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import inspect
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import math
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from typing import Any, Dict, List
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import cv2
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import numpy as np
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import torch
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import ultralytics
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if hasattr(ultralytics, "FastSAM"):
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from ultralytics import FastSAM as YOLO
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else:
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from ultralytics import YOLO
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class FastSAM:
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def __init__(
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self,
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checkpoint: str,
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) -> None:
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self.model_path = checkpoint
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self.model = YOLO(self.model_path)
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if not hasattr(torch.nn.Upsample, "recompute_scale_factor"):
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torch.nn.Upsample.recompute_scale_factor = None
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def to(self, device) -> None:
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self.model.to(device)
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@property
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def device(self) -> Any:
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return self.model.device
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def __call__(self, source=None, stream=False, **kwargs) -> Any:
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return self.model(source=source, stream=stream, **kwargs)
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class FastSamAutomaticMaskGenerator:
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def __init__(
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self,
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model: FastSAM,
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points_per_batch: int = None,
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pred_iou_thresh: float = None,
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stability_score_thresh: float = None,
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) -> None:
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self.model = model
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self.points_per_batch = points_per_batch
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self.pred_iou_thresh = pred_iou_thresh
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self.stability_score_thresh = stability_score_thresh
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self.conf = 0.25 if stability_score_thresh >= 0.95 else 0.15
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def generate(self, image: np.ndarray) -> List[Dict[str, Any]]:
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height, width = image.shape[:2]
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new_height = math.ceil(height / 32) * 32
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new_width = math.ceil(width / 32) * 32
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resize_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
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backup_nn_dict = {}
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for key, _ in torch.nn.__dict__.copy().items():
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if not inspect.isclass(torch.nn.__dict__.get(key)) and "Norm" in key:
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backup_nn_dict[key] = torch.nn.__dict__.pop(key)
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results = self.model(
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source=resize_image,
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stream=False,
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imgsz=max(new_height, new_width),
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device=self.model.device,
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retina_masks=True,
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iou=0.7,
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conf=self.conf,
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max_det=256)
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for key, value in backup_nn_dict.items():
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setattr(torch.nn, key, value)
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annotations = results[0].masks.data
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if isinstance(annotations[0], torch.Tensor):
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annotations = np.array(annotations.cpu())
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annotations_list = []
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for mask in annotations:
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mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
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mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((7, 7), np.uint8))
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mask = cv2.resize(mask, (width, height), interpolation=cv2.INTER_AREA)
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annotations_list.append(dict(segmentation=mask.astype(bool)))
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return annotations_list
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