# VerifyDataAnnotations
## Overview
VerifyDataAnnotations is a Python package specifically designed to validate and annotate image data with bounding boxes using annotations provided in YOLO format text files. It serves as a tool for verifying the correctness of annotations and ensuring the integrity of datasets intended for use in computer vision tasks, especially those utilizing the YOLO object detection framework.
## Features
- Validates the structure of image and label directories.
- Checks if label files contain valid annotations in YOLO format.
- Ensures that image files have the correct format.
- Annotates images with bounding boxes based on YOLO label annotations.
- Saves annotated images to an output directory.
## Input Parameters
- `label_folder`: Path to the directory containing label files with annotations in YOLO format.
- `raw_image_folder`: Path to the directory containing the raw image files.
- `output_image_folder`: Path to the directory where annotated images will be saved.
- `image_name_list_path`: Path to the text file listing the names of all images in the dataset.
- `class_path`: Path to the text file containing the list of classes or labels used in the dataset.
## Installation
You can install VerifyDataAnnotations via pip:
```bash
pip install verifyannotations
```
## Example
Suppose we have the following directory structure:
```
dataset/
│
├── labels/
│ ├── image1.txt
│ ├── image2.txt
│ └── ...
│
├── images/
│ ├── image1.bmp
│ ├── image2.bmp
│ └── ...
│
├── saved_annotations/
│
├── name_list.txt
└── classes.txt
```
The `labels` directory contains text files with annotation data in YOLO format. The `images` directory contains corresponding image files. `saved_annotations` will store the annotated images.
Using VerifyDataAnnotations:
```python
from verifyannotations import VerifyDataAnnotations
label_folder = "dataset/labels"
raw_image_folder = "dataset/images"
output_image_folder = "dataset/saved_annotations"
image_name_list_path = "dataset/name_list.txt"
class_path = "dataset/classes.txt"
verifier = VerifyDataAnnotations(
label_folder,
raw_image_folder,
output_image_folder,
image_name_list_path,
class_path,
)
verifier.verify_annotations()
```
This will validate the annotations, annotate the images with bounding boxes, and save the annotated images to the `saved_annotations` directory.
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"description": "\n# VerifyDataAnnotations\n\n## Overview\n\nVerifyDataAnnotations is a Python package specifically designed to validate and annotate image data with bounding boxes using annotations provided in YOLO format text files. It serves as a tool for verifying the correctness of annotations and ensuring the integrity of datasets intended for use in computer vision tasks, especially those utilizing the YOLO object detection framework.\n\n## Features\n\n- Validates the structure of image and label directories.\n- Checks if label files contain valid annotations in YOLO format.\n- Ensures that image files have the correct format.\n- Annotates images with bounding boxes based on YOLO label annotations.\n- Saves annotated images to an output directory.\n\n## Input Parameters\n\n- `label_folder`: Path to the directory containing label files with annotations in YOLO format.\n- `raw_image_folder`: Path to the directory containing the raw image files.\n- `output_image_folder`: Path to the directory where annotated images will be saved.\n- `image_name_list_path`: Path to the text file listing the names of all images in the dataset.\n- `class_path`: Path to the text file containing the list of classes or labels used in the dataset.\n\n\n\n## Installation\n\nYou can install VerifyDataAnnotations via pip:\n\n```bash\npip install verifyannotations\n```\n\n\n## Example\n\nSuppose we have the following directory structure:\n\n```\ndataset/\n\u2502\n\u251c\u2500\u2500 labels/\n\u2502 \u251c\u2500\u2500 image1.txt\n\u2502 \u251c\u2500\u2500 image2.txt\n\u2502 \u2514\u2500\u2500 ...\n\u2502\n\u251c\u2500\u2500 images/\n\u2502 \u251c\u2500\u2500 image1.bmp\n\u2502 \u251c\u2500\u2500 image2.bmp\n\u2502 \u2514\u2500\u2500 ...\n\u2502\n\u251c\u2500\u2500 saved_annotations/\n\u2502\n\u251c\u2500\u2500 name_list.txt\n\u2514\u2500\u2500 classes.txt\n```\n\nThe `labels` directory contains text files with annotation data in YOLO format. The `images` directory contains corresponding image files. `saved_annotations` will store the annotated images.\n\nUsing VerifyDataAnnotations:\n\n```python\nfrom verifyannotations import VerifyDataAnnotations\n\nlabel_folder = \"dataset/labels\"\nraw_image_folder = \"dataset/images\"\noutput_image_folder = \"dataset/saved_annotations\"\nimage_name_list_path = \"dataset/name_list.txt\"\nclass_path = \"dataset/classes.txt\"\n\nverifier = VerifyDataAnnotations(\n label_folder,\n raw_image_folder,\n output_image_folder,\n image_name_list_path,\n class_path,\n)\n\nverifier.verify_annotations()\n```\n\nThis will validate the annotations, annotate the images with bounding boxes, and save the annotated images to the `saved_annotations` directory.\n",
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