新增普通json转yolo格式代码

README.md

@@ -69,8 +69,18 @@
 
 
 
-## JSON2YOLO
+## JSON2YOLO_ignore_0
 
 直接执行即可，默认代码是放在COCOdataset 数据集根目录下执行。
 
 程序会跳过标签为0的标签，然后导出标签会-1 ，如果不需要这部分功能需要调整。
+
+这部分是针对公开数据集[SeaDronesSee](https://github.com/Ben93kie/SeaDronesSee)v2开发的。
+
+https://seadronessee.cs.uni-tuebingen.de/dataset
+
+
+
+## JSON2YOLO
+
+直接执行即可，默认代码是放在COCOdataset 数据集根目录下执行。

json2yolo.py

@@ -1,4 +1,3 @@
-
 import json
 import os
 from PIL import Image
@@ -10,17 +9,11 @@ def process_annotations(json_file, images_folder, labels_folder):
  data = json.load(file)
 
  annotations = data['annotations']
- ignore_count = 0
 
  for annotation in tqdm(annotations):
  category_id = annotation['category_id']
  image_id = annotation['image_id']
 
- # 跳过ignore标签
- if category_id == 0:
- ignore_count += 1
- continue
-
  # 获取图片尺寸
  file_path = os.path.join(images_folder, str(image_id) + '.jpg')
  try:
@@ -44,8 +37,6 @@ def process_annotations(json_file, images_folder, labels_folder):
  with open(label_file_path, 'a') as file:
  file.write(label_content + '\n')
 
- return ignore_count
-
 def create_labels_folder():
  # 创建labels文件夹及其子文件夹
  labels_folder = 'labels'
@@ -59,10 +50,10 @@ def main():
  train_labels_folder, val_labels_folder = create_labels_folder()
 
  # 处理训练和验证数据
- ignore_count_train = process_annotations('annotations/instances_train.json', 'images/train', train_labels_folder)
- ignore_count_val = process_annotations('annotations/instances_val.json', 'images/val', val_labels_folder)
+ process_annotations('annotations/instances_train.json', 'images/train', train_labels_folder)
+ process_annotations('annotations/instances_val.json', 'images/val', val_labels_folder)
 
- print(f"完成处理。忽略的标签数量：训练集 = {ignore_count_train}, 验证集 = {ignore_count_val}")
+ print("完成处理。")
 
 if __name__ == "__main__":
  main()

json2yolo_ignore_0.py

@@ -0,0 +1,68 @@
+
+import json
+import os
+from PIL import Image
+from tqdm import tqdm
+
+def process_annotations(json_file, images_folder, labels_folder):
+ # 读取JSON文件
+ with open(json_file, 'r') as file:
+ data = json.load(file)
+
+ annotations = data['annotations']
+ ignore_count = 0
+
+ for annotation in tqdm(annotations):
+ category_id = annotation['category_id']
+ image_id = annotation['image_id']
+
+ # 跳过ignore标签
+ if category_id == 0:
+ ignore_count += 1
+ continue
+
+ # 获取图片尺寸
+ file_path = os.path.join(images_folder, str(image_id) + '.jpg')
+ try:
+ with Image.open(file_path) as img:
+ width, height = img.size
+ except FileNotFoundError:
+ continue
+
+ # 计算归一化的坐标
+ bbox = annotation['bbox']
+ x_center = (bbox[0] + bbox[2] / 2) / width
+ y_center = (bbox[1] + bbox[3] / 2) / height
+ w_norm = bbox[2] / width
+ h_norm = bbox[3] / height
+
+ # 写入标签文件，标签编号减1
+ label = category_id - 1
+ label_content = f"{label} {x_center} {y_center} {w_norm} {h_norm}"
+
+ label_file_path = os.path.join(labels_folder, str(image_id) + '.txt')
+ with open(label_file_path, 'a') as file:
+ file.write(label_content + '\n')
+
+ return ignore_count
+
+def create_labels_folder():
+ # 创建labels文件夹及其子文件夹
+ labels_folder = 'labels'
+ train_labels_folder = os.path.join(labels_folder, 'train')
+ val_labels_folder = os.path.join(labels_folder, 'val')
+ os.makedirs(train_labels_folder, exist_ok=True)
+ os.makedirs(val_labels_folder, exist_ok=True)
+ return train_labels_folder, val_labels_folder
+
+def main():
+ train_labels_folder, val_labels_folder = create_labels_folder()
+
+ # 处理训练和验证数据
+ ignore_count_train = process_annotations('annotations/instances_train.json', 'images/train', train_labels_folder)
+ ignore_count_val = process_annotations('annotations/instances_val.json', 'images/val', val_labels_folder)
+
+ print(f"完成处理。忽略的标签数量：训练集 = {ignore_count_train}, 验证集 = {ignore_count_val}")
+
+if __name__ == "__main__":
+ main()