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labelme2yolo
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format code using back

This commit is contained in:
Wang Xin 2022-08-10 22:30:20 +08:00
parent 8a337f91a9
commit cf23412bfd
4 changed files with 127 additions and 104 deletions
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2
src/labelme2yolo/__about__.py
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@ -1,4 +1,4 @@
# SPDX-FileCopyrightText: 2022-present Wang Xin <xinwang614@gmail.com>
#
# SPDX-License-Identifier: MIT
__version__ = '0.0.2'
__version__ = "0.0.2"

2
src/labelme2yolo/__main__.py
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@ -3,7 +3,7 @@
# SPDX-License-Identifier: MIT
import sys
if __name__ == '__main__':
if __name__ == "__main__":
from .cli import run
sys.exit(run())

33
src/labelme2yolo/cli/__init__.py
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@ -2,19 +2,36 @@
#
# SPDX-License-Identifier: MIT
import argparse
from labelme2yolo.l2y import Labelme2YOLO
def run():
parser = argparse.ArgumentParser("labelme2yolo")
parser.add_argument('--json_dir', type=str,
help='Please input the path of the labelme json files.')
parser.add_argument('--val_size', type=float, nargs='?', default=None,
help='Please input the validation dataset size, for example 0.1 ')
parser.add_argument('--test_size', type=float, nargs='?', default=None,
help='Please input the validation dataset size, for example 0.1 ')
parser.add_argument('--json_name', type=str, nargs='?', default=None,
help='If you put json name, it would convert only one json file to YOLO.')
parser.add_argument(
"--json_dir", type=str, help="Please input the path of the labelme json files."
)
parser.add_argument(
"--val_size",
type=float,
nargs="?",
default=None,
help="Please input the validation dataset size, for example 0.1 ",
)
parser.add_argument(
"--test_size",
type=float,
nargs="?",
default=None,
help="Please input the validation dataset size, for example 0.1 ",
)
parser.add_argument(
"--json_name",
type=str,
nargs="?",
default=None,
help="If you put json name, it would convert only one json file to YOLO.",
)
args = parser.parse_args()
if not args.json_dir:

194
src/labelme2yolo/l2y.py
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@ -4,21 +4,21 @@ Created on Aug 18, 2021
@author: xiaosonh
@author: GreatV(Wang Xin)
"""
import os
import shutil
import math
import base64
import io
import json
import math
import os
import shutil
from collections import OrderedDict
from multiprocessing import Pool
import json
import cv2
from sklearn.model_selection import train_test_split
import numpy as np
import PIL.ExifTags
import PIL.Image
import PIL.ImageOps
from sklearn.model_selection import train_test_split
# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
@ -80,81 +80,87 @@ def get_label_id_map(json_dir):
label_set = set()
for file_name in os.listdir(json_dir):
if file_name.endswith('json'):
if file_name.endswith("json"):
json_path = os.path.join(json_dir, file_name)
data = json.load(open(json_path))
for shape in data['shapes']:
label_set.add(shape['label'])
for shape in data["shapes"]:
label_set.add(shape["label"])
return OrderedDict([(label, label_id)
for label_id, label in enumerate(label_set)])
return OrderedDict([(label, label_id) for label_id, label in enumerate(label_set)])
def save_yolo_label(json_name, label_dir_path, target_dir, yolo_obj_list):
txt_path = os.path.join(label_dir_path,
target_dir,
json_name.replace('.json', '.txt'))
txt_path = os.path.join(
label_dir_path, target_dir, json_name.replace(".json", ".txt")
)
with open(txt_path, 'w+') as f:
with open(txt_path, "w+") as f:
for yolo_obj_idx, yolo_obj in enumerate(yolo_obj_list):
yolo_obj_line = '%s %s %s %s %s\n' % yolo_obj \
if yolo_obj_idx + 1 != len(yolo_obj_list) else \
'%s %s %s %s %s' % yolo_obj
yolo_obj_line = (
"%s %s %s %s %s\n" % yolo_obj
if yolo_obj_idx + 1 != len(yolo_obj_list)
else "%s %s %s %s %s" % yolo_obj
)
f.write(yolo_obj_line)
def save_yolo_image(json_data, json_name, image_dir_path, target_dir):
img_name = json_name.replace('.json', '.png')
img_name = json_name.replace(".json", ".png")
img_path = os.path.join(image_dir_path, target_dir, img_name)
if not os.path.exists(img_path):
img = img_b64_to_arr(json_data['imageData'])
img = img_b64_to_arr(json_data["imageData"])
PIL.Image.fromarray(img).save(img_path)
return img_path
class Labelme2YOLO(object):
def __init__(self, json_dir):
self._json_dir = json_dir
self._label_id_map = get_label_id_map(self._json_dir)
def _make_train_val_dir(self):
self._label_dir_path = os.path.join(self._json_dir,
'YOLODataset/labels/')
self._image_dir_path = os.path.join(self._json_dir,
'YOLODataset/images/')
self._label_dir_path = os.path.join(self._json_dir, "YOLODataset/labels/")
self._image_dir_path = os.path.join(self._json_dir, "YOLODataset/images/")
for yolo_path in (os.path.join(self._label_dir_path + 'train/'),
os.path.join(self._label_dir_path + 'val/'),
os.path.join(self._label_dir_path + 'test/'),
os.path.join(self._image_dir_path + 'train/'),
os.path.join(self._image_dir_path + 'val/'),
os.path.join(self._image_dir_path + 'test/')):
for yolo_path in (
os.path.join(self._label_dir_path + "train/"),
os.path.join(self._label_dir_path + "val/"),
os.path.join(self._label_dir_path + "test/"),
os.path.join(self._image_dir_path + "train/"),
os.path.join(self._image_dir_path + "val/"),
os.path.join(self._image_dir_path + "test/"),
):
if os.path.exists(yolo_path):
shutil.rmtree(yolo_path)
os.makedirs(yolo_path)
def _train_test_split(self, folders, json_names, val_size, test_size):
if len(folders) > 0 and 'train' in folders and 'val' in folders and 'test' in folders:
train_json_names = self.get_json_names('train/')
val_json_names = self.get_json_names('val/')
test_json_names = self.get_json_names('test/')
if (
len(folders) > 0
and "train" in folders
and "val" in folders
and "test" in folders
):
train_json_names = self.get_json_names("train/")
val_json_names = self.get_json_names("val/")
test_json_names = self.get_json_names("test/")
return train_json_names, val_json_names, test_json_names
train_indexes, val_indexes = train_test_split(range(len(json_names)),
test_size=val_size)
train_indexes, val_indexes = train_test_split(
range(len(json_names)), test_size=val_size
)
tmp_train_len = len(train_indexes)
test_indexes = []
if test_size:
train_indexes, test_indexes = train_test_split(
range(tmp_train_len), test_size=test_size / (1 - val_size))
train_json_names = [json_names[train_idx]
for train_idx in train_indexes]
range(tmp_train_len), test_size=test_size / (1 - val_size)
)
train_json_names = [json_names[train_idx] for train_idx in train_indexes]
val_json_names = [json_names[val_idx] for val_idx in val_indexes]
test_json_names = [json_names[test_idx] for test_idx in test_indexes]
@ -162,90 +168,93 @@ class Labelme2YOLO(object):
def get_json_names(self, data_type: str):
data_folder = os.path.join(self._json_dir, data_type)
data_json_names = [data_sample_name + '.json'
for data_sample_name in os.listdir(data_folder)
if os.path.isdir(os.path.join(data_folder, data_sample_name))]
data_json_names = [
data_sample_name + ".json"
for data_sample_name in os.listdir(data_folder)
if os.path.isdir(os.path.join(data_folder, data_sample_name))
]
return data_json_names
def convert(self, val_size, test_size):
json_names = [file_name for file_name in os.listdir(self._json_dir)
if os.path.isfile(os.path.join(self._json_dir, file_name)) and
file_name.endswith('.json')]
folders = [file_name for file_name in os.listdir(self._json_dir)
if os.path.isdir(os.path.join(self._json_dir, file_name))]
json_names = [
file_name
for file_name in os.listdir(self._json_dir)
if os.path.isfile(os.path.join(self._json_dir, file_name))
and file_name.endswith(".json")
]
folders = [
file_name
for file_name in os.listdir(self._json_dir)
if os.path.isdir(os.path.join(self._json_dir, file_name))
]
train_json_names, val_json_names, test_json_names = self._train_test_split(
folders, json_names, val_size, test_size)
folders, json_names, val_size, test_size
)
self._make_train_val_dir()
# convert labelme object to yolo format object, and save them to files
# also get image from labelme json file and save them under images folder
for target_dir, json_names in zip(('train/', 'val/', 'test/'),
(train_json_names, val_json_names, test_json_names)):
for target_dir, json_names in zip(
("train/", "val/", "test/"),
(train_json_names, val_json_names, test_json_names),
):
pool = Pool(os.cpu_count() - 1)
for json_name in json_names:
pool.apply_async(self.covert_json_to_text,
args=(target_dir, json_name))
pool.apply_async(self.covert_json_to_text, args=(target_dir, json_name))
pool.close()
pool.join()
print('Generating dataset.yaml file ...')
print("Generating dataset.yaml file ...")
self._save_dataset_yaml()
def covert_json_to_text(self, target_dir, json_name):
json_path = os.path.join(self._json_dir, json_name)
json_data = json.load(open(json_path))
print('Converting %s for %s ...' %
(json_name, target_dir.replace('/', '')))
print("Converting %s for %s ..." % (json_name, target_dir.replace("/", "")))
img_path = save_yolo_image(json_data,
json_name,
self._image_dir_path,
target_dir)
img_path = save_yolo_image(
json_data, json_name, self._image_dir_path, target_dir
)
yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
save_yolo_label(json_name,
self._label_dir_path,
target_dir,
yolo_obj_list)
save_yolo_label(json_name, self._label_dir_path, target_dir, yolo_obj_list)
def convert_one(self, json_name):
json_path = os.path.join(self._json_dir, json_name)
json_data = json.load(open(json_path))
print('Converting %s ...' % json_name)
print("Converting %s ..." % json_name)
img_path = save_yolo_image(json_data, json_name,
self._json_dir, '')
img_path = save_yolo_image(json_data, json_name, self._json_dir, "")
yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
save_yolo_label(json_name, self._json_dir,
'', yolo_obj_list)
save_yolo_label(json_name, self._json_dir, "", yolo_obj_list)
def _get_yolo_object_list(self, json_data, img_path):
yolo_obj_list = []
img_h, img_w, _ = cv2.imread(img_path).shape
for shape in json_data['shapes']:
for shape in json_data["shapes"]:
# labelme circle shape is different from others
# it only has 2 points, 1st is circle center, 2nd is drag end point
if shape['shape_type'] == 'circle':
yolo_obj = self._get_circle_shape_yolo_object(
shape, img_h, img_w)
if shape["shape_type"] == "circle":
yolo_obj = self._get_circle_shape_yolo_object(shape, img_h, img_w)
else:
yolo_obj = self._get_other_shape_yolo_object(
shape, img_h, img_w)
yolo_obj = self._get_other_shape_yolo_object(shape, img_h, img_w)
yolo_obj_list.append(yolo_obj)
return yolo_obj_list
def _get_circle_shape_yolo_object(self, shape, img_h, img_w):
obj_center_x, obj_center_y = shape['points'][0]
obj_center_x, obj_center_y = shape["points"][0]
radius = math.sqrt((obj_center_x - shape['points'][1][0]) ** 2 +
(obj_center_y - shape['points'][1][1]) ** 2)
radius = math.sqrt(
(obj_center_x - shape["points"][1][0]) ** 2
+ (obj_center_y - shape["points"][1][1]) ** 2
)
obj_w = 2 * radius
obj_h = 2 * radius
@ -254,45 +263,42 @@ class Labelme2YOLO(object):
yolo_w = round(float(obj_w / img_w), 6)
yolo_h = round(float(obj_h / img_h), 6)
label_id = self._label_id_map[shape['label']]
label_id = self._label_id_map[shape["label"]]
return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
def _get_other_shape_yolo_object(self, shape, img_h, img_w):
def __get_object_desc(obj_port_list):
def __get_dist(int_list): return max(int_list) - min(int_list)
def __get_dist(int_list):
return max(int_list) - min(int_list)
x_lists = [port[0] for port in obj_port_list]
y_lists = [port[1] for port in obj_port_list]
return min(x_lists), __get_dist(x_lists), min(y_lists), __get_dist(y_lists)
obj_x_min, obj_w, obj_y_min, obj_h = __get_object_desc(shape['points'])
obj_x_min, obj_w, obj_y_min, obj_h = __get_object_desc(shape["points"])
yolo_center_x = round(float((obj_x_min + obj_w / 2.0) / img_w), 6)
yolo_center_y = round(float((obj_y_min + obj_h / 2.0) / img_h), 6)
yolo_w = round(float(obj_w / img_w), 6)
yolo_h = round(float(obj_h / img_h), 6)
label_id = self._label_id_map[shape['label']]
label_id = self._label_id_map[shape["label"]]
return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
def _save_dataset_yaml(self):
yaml_path = os.path.join(
self._json_dir, 'YOLODataset', 'dataset.yaml')
yaml_path = os.path.join(self._json_dir, "YOLODataset", "dataset.yaml")
with open(yaml_path, 'w+') as yaml_file:
yaml_file.write('train: %s\n' %
os.path.join(self._image_dir_path, 'train'))
yaml_file.write('val: %s\n\n' %
os.path.join(self._image_dir_path, 'val'))
yaml_file.write('test: %s\n\n' %
os.path.join(self._image_dir_path, 'test'))
yaml_file.write('nc: %i\n\n' % len(self._label_id_map))
with open(yaml_path, "w+") as yaml_file:
yaml_file.write("train: %s\n" % os.path.join(self._image_dir_path, "train"))
yaml_file.write("val: %s\n\n" % os.path.join(self._image_dir_path, "val"))
yaml_file.write("test: %s\n\n" % os.path.join(self._image_dir_path, "test"))
yaml_file.write("nc: %i\n\n" % len(self._label_id_map))
names_str = ''
names_str = ""
for label, _ in self._label_id_map.items():
names_str += "'%s', " % label
names_str = names_str.rstrip(', ')
yaml_file.write('names: [%s]' % names_str)
names_str = names_str.rstrip(", ")
yaml_file.write("names: [%s]" % names_str)