"""
def __init__(self,
data_dir,
image_dir,
anno_path,
label_list,
transforms=None,
num_workers='auto',
shuffle=False,
allow_empty=False,
empty_ratio=1.):
# matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
# or matplotlib.backends is imported for the first time
# pycocotools import matplotlib
import matplotlib
matplotlib.use('Agg')
from pycocotools.coco import COCO
super(COCODetection, self).__init__(data_dir, label_list, transforms,
num_workers, shuffle)
self.data_fields = None
self.num_max_boxes = 50
self.use_mix = False
if self.transforms is not None:
for op in self.transforms.transforms:
if isinstance(op, MixupImage):
self.mixup_op = copy.deepcopy(op)
self.use_mix = True
self.num_max_boxes *= 2
break
self.batch_transforms = None
self.allow_empty = allow_empty
self.empty_ratio = empty_ratio
self.file_list = list()
neg_file_list = list()
self.labels = list()
annotations = dict()
annotations['images'] = list()
annotations['categories'] = list()
annotations['annotations'] = list()
cname2cid = OrderedDict()
label_id = 0
with open(label_list, 'r', encoding=get_encoding(label_list)) as f:
for line in f.readlines():
cname2cid[line.strip()] = label_id
label_id += 1
self.labels.append(line.strip())
for k, v in cname2cid.items():
annotations['categories'].append({
'supercategory': 'component',
'id': v + 1,
'name': k
})
anno_path = norm_path(os.path.join(self.data_dir, anno_path))
image_dir = norm_path(os.path.join(self.data_dir, image_dir))
assert anno_path.endswith('.json'), \
'invalid coco annotation file: ' + anno_path
from pycocotools.coco import COCO
coco = COCO(anno_path)
img_ids = coco.getImgIds()
img_ids.sort()
cat_ids = coco.getCatIds()
ct = 0
catid2clsid = dict({catid: i for i, catid in enumerate(cat_ids)})
cname2cid = dict({
coco.loadCats(catid)[0]['name']: clsid
for catid, clsid in catid2clsid.items()
})
for img_id in img_ids:
img_anno = coco.loadImgs([img_id])[0]
im_fname = img_anno['file_name']
im_w = float(img_anno['width'])
im_h = float(img_anno['height'])
im_path = os.path.join(image_dir,
im_fname) if image_dir else im_fname
if not os.path.exists(im_path):
logging.warning('Illegal image file: {}, and it will be '
'ignored'.format(im_path))
continue
if im_w < 0 or im_h < 0:
logging.warning(
'Illegal width: {} or height: {} in annotation, '
'and im_id: {} will be ignored'.format(im_w, im_h, img_id))
continue
im_info = {
'image': im_path,
'im_id': np.array([img_id]),
'image_shape': np.array(
[im_h, im_w], dtype=np.int32)
}
ins_anno_ids = coco.getAnnIds(imgIds=[img_id], iscrowd=False)
instances = coco.loadAnns(ins_anno_ids)
is_crowds = []
gt_classes = []
gt_bboxs = []
gt_scores = []
difficults = []
for inst in instances:
# check gt bbox
if inst.get('ignore', False):
continue
if 'bbox' not in inst.keys():
continue
else:
if not any(np.array(inst['bbox'])):
continue
# read box
x1, y1, box_w, box_h = inst['bbox']
x2 = x1 + box_w
y2 = y1 + box_h
eps = 1e-5
if inst['area'] > 0 and x2 - x1 > eps and y2 - y1 > eps:
inst['clean_bbox'] = [
round(float(x), 3) for x in [x1, y1, x2, y2]
]
else:
logging.warning(
'Found an invalid bbox in annotations: im_id: {}, '
'area: {} x1: {}, y1: {}, x2: {}, y2: {}.'.format(
img_id, float(inst['area']), x1, y1, x2, y2))
is_crowds.append([inst['iscrowd']])
gt_classes.append([inst['category_id']])
gt_bboxs.append(inst['clean_bbox'])
gt_scores.append([1.])
difficults.append([0])
annotations['annotations'].append({
'iscrowd': inst['iscrowd'],
'image_id': int(inst['image_id']),
'bbox': inst['clean_bbox'],
'area': inst['area'],
'category_id': inst['category_id'],
'id': inst['id'],
'difficult': 0
})
label_info = {
'is_crowd': np.array(is_crowds),
'gt_class': np.array(gt_classes),
'gt_bbox': np.array(gt_bboxs).astype(np.float32),
'gt_score': np.array(gt_scores).astype(np.float32),
'difficult': np.array(difficults),
}
if label_info['gt_bbox'].size > 0:
self.file_list.append({ ** im_info, ** label_info})
annotations['images'].append({
'height': im_h,
'width': im_w,
'id': int(im_info['im_id']),
'file_name': osp.split(im_info['image'])[1]
})
else:
neg_file_list.append({ ** im_info, ** label_info})
ct += 1
if self.use_mix:
self.num_max_boxes = max(self.num_max_boxes, 2 * len(instances))
else:
self.num_max_boxes = max(self.num_max_boxes, len(instances))
if not ct:
logging.error(
"No coco record found in %s' % (file_list)", exit=True)
self.pos_num = len(self.file_list)
if self.allow_empty and neg_file_list:
self.file_list += self._sample_empty(neg_file_list)
logging.info(
"{} samples in file {}, including {} positive samples and {} negative samples.".
format(
len(self.file_list), anno_path, self.pos_num,
len(self.file_list) - self.pos_num))
self.num_samples = len(self.file_list)
self.coco_gt = COCO()
self.coco_gt.dataset = annotations
self.coco_gt.createIndex()
self._epoch = 0
def __getitem__(self, idx):
sample = copy.deepcopy(self.file_list[idx])
if self.data_fields is not None:
sample = {k: sample[k] for k in self.data_fields}
if self.use_mix and (self.mixup_op.mixup_epoch == -1 or
self._epoch < self.mixup_op.mixup_epoch):
if self.num_samples > 1:
mix_idx = random.randint(1, self.num_samples - 1)
mix_pos = (mix_idx + idx) % self.num_samples
else:
mix_pos = 0
sample_mix = copy.deepcopy(self.file_list[mix_pos])
if self.data_fields is not None:
sample_mix = {k: sample_mix[k] for k in self.data_fields}
sample = self.mixup_op(sample=[
DecodeImg(to_rgb=False)(sample),
DecodeImg(to_rgb=False)(sample_mix)
])
sample = self.transforms(sample)
return sample
def __len__(self):
return self.num_samples
def set_epoch(self, epoch_id):
self._epoch = epoch_id
def cluster_yolo_anchor(self,
num_anchors,
image_size,
cache=True,
cache_path=None,
iters=300,
gen_iters=1000,
thresh=.25):
"""
Cluster YOLO anchors.
Reference:
https://github.com/ultralytics/yolov5/blob/master/utils/autoanchor.py
Args:
num_anchors (int): number of clusters
image_size (list or int): [h, w], being an int means image height and image width are the same.
cache (bool): whether using cache
cache_path (str or None, optional): cache directory path. If None, use `data_dir` of dataset.
iters (int, optional): iters of kmeans algorithm
gen_iters (int, optional): iters of genetic algorithm
threshold (float, optional): anchor scale threshold
verbose (bool, optional): whether print results
"""
if cache_path is None:
cache_path = self.data_dir
cluster = YOLOAnchorCluster(
num_anchors=num_anchors,
dataset=self,
image_size=image_size,
cache=cache,
cache_path=cache_path,
iters=iters,
gen_iters=gen_iters,
thresh=thresh)
anchors = cluster()
return anchors
def add_negative_samples(self, image_dir, empty_ratio=1):
"""将背景图片加入训练
Args:
image_dir (str):背景图片所在的文件夹目录。
empty_ratio (float or None): 用于指定负样本占总样本数的比例。如果为None,保留数据集初始化是设置的`empty_ratio`值,
否则更新原有`empty_ratio`值。如果小于0或大于等于1,则保留全部的负样本。默认为1。
"""
import cv2
if not osp.isdir(image_dir):
raise Exception("{} is not a valid image directory.".format(
image_dir))
if empty_ratio is not None:
self.empty_ratio = empty_ratio
image_list = os.listdir(image_dir)
max_img_id = max(len(self.file_list) - 1, max(self.coco_gt.getImgIds()))
neg_file_list = list()
for image in image_list:
if not is_pic(image):
continue
gt_bbox = np.zeros((0, 4), dtype=np.float32)
gt_class = np.zeros((0, 1), dtype=np.int32)
gt_score = np.zeros((0, 1), dtype=np.float32)
is_crowd = np.zeros((0, 1), dtype=np.int32)
difficult = np.zeros((0, 1), dtype=np.int32)
max_img_id += 1
im_fname = osp.join(image_dir, image)
img_data = cv2.imread(im_fname, cv2.IMREAD_UNCHANGED)
im_h, im_w, im_c = img_data.shape
im_info = {
'im_id': np.asarray([max_img_id]),
'image_shape': np.array(
[im_h, im_w], dtype=np.int32)
}
label_info = {
'is_crowd': is_crowd,
'gt_class': gt_class,
'gt_bbox': gt_bbox,
'gt_score': gt_score,
'difficult': difficult
}
if 'gt_poly' in self.file_list[0]:
label_info['gt_poly'] = []
neg_file_list.append({'image': im_fname, ** im_info, ** label_info})
if neg_file_list:
self.allow_empty = True
self.file_list += self._sample_empty(neg_file_list)
logging.info(
"{} negative samples added. Dataset contains {} positive samples and {} negative samples.".
format(
len(self.file_list) - self.num_samples, self.pos_num,
len(self.file_list) - self.pos_num))
self.num_samples = len(self.file_list)
def _sample_empty(self, neg_file_list):
if 0. <= self.empty_ratio < 1.:
import random
total_num = len(self.file_list)
neg_num = total_num - self.pos_num
sample_num = min((total_num * self.empty_ratio - neg_num) //
(1 - self.empty_ratio), len(neg_file_list))
return random.sample(neg_file_list, sample_num)
else:
return neg_file_list