Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
80 lines
3.3 KiB
Python
80 lines
3.3 KiB
Python
# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
|
|
|
|
from __future__ import annotations
|
|
|
|
from pathlib import Path
|
|
from typing import Any
|
|
|
|
from ultralytics.engine.model import Model
|
|
|
|
from .predict import FastSAMPredictor
|
|
from .val import FastSAMValidator
|
|
|
|
|
|
class FastSAM(Model):
|
|
"""FastSAM model interface for Segment Anything tasks.
|
|
|
|
This class extends the base Model class to provide specific functionality for the FastSAM (Fast Segment Anything
|
|
Model) implementation, allowing for efficient and accurate image segmentation with optional prompting support.
|
|
|
|
Attributes:
|
|
model (str): Path to the pre-trained FastSAM model file.
|
|
task (str): The task type, set to "segment" for FastSAM models.
|
|
|
|
Methods:
|
|
predict: Perform segmentation prediction on image or video source with optional prompts.
|
|
task_map: Returns mapping of segment task to predictor and validator classes.
|
|
|
|
Examples:
|
|
Initialize FastSAM model and run prediction
|
|
>>> from ultralytics import FastSAM
|
|
>>> model = FastSAM("FastSAM-x.pt")
|
|
>>> results = model.predict("ultralytics/assets/bus.jpg")
|
|
|
|
Run prediction with bounding box prompts
|
|
>>> results = model.predict("image.jpg", bboxes=[[100, 100, 200, 200]])
|
|
"""
|
|
|
|
def __init__(self, model: str | Path = "FastSAM-x.pt"):
|
|
"""Initialize the FastSAM model with the specified pre-trained weights."""
|
|
if str(model) == "FastSAM.pt":
|
|
model = "FastSAM-x.pt"
|
|
assert Path(model).suffix not in {".yaml", ".yml"}, "FastSAM only supports pre-trained weights."
|
|
super().__init__(model=model, task="segment")
|
|
|
|
def predict(
|
|
self,
|
|
source,
|
|
stream: bool = False,
|
|
bboxes: list | None = None,
|
|
points: list | None = None,
|
|
labels: list | None = None,
|
|
texts: list | None = None,
|
|
**kwargs: Any,
|
|
):
|
|
"""Perform segmentation prediction on image or video source.
|
|
|
|
Supports prompted segmentation with bounding boxes, points, labels, and texts. The method packages these prompts
|
|
and passes them to the parent class predict method for processing.
|
|
|
|
Args:
|
|
source (str | PIL.Image | np.ndarray): Input source for prediction, can be a file path, URL, PIL image, or
|
|
numpy array.
|
|
stream (bool): Whether to enable real-time streaming mode for video inputs.
|
|
bboxes (list, optional): Bounding box coordinates for prompted segmentation in format [[x1, y1, x2, y2]].
|
|
points (list, optional): Point coordinates for prompted segmentation in format [[x, y]].
|
|
labels (list, optional): Class labels for prompted segmentation.
|
|
texts (list, optional): Text prompts for segmentation guidance.
|
|
**kwargs (Any): Additional keyword arguments passed to the predictor.
|
|
|
|
Returns:
|
|
(list): List of Results objects containing the prediction results.
|
|
"""
|
|
prompts = dict(bboxes=bboxes, points=points, labels=labels, texts=texts)
|
|
return super().predict(source, stream, prompts=prompts, **kwargs)
|
|
|
|
@property
|
|
def task_map(self) -> dict[str, dict[str, Any]]:
|
|
"""Returns a dictionary mapping segment task to corresponding predictor and validator classes."""
|
|
return {"segment": {"predictor": FastSAMPredictor, "validator": FastSAMValidator}}
|