# Copyright (c) Alibaba, Inc. and its affiliates.
import os
import xml.etree.ElementTree as ET
from multiprocessing import cpu_count
import numpy as np
from easycv.datasets.registry import DATASOURCES
from easycv.file import io
from .base import DetSourceBase
def parse_xml(source_item, classes):
img_path, xml_path = source_item
with io.open(xml_path[0], 'r') as f:
tree = ET.parse(f)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
gt_bboxes = []
gt_labels = []
for obj in root.iter('object'):
difficult = obj.find('difficult').text
if int(difficult) == 1:
continue
cls_id = classes.index(int(xml_path[1]))
xmlbox = obj.find('bndbox')
box = (float(xmlbox.find('xmin').text),
float(xmlbox.find('ymin').text),
float(xmlbox.find('xmax').text),
float(xmlbox.find('ymax').text))
gt_bboxes.append(box)
gt_labels.append(cls_id)
if len(gt_bboxes) == 0:
gt_bboxes = np.zeros((0, 4), dtype=np.float32)
img_info = {
'gt_bboxes': np.array(gt_bboxes, dtype=np.float32),
'gt_labels': np.array(gt_labels, dtype=np.int64),
'filename': img_path,
}
return img_info
[docs]@DATASOURCES.register_module
class DetSourcePet(DetSourceBase):
"""
data dir is as follows:
```
|- data
|-annotations
|-annotations
|-list.txt
|-test.txt
|-trainval.txt
|-xmls
|-Abyssinian_6.xml
|-...
|-images
|-images
|-Abyssinian_6.jpg
|-...
```
Example0:
data_source = DetSourcePet(
path='/your/data/annotations/annotations/trainval.txt',
classes_id=1 or 2 or 3,
Example1:
data_source = DetSourcePet(
path='/your/data/annotations/annotations/trainval.txt',
classes_id=1 or 2 or 3,
img_root_path='/your/data//images',
img_root_path='/your/data/annotations/annotations/xmls'
)
"""
CLASSES_CFG = {
# 1:37 Class ids
1: list(range(1, 38)),
# 1:Cat 2:Dog
2: list(range(1, 3)),
# 1-25:Cat 1:12:Dog
3: list(range(1, 26))
}
[docs] def __init__(self,
path,
classes_id=1,
img_root_path=None,
label_root_path=None,
cache_at_init=False,
cache_on_the_fly=False,
img_suffix='.jpg',
label_suffix='.xml',
parse_fn=parse_xml,
num_processes=int(cpu_count() / 2),
**kwargs):
"""
Args:
path: path of img id list file in pet format
classes_id: 1= 1:37 Class ids, 2 = 1:Cat 2:Dog, 3 = 1-25:Cat 1:12:Dog
cache_at_init: if set True, will cache in memory in __init__ for faster training
cache_on_the_fly: if set True, will cache in memroy during training
img_suffix: suffix of image file
label_suffix: suffix of label file
parse_fn: parse function to parse item of source iterator
num_processes: number of processes to parse samples
"""
self.classes_id = classes_id
self.img_root_path = img_root_path
self.label_root_path = label_root_path
self.path = path
self.img_suffix = img_suffix
self.label_suffix = label_suffix
super(DetSourcePet, self).__init__(
classes=self.CLASSES_CFG[classes_id],
cache_at_init=cache_at_init,
cache_on_the_fly=cache_on_the_fly,
parse_fn=parse_fn,
num_processes=num_processes)
[docs] def get_source_iterator(self):
if not self.img_root_path:
self.img_root_path = os.path.join(self.path, '../../..',
'images/images')
if not self.label_root_path:
self.label_root_path = os.path.join(
os.path.dirname(self.path), 'xmls')
assert os.path.exists(self.path), f'{self.path} is not exists'
imgs_path_list = []
labels_path_list = []
with io.open(self.path, 'r') as t:
id_lines = t.read().splitlines()
for id_line in id_lines:
img_id = id_line.strip()
if img_id == '':
continue
line = img_id.split()
img_path = os.path.join(self.img_root_path,
line[0] + self.img_suffix)
label_path = os.path.join(self.label_root_path,
line[0] + self.label_suffix)
if not os.path.exists(label_path):
continue
labels_path_list.append((label_path, line[self.classes_id]))
imgs_path_list.append(img_path)
return list(zip(imgs_path_list, labels_path_list))