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+# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
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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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+
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+import os
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+import json
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+from .....utils.file_interface import custom_open
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+
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+
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+def check_src_dataset(root_dir, dataset_type):
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+ """check src dataset format validity"""
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+ if dataset_type in ("LabelMe"):
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+ anno_suffix = ".json"
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+ else:
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+ raise ConvertFailedError(
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+ message=f"数据格式转换失败!不支持{dataset_type}格式数据集。当前仅支持 LabelMe 格式。"
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+ )
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+
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+ err_msg_prefix = f"数据格式转换失败!请参考上述`{dataset_type}格式数据集示例`检查待转换数据集格式。"
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+
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+ for anno in ["label.txt", "annotations", "images"]:
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+ src_anno_path = os.path.join(root_dir, anno)
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+ if not os.path.exists(src_anno_path):
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+ raise ConvertFailedError(
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+ message=f"{err_msg_prefix}保证{src_anno_path}文件存在。"
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+ )
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+ return None
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+
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+
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+def convert(dataset_type, input_dir):
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+ """convert dataset to multilabel format"""
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+ # check format validity
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+ check_src_dataset(input_dir, dataset_type)
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+
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+ if dataset_type in ("LabelMe"):
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+ convert_labelme_dataset(input_dir)
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+ else:
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+ raise ConvertFailedError(
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+ message=f"数据格式转换失败!不支持{dataset_type}格式数据集。当前仅支持 LabelMe 格式。"
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+ )
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+
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+
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+def convert_labelme_dataset(root_dir):
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+ image_dir = os.path.join(root_dir, "images")
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+ anno_path = os.path.join(root_dir, "annotations")
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+ label_path = os.path.join(root_dir, "label.txt")
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+ train_rate = 50
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+ gallery_rate = 30
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+ query_rate = 20
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+ tags = ["train", "gallery", "query"]
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+ label_dict = {}
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+ image_files = []
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+
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+ with custom_open(label_path, "r") as f:
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+ lines = f.readlines()
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+ for idx, line in enumerate(lines):
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+ line = line.strip()
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+ label_dict[line] = str(idx)
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+
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+ for json_file in os.listdir(anno_path):
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+ with custom_open(os.path.join(anno_path, json_file), "r") as f:
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+ data = json.load(f)
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+ filename = data["imagePath"].strip().split("/")[2]
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+ image_path = os.path.join("images", filename)
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+ for label, value in data["flags"].items():
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+ if value:
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+ image_files.append(f"{image_path} {label_dict[label]}\n")
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+
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+ start = 0
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+ image_num = len(image_files)
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+ rate_list = [train_rate, gallery_rate, query_rate]
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+ for i, tag in enumerate(tags):
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+ rate = rate_list[i]
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+ if rate == 0:
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+ continue
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+
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+ end = start + round(image_num * rate / 100)
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+ if sum(rate_list[i + 1 :]) == 0:
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+ end = image_num
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+
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+ txt_file = os.path.abspath(os.path.join(root_dir, tag + ".txt"))
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+ with custom_open(txt_file, "w") as f:
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+ m = 0
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+ for id in range(start, end):
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+ m += 1
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+ f.write(image_files[id])
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+ start = end
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