Pros of labelImg

• Extensive Documentation: The labelImg repository provides detailed documentation, including installation instructions, usage examples, and a comprehensive guide to the tool's features.
• Active Development: The project has an active community, with regular updates and bug fixes.
• Supported Formats: labelImg supports a wide range of image and annotation formats, making it versatile for various computer vision tasks.

Cons of labelImg

• Limited Customization: The tool's user interface and functionality are relatively fixed, with limited options for customization or extension.
• Dependency on Qt: labelImg relies on the Qt framework, which may not be preferred by all users or may require additional setup.

Code Comparison

Here's a brief comparison of the code structure between the two repositories:

# vietanhdev/anylabeling
from PyQt5.QtWidgets import QApplication, QMainWindow
from PyQt5.QtGui import QImage, QPixmap
from PyQt5.QtCore import Qt, QRect, QPoint

class AnyLabelingWindow(QMainWindow):
    def __init__(self):
        super().__init__()
        self.initUI()

    def initUI(self):
        self.setWindowTitle('AnyLabeling')
        self.setGeometry(100, 100, 800, 600)

# HumanSignal/labelImg
from PyQt5 import QtGui, QtCore, QtWidgets
from libs.pascal_voc_io import PascalVocReader, PascalVocWriter
from libs.ustr import ustr
from libs.shape import Shape, DEFAULT_LINE_COLOR, DEFAULT_FILL_COLOR

class MainWindow(QtWidgets.QMainWindow):
    def __init__(self, parent=None):
        super(MainWindow, self).__init__(parent)
        self.setWindowTitle(__appname__)
        self.setWindowIcon(QtGui.QIcon(os.path.join('resources', 'icon.png')))

As you can see, both repositories use PyQt5 for their graphical user interface, but the overall structure and organization of the code differ.

Pros of LabelMe

• LabelMe provides a web-based interface for annotating images, making it accessible to a wider range of users.
• The project has a large and active community, with regular updates and a wealth of documentation and resources.
• LabelMe supports a wide range of annotation types, including polygons, bounding boxes, and segmentation masks.

Cons of LabelMe

• LabelMe may have a steeper learning curve compared to AnyLabeling, especially for users who are more comfortable with a desktop-based application.
• The web-based nature of LabelMe may introduce additional complexity in terms of deployment and hosting.
• LabelMe may have a larger footprint in terms of dependencies and runtime requirements compared to the more lightweight AnyLabeling.

Code Comparison

AnyLabeling:

from anylabeling.app import App

app = App()
app.run()

LabelMe:

from labelme.app import MainWindow
from qtpy.QtWidgets import QApplication
import sys

app = QApplication(sys.argv)
window = MainWindow()
window.show()
app.exec_()

Both code snippets demonstrate the basic setup and execution of the respective annotation tools. The AnyLabeling code is more concise, while the LabelMe code includes additional setup for the Qt-based GUI framework.

Pros of Label Studio

• Extensive Functionality: Label Studio offers a wide range of features for data annotation, including support for various data types (images, text, audio, and video) and a variety of annotation tools.
• Collaborative Workflow: Label Studio enables collaborative annotation, allowing multiple users to work on the same dataset simultaneously.
• Customizable Interface: The platform provides a customizable user interface, allowing users to tailor the experience to their specific needs.

Cons of Label Studio

• Steeper Learning Curve: Compared to AnyLabeling, Label Studio may have a more complex setup and configuration process, which could be a barrier for some users.
• Resource Intensive: Label Studio requires more system resources, such as CPU and memory, to run effectively, which could be a limitation for users with less powerful hardware.

Code Comparison

AnyLabeling:

from anylabeling.app import create_app

app = create_app()

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)

Label Studio:

from label_studio.server import run_server

if __name__ == "__main__":
    run_server()

Both code snippets demonstrate the basic setup and execution of the respective applications. The AnyLabeling code creates an instance of the Flask application and runs it on the specified host and port, while the Label Studio code runs the server using the run_server() function.

Pros of CVAT

• Extensive documentation and active community support
• Supports a wide range of annotation types, including bounding boxes, polygons, and keypoints
• Provides a web-based interface for easy collaboration and task management

Cons of CVAT

• Relatively complex setup process compared to AnyLabeling
• Larger codebase and dependencies, which may be overkill for smaller projects
• Limited support for custom data formats out of the box

Code Comparison

CVAT:

from cvat.apps.dataset_manager.formats.ms_coco import dumper
from cvat.apps.dataset_manager.task import TaskData

task_data = TaskData(annotation_path, image_dir)
dumper.dump(task_data, output_path)

AnyLabeling:

from anylabeling.utils import save_annotations
annotations = [
    {'class': 'person', 'bbox': [10, 20, 50, 60]},
    {'class': 'car', 'bbox': [100, 150, 80, 120]}
]
save_annotations(annotations, output_path)

Pros of VoTT

• VoTT provides a more comprehensive set of features for video annotation, including support for multiple video formats, object tracking, and export to various annotation formats.
• VoTT has a larger and more active community, with more contributors and a more extensive documentation.
• VoTT is backed by Microsoft, which may provide more reliable long-term support and updates.

Cons of VoTT

• VoTT has a more complex and less intuitive user interface compared to AnyLabeling.
• VoTT may have a steeper learning curve, especially for users who are new to video annotation tools.
• VoTT may have a larger footprint and require more system resources compared to AnyLabeling.

Code Comparison

AnyLabeling:

def load_image(self, image_path):
    """Load an image from the specified path."""
    image = Image.open(image_path)
    image = image.convert('RGB')
    return image

VoTT:

function loadImage(imagePath) {
  return new Promise((resolve, reject) => {
    const img = new Image();
    img.onload = () => resolve(img);
    img.onerror = reject;
    img.src = imagePath;
  });
}

Both functions load an image from a specified path, but the implementation differs. AnyLabeling uses the Pillow library, while VoTT uses the built-in Image object in JavaScript.