Skip to content

Anotación de Imágenes

Anota imágenes con clasificación, cuadros delimitadores y etiquetado de regiones.

Anotación de Imágenes

Potato soporta anotación de imágenes para tareas de clasificación, detección de objetos y etiquetado de regiones.

Habilitar la Visualización de Imágenes

yaml
image:
  enabled: true
  max_width: 800
  max_height: 600

Formato de Datos

Referencia imágenes en tus datos:

json
{
  "id": "img_1",
  "image_path": "images/photo_001.jpg",
  "description": "Optional description"
}

Configura el campo de imagen:

yaml
data_files:
  - path: data/image_tasks.json
    image_field: image_path

Clasificación de Imágenes

Clasifica imágenes completas:

yaml
annotation_schemes:
  - annotation_type: radio
    name: category
    description: "What is shown in this image?"
    labels:
      - Cat
      - Dog
      - Bird
      - Other
 
  - annotation_type: multiselect
    name: attributes
    description: "Select all that apply"
    labels:
      - Indoor
      - Outdoor
      - Multiple animals
      - Human present

Clasificación Multi-Etiqueta

yaml
annotation_schemes:
  - annotation_type: multiselect
    name: objects
    description: "What objects are visible?"
    labels:
      - Person
      - Car
      - Building
      - Tree
      - Animal
      - Furniture
      - Food
      - Electronic device

Evaluación de Calidad de Imagen

yaml
annotation_schemes:
  - annotation_type: likert
    name: quality
    description: "Overall image quality"
    size: 5
    min_label: "Very poor"
    max_label: "Excellent"
 
  - annotation_type: multiselect
    name: issues
    description: "Select any quality issues"
    labels:
      - Blurry
      - Overexposed
      - Underexposed
      - Noisy
      - Low resolution
      - Watermark visible

Anotación de Cuadros Delimitadores

Dibuja cuadros alrededor de objetos:

yaml
annotation_schemes:
  - annotation_type: bbox
    name: objects
    description: "Draw boxes around objects"
    labels:
      - Person
      - Car
      - Bicycle
      - Traffic sign
    label_colors:
      Person: "#3b82f6"
      Car: "#10b981"
      Bicycle: "#f59e0b"
      "Traffic sign": "#ef4444"

Salida de Cuadros Delimitadores

json
{
  "id": "img_1",
  "objects": [
    {
      "label": "Person",
      "x": 100,
      "y": 50,
      "width": 80,
      "height": 200
    },
    {
      "label": "Car",
      "x": 300,
      "y": 150,
      "width": 150,
      "height": 100
    }
  ]
}

Cuadros Delimitadores Precargados

Carga anotaciones existentes para revisión:

json
{
  "id": "img_1",
  "image_path": "images/photo_001.jpg",
  "predictions": [
    {"label": "Person", "x": 100, "y": 50, "width": 80, "height": 200, "confidence": 0.95}
  ]
}
yaml
annotation_schemes:
  - annotation_type: bbox
    name: objects
    load_predictions: true
    prediction_field: predictions

Anotación de Regiones/Polígonos

Para regiones no rectangulares:

yaml
annotation_schemes:
  - annotation_type: polygon
    name: regions
    description: "Outline regions of interest"
    labels:
      - Building
      - Road
      - Vegetation
      - Water

Comparación de Imágenes

Compara dos imágenes:

yaml
data_files:
  - path: data/image_pairs.json
    item_a_field: image_original
    item_b_field: image_edited
 
annotation_schemes:
  - annotation_type: pairwise
    name: preference
    description: "Which image looks better?"
    options:
      - label: "Original"
        value: "A"
      - label: "Edited"
        value: "B"
      - label: "Same"
        value: "tie"

Subtitulado de Imágenes

yaml
annotation_schemes:
  - annotation_type: text
    name: caption
    description: "Write a caption for this image"
    textarea: true
    placeholder: "Describe what you see..."
    min_length: 10
    max_length: 300

Revisión de Calidad de Subtítulos

yaml
data_files:
  - path: data/captions.json
    image_field: image_path
    text_field: generated_caption
 
annotation_schemes:
  - annotation_type: likert
    name: accuracy
    description: "How accurate is this caption?"
    size: 5
    min_label: "Very inaccurate"
    max_label: "Very accurate"
 
  - annotation_type: likert
    name: fluency
    description: "How natural is the language?"
    size: 5
    min_label: "Very awkward"
    max_label: "Very natural"
 
  - annotation_type: text
    name: improved_caption
    description: "Suggest a better caption (optional)"
    textarea: true

Opciones de Visualización

Tamaño de Imagen

yaml
image:
  max_width: 800
  max_height: 600
  preserve_aspect_ratio: true

Controles de Zoom

yaml
image:
  zoom_enabled: true
  initial_zoom: fit  # 'fit', 'actual', or percentage

Modo de Pantalla Completa

yaml
image:
  fullscreen_enabled: true

Moderación de Contenido

yaml
annotation_schemes:
  - annotation_type: radio
    name: safe_for_work
    description: "Is this image safe for work?"
    labels:
      - Safe
      - Questionable
      - Not Safe
 
  - annotation_type: multiselect
    name: violation_types
    description: "Select all violations (if any)"
    labels:
      - Violence
      - Adult content
      - Hate symbols
      - Graphic content
      - Spam/advertisement
    show_if:
      scheme: safe_for_work
      value: ["Questionable", "Not Safe"]

Formatos Soportados

Formatos de imagen comunes soportados:

  • JPEG/JPG
  • PNG
  • GIF
  • WebP
  • BMP
yaml
image:
  allowed_formats: ["jpg", "jpeg", "png", "webp"]

Ejemplo Completo: Revisión de Detección de Objetos

yaml
task_name: "Object Detection Verification"
 
image:
  enabled: true
  max_width: 1000
  zoom_enabled: true
 
data_files:
  - path: data/detections.json
    image_field: image_path
 
annotation_schemes:
  # Review pre-loaded predictions
  - annotation_type: bbox
    name: objects
    description: "Verify and correct object boxes"
    labels:
      - Person
      - Vehicle
      - Animal
      - Object
    load_predictions: true
    prediction_field: model_predictions
    label_colors:
      Person: "#3b82f6"
      Vehicle: "#10b981"
      Animal: "#f59e0b"
      Object: "#6b7280"
 
  # Overall assessment
  - annotation_type: radio
    name: prediction_quality
    description: "How accurate were the predictions?"
    labels:
      - All correct
      - Minor corrections needed
      - Major corrections needed
      - Mostly incorrect
 
  - annotation_type: number
    name: missed_objects
    description: "How many objects were missed?"
    min: 0
    max: 50
 
  - annotation_type: text
    name: notes
    description: "Any issues or comments?"
    textarea: true
    required: false

Consejos de Rendimiento

  1. Optimiza el tamaño de imagen - Redimensiona imágenes grandes antes de la anotación
  2. Usa JPEG para fotos - Tamaños de archivo más pequeños, carga más rápida
  3. Usa PNG para gráficos - Mejor calidad para diagramas/capturas de pantalla
  4. Habilita la carga diferida - Para conjuntos de datos grandes
  5. Considera miniaturas - Muestra vistas previas en vistas de lista
  6. Pre-procesa consistentemente - Normaliza tamaños y formatos