fast_sam/fast_sam_wrapper.py

import inspect
import math
from typing import Any, Dict, List

import cv2
import numpy as np
import torch
import ultralytics

if hasattr(ultralytics, "FastSAM"):
    from ultralytics import FastSAM as YOLO
else:
    from ultralytics import YOLO


class FastSAM:
    def __init__(
        self,
        checkpoint: str,
    ) -> None:
        self.model_path = checkpoint
        self.model = YOLO(self.model_path)

        if not hasattr(torch.nn.Upsample, "recompute_scale_factor"):
            torch.nn.Upsample.recompute_scale_factor = None

    def to(self, device) -> None:
        self.model.to(device)

    @property
    def device(self) -> Any:
        return self.model.device

    def __call__(self, source=None, stream=False, **kwargs) -> Any:
        return self.model(source=source, stream=stream, **kwargs)


class FastSamAutomaticMaskGenerator:
    def __init__(
        self,
        model: FastSAM,
        points_per_batch: int = None,
        pred_iou_thresh: float = None,
        stability_score_thresh: float = None,
    ) -> None:
        self.model = model
        self.points_per_batch = points_per_batch
        self.pred_iou_thresh = pred_iou_thresh
        self.stability_score_thresh = stability_score_thresh
        self.conf = 0.25 if stability_score_thresh >= 0.95 else 0.15

    def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
        height, width = image.shape[:2]
        new_height = math.ceil(height / 32) * 32
        new_width = math.ceil(width / 32) * 32
        resize_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_CUBIC)

        backup_nn_dict = {}
        for key, _ in torch.nn.__dict__.copy().items():
            if not inspect.isclass(torch.nn.__dict__.get(key)) and "Norm" in key:
                backup_nn_dict[key] = torch.nn.__dict__.pop(key)

        results = self.model(
            source=resize_image,
            stream=False,
            imgsz=max(new_height, new_width),
            device=self.model.device,
            retina_masks=True,
            iou=0.7,
            conf=self.conf,
            max_det=256)

        for key, value in backup_nn_dict.items():
            setattr(torch.nn, key, value)
            # assert backup_nn_dict[key] == torch.nn.__dict__[key]

        annotations = results[0].masks.data

        if isinstance(annotations[0], torch.Tensor):
            annotations = np.array(annotations.cpu())

        annotations_list = []
        for mask in annotations:
            mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
            mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((7, 7), np.uint8))
            mask = cv2.resize(mask, (width, height), interpolation=cv2.INTER_AREA)

            annotations_list.append(dict(segmentation=mask.astype(bool)))

        return annotations_list