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PaddleRS/tests/data/data_utils.py

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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.
import os.path as osp
import re
import imghdr
import platform
from collections import OrderedDict
from functools import partial, wraps
import numpy as np
__all__ = ['build_input_from_file']
def norm_path(path):
win_sep = "\\"
other_sep = "/"
if platform.system() == "Windows":
path = win_sep.join(path.split(other_sep))
else:
path = other_sep.join(path.split(win_sep))
return path
def is_pic(im_path):
valid_suffix = [
'JPEG', 'jpeg', 'JPG', 'jpg', 'BMP', 'bmp', 'PNG', 'png', 'npy'
]
suffix = im_path.split('.')[-1]
if suffix in valid_suffix:
return True
im_format = imghdr.what(im_path)
_, ext = osp.splitext(im_path)
if im_format == 'tiff' or ext == '.img':
return True
return False
def get_full_path(p, prefix=''):
p = norm_path(p)
return osp.join(prefix, p)
def silent(func):
def _do_nothing(*args, **kwargs):
pass
@wraps(func)
def _wrapper(*args, **kwargs):
import builtins
print = builtins.print
builtins.print = _do_nothing
ret = func(*args, **kwargs)
builtins.print = print
return ret
return _wrapper
class ConstrSample(object):
def __init__(self, prefix, label_list):
super().__init__()
self.prefix = prefix
self.label_list_obj = self.read_label_list(label_list)
self.get_full_path = partial(get_full_path, prefix=self.prefix)
def read_label_list(self, label_list):
if label_list is None:
return None
cname2cid = OrderedDict()
label_id = 0
with open(label_list, 'r') as f:
for line in f:
cname2cid[line.strip()] = label_id
label_id += 1
return cname2cid
def __call__(self, *parts):
raise NotImplementedError
class ConstrSegSample(ConstrSample):
def __call__(self, im_path, mask_path):
return {
'image': self.get_full_path(im_path),
'mask': self.get_full_path(mask_path)
}
class ConstrCdSample(ConstrSample):
def __call__(self, im1_path, im2_path, mask_path, *aux_mask_paths):
sample = {
'image_t1': self.get_full_path(im1_path),
'image_t2': self.get_full_path(im2_path),
'mask': self.get_full_path(mask_path)
}
if len(aux_mask_paths) > 0:
sample['aux_masks'] = [
self.get_full_path(p) for p in aux_mask_paths
]
return sample
class ConstrClasSample(ConstrSample):
def __call__(self, im_path, label):
return {'image': self.get_full_path(im_path), 'label': int(label)}
class ConstrDetSample(ConstrSample):
def __init__(self, prefix, label_list):
super().__init__(prefix, label_list)
self.ct = 0
def __call__(self, im_path, ann_path):
im_path = self.get_full_path(im_path)
ann_path = self.get_full_path(ann_path)
# TODO: Precisely recognize the annotation format
if ann_path.endswith('.json'):
im_dir = im_path
return self._parse_coco_files(im_dir, ann_path)
elif ann_path.endswith('.xml'):
return self._parse_voc_files(im_path, ann_path)
else:
raise ValueError("Cannot recognize the annotation format")
def _parse_voc_files(self, im_path, ann_path):
import xml.etree.ElementTree as ET
cname2cid = self.label_list_obj
tree = ET.parse(ann_path)
# The xml file must contain id.
if tree.find('id') is None:
im_id = np.asarray([self.ct])
else:
self.ct = int(tree.find('id').text)
im_id = np.asarray([int(tree.find('id').text)])
pattern = re.compile('<size>', re.IGNORECASE)
size_tag = pattern.findall(str(ET.tostringlist(tree.getroot())))
if len(size_tag) > 0:
size_tag = size_tag[0][1:-1]
size_element = tree.find(size_tag)
pattern = re.compile('<width>', re.IGNORECASE)
width_tag = pattern.findall(str(ET.tostringlist(size_element)))[0][
1:-1]
im_w = float(size_element.find(width_tag).text)
pattern = re.compile('<height>', re.IGNORECASE)
height_tag = pattern.findall(str(ET.tostringlist(size_element)))[0][
1:-1]
im_h = float(size_element.find(height_tag).text)
else:
im_w = 0
im_h = 0
pattern = re.compile('<object>', re.IGNORECASE)
obj_match = pattern.findall(str(ET.tostringlist(tree.getroot())))
if len(obj_match) > 0:
obj_tag = obj_match[0][1:-1]
objs = tree.findall(obj_tag)
else:
objs = list()
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)
is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)
difficult = np.zeros((num_bbox, 1), dtype=np.int32)
for obj in objs:
pattern = re.compile('<name>', re.IGNORECASE)
name_tag = pattern.findall(str(ET.tostringlist(obj)))[0][1:-1]
cname = obj.find(name_tag).text.strip()
pattern = re.compile('<difficult>', re.IGNORECASE)
diff_tag = pattern.findall(str(ET.tostringlist(obj)))
if len(diff_tag) == 0:
_difficult = 0
else:
diff_tag = diff_tag[0][1:-1]
try:
_difficult = int(obj.find(diff_tag).text)
except Exception:
_difficult = 0
pattern = re.compile('<bndbox>', re.IGNORECASE)
box_tag = pattern.findall(str(ET.tostringlist(obj)))
if len(box_tag) == 0:
continue
box_tag = box_tag[0][1:-1]
box_element = obj.find(box_tag)
pattern = re.compile('<xmin>', re.IGNORECASE)
xmin_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
-1]
x1 = float(box_element.find(xmin_tag).text)
pattern = re.compile('<ymin>', re.IGNORECASE)
ymin_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
-1]
y1 = float(box_element.find(ymin_tag).text)
pattern = re.compile('<xmax>', re.IGNORECASE)
xmax_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
-1]
x2 = float(box_element.find(xmax_tag).text)
pattern = re.compile('<ymax>', re.IGNORECASE)
ymax_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
-1]
y2 = float(box_element.find(ymax_tag).text)
x1 = max(0, x1)
y1 = max(0, y1)
if im_w > 0.5 and im_h > 0.5:
x2 = min(im_w - 1, x2)
y2 = min(im_h - 1, y2)
if not (x2 >= x1 and y2 >= y1):
continue
gt_bbox[i, :] = [x1, y1, x2, y2]
gt_class[i, 0] = cname2cid[cname]
gt_score[i, 0] = 1.
is_crowd[i, 0] = 0
difficult[i, 0] = _difficult
i += 1
gt_bbox = gt_bbox[:i, :]
gt_class = gt_class[:i, :]
gt_score = gt_score[:i, :]
is_crowd = is_crowd[:i, :]
difficult = difficult[:i, :]
im_info = {
'im_id': im_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
}
self.ct += 1
return {'image': im_path, ** im_info, ** label_info}
@silent
def _parse_coco_files(self, im_dir, ann_path):
from pycocotools.coco import COCO
coco = COCO(ann_path)
img_ids = coco.getImgIds()
img_ids.sort()
samples = []
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 = osp.join(im_dir, im_fname) if im_dir else im_fname
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]
]
is_crowds.append([inst['iscrowd']])
gt_classes.append([inst['category_id']])
gt_bboxs.append(inst['clean_bbox'])
gt_scores.append([1.])
difficults.append([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),
}
samples.append({ ** im_info, ** label_info})
return samples
def build_input_from_file(file_list, prefix='', task='auto', label_list=None):
"""
Construct a list of dictionaries from file. Each dict in the list can be used as the input to paddlers.transforms.Transform objects.
Args:
file_list (str): Path of file_list.
prefix (str, optional): A nonempty `prefix` specifies the directory that stores the images and annotation files. Default: ''.
task (str, optional): Supported values are 'seg', 'det', 'cd', 'clas', and 'auto'. When `task` is set to 'auto', automatically determine the task based on the input.
Default: 'auto'.
label_list (str|None, optional): Path of label_list. Default: None.
Returns:
list: List of samples.
"""
def _determine_task(parts):
if len(parts) in (3, 5):
task = 'cd'
elif len(parts) == 2:
if parts[1].isdigit():
task = 'clas'
elif is_pic(osp.join(prefix, parts[1])):
task = 'seg'
else:
task = 'det'
else:
raise RuntimeError(
"Cannot automatically determine the task type. Please specify `task` manually."
)
return task
if task not in ('seg', 'det', 'cd', 'clas', 'auto'):
raise ValueError("Invalid value of `task`")
samples = []
ctor = None
with open(file_list, 'r') as f:
for line in f:
line = line.strip()
parts = line.split()
if task == 'auto':
task = _determine_task(parts)
if ctor is None:
# Select and build sample constructor
ctor_class = globals()['Constr' + task.capitalize() + 'Sample']
ctor = ctor_class(prefix, label_list)
sample = ctor(*parts)
if isinstance(sample, list):
samples.extend(sample)
else:
samples.append(sample)
return samples