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279 lines
11 KiB
Python
279 lines
11 KiB
Python
# copyright (c) 2020 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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import numpy as np
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import cv2
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import math
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import os
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import json
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import random
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import traceback
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from paddle.io import Dataset
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from .imaug import transform, create_operators
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class SimpleDataSet(Dataset):
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def __init__(self, config, mode, logger, seed=None):
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super(SimpleDataSet, self).__init__()
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self.logger = logger
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self.mode = mode.lower()
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global_config = config["Global"]
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dataset_config = config[mode]["dataset"]
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loader_config = config[mode]["loader"]
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self.delimiter = dataset_config.get("delimiter", "\t")
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label_file_list = dataset_config.pop("label_file_list")
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data_source_num = len(label_file_list)
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ratio_list = dataset_config.get("ratio_list", 1.0)
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if isinstance(ratio_list, (float, int)):
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ratio_list = [float(ratio_list)] * int(data_source_num)
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assert (
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len(ratio_list) == data_source_num
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), "The length of ratio_list should be the same as the file_list."
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self.data_dir = dataset_config["data_dir"]
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self.do_shuffle = loader_config["shuffle"]
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self.seed = seed
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logger.info("Initialize indexs of datasets:%s" % label_file_list)
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self.data_lines = self.get_image_info_list(label_file_list, ratio_list)
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self.data_idx_order_list = list(range(len(self.data_lines)))
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if self.mode == "train" and self.do_shuffle:
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self.shuffle_data_random()
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self.set_epoch_as_seed(self.seed, dataset_config)
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self.ops = create_operators(dataset_config["transforms"], global_config)
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self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx", 2)
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self.need_reset = True in [x < 1 for x in ratio_list]
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def set_epoch_as_seed(self, seed, dataset_config):
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if self.mode == "train":
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try:
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border_map_id = [
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index
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for index, dictionary in enumerate(dataset_config["transforms"])
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if "MakeBorderMap" in dictionary
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][0]
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shrink_map_id = [
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index
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for index, dictionary in enumerate(dataset_config["transforms"])
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if "MakeShrinkMap" in dictionary
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][0]
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dataset_config["transforms"][border_map_id]["MakeBorderMap"][
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"epoch"
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] = (seed if seed is not None else 0)
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dataset_config["transforms"][shrink_map_id]["MakeShrinkMap"][
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"epoch"
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] = (seed if seed is not None else 0)
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except Exception as E:
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print(E)
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return
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def get_image_info_list(self, file_list, ratio_list):
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if isinstance(file_list, str):
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file_list = [file_list]
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data_lines = []
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for idx, file in enumerate(file_list):
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with open(file, "rb") as f:
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lines = f.readlines()
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if self.mode == "train" or ratio_list[idx] < 1.0:
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random.seed(self.seed)
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lines = random.sample(lines, round(len(lines) * ratio_list[idx]))
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data_lines.extend(lines)
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return data_lines
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def shuffle_data_random(self):
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random.seed(self.seed)
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random.shuffle(self.data_lines)
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return
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def _try_parse_filename_list(self, file_name):
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# multiple images -> one gt label
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if len(file_name) > 0 and file_name[0] == "[":
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try:
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info = json.loads(file_name)
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file_name = random.choice(info)
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except:
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pass
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return file_name
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def get_ext_data(self):
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ext_data_num = 0
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for op in self.ops:
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if hasattr(op, "ext_data_num"):
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ext_data_num = getattr(op, "ext_data_num")
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break
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load_data_ops = self.ops[: self.ext_op_transform_idx]
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ext_data = []
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while len(ext_data) < ext_data_num:
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file_idx = self.data_idx_order_list[np.random.randint(self.__len__())]
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data_line = self.data_lines[file_idx]
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data_line = data_line.decode("utf-8")
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substr = data_line.strip("\n").split(self.delimiter)
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file_name = substr[0]
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file_name = self._try_parse_filename_list(file_name)
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label = substr[1]
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img_path = os.path.join(self.data_dir, file_name)
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data = {"img_path": img_path, "label": label}
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if not os.path.exists(img_path):
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continue
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with open(data["img_path"], "rb") as f:
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img = f.read()
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data["image"] = img
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data = transform(data, load_data_ops)
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if data is None:
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continue
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if "polys" in data.keys():
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if data["polys"].shape[1] != 4:
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continue
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ext_data.append(data)
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return ext_data
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def __getitem__(self, idx):
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file_idx = self.data_idx_order_list[idx]
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data_line = self.data_lines[file_idx]
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try:
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data_line = data_line.decode("utf-8")
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substr = data_line.strip("\n").split(self.delimiter)
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file_name = substr[0]
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file_name = self._try_parse_filename_list(file_name)
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label = substr[1]
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img_path = os.path.join(self.data_dir, file_name)
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data = {"img_path": img_path, "label": label}
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if not os.path.exists(img_path):
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raise Exception("{} does not exist!".format(img_path))
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with open(data["img_path"], "rb") as f:
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img = f.read()
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data["image"] = img
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data["ext_data"] = self.get_ext_data()
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outs = transform(data, self.ops)
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except:
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self.logger.error(
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"When parsing line {}, error happened with msg: {}".format(
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data_line, traceback.format_exc()
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)
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)
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outs = None
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if outs is None:
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# during evaluation, we should fix the idx to get same results for many times of evaluation.
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rnd_idx = (
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np.random.randint(self.__len__())
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if self.mode == "train"
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else (idx + 1) % self.__len__()
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)
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return self.__getitem__(rnd_idx)
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return outs
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def __len__(self):
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return len(self.data_idx_order_list)
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class MultiScaleDataSet(SimpleDataSet):
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def __init__(self, config, mode, logger, seed=None):
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super(MultiScaleDataSet, self).__init__(config, mode, logger, seed)
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self.ds_width = config[mode]["dataset"].get("ds_width", False)
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if self.ds_width:
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self.wh_aware()
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def wh_aware(self):
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data_line_new = []
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wh_ratio = []
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for lins in self.data_lines:
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data_line_new.append(lins)
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lins = lins.decode("utf-8")
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name, label, w, h = lins.strip("\n").split(self.delimiter)
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wh_ratio.append(float(w) / float(h))
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self.data_lines = data_line_new
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self.wh_ratio = np.array(wh_ratio)
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self.wh_ratio_sort = np.argsort(self.wh_ratio)
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self.data_idx_order_list = list(range(len(self.data_lines)))
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def resize_norm_img(self, data, imgW, imgH, padding=True):
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img = data["image"]
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h = img.shape[0]
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w = img.shape[1]
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if not padding:
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resized_image = cv2.resize(
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img, (imgW, imgH), interpolation=cv2.INTER_LINEAR
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)
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resized_w = imgW
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else:
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ratio = w / float(h)
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if math.ceil(imgH * ratio) > imgW:
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resized_w = imgW
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else:
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resized_w = int(math.ceil(imgH * ratio))
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resized_image = cv2.resize(img, (resized_w, imgH))
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resized_image = resized_image.astype("float32")
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resized_image = resized_image.transpose((2, 0, 1)) / 255
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resized_image -= 0.5
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resized_image /= 0.5
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padding_im = np.zeros((3, imgH, imgW), dtype=np.float32)
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padding_im[:, :, :resized_w] = resized_image
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valid_ratio = min(1.0, float(resized_w / imgW))
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data["image"] = padding_im
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data["valid_ratio"] = valid_ratio
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return data
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def __getitem__(self, properties):
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# properites is a tuple, contains (width, height, index)
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img_height = properties[1]
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idx = properties[2]
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if self.ds_width and properties[3] is not None:
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wh_ratio = properties[3]
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img_width = img_height * (
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1 if int(round(wh_ratio)) == 0 else int(round(wh_ratio))
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)
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file_idx = self.wh_ratio_sort[idx]
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else:
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file_idx = self.data_idx_order_list[idx]
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img_width = properties[0]
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wh_ratio = None
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data_line = self.data_lines[file_idx]
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try:
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data_line = data_line.decode("utf-8")
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substr = data_line.strip("\n").split(self.delimiter)
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file_name = substr[0]
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file_name = self._try_parse_filename_list(file_name)
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label = substr[1]
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img_path = os.path.join(self.data_dir, file_name)
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data = {"img_path": img_path, "label": label}
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if not os.path.exists(img_path):
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raise Exception("{} does not exist!".format(img_path))
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with open(data["img_path"], "rb") as f:
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img = f.read()
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data["image"] = img
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data["ext_data"] = self.get_ext_data()
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outs = transform(data, self.ops[:-1])
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if outs is not None:
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outs = self.resize_norm_img(outs, img_width, img_height)
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outs = transform(outs, self.ops[-1:])
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except:
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self.logger.error(
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"When parsing line {}, error happened with msg: {}".format(
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data_line, traceback.format_exc()
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)
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)
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outs = None
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if outs is None:
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# during evaluation, we should fix the idx to get same results for many times of evaluation.
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rnd_idx = (idx + 1) % self.__len__()
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return self.__getitem__([img_width, img_height, rnd_idx, wh_ratio])
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return outs
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