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Segment Image Rle
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientSet FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("fal-ai/sam-3-1/image-rle", {
input: {
image_url: "https://raw.githubusercontent.com/facebookresearch/segment-anything-2/main/notebooks/images/truck.jpg"
},
logs: true,
onQueueUpdate: (update) => {
if (update.status === "IN_PROGRESS") {
update.logs.map((log) => log.message).forEach(console.log);
}
},
});
console.log(result.data);
console.log(result.requestId);This model has a real-time mode via WebSockets, supported by fal.realtime.connect.
import { fal } from "@fal-ai/client";
const connection = fal.realtime.connect("fal-ai/sam-3-1/image-rle", {
onResult: (result) => {
console.log(result);
},
onError: (error) => {
console.error(error);
},
// Fetch short-lived JWT token from your backend
tokenProvider: async (app) => {
const response = await fetch("/api/fal/realtime-token", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ app }),
});
return response.text();
},
tokenExpirationSeconds: 10,
});
connection.send({
image_url: "https://raw.githubusercontent.com/facebookresearch/segment-anything-2/main/notebooks/images/truck.jpg"
});The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_KEY"
});The client API provides a convenient way to submit requests to the model.
import { fal } from "@fal-ai/client";
const { request_id } = await fal.queue.submit("fal-ai/sam-3-1/image-rle", {
input: {
image_url: "https://raw.githubusercontent.com/facebookresearch/segment-anything-2/main/notebooks/images/truck.jpg"
},
webhookUrl: "https://optional.webhook.url/for/results",
});You can fetch the status of a request to check if it is completed or still in progress.
import { fal } from "@fal-ai/client";
const status = await fal.queue.status("fal-ai/sam-3-1/image-rle", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.
import { fal } from "@fal-ai/client";
const result = await fal.queue.result("fal-ai/sam-3-1/image-rle", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.
You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.
You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.
We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.
import { fal } from "@fal-ai/client";
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);Read more about file handling in our file upload guide.
image_url string* requiredURL of the image to be segmented
prompt stringText prompt for segmentation Default value: "wheel"
List of point prompts
Box prompt coordinates (x_min, y_min, x_max, y_max). Multiple boxes supported - use object_id to group boxes for the same object or leave empty for separate objects.
apply_mask booleanApply the mask on the image. Default value: true
sync_mode booleanIf True, the media will be returned as a data URI.
output_format OutputFormatEnumThe format of the generated image. Default value: "png"
Possible enum values: jpeg, png, webp
return_multiple_masks booleanIf True, upload and return multiple generated masks as defined by max_masks.
max_masks integerMaximum number of masks to return when return_multiple_masks is enabled. Default value: 3
include_scores booleanWhether to include mask confidence scores.
include_boxes booleanWhether to include bounding boxes for each mask (when available).
{
"image_url": "https://raw.githubusercontent.com/facebookresearch/segment-anything-2/main/notebooks/images/truck.jpg",
"prompt": "wheel",
"point_prompts": [],
"box_prompts": [],
"apply_mask": true,
"output_format": "png",
"max_masks": 3
}Run Length Encoding of the mask.
Zip file containing per-frame bounding box overlays.
Per-mask metadata when multiple RLEs are returned.
Per-mask confidence scores when requested.
Per-mask normalized bounding boxes [cx, cy, w, h] when requested.
{
"metadata": [
{}
]
}url string* requiredThe URL where the file can be downloaded from.
content_type stringThe mime type of the file.
file_name stringThe name of the file. It will be auto-generated if not provided.
file_size integerThe size of the file in bytes.
index integer* requiredIndex of the mask inside the model output.
score floatScore for this mask.
Bounding box for the mask in normalized cxcywh coordinates.
x integerX Coordinate of the prompt
y integerY Coordinate of the prompt
label Enum1 for foreground, 0 for background
Possible enum values: 0, 1
object_id integerOptional object identifier. Prompts sharing an object id refine the same object. When a text prompt is also given, the id selects which detected object the points refine.
frame_index integerThe frame index to interact with.
x_min integerX Min Coordinate of the box
y_min integerY Min Coordinate of the box
x_max integerX Max Coordinate of the box
y_max integerY Max Coordinate of the box
object_id integerOptional object identifier. Boxes sharing an object id refine the same object.
frame_index integerThe frame index to interact with.