Documentation v1.0

Official User Guide

Comprehensive documentation on how to use Image2JSON, understand the data structure, and integrate it into your workflow.

Analysis Modes

JSON Structure

Use Cases

Analysis Modes Explained

Our AI model adapts its focus based on the mode you select. Choosing the correct mode is critical for obtaining high-quality data.

Avatars

Best for: Profile pictures, portraits, and character design.
Focus: Facial features, emotions, accessories (glasses, hats), and artistic style.

Object

Best for: E-commerce products, singular items.
Focus: Brand detection, material analysis (wood, plastic), defects, and dimensions relative to the frame.

Place

Best for: Architecture, landscapes, real estate.
Focus: Lighting conditions, architectural style (Brutalist, Gothic), weather, and spatial layout.

Document

Best for: Invoices, receipts, handwritten notes.
Focus: OCR (Text extraction), table structure preservation, and key-value pair identification.

Understanding the Output

The generated JSON is standardized. Below is a breakdown of the key fields you will receive in your response.

Key	Type	Description
`summary`	String	A concise, human-readable description of the image content (Alt Text ready).
`dominant_colors`	Array	List of hex codes (e.g., #FF5733) and color names found in the image.
`objects_detected`	Array	List of identified items, often including confidence scores or counts.
`ocr_text`	String	(Document mode only) Raw text extracted from the image.
`style_analysis`	Object	Artistic movements, lighting types (diffuse, harsh), and composition rules.

Real-World Use Cases

1. Education: Art History Analysis

Scenario: A professor wants to compare the color palettes of Impressionism vs. Expressionism.
Workflow:

Upload "The Starry Night" (Van Gogh) and "Water Lilies" (Monet).
Extract the dominant_colors array from both JSON files.
Use the data to visualize the shift from pastel blues to vibrant yellows/oranges.

2. E-commerce: Auto-Tagging

Scenario: An online store has 1,000 product photos but no descriptions.
Workflow:

Process images using "Object" mode.
Use the keywords field to automatically populate SEO tags (e.g., "Leather", "Vintage", "Brown").
Use the summary field as the product's Alt Text for accessibility.

3. Accessibility & SEO

Scenario: Making a website WCAG compliant.
Workflow:

Upload website assets to Image2JSON.
Copy the "Summary" field directly into the HTML <img alt="..."> tag.
This improves Google Image Search rankings and helps screen reader users.

Python Integration Example

Once you have your JSON file, you can parse it easily with Python.

import json

# Load the downloaded JSON file
with open('image_data.json', 'r') as file:
    data = json.load(file)

# Extract SEO Keywords
keywords = data.get('tags', [])
print(f"SEO Keywords: {', '.join(keywords)}")

# Check if image contains specific content
if 'person' in data.get('objects', []):
    print("This image contains a person.")
                

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