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PaddleDetection
未验证 提交 56cbc020 编写于 作者: G Guanghua Yu 提交者： GitHub
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add tools for voc dataset to coco (#1348)

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docs/advanced_tutorials/READER.md
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......@@ -34,8 +34,6 @@ PaddleDetection的数据处理模块是一个Python模块，所有代码逻辑
├── tests # 单元测试模块
│ ├── test_dataset.py # 对数据集解析、加载等进行单元测试
│ │ ...
├── tools # 一些有用的工具
│ ├── x2coco.py # 将其他数据集转换为COCO数据集格式
├── transform # 数据预处理模块
│ ├── batch_operators.py # 定义各类基于批量数据的预处理算子
│ ├── op_helper.py # 预处理算子的辅助函数
......
docs/advanced_tutorials/TRANSFER_LEARNING.md
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......@@ -8,7 +8,7 @@ In transfer learning, if different dataset and the number of classes is used, th
### Use custom dataset
Transfer learning needs custom dataset and annotation in COCO-format and VOC-format is supported now. The script converts the annotation from labelme or cityscape to COCO is provided in ```ppdet/data/tools/x2coco.py```. More details please refer to [READER](READER.md). After data preparation, update the data parameters in configuration file.
Transfer learning needs custom dataset and annotation in COCO-format and VOC-format is supported now. The script converts the annotation from voc, labelme or cityscape to COCO is provided in ```tools/x2coco.py```. More details please refer to [READER](READER.md). After data preparation, update the data parameters in configuration file.
1. COCO-format dataset, take [yolov3\_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml#L66) for example, modify the COCODataSet in yolov3\_reader:
......
docs/advanced_tutorials/TRANSFER_LEARNING_cn.md
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......@@ -7,7 +7,7 @@
### 选择数据
迁移学习需要使用自己的数据集，目前已支持COCO和VOC的数据标注格式，在```ppdet/data/tools/x2coco.py```中给出了labelme和cityscape标注格式转换为COCO格式的脚本，具体使用方式可以参考[自定义数据源](READER.md)。数据准备完成后，在配置文件中配置数据路径，对应修改reader中的路径参数即可。
迁移学习需要使用自己的数据集，目前已支持COCO和VOC的数据标注格式，在```tools/x2coco.py```中给出了voc、labelme和cityscape标注格式转换为COCO格式的脚本，具体使用方式可以参考[自定义数据源](READER.md)。数据准备完成后，在配置文件中配置数据路径，对应修改reader中的路径参数即可。
1. COCO数据集需要修改COCODataSet中的参数，以[yolov3\_darknet.yml](https://github.com/PaddlePaddle/PaddleDetection/blob/master/configs/yolov3_darknet.yml#L66)为例，修改yolov3\_reader中的配置：
......
docs/tutorials/Custom_DataSet.md
浏览文件 @ 56cbc020
......@@ -15,26 +15,42 @@
### 方式一：将数据集转换为COCO格式
在`./tools/`中提供了`x2coco.py`用于将labelme标注的数据集或cityscape数据集转换为COCO数据集:
在`./tools/`中提供了`x2coco.py`用于将voc格式数据集、labelme标注的数据集或cityscape数据集转换为COCO数据集，例如:
（1）labelmes数据转换为COCO格式：
```bash
python ./ppdet/data/tools/x2coco.py \
python tools/x2coco.py \
--dataset_type labelme \
--json_input_dir ./labelme_annos/ \
--image_input_dir ./labelme_imgs/ \
--output_dir ./cocome/ \
--train_proportion 0.8 \
--val_proportion 0.2 \
--test_proportion 0.0 \
--test_proportion 0.0
```
（2）voc数据转换为COCO格式：
```bash
python tools/x2coco.py \
--dataset_type voc \
--voc_anno_dir path/to/VOCdevkit/VOC2007/Annotations/ \
--voc_anno_list path/to/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt \
--voc_label_list dataset/voc/label_list.txt \
--voc_out_name voc_train.json
```
**参数说明：**
- `--dataset_type`：需要转换的数据格式，目前支持：’labelme‘和’cityscape‘
- `--dataset_type`：需要转换的数据格式，目前支持：’voc‘、’labelme‘和’cityscape‘
- `--json_input_dir`：使用labelme标注的json文件所在文件夹
- `--image_input_dir`：图像文件所在文件夹
- `--output_dir`：转换后的COCO格式数据集存放位置
- `--train_proportion`：标注数据中用于train的比例
- `--val_proportion`：标注数据中用于validation的比例
- `--test_proportion`：标注数据中用于infer的比例
- `--voc_anno_dir`：VOC数据转换为COCO数据集时的voc数据集标注文件路径
- `--voc_anno_list`：VOC数据转换为COCO数据集时的标注列表文件，一般是`ImageSets/Main`下trainval.txt和test.txt文件
- `--voc_label_list`：VOC数据转换为COCO数据集时的类别列表文件，文件中每一行表示一种物体类别
- `--voc_out_name`：VOC数据转换为COCO数据集时的输出的COCO数据集格式json文件名
### 方式二：将数据集转换为VOC格式
......
ppdet/data/source/coco.py
浏览文件 @ 56cbc020
......@@ -137,14 +137,14 @@ class COCODataSet(DataSet):
y1 = max(0, y)
x2 = min(im_w - 1, x1 + max(0, box_w - 1))
y2 = min(im_h - 1, y1 + max(0, box_h - 1))
if inst['area'] > 0 and x2 >= x1 and y2 >= y1:
if x2 >= x1 and y2 >= y1:
inst['clean_bbox'] = [x1, y1, x2, y2]
bboxes.append(inst)
else:
logger.warn(
'Found an invalid bbox in annotations: im_id: {}, '
'area: {} x1: {}, y1: {}, x2: {}, y2: {}.'.format(
img_id, float(inst['area']), x1, y1, x2, y2))
'x1: {}, y1: {}, x2: {}, y2: {}.'.format(
img_id, x1, y1, x2, y2))
num_bbox = len(bboxes)
gt_bbox = np.zeros((num_bbox, 4), dtype=np.float32)
......
tools/cpp_infer.py 已删除 100644 → 0
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此差异已折叠。
ppdet/data/tools/x2coco.py → tools/x2coco.py
浏览文件 @ 56cbc020
......@@ -21,6 +21,9 @@ import os
import os.path as osp
import sys
import shutil
import xml.etree.ElementTree as ET
from tqdm import tqdm
import re
import numpy as np
import PIL.ImageDraw
......@@ -154,17 +157,19 @@ def deal_json(ds_type, img_path, json_path):
categories_list.append(categories(label, labels_list))
labels_list.append(label)
label_to_num[label] = len(labels_list)
points = shapes['points']
p_type = shapes['shape_type']
if p_type == 'polygon':
points = shapes['points']
annotations_list.append(
annotations_polygon(data['imageHeight'], data[
'imageWidth'], points, label, image_num,
object_num, label_to_num))
if p_type == 'rectangle':
points.append([points[0][0], points[1][1]])
points.append([points[1][0], points[0][1]])
(x1, y1), (x2, y2) = shapes['points']
x1, x2 = sorted([x1, x2])
y1, y2 = sorted([y1, y2])
points = [[x1, y1], [x2, y2], [x1, y2], [x2, y1]]
annotations_list.append(
annotations_rectangle(points, label, image_num,
object_num, label_to_num))
......@@ -187,6 +192,100 @@ def deal_json(ds_type, img_path, json_path):
return data_coco
def voc_get_label_anno(ann_dir_path, ann_ids_path, labels_path):
with open(labels_path, 'r') as f:
labels_str = f.read().split()
labels_ids = list(range(1, len(labels_str) + 1))
with open(ann_ids_path, 'r') as f:
ann_ids = f.read().split()
ann_paths = []
for aid in ann_ids:
if aid.endswith('xml'):
ann_path = os.path.join(ann_dir_path, aid)
else:
ann_path = os.path.join(ann_dir_path, aid + '.xml')
ann_paths.append(ann_path)
return dict(zip(labels_str, labels_ids)), ann_paths
def voc_get_image_info(annotation_root, im_id):
filename = annotation_root.findtext('filename')
assert filename is not None
img_name = os.path.basename(filename)
size = annotation_root.find('size')
width = int(size.findtext('width'))
height = int(size.findtext('height'))
image_info = {
'file_name': filename,
'height': height,
'width': width,
'id': im_id
}
return image_info
def voc_get_coco_annotation(obj, label2id):
label = obj.findtext('name')
assert label in label2id, "label is not in label2id."
category_id = label2id[label]
bndbox = obj.find('bndbox')
xmin = int(bndbox.findtext('xmin')) - 1
ymin = int(bndbox.findtext('ymin')) - 1
xmax = int(bndbox.findtext('xmax'))
ymax = int(bndbox.findtext('ymax'))
assert xmax > xmin and ymax > ymin, "Box size error."
o_width = xmax - xmin
o_height = ymax - ymin
anno = {
'area': o_width * o_height,
'iscrowd': 0,
'bbox': [xmin, ymin, o_width, o_height],
'category_id': category_id,
'ignore': 0,
'segmentation': [] # This script is not for segmentation
}
return anno
def voc_xmls_to_cocojson(annotation_paths, label2id, output_dir, output_file):
output_json_dict = {
"images": [],
"type": "instances",
"annotations": [],
"categories": []
}
bnd_id = 1 # bounding box start id
im_id = 0
print('Start converting !')
for a_path in tqdm(annotation_paths):
# Read annotation xml
ann_tree = ET.parse(a_path)
ann_root = ann_tree.getroot()
img_info = voc_get_image_info(ann_root, im_id)
im_id += 1
img_id = img_info['id']
output_json_dict['images'].append(img_info)
for obj in ann_root.findall('object'):
ann = voc_get_coco_annotation(obj=obj, label2id=label2id)
ann.update({'image_id': img_id, 'id': bnd_id})
output_json_dict['annotations'].append(ann)
bnd_id = bnd_id + 1
for label, label_id in label2id.items():
category_info = {'supercategory': 'none', 'id': label_id, 'name': label}
output_json_dict['categories'].append(category_info)
output_file = os.path.join(output_dir, output_file)
with open(output_file, 'w') as f:
output_json = json.dumps(output_json_dict)
f.write(output_json)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
......@@ -194,7 +293,7 @@ def main():
parser.add_argument('--json_input_dir', help='input annotated directory')
parser.add_argument('--image_input_dir', help='image directory')
parser.add_argument(
'--output_dir', help='output dataset directory', default='../../../')
'--output_dir', help='output dataset directory', default='./')
parser.add_argument(
'--train_proportion',
help='the proportion of train dataset',
......@@ -210,96 +309,137 @@ def main():
help='the proportion of test dataset',
type=float,
default=0.0)
parser.add_argument(
'--voc_anno_dir',
help='In Voc format dataset, path to annotation files directory.',
type=str,
default=None)
parser.add_argument(
'--voc_anno_list',
help='In Voc format dataset, path to annotation files ids list.',
type=str,
default=None)
parser.add_argument(
'--voc_label_list',
help='In Voc format dataset, path to label list. The content of each line is a category.',
type=str,
default=None)
parser.add_argument(
'--voc_out_name',
type=str,
default='voc.json',
help='In Voc format dataset, path to output json file')
args = parser.parse_args()
try:
assert args.dataset_type in ['labelme', 'cityscape']
except AssertionError as e:
print('Now only support the cityscape dataset and labelme dataset!!')
os._exit(0)
try:
assert os.path.exists(args.json_input_dir)
except AssertionError as e:
print('The json folder does not exist!')
os._exit(0)
try:
assert os.path.exists(args.image_input_dir)
except AssertionError as e:
print('The image folder does not exist!')
os._exit(0)
try:
assert abs(args.train_proportion + args.val_proportion \
+ args.test_proportion - 1.0) < 1e-5
assert args.dataset_type in ['voc', 'labelme', 'cityscape']
except AssertionError as e:
print(
'The sum of pqoportion of training, validation and test datase must be 1!'
)
'Now only support the voc, cityscape dataset and labelme dataset!!')
os._exit(0)
# Allocate the dataset.
total_num = len(glob.glob(osp.join(args.json_input_dir, '*.json')))
if args.train_proportion != 0:
train_num = int(total_num * args.train_proportion)
os.makedirs(args.output_dir + '/train')
if args.dataset_type == 'voc':
assert args.voc_anno_dir and args.voc_anno_list and args.voc_label_list
label2id, ann_paths = voc_get_label_anno(
args.voc_anno_dir, args.voc_anno_list, args.voc_label_list)
voc_xmls_to_cocojson(
annotation_paths=ann_paths,
label2id=label2id,
output_dir=args.output_dir,
output_file=args.voc_out_name)
else:
train_num = 0
if args.val_proportion == 0.0:
val_num = 0
test_num = total_num - train_num
if args.test_proportion != 0.0:
os.makedirs(args.output_dir + '/test')
else:
val_num = int(total_num * args.val_proportion)
test_num = total_num - train_num - val_num
os.makedirs(args.output_dir + '/val')
if args.test_proportion != 0.0:
os.makedirs(args.output_dir + '/test')
count = 1
for img_name in os.listdir(args.image_input_dir):
if count <= train_num:
if osp.exists(args.output_dir + '/train/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/train/', img_name))
try:
assert os.path.exists(args.json_input_dir)
except AssertionError as e:
print('The json folder does not exist!')
os._exit(0)
try:
assert os.path.exists(args.image_input_dir)
except AssertionError as e:
print('The image folder does not exist!')
os._exit(0)
try:
assert abs(args.train_proportion + args.val_proportion \
+ args.test_proportion - 1.0) < 1e-5
except AssertionError as e:
print(
'The sum of pqoportion of training, validation and test datase must be 1!'
)
os._exit(0)
# Allocate the dataset.
total_num = len(glob.glob(osp.join(args.json_input_dir, '*.json')))
if args.train_proportion != 0:
train_num = int(total_num * args.train_proportion)
os.makedirs(args.output_dir + '/train')
else:
if count <= train_num + val_num:
if osp.exists(args.output_dir + '/val/'):
train_num = 0
if args.val_proportion == 0.0:
val_num = 0
test_num = total_num - train_num
if args.test_proportion != 0.0:
os.makedirs(args.output_dir + '/test')
else:
val_num = int(total_num * args.val_proportion)
test_num = total_num - train_num - val_num
os.makedirs(args.output_dir + '/val')
if args.test_proportion != 0.0:
os.makedirs(args.output_dir + '/test')
count = 1
for img_name in os.listdir(args.image_input_dir):
if count <= train_num:
if osp.exists(args.output_dir + '/train/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/val/', img_name))
osp.join(args.output_dir + '/train/', img_name))
else:
if osp.exists(args.output_dir + '/test/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/test/', img_name))
count = count + 1
# Deal with the json files.
if not os.path.exists(args.output_dir + '/annotations'):
os.makedirs(args.output_dir + '/annotations')
if args.train_proportion != 0:
train_data_coco = deal_json(
args.dataset_type, args.output_dir + '/train', args.json_input_dir)
train_json_path = osp.join(args.output_dir + '/annotations',
'instance_train.json')
json.dump(
train_data_coco,
open(train_json_path, 'w'),
indent=4,
cls=MyEncoder)
if args.val_proportion != 0:
val_data_coco = deal_json(args.dataset_type, args.output_dir + '/val',
args.json_input_dir)
val_json_path = osp.join(args.output_dir + '/annotations',
'instance_val.json')
json.dump(
val_data_coco, open(val_json_path, 'w'), indent=4, cls=MyEncoder)
if args.test_proportion != 0:
test_data_coco = deal_json(args.dataset_type, args.output_dir + '/test',
args.json_input_dir)
test_json_path = osp.join(args.output_dir + '/annotations',
'instance_test.json')
json.dump(
test_data_coco, open(test_json_path, 'w'), indent=4, cls=MyEncoder)
if count <= train_num + val_num:
if osp.exists(args.output_dir + '/val/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/val/', img_name))
else:
if osp.exists(args.output_dir + '/test/'):
shutil.copyfile(
osp.join(args.image_input_dir, img_name),
osp.join(args.output_dir + '/test/', img_name))
count = count + 1
# Deal with the json files.
if not os.path.exists(args.output_dir + '/annotations'):
os.makedirs(args.output_dir + '/annotations')
if args.train_proportion != 0:
train_data_coco = deal_json(args.dataset_type,
args.output_dir + '/train',
args.json_input_dir)
train_json_path = osp.join(args.output_dir + '/annotations',
'instance_train.json')
json.dump(
train_data_coco,
open(train_json_path, 'w'),
indent=4,
cls=MyEncoder)
if args.val_proportion != 0:
val_data_coco = deal_json(args.dataset_type,
args.output_dir + '/val',
args.json_input_dir)
val_json_path = osp.join(args.output_dir + '/annotations',
'instance_val.json')
json.dump(
val_data_coco,
open(val_json_path, 'w'),
indent=4,
cls=MyEncoder)
if args.test_proportion != 0:
test_data_coco = deal_json(args.dataset_type,
args.output_dir + '/test',
args.json_input_dir)
test_json_path = osp.join(args.output_dir + '/annotations',
'instance_test.json')
json.dump(
test_data_coco,
open(test_json_path, 'w'),
indent=4,
cls=MyEncoder)
if __name__ == '__main__':
......
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