API
If you're looking for an API, you can choose from your desired programming language.
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import requests
import base64
# Use this function to convert an image file from the filesystem to base64
def image_file_to_base64(image_path):
with open(image_path, 'rb') as f:
image_data = f.read()
return base64.b64encode(image_data).decode('utf-8')
# Use this function to fetch an image from a URL and convert it to base64
def image_url_to_base64(image_url):
response = requests.get(image_url)
image_data = response.content
return base64.b64encode(image_data).decode('utf-8')
# Use this function to convert a list of image URLs to base64
def image_urls_to_base64(image_urls):
return [image_url_to_base64(url) for url in image_urls]
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/segfit"
# Request payload
data = {
"outfit_image": image_url_to_base64("https://segmind-inference-inputs.s3.ap-south-1.amazonaws.com/a8b0d0b4-8b45-4ff4-b42e-66c96c0070ee.jpeg"), # Or use image_file_to_base64("IMAGE_PATH")
"gender": "woman",
"nationality": "indian",
"location": "garden",
"prompt_clothing": "cloth,dress,stripes,lace",
"seed": 42,
"image_format": "png",
"image_quality": 95,
"base64": False
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
URL of the outfit image to use as reference
url or base64 of the model image (optional)
url or base64 of the model mask (optional)
Gender of the model
Nationality of the model
Background location for the image
Cloth details, use it when model image/mask is not provided
Seed for image generation
min : -1,
max : 999999999999999
Format of the output image
Allowed values:
Quality of the output image (1-100)
min : 1,
max : 100
Return image as base64 string
To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.
SegFIT: Revolutionizing Virtual Try-On Technology
Introduction
SegFIT, developed in-house by Segmind, stands out as a premier virtual try-on (VTON) model, revolutionizing how consumers and retailers approach fashion. This innovative solution allows users to visualize clothing on custom fashion models with remarkable accuracy, making it a one-stop platform for seamless try-on experiences. Whether you're a fashion enthusiast seeking the perfect fit or an e-commerce business aiming to reduce returns, SegFIT delivers unmatched precision and convenience, setting a new standard in virtual try-on technology.
Key Features
What makes SegFIT one of the best VTON models available? Its key features include:
- High-precision fit visualization
- Fast processing speeds
- Compatibility with diverse platforms (web, mobile, and AR)
- Support for custom fashion models
- Catering to all body types and clothing styles
- Scalability and inclusivity
Retailers benefit from happier customers and lower return rates, while shoppers enjoy a confident, hassle-free buying process. SegFIT's advanced fashion technology empowers businesses to stand out in a competitive market.
Use Cases and Impact
From e-commerce to augmented reality shopping, SegFIT's use cases are as versatile as they are impactful:
- Online stores can integrate this virtual try-on model to enhance product pages
- Designers can showcase bespoke creations
- Influencers can engage followers with interactive demos
Backed by Segmind's expertise, SegFIT combines technical excellence—such as 4K resolution support and a swift 60-seconds processing time—with a user-focused design.
Discover how SegFIT transforms the fashion industry and elevates your shopping experience today.
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