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')
api_key = "YOUR_API_KEY"
url = "https://api.segmind.com/v1/inpaint-mask-maker"
# Request payload
data = {
"prompt": "white horse",
"image": image_url_to_base64("https://segmind-sd-models.s3.amazonaws.com/display_images/inpaint-mask-maker-input.jpg"), # Or use image_file_to_base64("IMAGE_PATH")
"invert_mask": False,
"confidence_thresh": 0.5,
"iou_thresh": 0.2,
"return_mask": False,
"grow_mask": 10,
"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
Prompt to render
Upload your input image
Refers to inverting the mask.
Lowering it reduces false positives and increases sensitivity to desired objects. Increasing it minimizes false positives and prevents identifying undesired objects.
min : 0,
max : 1
Lowering the value reduces bounding box overlap, making detection more stringent. Increasing it allows more overlap and adapts to a wider detection range.
min : 0,
max : 1
Check this to obtain the mask as the output.
Selectively expand image regions
min : 0,
max : 100
Base64 encoding of the output image.
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.
PixelFlow allows you to use all these features
Unlock the full potential of generative AI with Segmind. Create stunning visuals and innovative designs with total creative control. Take advantage of powerful development tools to automate processes and models, elevating your creative workflow.
Segmented Creation Workflow
Gain greater control by dividing the creative process into distinct steps, refining each phase.
Customized Output
Customize at various stages, from initial generation to final adjustments, ensuring tailored creative outputs.
Layering Different Models
Integrate and utilize multiple models simultaneously, producing complex and polished creative results.
Workflow APIs
Deploy Pixelflows as APIs quickly, without server setup, ensuring scalability and efficiency.
Inpaint Mask Maker
Inpaint Mask Maker leverages a two-stage deep learning pipeline to generate high-fidelity masks for image inpainting tasks. This approach combines the object detection capabilities of You Only Look Once (YOLO) with the precise segmentation abilities of the Segment Anything Model (SAM) to achieve robust mask creation.
The model first identifies and classify objects within an input image by dividing the image into a grid and predicting bounding boxes and class probabilities for each grid cell. This process effectively localizes and classifies all objects present in the image. Following successful object identification, the pipeline leverages semantic segmentation based on the object detections. The model then segments the image based on the identified object classes, generating a binary mask for each object. This binary mask isolates the specific region of the image occupied by the corresponding object, providing a precise delineation of its boundaries.
Applications of Inpaint Mask Maker
This two-stage approach is particularly well-suited for scenarios where both accurate object localization/classification and precise object boundary delineation are crucial. This includes applications such as:
-
Content removal and image restoration: Precise masks facilitate the removal of unwanted objects while preserving surrounding image details during inpainting.
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Object manipulation: Accurate masks enable targeted manipulation of specific objects within an image for tasks like image editing or object replacement.
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