只需要有图片和yolo格式的标签就可以转换为coco格式的标签
第一步:将yolo格式的标签:classId, xCenter, yCenter, w, h转换为coco格式:classId, xMin, yMim, xMax, yMax格式。coco的id编号从1开始计算,所以这里classId应该从1开始计算。最终annos.txt中每行为imageName, classId, xMin, yMim, xMax, yMax, 一个bbox对应一行
import os
import cv2
# 原始标签路径
originLabelsDir = r'G:\data\cell_phone_samples\correct_images_and_labels' \
r'\cellphone_labels_cut_person_and_cellphone_total\labels\val'
# 转换后的文件保存路径
saveDir = r'G:\data\cell_phone_samples\correct_images_and_labels' \
r'\cellphone_labels_cut_person_and_cellphone_total\labels_coco_format\annos.txt'
# 原始标签对应的图片路径
originImagesDir = r'G:\data\cell_phone_samples\correct_images_and_labels' \
r'\cellphone_labels_cut_person_and_cellphone_total\images\val'
txtFileList = os.listdir(originLabelsDir)
with open(saveDir, 'w') as fw:
for txtFile in txtFileList:
with open(os.path.join(originLabelsDir, txtFile), 'r') as fr:
labelList = fr.readlines()
for label in labelList:
label = label.strip().split()
x = float(label[1])
y = float(label[2])
w = float(label[3])
h = float(label[4])
# convert x,y,w,h to x1,y1,x2,y2
imagePath = os.path.join(originImagesDir,
txtFile.replace('txt', 'jpg'))
image = cv2.imread(imagePath)
H, W, _ = image.shape
x1 = (x - w / 2) * W
y1 = (y - h / 2) * H
x2 = (x + w / 2) * W
y2 = (y + h / 2) * H
# 为了与coco标签方式对,标签序号从1开始计算
fw.write(txtFile.replace('txt', 'jpg') + ' {} {} {} {} {}\n'.format(int(label[0]) + 1, x1, y1, x2, y2))
print('{} done'.format(txtFile))
第二步:将标签转换为coco格式并以json格式保存,代码如下。根路径root_path中,包含images(图片文件夹),annos.txt(bbox标注),classes.txt(一行对应一种类别名字), 以及annotations文件夹(如果没有则会自动创建,用于保存最后的json)
import json
import os
import cv2
# ------------用os提取images文件夹中的图片名称,并且将BBox都读进去------------
# 根路径,里面包含images(图片文件夹),annos.txt(bbox标注),classes.txt(类别标签),
# 以及annotations文件夹(如果没有则会自动创建,用于保存最后的json)
root_path = r'G:\data\cell_phone_samples\correct_images_and_labels\cellphone_labels_cut_person_and_cellphone_total\labels_coco_format'
# 用于创建训练集或验证集
phase = 'train' # 需要修正
# dataset用于保存所有数据的图片信息和标注信息
dataset = {'categories': [], 'annotations': [], 'images': []}
# 打开类别标签
with open(os.path.join(root_path, 'classes.txt')) as f:
classes = f.read().strip().split()
# 建立类别标签和数字id的对应关系
for i, cls in enumerate(classes, 1):
dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'})
# 读取images文件夹的图片名称
indexes = os.listdir(os.path.join(root_path, 'images'))
# 统计处理图片的数量
global count
count = 0
# 读取Bbox信息
with open(os.path.join(root_path, 'annos.txt')) as tr:
annos = tr.readlines()
# ---------------接着将,以上数据转换为COCO所需要的格式---------------
for k, index in enumerate(indexes):
count += 1
# 用opencv读取图片,得到图像的宽和高
im = cv2.imread(os.path.join(root_path, 'images/') + index)
height, width, _ = im.shape
# 添加图像的信息到dataset中
dataset['images'].append({'file_name': index,
'id': k,
'width': width,
'height': height})
for ii, anno in enumerate(annos):
parts = anno.strip().split()
# 如果图像的名称和标记的名称对上,则添加标记
if parts[0] == index:
# 类别
cls_id = parts[1]
# x_min
x1 = float(parts[2])
# y_min
y1 = float(parts[3])
# x_max
x2 = float(parts[4])
# y_max
y2 = float(parts[5])
width = max(0, x2 - x1)
height = max(0, y2 - y1)
dataset['annotations'].append({
'area': width * height,
'bbox': [x1, y1, width, height],
'category_id': int(cls_id),
'id': i,
'image_id': k,
'iscrowd': 0,
# mask, 矩形是从左上角点按顺时针的四个顶点
'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]]
})
print('{} images handled'.format(count))
# 保存结果的文件夹
folder = os.path.join(root_path, 'annotations')
if not os.path.exists(folder):
os.makedirs(folder)
json_name = os.path.join(root_path, 'annotations/{}.json'.format(phase))
with open(json_name, 'w') as f:
json.dump(dataset, f)