File size: 6,959 Bytes
8207382 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | # -*- coding: utf-8 -*-
# @Time : 2019/8/23 21:54
# @Author : zhoujun
import pathlib
import os
import cv2
import numpy as np
import scipy.io as sio
from tqdm.auto import tqdm
from base import BaseDataSet
from utils import order_points_clockwise, get_datalist, load, expand_polygon
class ICDAR2015Dataset(BaseDataSet):
def __init__(
self,
data_path: str,
img_mode,
pre_processes,
filter_keys,
ignore_tags,
transform=None,
**kwargs,
):
super().__init__(
data_path, img_mode, pre_processes, filter_keys, ignore_tags, transform
)
def load_data(self, data_path: str) -> list:
data_list = get_datalist(data_path)
t_data_list = []
for img_path, label_path in data_list:
data = self._get_annotation(label_path)
if len(data["text_polys"]) > 0:
item = {"img_path": img_path, "img_name": pathlib.Path(img_path).stem}
item.update(data)
t_data_list.append(item)
else:
print("there is no suit bbox in {}".format(label_path))
return t_data_list
def _get_annotation(self, label_path: str) -> dict:
boxes = []
texts = []
ignores = []
with open(label_path, encoding="utf-8", mode="r") as f:
for line in f.readlines():
params = line.strip().strip("\ufeff").strip("\xef\xbb\xbf").split(",")
try:
box = order_points_clockwise(
np.array(list(map(float, params[:8]))).reshape(-1, 2)
)
if cv2.contourArea(box) > 0:
boxes.append(box)
label = params[8]
texts.append(label)
ignores.append(label in self.ignore_tags)
except:
print("load label failed on {}".format(label_path))
data = {
"text_polys": np.array(boxes),
"texts": texts,
"ignore_tags": ignores,
}
return data
class DetDataset(BaseDataSet):
def __init__(
self,
data_path: str,
img_mode,
pre_processes,
filter_keys,
ignore_tags,
transform=None,
**kwargs,
):
self.load_char_annotation = kwargs["load_char_annotation"]
self.expand_one_char = kwargs["expand_one_char"]
super().__init__(
data_path, img_mode, pre_processes, filter_keys, ignore_tags, transform
)
def load_data(self, data_path: str) -> list:
"""
从json文件中读取出 文本行的坐标和gt，字符的坐标和gt
:param data_path:
:return:
"""
data_list = []
for path in data_path:
content = load(path)
for gt in tqdm(content["data_list"], desc="read file {}".format(path)):
img_path = os.path.join(content["data_root"], gt["img_name"])
polygons = []
texts = []
illegibility_list = []
language_list = []
for annotation in gt["annotations"]:
if len(annotation["polygon"]) == 0 or len(annotation["text"]) == 0:
continue
if len(annotation["text"]) > 1 and self.expand_one_char:
annotation["polygon"] = expand_polygon(annotation["polygon"])
polygons.append(annotation["polygon"])
texts.append(annotation["text"])
illegibility_list.append(annotation["illegibility"])
language_list.append(annotation["language"])
if self.load_char_annotation:
for char_annotation in annotation["chars"]:
if (
len(char_annotation["polygon"]) == 0
or len(char_annotation["char"]) == 0
):
continue
polygons.append(char_annotation["polygon"])
texts.append(char_annotation["char"])
illegibility_list.append(char_annotation["illegibility"])
language_list.append(char_annotation["language"])
data_list.append(
{
"img_path": img_path,
"img_name": gt["img_name"],
"text_polys": np.array(polygons),
"texts": texts,
"ignore_tags": illegibility_list,
}
)
return data_list
class SynthTextDataset(BaseDataSet):
def __init__(
self,
data_path: str,
img_mode,
pre_processes,
filter_keys,
transform=None,
**kwargs,
):
self.transform = transform
self.dataRoot = pathlib.Path(data_path)
if not self.dataRoot.exists():
raise FileNotFoundError("Dataset folder is not exist.")
self.targetFilePath = self.dataRoot / "gt.mat"
if not self.targetFilePath.exists():
raise FileExistsError("Target file is not exist.")
targets = {}
sio.loadmat(
self.targetFilePath,
targets,
squeeze_me=True,
struct_as_record=False,
variable_names=["imnames", "wordBB", "txt"],
)
self.imageNames = targets["imnames"]
self.wordBBoxes = targets["wordBB"]
self.transcripts = targets["txt"]
super().__init__(data_path, img_mode, pre_processes, filter_keys, transform)
def load_data(self, data_path: str) -> list:
t_data_list = []
for imageName, wordBBoxes, texts in zip(
self.imageNames, self.wordBBoxes, self.transcripts
):
item = {}
wordBBoxes = (
np.expand_dims(wordBBoxes, axis=2)
if (wordBBoxes.ndim == 2)
else wordBBoxes
)
_, _, numOfWords = wordBBoxes.shape
text_polys = wordBBoxes.reshape(
[8, numOfWords], order="F"
).T # num_words * 8
text_polys = text_polys.reshape(numOfWords, 4, 2) # num_of_words * 4 * 2
transcripts = [word for line in texts for word in line.split()]
if numOfWords != len(transcripts):
continue
item["img_path"] = str(self.dataRoot / imageName)
item["img_name"] = (self.dataRoot / imageName).stem
item["text_polys"] = text_polys
item["texts"] = transcripts
item["ignore_tags"] = [x in self.ignore_tags for x in transcripts]
t_data_list.append(item)
return t_data_list
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