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+# Copyright (c) 2022 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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+
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+import os.path as osp
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+import re
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+import imghdr
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+import platform
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+from collections import OrderedDict
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+from functools import partial
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+
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+import numpy as np
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+
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+__all__ = ['build_input_from_file']
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+
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+
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+def norm_path(path):
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+ win_sep = "\\"
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+ other_sep = "/"
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+ if platform.system() == "Windows":
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+ path = win_sep.join(path.split(other_sep))
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+ else:
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+ path = other_sep.join(path.split(win_sep))
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+ return path
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+
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+
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+def is_pic(im_path):
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+ valid_suffix = [
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+ 'JPEG', 'jpeg', 'JPG', 'jpg', 'BMP', 'bmp', 'PNG', 'png', 'npy'
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+ ]
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+ suffix = im_path.split('.')[-1]
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+ if suffix in valid_suffix:
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+ return True
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+ im_format = imghdr.what(im_path)
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+ _, ext = osp.splitext(im_path)
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+ if im_format == 'tiff' or ext == '.img':
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+ return True
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+ return False
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+
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+
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+def get_full_path(p, prefix=''):
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+ p = norm_path(p)
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+ return osp.join(prefix, p)
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+
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+
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+class ConstrSample(object):
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+ def __init__(self, prefix, label_list):
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+ super().__init__()
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+ self.prefix = prefix
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+ self.label_list_obj = self.read_label_list(label_list)
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+ self.get_full_path = partial(get_full_path, prefix=self.prefix)
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+
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+ def read_label_list(self, label_list):
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+ if label_list is None:
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+ return None
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+ cname2cid = OrderedDict()
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+ label_id = 0
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+ with open(label_list, 'r') as f:
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+ for line in f:
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+ cname2cid[line.strip()] = label_id
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+ label_id += 1
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+ return cname2cid
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+
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+ def __call__(self, *parts):
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+ raise NotImplementedError
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+
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+
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+class ConstrSegSample(ConstrSample):
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+ def __call__(self, im_path, mask_path):
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+ return {
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+ 'image': self.get_full_path(im_path),
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+ 'mask': self.get_full_path(mask_path)
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+ }
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+
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+
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+class ConstrCdSample(ConstrSample):
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+ def __call__(self, im1_path, im2_path, mask_path, *aux_mask_paths):
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+ sample = {
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+ 'image_t1': self.get_full_path(im1_path),
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+ 'image_t2': self.get_full_path(im2_path),
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+ 'mask': self.get_full_path(mask_path)
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+ }
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+ if len(aux_mask_paths) > 0:
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+ sample['aux_masks'] = [
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+ self.get_full_path(p) for p in aux_mask_paths
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+ ]
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+ return sample
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+
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+
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+class ConstrClasSample(ConstrSample):
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+ def __call__(self, im_path, label):
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+ return {'image': self.get_full_path(im_path), 'label': int(label)}
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+
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+
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+class ConstrDetSample(ConstrSample):
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+ def __init__(self, prefix, label_list):
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+ super().__init__(prefix, label_list)
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+ self.ct = 0
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+
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+ def __call__(self, im_path, ann_path):
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+ im_path = self.get_full_path(im_path)
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+ ann_path = self.get_full_path(ann_path)
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+ # TODO: Precisely recognize the annotation format
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+ if ann_path.endswith('.json'):
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+ im_dir = im_path
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+ return self._parse_coco_files(im_dir, ann_path)
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+ elif ann_path.endswith('.xml'):
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+ return self._parse_voc_files(im_path, ann_path)
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+ else:
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+ raise ValueError("Cannot recognize the annotation format")
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+
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+ def _parse_voc_files(self, im_path, ann_path):
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+ import xml.etree.ElementTree as ET
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+
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+ cname2cid = self.label_list_obj
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+ tree = ET.parse(ann_path)
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+ # The xml file must contain id.
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+ if tree.find('id') is None:
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+ im_id = np.asarray([self.ct])
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+ else:
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+ self.ct = int(tree.find('id').text)
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+ im_id = np.asarray([int(tree.find('id').text)])
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+ pattern = re.compile('<size>', re.IGNORECASE)
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+ size_tag = pattern.findall(str(ET.tostringlist(tree.getroot())))
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+ if len(size_tag) > 0:
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+ size_tag = size_tag[0][1:-1]
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+ size_element = tree.find(size_tag)
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+ pattern = re.compile('<width>', re.IGNORECASE)
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+ width_tag = pattern.findall(str(ET.tostringlist(size_element)))[0][
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+ 1:-1]
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+ im_w = float(size_element.find(width_tag).text)
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+ pattern = re.compile('<height>', re.IGNORECASE)
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+ height_tag = pattern.findall(str(ET.tostringlist(size_element)))[0][
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+ 1:-1]
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+ im_h = float(size_element.find(height_tag).text)
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+ else:
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+ im_w = 0
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+ im_h = 0
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+
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+ pattern = re.compile('<object>', re.IGNORECASE)
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+ obj_match = pattern.findall(str(ET.tostringlist(tree.getroot())))
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+ if len(obj_match) > 0:
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+ obj_tag = obj_match[0][1:-1]
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+ objs = tree.findall(obj_tag)
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+ else:
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+ objs = list()
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+
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+ num_bbox, i = len(objs), 0
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+ gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
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+ gt_class = np.zeros((num_bbox, 1), dtype=np.int32)
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+ gt_score = np.zeros((num_bbox, 1), dtype=np.float32)
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+ is_crowd = np.zeros((num_bbox, 1), dtype=np.int32)
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+ difficult = np.zeros((num_bbox, 1), dtype=np.int32)
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+ for obj in objs:
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+ pattern = re.compile('<name>', re.IGNORECASE)
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+ name_tag = pattern.findall(str(ET.tostringlist(obj)))[0][1:-1]
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+ cname = obj.find(name_tag).text.strip()
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+ pattern = re.compile('<difficult>', re.IGNORECASE)
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+ diff_tag = pattern.findall(str(ET.tostringlist(obj)))
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+ if len(diff_tag) == 0:
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+ _difficult = 0
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+ else:
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+ diff_tag = diff_tag[0][1:-1]
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+ try:
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+ _difficult = int(obj.find(diff_tag).text)
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+ except Exception:
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+ _difficult = 0
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+ pattern = re.compile('<bndbox>', re.IGNORECASE)
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+ box_tag = pattern.findall(str(ET.tostringlist(obj)))
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+ if len(box_tag) == 0:
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+ continue
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+ box_tag = box_tag[0][1:-1]
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+ box_element = obj.find(box_tag)
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+ pattern = re.compile('<xmin>', re.IGNORECASE)
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+ xmin_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
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+ -1]
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+ x1 = float(box_element.find(xmin_tag).text)
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+ pattern = re.compile('<ymin>', re.IGNORECASE)
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+ ymin_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
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+ -1]
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+ y1 = float(box_element.find(ymin_tag).text)
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+ pattern = re.compile('<xmax>', re.IGNORECASE)
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+ xmax_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
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+ -1]
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+ x2 = float(box_element.find(xmax_tag).text)
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+ pattern = re.compile('<ymax>', re.IGNORECASE)
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+ ymax_tag = pattern.findall(str(ET.tostringlist(box_element)))[0][1:
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+ -1]
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+ y2 = float(box_element.find(ymax_tag).text)
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+ x1 = max(0, x1)
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+ y1 = max(0, y1)
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+ if im_w > 0.5 and im_h > 0.5:
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+ x2 = min(im_w - 1, x2)
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+ y2 = min(im_h - 1, y2)
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+
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+ if not (x2 >= x1 and y2 >= y1):
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+ continue
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+
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+ gt_bbox[i, :] = [x1, y1, x2, y2]
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+ gt_class[i, 0] = cname2cid[cname]
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+ gt_score[i, 0] = 1.
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+ is_crowd[i, 0] = 0
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+ difficult[i, 0] = _difficult
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+ i += 1
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+
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+ gt_bbox = gt_bbox[:i, :]
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+ gt_class = gt_class[:i, :]
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+ gt_score = gt_score[:i, :]
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+ is_crowd = is_crowd[:i, :]
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+ difficult = difficult[:i, :]
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+
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+ im_info = {
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+ 'im_id': im_id,
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+ 'image_shape': np.array(
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+ [im_h, im_w], dtype=np.int32)
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+ }
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+ label_info = {
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+ 'is_crowd': is_crowd,
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+ 'gt_class': gt_class,
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+ 'gt_bbox': gt_bbox,
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+ 'gt_score': gt_score,
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+ 'difficult': difficult
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+ }
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+
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+ self.ct += 1
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+ return {'image': im_path, ** im_info, ** label_info}
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+
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+ def _parse_coco_files(self, im_dir, ann_path):
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+ from pycocotools.coco import COCO
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+
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+ coco = COCO(ann_path)
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+ img_ids = coco.getImgIds()
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+ img_ids.sort()
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+
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+ samples = []
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+ for img_id in img_ids:
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+ img_anno = coco.loadImgs([img_id])[0]
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+ im_fname = img_anno['file_name']
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+ im_w = float(img_anno['width'])
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+ im_h = float(img_anno['height'])
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+
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+ im_path = osp.join(im_dir, im_fname) if im_dir else im_fname
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+
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+ im_info = {
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+ 'image': im_path,
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+ 'im_id': np.array([img_id]),
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+ 'image_shape': np.array(
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+ [im_h, im_w], dtype=np.int32)
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+ }
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+
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+ ins_anno_ids = coco.getAnnIds(imgIds=[img_id], iscrowd=False)
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+ instances = coco.loadAnns(ins_anno_ids)
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+
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+ is_crowds = []
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+ gt_classes = []
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+ gt_bboxs = []
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+ gt_scores = []
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+ difficults = []
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+
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+ for inst in instances:
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+ # Check gt bbox
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+ if inst.get('ignore', False):
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+ continue
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+ if 'bbox' not in inst.keys():
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+ continue
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+ else:
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+ if not any(np.array(inst['bbox'])):
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+ continue
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+
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+ # Read box
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+ x1, y1, box_w, box_h = inst['bbox']
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+ x2 = x1 + box_w
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+ y2 = y1 + box_h
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+ eps = 1e-5
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+ if inst['area'] > 0 and x2 - x1 > eps and y2 - y1 > eps:
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+ inst['clean_bbox'] = [
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+ round(float(x), 3) for x in [x1, y1, x2, y2]
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+ ]
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+
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+ is_crowds.append([inst['iscrowd']])
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+ gt_classes.append([inst['category_id']])
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+ gt_bboxs.append(inst['clean_bbox'])
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+ gt_scores.append([1.])
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+ difficults.append([0])
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+
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+ label_info = {
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+ 'is_crowd': np.array(is_crowds),
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+ 'gt_class': np.array(gt_classes),
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+ 'gt_bbox': np.array(gt_bboxs).astype(np.float32),
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+ 'gt_score': np.array(gt_scores).astype(np.float32),
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+ 'difficult': np.array(difficults),
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+ }
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+
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+ samples.append({ ** im_info, ** label_info})
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+
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+ return samples
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+
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+
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+def build_input_from_file(file_list, prefix='', task='auto', label_list=None):
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+ """
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+ Construct a list of dictionaries from file. Each dict in the list can be used as the input to `paddlers.transforms.Transform` objects.
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+
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+ Args:
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+ file_list (str): Path of file_list.
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+ prefix (str, optional): A nonempty `prefix` specifies the directory that stores the images and annotation files. Default: ''.
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+ 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.
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+ Default: 'auto'.
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+ label_list (str | None, optional): Path of label_list. Default: None.
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+
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+ Returns:
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+ list: List of samples.
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+ """
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+
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+ def _determine_task(parts):
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+ if len(parts) in (3, 5):
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+ task = 'cd'
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+ elif len(parts) == 2:
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+ if parts[1].isdigit():
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+ task = 'clas'
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+ elif is_pic(osp.join(prefix, parts[1])):
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+ task = 'seg'
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+ else:
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+ task = 'det'
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+ else:
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+ raise RuntimeError(
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+ "Cannot automatically determine the task type. Please specify `task` manually."
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+ )
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+ return task
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+
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+ if task not in ('seg', 'det', 'cd', 'clas', 'auto'):
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+ raise ValueError("Invalid value of `task`")
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+
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+ samples = []
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+ ctor = None
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+ with open(file_list, 'r') as f:
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+ for line in f:
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+ line = line.strip()
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+ parts = line.split()
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+ if task == 'auto':
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+ task = _determine_task(parts)
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+ if ctor is None:
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+ # Select and build sample constructor
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+ ctor_class = globals()['Constr' + task.capitalize() + 'Sample']
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+ ctor = ctor_class(prefix, label_list)
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+ sample = ctor(*parts)
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+ if isinstance(sample, list):
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+ samples.extend(sample)
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+ else:
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+ samples.append(sample)
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+
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+ return samples
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Bobholamovic
