自定义coco数据集

1、环境

anaconda环境安装配置

2、工具

安装labelme工具

3、安装软件

3.1、打开anaconda控制台

3.2、创建虚拟环境

conda create -n labelme python=3.7

3.3、激活环境

conda activate labelme

3.4、下载labelme

pip install labelme

3.5、输入labelme打开软件

以后打开跳过3.2和3.4打开即可

labelme

4、制作labelme数据集

4.1、打开文件夹

存有多张图片的文件夹
图片为统一格式（比如都为.png或者.jpg）

4.2、创建矩形框

4.3、label名称

为框选住的类别起一个名字

再次框选的时候会保存已经存在的label

4.4、保存

保存名字和图片在同一个路径，同样的名字

4.5、结果

点击下一个继续标注label

把需要的图片全部做标签保存

5、转换coco数据

5.1、创建目录

• dataset中放第4步制作好的数据集

  ├─data-labelme
  │  ├─coco
  │  │  ├─annotations
  │  │  ├─train2017
  │  │  └─val2017
  │  ├─dataset
  ├─json2coco.py
  
  

5.2、运行文件

• 然后执行json2coco.py文件
  将代码中标有修改的注释下面代码进行替换

  import os
  import json
  import numpy as np
  import glob
  import shutil
  import cv2
  from sklearn.model_selection import train_test_split
  
  np.random.seed(41)
  
  # 修改1->改成自己的类别
  classname_to_id = {
        
        
      "green": 0,  
      "purple": 1,
      "yellow": 2
  }
  
  
  class Lableme2CoCo:
  
      def __init__(self):
          self.images = []
          self.annotations = []
          self.categories = []
          self.img_id = 0
          self.ann_id = 0
  
      def save_coco_json(self, instance, save_path):
          json.dump(instance, open(save_path, 'w', encoding='utf-8'), ensure_ascii=False, indent=1)  # indent=2 更加美观显示
  
      # 由json文件构建COCO
      def to_coco(self, json_path_list):
          self._init_categories()
          for json_path in json_path_list:
              obj = self.read_jsonfile(json_path)
              self.images.append(self._image(obj, json_path))
              shapes = obj['shapes']
              for shape in shapes:
                  annotation = self._annotation(shape)
                  self.annotations.append(annotation)
                  self.ann_id += 1
              self.img_id += 1
          instance = {
        
        }
          instance['info'] = 'spytensor created'
          instance['license'] = ['license']
          instance['images'] = self.images
          instance['annotations'] = self.annotations
          instance['categories'] = self.categories
          return instance
  
      # 构建类别
      def _init_categories(self):
          for k, v in classname_to_id.items():
              category = {
        
        }
              category['id'] = v
              category['name'] = k
              self.categories.append(category)
  
      # 构建COCO的image字段
      def _image(self, obj, path):
          image = {
        
        }
          from labelme import utils
          img_x = utils.img_b64_to_arr(obj['imageData'])
          h, w = img_x.shape[:-1]
          image['height'] = h
          image['width'] = w
          image['id'] = self.img_id
          image['file_name'] = os.path.basename(path).replace(".json", ".jpg")
          return image
  
      # 构建COCO的annotation字段
      def _annotation(self, shape):
          # print('shape', shape)
          label = shape['label']
          points = shape['points']
          annotation = {
        
        }
          annotation['id'] = self.ann_id
          annotation['image_id'] = self.img_id
          annotation['category_id'] = int(classname_to_id[label])
          annotation['segmentation'] = [np.asarray(points).flatten().tolist()]
          annotation['bbox'] = self._get_box(points)
          annotation['iscrowd'] = 0
          annotation['area'] = 1.0
          return annotation
  
      # 读取json文件，返回一个json对象
      def read_jsonfile(self, path):
          with open(path, "r", encoding='utf-8') as f:
              return json.load(f)
  
      # COCO的格式： [x1,y1,w,h] 对应COCO的bbox格式
      def _get_box(self, points):
          min_x = min_y = np.inf
          max_x = max_y = 0
          for x, y in points:
              min_x = min(min_x, x)
              min_y = min(min_y, y)
              max_x = max(max_x, x)
              max_y = max(max_y, y)
          return [min_x, min_y, max_x - min_x, max_y - min_y]
  
  # 训练过程中，如果遇到Index put requires the source and destination dtypes match, got Long for the destination and Int for the source
  # 参考：https://github.com/open-mmlab/mmdetection/issues/6706
  
  
  if __name__ == '__main__':
      labelme_path = "./data-labelme/dataset"
      saved_coco_path = "./data-labelme/"
      print('reading...')
      # 创建文件
      if not os.path.exists("%scoco/annotations/" % saved_coco_path):
          os.makedirs("%scoco/annotations/" % saved_coco_path)
      if not os.path.exists("%scoco/train2017/" % saved_coco_path):
          os.makedirs("%scoco/train2017" % saved_coco_path)
      if not os.path.exists("%scoco/val2017/" % saved_coco_path):
          os.makedirs("%scoco/val2017" % saved_coco_path)
      # 获取images目录下所有的joson文件列表
      print(labelme_path + "/*.json")
      json_list_path = glob.glob(labelme_path + "/*.json")
      print('json_list_path: ', len(json_list_path))
      # 修改2训练集和测试集的比例
      # 数据划分,这里没有区分val2017和tran2017目录，所有图片都放在images目录下
      train_path, val_path = train_test_split(json_list_path, test_size=0.1, train_size=0.9)
      print("train_n:", len(train_path), 'val_n:', len(val_path))
      # 把训练集转化为COCO的json格式
      l2c_train = Lableme2CoCo()
      train_instance = l2c_train.to_coco(train_path)
      l2c_train.save_coco_json(train_instance, '%scoco/annotations/instances_train2017.json' % saved_coco_path)
      for file in train_path:
          # 修改3 换成自己图片的后缀名
          img_name = file.replace('json', 'jpg')
          temp_img = cv2.imread(img_name)
          try:
              cv2.imwrite("{}coco/train2017/{}".format(saved_coco_path, img_name.split('\\')[-1]), temp_img)
          except Exception as e:
              print(e)
              print('Wrong Image:', img_name )
              continue
          print(img_name + '-->', img_name)
  
      for file in val_path:
          # 修改4 换成自己图片的后缀名
          img_name = file.replace('json', 'jpg')
          temp_img = cv2.imread(img_name)
          try:
              cv2.imwrite("{}coco/val2017/{}".format(saved_coco_path, img_name.split('\\')[-1]), temp_img)
          except Exception as e:
              print(e)
              print('Wrong Image:', img_name)
              continue
          print(img_name + '-->', img_name)
  
      # 把验证集转化为COCO的json格式
      l2c_val = Lableme2CoCo()
      val_instance = l2c_val.to_coco(val_path)
      l2c_val.save_coco_json(val_instance, '%scoco/annotations/instances_val2017.json' % saved_coco_path)
  
  
  

5.3、运行结果

制作数据集完毕，可以进行自己项目的训练了