Bria Mask Generator
Bria AI Get Masks automatically generates accurate object masks for advanced image editing and enhancement.
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/bria-mask-generator"
# Request payload
data = {
"image_url": "https://segmind-inference-inputs.s3.amazonaws.com/fc8ac0d8-9459-49ba-afeb-7b0fb6fc0041-e22ba32d6851fb33157dca6b1172533a.png",
"content_moderation": False
}
headers = {'x-api-key': api_key}
response = requests.post(url, json=data, headers=headers)
print(response.content) # The response is the generated imageAttributes
Enter the URL or Base64 string of your image. Use a URL for online images, Base64 for local images.
Toggle to enable or disable automatic content moderation. Enable for unknown sources, disable for known safe images.
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.
Resources to get you started
Everything you need to know to get the most out of Bria Mask Generator
Bria AI Get Masks Usage Guide
Bria AI Get Masks is an advanced image segmentation model that automatically generates precise masks for multiple objects within an image. Follow this guide to harness its full potential, select the right parameters, and optimize workflows across different use cases.
1. Getting Started
- •Input Format
- •Image URL: Use for online-hosted images (HTTP/HTTPS).
- •Base64 String: Ideal for local images or when embedding data directly.
- •Authentication
- •Include your API key in the request headers.
- •Endpoint
- •
/v1/bria/get-masks
- •
2. Core Parameters
{
"image_url": "https://example.com/image.jpg",
"content_moderation": false
}
- •image_url (required): URL or Base64 of the target image.
- •content_moderation (optional):
trueto enable safe-content filtering (recommended for user-generated or unknown sources),falseto skip moderation for trusted assets.
3. Best Practices for High-Quality Masks
- •Supply high-resolution (≥1024×768) images.
- •Ensure clear object contours and good foreground–background contrast.
- •Use well-lit, evenly exposed photos to improve detection accuracy.
- •Simplify overly complex scenes by cropping or focusing on target objects.
- •Test various object types (people, products, animals) to gauge model behavior.
4. Parameter Recommendations by Use Case
A. E-Commerce Product Editing
- •content_moderation: false (trusted brand images)
- •Workflow: Fetch product photos via URL → segment → auto-replace backgrounds → export PNG with alpha masks.
B. Content Creation & Marketing
- •content_moderation: true (external/UGC sources)
- •Workflow: Batch process campaign assets → isolate subjects → apply dynamic overlays or filters.
C. Background Removal & Replacement
- •content_moderation: false (in-house images)
- •Workflow: Send high-contrast portrait shots → retrieve detailed masks → composite subjects onto new backgrounds.
D. High-Throughput Batch Processing
- •content_moderation: conditional (true for unknown)
- •Concurrency: Utilize asynchronous API calls to handle large queues.
- •Retries & Timeouts: Implement exponential backoff to manage occasional rate limits.
5. Scaling & Integration Tips
- •Asynchronous Calls: Leverage non-blocking requests for parallel processing.
- •Error Handling: Check for HTTP 4xx/5xx responses and implement retry logic.
- •Monitoring: Log request IDs and durations to trace performance bottlenecks.
- •Versioning: Pin the model version in your API URL to prevent unexpected updates.
6. Troubleshooting
- •Incomplete Masks: Increase image resolution or adjust framing.
- •False Positives/Negatives: Enable moderation to filter unusual content, or fine-tune image lighting and contrast.
- •API Errors: Verify URL encoding and authentication headers.
By following these guidelines and selecting the right parameters, you’ll unlock the full power of Bria AI Get Masks for precise, scalable, and automated image segmentation.
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