CV图像处理小工具——语义分割json生成检测框json
import json
import os
from os import listdir, getcwd
from os.path import join
import os.path
rootdir = 'F:/dataset/'# 写自己存放图片的数据地址
input_dir = 'F:/dataset/labels_json/'
output_dir = 'F:/dataset/labels_box/'
def position(pos):
# 该函数用来找出xmin,ymin,xmax,ymax即bbox包围框
x = []
y = []
nums = len(pos)
for i in range(nums):
x.append(pos[i][0])
y.append(pos[i][1])
x_max = max(x)
x_min = min(x)
y_max = max(y)
y_min = min(y)
b = (float(x_min), float(x_max), float(y_min), float(y_max))
return b
def convert_annotation(image_ids, input_dir, output_dir):
for image_id in image_ids:
print(image_id)
input_file_path = os.path.join(input_dir, f"{image_id}.json")
output_file_path = os.path.join(output_dir, f"{image_id}.json")
try:
with open(input_file_path, 'r') as load_f:
load_dict = json.load(load_f)
w = load_dict['imageWidth']
h = load_dict['imageHeight']
objects = load_dict['shapes']
annotations = [] # 创建一个空列表来存储注解
for obj in objects:
# 去除可能的额外空格，并设置默认标签（如果需要）
labels = obj['label'].strip()
# 根据标签处理形状
if labels in [
"class1", "class2",
"class3", "class4"
]:
pos = obj['points']
b = position(pos)
cls_id_mapping = {
"class1": 1,
"class2": 2,
"class3": 3,
"class4": 4,
}
cls_id = cls_id_mapping[labels]
annotation = {
'label': labels,
'class_id': cls_id,
'bbox': b
}
annotations.append(annotation)
output_dict = {
"version": "5.0.2",
"flags": {},
"shapes": [], # shapes数组为空，因为注解信息已经放在annotations中
"imagePath": load_dict['imagePath'],
"imageData": None,
"imageHeight": h,
"imageWidth": w,
"annotations": annotations # 添加注解列表
}
for shape in load_dict['shapes']:
# 假设每个多边形都有四个点，我们可以直接取对角线上的两个点来定义矩形
# 这里我们取第一个点和第三个点（或者您可以根据具体情况选择其他点对）
objects = load_dict['shapes']
for obj in objects:
# 去除可能的额外空格，并设置默认标签（如果需要）
labels = obj['label'].strip()
if labels in [
"class1", "class2",
"class3", "class4"
]:
pos = obj['points']
b = position(pos)
rect_points = [
[b[0], b[3]],
[b[1], b[2]]
]
new_shape = {
"label": labels, # 保留原多边形的标签（或者您可以根据需要生成新的标签）
"points": rect_points, # 使用调整后的点来表示矩形（但这里我们实际上只使用了两个点）
# 注意：由于我们简化了问题，并没有真正地使用四个点来定义一个完整的矩形
# 在实际应用中，您可能需要更精确地处理这个问题。
"group_id": None, # 您可以根据需要设置这个字段的值
"shape_type": "rectangle", # 指定形状类型为矩形
"flags": {} # 保留空的flags字段（或者您可以根据需要填充它）
}
output_dict['shapes'].append(new_shape)
# 写入新的JSON文件
with open(output_file_path, 'w') as output_f:
json.dump(output_dict, output_f, indent=4)
except Exception as e:
print(f"Error processing {input_file_path}: {e}")
def image_id(rootdir):
a = []
for parent, dirnames, filenames in os.walk(rootdir):
for filename in filenames:
# print(filename)
if filename.endswith('.jpg'):
filename = os.path.splitext(filename)[0]
if filename.endswith('.jpeg'):
filename = os.path.splitext(filename)[0]
if filename.endswith('.JPG'):
filename = os.path.splitext(filename)[0]
if filename.endswith('.JPEG'):
filename = os.path.splitext(filename)[0]
a.append(filename)
return a
names = image_id("F:/dataset/images/"),
for image_id in names:
convert_annotation(image_id, input_dir, output_dir)