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yolov8 训练自己的数据集_volov8 dataset train test val

作者：秋刀鱼在做梦 | 2024-07-28 21:28:38

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volov8 dataset train test val

1，准备数据集

1.1voc数据转yolo数据

voc格式

  1. 文件联级如下所述：
  2. VOCdevkit
  3.     ---VOC2007
  4.         ---Annotations
  5.         ---ImageSets
  6.         ---JPEGImages

yolo格式

  1. voc_to_yolo.py
  2.  
  3. from tqdm import tqdm
  4. import shutil
  5. from pathlib import Path
  6. import xml.etree.ElementTree as ET
  7.  
  8. def convert_label(path, lb_path, year, image_id, names):
  9.     def convert_box(size, box):
  10.         dw, dh = 1. / size[0], 1. / size[1]
  11.         x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
  12.         return x * dw, y * dh, w * dw, h * dh
  13.  
  14.     in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
  15.     out_file = open(lb_path, 'w')
  16.     tree = ET.parse(in_file)
  17.     root = tree.getroot()
  18.     size = root.find('size')
  19.     w = int(size.find('width').text)
  20.     h = int(size.find('height').text)
  21.  
  22.     for obj in root.iter('object'):
  23.         cls = obj.find('name').text
  24.         if cls in names:
  25.             xmlbox = obj.find('bndbox')
  26.             bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
  27.             cls_id = names.index(cls)  # class id
  28.             out_file.write(" ".join(str(a) for a in (cls_id, *bb)) + '\n')
  29.         else:
  30.             print("category error: ", cls)
  31.  
  32. year = "2007"
  33. image_sets = ["train", "val"]
  34. path = Path("H:\\work\\daodan_move\\ultralytics-main\\ultralytics\\datasets\\VOCdevkit\\")
  35. class_names = ["call","dislike","fist","four","like","mute","ok","one","palm","1","2","3","4","5","6","7","8","9","10"]
  36.  
  37. for image_set in image_sets:
  38.     imgs_path = path / 'images' / f'{image_set}'
  39.     lbs_path = path / 'labels' / f'{image_set}'
  40.     imgs_path.mkdir(exist_ok=True, parents=True)
  41.     lbs_path.mkdir(exist_ok=True, parents=True)
  42.     
  43.     with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
  44.         image_ids = f.read().strip().split()
  45.     for id in tqdm(image_ids, desc=f'{image_set}'):
  46.         f = path / f'VOC{year}/JPEGImages/{id}.jpg'  # old img path
  47.         lb_path = (lbs_path / f.name).with_suffix('.txt')  # new label path
  48.         # f.rename(imgs_path / f.name)  # move image
  49.         shutil.copyfile(f, imgs_path / f.name) # copy image
  50.         convert_label(path, lb_path, year, id, class_names)  # convert labels to YOLO format

1.2 COCO格式转YOLO格式 转换脚本

coco格式

  1. ── VOCdevkit
  2. ├── images
  3. │   ├── train	# 存放训练集图片
  4. │   └── val	# 存放验证集图片
  5. └── labels
  6.     ├── train	# 存放训练集标注文件
  7.     └── val	# 存放验证集标注文件

  1. import json
  2. import os
  3. import shutil
  4. from tqdm import tqdm
  5.  
  6. coco_path = "F:/datasets/Apple_Detection_Swift-YOLO_192"
  7. output_path = "F:/vsCode/ultralytics/datasets/Apple"
  8.  
  9. os.makedirs(os.path.join(output_path, "images", "train"), exist_ok=True)
  10. os.makedirs(os.path.join(output_path, "images", "val"), exist_ok=True)
  11. os.makedirs(os.path.join(output_path, "labels", "train"), exist_ok=True)
  12. os.makedirs(os.path.join(output_path, "labels", "val"), exist_ok=True)
  13.  
  14. with open(os.path.join(coco_path, "train", "_annotations.coco.json"), "r") as f:
  15.     train_annotations = json.load(f)
  16.  
  17. with open(os.path.join(coco_path, "valid", "_annotations.coco.json"), "r") as f:
  18.     val_annotations = json.load(f)
  19.     
  20. # Iterate over the training images
  21. for image in tqdm(train_annotations["images"]):
  22.     width, height = image["width"], image["height"]
  23.     scale_x = 1.0 / width
  24.     scale_y = 1.0 / height
  25.     
  26.     label = ""
  27.     for annotation in train_annotations["annotations"]:
  28.         if annotation["image_id"] == image["id"]:
  29.             # Convert the annotation to YOLO format
  30.             x, y, w, h = annotation["bbox"]
  31.             x_center = x + w / 2.0
  32.             y_center = y + h / 2.0
  33.             x_center *= scale_x
  34.             y_center *= scale_y
  35.             w *= scale_x
  36.             h *= scale_y
  37.             class_id = annotation["category_id"]
  38.             label += "{} {} {} {} {}\n".format(class_id, x_center, y_center, w, h)
  39.             
  40.     # Save the image and label
  41.     shutil.copy(os.path.join(coco_path, "train", image["file_name"]), os.path.join(output_path, "images", "train", image["file_name"]))
  42.     with open(os.path.join(output_path, "labels", "train", image["file_name"].replace(".jpg", ".txt")), "w") as f:
  43.         f.write(label)
  44.         
  45. # Iterate over the validation images
  46. for image in tqdm(val_annotations["images"]):
  47.     width, height = image["width"], image["height"]
  48.     scale_x = 1.0 / width
  49.     scale_y = 1.0 / height
  50.     
  51.     label = ""
  52.     for annotation in val_annotations["annotations"]:
  53.         if annotation["image_id"] == image["id"]:
  54.             # Convert the annotation to YOLO format
  55.             x, y, w, h = annotation["bbox"]
  56.             x_center = x + w / 2.0
  57.             y_center = y + h / 2.0
  58.             x_center *= scale_x
  59.             y_center *= scale_y
  60.             w *= scale_x
  61.             h *= scale_y
  62.             class_id = annotation["category_id"]
  63.             label += "{} {} {} {} {}\n".format(class_id, x_center, y_center, w, h)
  64.             
  65.     # Save the image and label
  66.     shutil.copy(os.path.join(coco_path, "valid", image["file_name"]), os.path.join(output_path, "images", "val", image["file_name"]))
  67.     with open(os.path.join(output_path, "labels", "val", image["file_name"].replace(".jpg", ".txt")), "w") as f:
  68.         f.write(label)

2.训练数据

将voc.yaml复制为voc_self.yaml,然后修改如下：

  1. # Ultralytics YOLO 
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