AliMostafaRadwan / autoAnnoter

autoAnnoter its a tool to auto annotate data using a exisiting model

autoAnnoter

autoAnnoter its a tool to auto annotate data using a exisiting model

🚀 New Update

• (02-11-2022) Added tools to this repository, that can help you to setup your Dataset for the Training.
  • partition_dataset.py : Partition your Dataset (XML & TXT & Images) into Train and Test in ratio
  • txt_to_xml.py : Convert your TXT Annotation files into XML Format
  • xml_to_txt.py : Convert your XML Annotation files into TXT Format
  • xml_neg_annotation.py : Annoatate your Negative Dataset
  • find_oneClass_from_xml.py : To filter your PASCAL VOC annotaion XML file based on class name

Clone this GitHub Repository

git clone https://github.com/naseemap47/autoAnnoter.git

Install Dependencies

cd autoAnnoter/
pip3 install -r requirements.txt

1. ONNX Model

To XML Format

python3 autoAnnot.py -x -i <PATH_TO_DATA> -t <PATH_TO_classes.txt> -m <ONNX_MODEL_PATH> -s <SIZE_OF_IMAGE_WHEN_TRAIN_YOUR_MODEL> -c <MODEL_OBJCET_DETECTION_CONFIDENCE>

To TXT Format

python3 autoAnnot.py -y -i <PATH_TO_DATA> -t <PATH_TO_classes.txt> -m <ONNX_MODEL_PATH> -s <SIZE_OF_IMAGE_WHEN_TRAIN_YOUR_MODEL> -c <MODEL_OBJCET_DETECTION_CONFIDENCE>

2. YOLOv7 Model

python3 autoAnotYolov7.py -i <PATH_TO_DATA> -m <YOLOv7_MODEL_PATH> -c <MODEL_OBJCET_DETECTION_CONFIDENCE>

🛠️ Tools

• 1. partition_dataset.py : Partition your Dataset (XML & TXT & Images) into Train and Test in ratio

  • -x, --xml : To partition XML files
  • -t, --txt : To partition TXT files
  • -i, --imageDir : path to image Dir, it should contain both images and Annotation files (XML or TXT)
  • -o, --outputDir : path to save Train and Test Dir, If not given - it will save inside image Dir
  • -r, --ratio : Ratio to partition Dataset into Train and Test (0 < ratio < 1)
  • Example Image Dir:

    ├── path_to/images
    │   ├── 1.jpg
    │   ├── 1.xml
    │   ├── 2.jpeg
    │   ├── 2.xml
    │   ├── ...
    .   .
    .   .
    

    ├── path_to/images
    │   ├── 1.jpeg
    │   ├── 1.txt
    │   ├── 2.jpg
    │   ├── 2.txt
    │   ├── ...
    .   .
    .   .
    

  Example:

  # XML
  python3 tools/partition_dataset.py -x -i path_to/images -r 0.1
  
  # TXT
  python3 tools/partition_dataset.py -t -i path_to/images -r 0.1
  

• 2. txt_to_xml.py : Convert your TXT Annotation files into XML Format

  • -i, --image : path to image/dir
  • -t, --txt : path to txt/dir
  • -c, --classes : path to classes.txt

  classes.txt Example:

  car
  person
  apple
  ....
  

  Example:

  python3 tools/txt_to_xml.py -i path_to/imageDir -t path_to/txt_Dir -c path_to/classes.txt
  

• 3. xml_to_txt.py : Convert your XML Annotation files into TXT Format

  • -i, --image : path to image/dir
  • -x, --xml : path to xml/dir
  • -c, --classes : path to classes.txt

  classes.txt Example:

  car
  person
  apple
  ....
  

  Example:

  python3 tools/xml_to_txt.py -i path_to/imageDir -x path_to/xml_Dir -c path_to/classes.txt
  

• 4. xml_neg_annotation.py : Annoatate your Negative Dataset

  • -i, --dataset : path to negative dataset
  • -o, --save : path to save Dir, if not exist it will create

  Example:

  python3 tools/xml_neg_annotation.py -i path_to/negDir -o path_to/saveDir
  

• 5. find_oneClass_from_xml.py : To filter your PASCAL VOC annotaion XML file based on class name

  • -i, --dataset : path to negative dataset
  • -o, --save : path to save Dir
  • -n, --name : name of class, that wants filter

  Example:

  python3 tools/find_oneClass_from_xml.py -i path_to/dataset -o path_to/saveDir -n 'class_name'