我们用yolov8做实例分割时，需要制作标签，如果用labelme做，不能直接用模型训练，需要利用一个脚本文件进行转换。
import base64
import random
import shutil
from tqdm import tqdm
import math
import json
import os
import numpy as np
import PIL.Image
import PIL.ImageDraw
import cv2
class ConvertManager(object):
def __init__(self):
pass
def base64_to_numpy(self, img_bs64):
img_bs64 = base64.b64decode(img_bs64)
img_array = np.frombuffer(img_bs64, np.uint8)
cv2_img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
return cv2_img
@classmethod
def load_labels(cls, name_file):
'''
load names from file.one name one line
:param name_file:
:return:
'''
with open(name_file, 'r') as f:
lines = f.read().rstrip('\n').split('\n')
return lines
def get_class_names_from_all_json(self, json_dir):
classnames = []
for file in os.listdir(json_dir):
if not file.endswith('.json'):
continue
with open(os.path.join(json_dir, file), 'r', encoding='utf-8') as f:
data_dict = json.load(f)
for shape in data_dict['shapes']:
if not shape['label'] in classnames:
classnames.append(shape['label'])
return classnames
def create_save_dir(self, save_dir):
images_dir = os.path.join(save_dir, 'images')
labels_dir = os.path.join(save_dir, 'labels')
if not os.path.exists(save_dir):
os.makedirs(save_dir)
os.mkdir(images_dir)
os.mkdir(labels_dir)
else:
if not os.path.exists(images_dir):
os.mkdir(images_dir)
if not os.path.exists(labels_dir):
os.mkdir(labels_dir)
return images_dir + os.sep, labels_dir + os.sep
def save_list(self, data_list, save_file):
with open(save_file, 'w') as f:
f.write('\n'.join(data_list))
def __rectangle_points_to_polygon(self, points):
xmin = 0
ymin = 0
xmax = 0
ymax = 0
if points[0][0] > points[1][0]:
xmax = points[0][0]
ymax = points[0][1]
xmin = points[1][0]
ymin = points[1][1]
else:
xmax = points[1][0]
ymax = points[1][1]
xmin = points[0][0]
ymin = points[0][1]
return [[xmin, ymin], [xmax, ymin], [xmax, ymax], [xmin, ymax]]
def convert_dataset(self, json_dir, json_list, images_dir, labels_dir, names, save_mode='train'):
images_dir = os.path.join(images_dir, save_mode)+os.sep
labels_dir = os.path.join(labels_dir, save_mode)+os.sep
if not os.path.exists(images_dir):
os.mkdir(images_dir)
if not os.path.exists(labels_dir):
os.mkdir(labels_dir)
for file in tqdm(json_list):
with open(os.path.join(json_dir, file), 'r', encoding='utf-8') as f:
data_dict = json.load(f)
image_file = os.path.join(json_dir, os.path.basename(data_dict['imagePath']))
if os.path.exists(image_file):
shutil.copyfile(image_file, images_dir + os.path.basename(image_file))
else:
imageData = data_dict.get('imageData')
if not imageData:
imageData = base64.b64encode(imageData).decode('utf-8')
img = self.img_b64_to_arr(imageData)
PIL.Image.fromarray(img).save(images_dir + file[:-4] + 'png')
# convert to txt
width = data_dict['imageWidth']
height = data_dict['imageHeight']
line_list = []
for shape in data_dict['shapes']:
data_list = []
data_list.append(str(names.index(shape['label'])))
if shape['shape_type'] == 'rectangle':
points = self.__rectangle_points_to_polygon(shape['points'])
for point in points:
data_list.append(str(point[0] / width))
data_list.append(str(point[1] / height))
elif shape['shape_type'] == 'polygon':
points = shape['points']
for point in points:
data_list.append(str(point[0] / width))
data_list.append(str(point[1] / height))
line_list.append(' '.join(data_list))
self.save_list(line_list, labels_dir + file[:-4] + "txt")
def split_train_val_test_dataset(self, file_list, train_ratio=0.9, trainval_ratio=0.9, need_test_dataset=False,
shuffle_list=True):
if shuffle_list:
random.shuffle(file_list)
total_file_count = len(file_list)
train_list = []
val_list = []
test_list = []
if need_test_dataset:
trainval_count = int(total_file_count * trainval_ratio)
trainval_list = file_list[:trainval_count]
test_list = file_list[trainval_count:]
train_count = int(train_ratio * len(trainval_list))
train_list = trainval_list[:train_count]
val_list = trainval_list[train_count:]
else:
train_count = int(train_ratio * total_file_count)
train_list = file_list[:train_count]
val_list = file_list[train_count:]
return train_list, val_list, test_list
def start(self, json_dir, save_dir, names=None, train_ratio=0.9):
images_dir, labels_dir = self.create_save_dir(save_dir)
if names is None or len(names) == 0:
print('class names will load from all json file')
names = self.get_class_names_from_all_json(json_dir)
print('find {} class names :'.format(len(names)), names)
if len(names) == 0:
return
self.save_list(names, os.path.join(save_dir, 'labels.txt'))
print('start convert')
all_json_list = []
for file in os.listdir(json_dir):
if not file.endswith('.json'):
continue
all_json_list.append(file)
train_list, val_list, test_list = self.split_train_val_test_dataset(all_json_list, train_ratio)
self.convert_dataset(json_dir, train_list, images_dir, labels_dir, names, 'train')
self.convert_dataset(json_dir, val_list, images_dir, labels_dir, names, 'val')
if __name__ == '__main__':
cm = ConvertManager()
cm.start(r'源文件路径', r'保存新位置路径')
