Pros of labelme

• More active development with recent updates and contributions
• Larger community support and user base
• Better documentation and examples for usage

Cons of labelme

• May have more complex setup and installation process
• Potentially steeper learning curve for new users
• Could have more dependencies and requirements

Code Comparison

labelme:

from labelme import utils
from labelme.app import MainWindow
from labelme.config import get_config

labelme>:

from labelme.label_file import LabelFile
from labelme.shape import Shape
from labelme.canvas import Canvas

Both repositories provide tools for image annotation, but labelme appears to be more actively maintained and widely used. It likely offers more features and better support, though it may be more complex to set up and use initially. The code snippets show that both projects have similar core functionalities, but labelme might have a more comprehensive set of utilities and a dedicated application interface. labelme> seems to focus more on core labeling components. Users should consider their specific needs and level of expertise when choosing between these tools.

Pros of labelImg

• Simpler interface focused specifically on bounding box annotations
• Faster performance for basic image labeling tasks
• Supports PascalVOC format natively

Cons of labelImg

• Limited annotation types (mainly bounding boxes)
• Less extensible for custom labeling workflows
• Fewer built-in image processing features

Code Comparison

labelImg:

def saveFile(self, _value=False):
    if self.filePath:
        try:
            self.saveLabels(self.filePath)
            self.setClean()
            return True
        except:
            self.errorMessage(u'Error saving file')
            return False
    return self.saveFileAs()

labelme:

def save_labels(self, filename):
    lf = LabelFile()
    def format_shape(s):
        return dict(label=s.label.encode('utf-8') if PY2 else s.label,
                    line_color=s.line_color.getRgb(),
                    fill_color=s.fill_color.getRgb(),
                    points=[(p.x(), p.y()) for p in s.points],
                    shape_type=s.shape_type)

    shapes = [format_shape(shape) for shape in self.canvas.shapes]
    try:
        lf.save_shapes(filename, shapes, self.imagePath, self.imageData,
                       self.lineColor.getRgb(), self.fillColor.getRgb())
        self.labelFile = lf
        self.filename = filename
        return True
    except LabelFileError as e:
        self.errorMessage(u'Error saving label data', u'<b>%s</b>' % e)
        return False

Pros of Label Studio

• More comprehensive and feature-rich, supporting a wider range of data types and annotation tasks
• Offers a web-based interface, making it easier to collaborate and manage large-scale annotation projects
• Provides integration with popular machine learning frameworks and cloud storage services

Cons of Label Studio

• More complex setup and configuration compared to LabelMe
• Requires more system resources due to its extensive features
• Steeper learning curve for new users

Code Comparison

LabelMe:

from labelme import utils
img = utils.img_b64_to_arr(json_data['imageData'])
label_name_to_value = {'_background_': 0}
for shape in sorted(json_data['shapes'], key=lambda x: x['label']):
    label_name = shape['label']
    if label_name in label_name_to_value:
        label_value = label_name_to_value[label_name]
    else:
        label_value = len(label_name_to_value)
        label_name_to_value[label_name] = label_value

Label Studio:

import label_studio_sdk
ls = label_studio_sdk.Client(url='http://localhost:8080', api_key='your-api-key')
project = ls.start_project(
    title='Image Classification',
    label_config='<View><Image name="image" value="$image"/><Choices name="label" toName="image"><Choice value="Cat"/><Choice value="Dog"/></Choices></View>'
)
ls.upload_data(project.id, [{'image': 'https://example.com/image1.jpg'}])

Pros of CVAT

• More comprehensive and feature-rich annotation tool
• Supports a wider range of annotation tasks and formats
• Better suited for large-scale, collaborative projects

Cons of CVAT

• More complex setup and deployment process
• Steeper learning curve for new users
• Requires more system resources to run effectively

Code Comparison

LabelMe:

from labelme import utils
img = utils.img_b64_to_arr(json_data['imageData'])
labels, shapes = utils.labelme_shapes_to_label(img.shape, json_data['shapes'])

CVAT:

from cvat_sdk import make_client
client = make_client('https://your-cvat-server.com')
client.tasks.create_from_data(name='New Task', labels=[{'name': 'car'}, {'name': 'person'}])

Summary

CVAT offers a more powerful and versatile annotation platform suitable for complex, large-scale projects, while LabelMe provides a simpler, more lightweight solution for basic image annotation tasks. CVAT's advanced features come at the cost of increased complexity and resource requirements, whereas LabelMe is easier to set up and use but has limited functionality compared to CVAT.

Pros of VoTT

• More comprehensive and feature-rich UI for annotation tasks
• Supports a wider range of annotation types, including bounding boxes, polygons, and tags
• Better integration with cloud storage services and export options

Cons of VoTT

• Steeper learning curve due to more complex interface
• Heavier resource usage, which may impact performance on lower-end machines
• Less frequent updates and maintenance compared to LabelMe

Code Comparison

LabelMe:

from labelme import utils
img = utils.img_data_to_arr(img_data)
label_name_to_value = {'_background_': 0}
for shape in sorted(data['shapes'], key=lambda x: x['label']):
    label_name = shape['label']
    if label_name in label_name_to_value:
        label_value = label_name_to_value[label_name]
    else:
        label_value = len(label_name_to_value)
        label_name_to_value[label_name] = label_value

VoTT:

export interface IAsset {
    id: string;
    format: string;
    name: string;
    path: string;
    size: ISize;
    state: number;
    type: AssetType;
}

export interface IProject {
    id: string;
    name: string;
    description?: string;
    tags: ITag[];
    sourceConnection: IConnection;
    targetConnection: IConnection;
    exportFormat: IExportFormat;
    assets?: { [index: string]: IAsset };
}