├── data # 数据集 │ ├── data_annotated # 标注的文件 │ │ ├── 0000.bmp │ │ ├── 0000.json │ ├── creat_txt.py │ ├── classify_file.py │ ├── labelme2coco.py │ ├── labels.txt创建labels.txt
__ignore__ _background_ A B划分数据集creat_txt.py
# -*- coding: utf-8 -*-
import os
import glob
import random
root = os.getcwd()
data_annotated_path = os.path.join(root, 'data_annotated')
txt_save_path = root
json_file_path = glob.glob(os.path.join(data_annotated_path, '*.json'))
img_file_path = glob.glob(os.path.join(data_annotated_path, '*.bmp'))
assert len(json_file_path) == len(img_file_path)
trainval_percent = 1 # No test sample
train_percent = 0.9
num = len(json_file_path)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
f_trainval = open(os.path.join(root, 'trainval2017.txt'), 'w')
f_train = open(os.path.join(root, 'train2017.txt'), 'w')
f_val = open(os.path.join(root, 'val2017.txt'), 'w')
f_test = open(os.path.join(root, 'test2017.txt'), 'w')
for i in list:
name = os.path.basename(json_file_path[i]).split('.')[0] + 'n'
if i in trainval:
f_trainval.write(name)
if i in train:
f_train.write(name)
else:
f_val.write(name)
else:
f_test.write(name)
f_trainval.close()
f_train.close()
f_val.close()
f_test.close()
print('Create_txt Done')
得到以下几个文件
├── data # 数据集 │ ├── test2017.txt │ ├── train2017.txt │ ├── trainval2017.txt │ ├── val2017.txt拷贝到对应文件夹classify_file.py
import shutil
import os
import os.path as osp
sets = ['train2017', 'val2017', 'test2017']
for image_set in sets:
if osp.exists(image_set):
shutil.rmtree(image_set)
print('Deleted previous %s file and created a new one' % image_set)
os.makedirs(image_set)
json_path = '%s' % image_set
if osp.exists(json_path):
shutil.rmtree(json_path)
print('Deleted previous %s file and created a new one' % json_path)
os.makedirs(json_path)
image_ids = open('./%s.txt' % image_set).read().strip().split()
for image_id in image_ids:
img = 'data_annotated/%s.bmp' % image_id
json = 'data_annotated/%s.json' % image_id
shutil.copy(img, image_set)
shutil.copy(json, '%s/' % image_set)
print("Done")
处理后得到以下文件
├── data # 数据集 │ ├── train2017 │ │ ├── 0000.bmp │ │ ├── 0000.json │ │ ├── ··· │ ├── val2017 │ │ ├── 0001.bmp │ │ ├── 0001.json │ │ ├── ··· │ ├── test2017转换为coco格式labelme2coco.py
import collections
import datetime
import glob
import json
import uuid
import os
import os.path as osp
import sys
import numpy as np
import imgviz
import labelme
import shutil
try:
import pycocotools.mask
except ImportError:
print('Please install pycocotools:nn pip install pycocotoolsn')
sys.exit(1)
def main():
noviz = False
root = os.getcwd()
dataset = 'hanfengseg'
sets = ['train2017', 'val2017', 'test2017']
output_dir = osp.join(root, dataset)
if not osp.exists(output_dir):
os.makedirs(output_dir)
print('Creating dataset:', output_dir)
else:
shutil.rmtree(output_dir)
print('Output directory already exists:', output_dir)
if not osp.exists(osp.join(output_dir, 'annotations')):
os.makedirs(osp.join(output_dir, 'annotations'))
if not osp.exists(osp.join(output_dir, 'JPEGImages')):
os.makedirs(osp.join(output_dir, 'JPEGImages'))
if not osp.exists(osp.join(output_dir, 'Visualization')):
os.makedirs(osp.join(output_dir, 'Visualization'))
for set in sets:
input_dir = './%s' % set # ./train2017
filename = 'instances_%s' % set # instances_train2017
now = datetime.datetime.now()
data = dict(
info=dict(
description='HanFeng',
url=None,
version="5.0.1",
year="2022",
contributor=None,
date_created=now.strftime("%Y-%m-%d %H:%M:%S.%f"),
),
licenses=[dict(url=None, id=0, name=None, )],
images=[
# license, url, file_name, height, width, date_captured, id
],
type="instances",
annotations=[
# segmentation, area, iscrowd, image_id, bbox, category_id, id
],
categories=[
# supercategory, id, name
],
)
class_name_to_id = {}
for i, line in enumerate(open(osp.join(root, 'labels.txt')).readlines()):
class_id = i - 1 # starts with -1
class_name = line.strip()
if class_id == -1:
assert class_name == "__ignore__"
continue
class_name_to_id[class_name] = class_id
data["categories"].append(
dict(supercategory=None, id=class_id, name=class_name, )
)
out_ann_file = osp.join(output_dir, 'annotations', filename + '.json') # ./annotationsinstances_test2017.json
label_files = glob.glob(osp.join(input_dir, '*.json')) # [./train2017\0.json, ...]
for image_id, filename in enumerate(label_files):
print("Generating dataset from:", filename)
label_file = labelme.LabelFile(filename=filename)
base = osp.splitext(osp.basename(filename))[0]
out_img_file = osp.join(output_dir, "JPEGImages", set, base + ".jpg")
img = labelme.utils.img_data_to_arr(label_file.imageData)
imgviz.io.imsave(out_img_file, img)
data["images"].append(
dict(
license=0,
url=None,
file_name=base + ".jpg",
height=img.shape[0],
width=img.shape[1],
date_captured=None,
id=image_id,
)
)
masks = {} # for area
segmentations = collections.defaultdict(list) # for segmentation
for shape in label_file.shapes:
points = shape["points"]
label = shape["label"]
group_id = shape.get("group_id")
shape_type = shape.get("shape_type", "polygon")
mask = labelme.utils.shape_to_mask(
img.shape[:2], points, shape_type
)
if group_id is None:
group_id = uuid.uuid1()
instance = (label, group_id)
if instance in masks:
masks[instance] = masks[instance] | mask
else:
masks[instance] = mask
if shape_type == "rectangle":
(x1, y1), (x2, y2) = points
x1, x2 = sorted([x1, x2])
y1, y2 = sorted([y1, y2])
points = [x1, y1, x2, y1, x2, y2, x1, y2]
if shape_type == "circle":
(x1, y1), (x2, y2) = points
r = np.linalg.norm([x2 - x1, y2 - y1])
# r(1-cos(a/2)) N>pi/arccos(1-x/r)
# x: tolerance of the gap between the arc and the line segment
n_points_circle = max(int(np.pi / np.arccos(1 - 1 / r)), 12)
i = np.arange(n_points_circle)
x = x1 + r * np.sin(2 * np.pi / n_points_circle * i)
y = y1 + r * np.cos(2 * np.pi / n_points_circle * i)
points = np.stack((x, y), axis=1).flatten().tolist()
else:
points = np.asarray(points).flatten().tolist()
segmentations[instance].append(points)
segmentations = dict(segmentations)
for instance, mask in masks.items():
cls_name, group_id = instance
if cls_name not in class_name_to_id:
continue
cls_id = class_name_to_id[cls_name]
mask = np.asfortranarray(mask.astype(np.uint8))
mask = pycocotools.mask.encode(mask)
area = float(pycocotools.mask.area(mask))
bbox = pycocotools.mask.toBbox(mask).flatten().tolist()
data["annotations"].append(
dict(
id=len(data["annotations"]),
image_id=image_id,
category_id=cls_id,
segmentation=segmentations[instance],
area=area,
bbox=bbox,
iscrowd=0,
)
)
if not noviz:
viz = img
if masks:
labels, captions, masks = zip(
*[
(class_name_to_id[cnm], cnm, msk)
for (cnm, gid), msk in masks.items()
if cnm in class_name_to_id
]
)
viz = imgviz.instances2rgb(
image=img,
labels=labels,
masks=masks,
captions=captions,
font_size=15,
line_width=2,
)
out_viz_file = osp.join(
output_dir, "Visualization", set, base + ".jpg"
)
imgviz.io.imsave(out_viz_file, viz)
with open(out_ann_file, "w") as f:
json.dump(data, f)
if __name__ == '__main__':
main()
生成的数据集
├── hanfengseg # 生成的数据集 │ ├── annotations # 标注文件 │ │ ├── instances_train2017.json │ │ ├── instances_val2017.json │ │ ├── instances_test2017.json │ ├── JPEGImages # 图片 │ │ ├── train2017 │ │ │ ├── 0000.jpg │ │ │ ├── 0001.jpg │ │ │ ├── ··· │ │ ├── val2017 │ ├── Visualization # 可视化 │ │ ├── train2017 │ │ │ ├── 0000.jpg │ │ │ ├── 0001.jpg │ │ │ ├── ··· │ │ ├── val2017
