API Documentation

import requests

url = "https://service.veedrobe.com/api/v1/process/crop-and-remove"

# Simple: Just send the image - AI auto-detects category
with open("clothing_image.jpg", "rb") as f:
    files = {"file": f}
    response = requests.post(url, files=files)

    # Save processed image
    with open("processed.jpg", "wb") as out:
        out.write(response.content)

    # Check auto-detected category and metadata
    print(f"Detected category: {response.headers.get('X-Category')}")
    print(f"Confidence: {response.headers.get('X-Confidence')}")
    print(f"Processing time: {response.headers.get('X-Total-Time-Ms')}ms")

import requests

url = "https://service.veedrobe.com/api/v1/process/crop-and-remove"

# Advanced: Specify category and options manually
with open("clothing_image.jpg", "rb") as f:
    files = {"file": f}
    data = {
        "category": "top",
        "confidence": 0.85,
        "max_size": 800
    }

    response = requests.post(url, files=files, data=data)

    # Save processed image
    with open("processed.jpg", "wb") as out:
        out.write(response.content)

    # Check metadata headers
    print(f"Confidence: {response.headers.get('X-Confidence')}")
    print(f"Processing time: {response.headers.get('X-Total-Time-Ms')}ms")
    print(f"Output size: {response.headers.get('X-Output-Size-KB')}KB")

const url = 'https://service.veedrobe.com/api/v1/process/crop-and-remove';

// From file input element
const fileInput = document.querySelector('input[type="file"]');
const file = fileInput.files[0];

// Create FormData - just the file, AI auto-detects category
const formData = new FormData();
formData.append('file', file);

// Send request
fetch(url, {
  method: 'POST',
  body: formData
})
  .then(response => {
    // Read metadata headers - including auto-detected category
    console.log('Detected category:', response.headers.get('X-Category'));
    console.log('Confidence:', response.headers.get('X-Confidence'));
    console.log('Processing time:', response.headers.get('X-Total-Time-Ms'), 'ms');

    // Get image blob
    return response.blob();
  })
  .then(blob => {
    // Create download link
    const url = URL.createObjectURL(blob);
    const a = document.createElement('a');
    a.href = url;
    a.download = 'processed.jpg';
    a.click();
    URL.revokeObjectURL(url);
  })
  .catch(error => console.error('Error:', error));

const url = 'https://service.veedrobe.com/api/v1/process/crop-and-remove';

// From file input element
const fileInput = document.querySelector('input[type="file"]');
const file = fileInput.files[0];

// Create FormData with manual settings
const formData = new FormData();
formData.append('file', file);
formData.append('category', 'top');
formData.append('confidence', '0.85');
formData.append('max_size', '800');

// Send request
fetch(url, {
  method: 'POST',
  body: formData
})
  .then(response => {
    // Read metadata headers
    console.log('Confidence:', response.headers.get('X-Confidence'));
    console.log('Processing time:', response.headers.get('X-Total-Time-Ms'), 'ms');
    console.log('Output size:', response.headers.get('X-Output-Size-KB'), 'KB');

    // Get image blob
    return response.blob();
  })
  .then(blob => {
    // Create download link
    const url = URL.createObjectURL(blob);
    const a = document.createElement('a');
    a.href = url;
    a.download = 'processed.jpg';
    a.click();
    URL.revokeObjectURL(url);
  })
  .catch(error => console.error('Error:', error));