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| # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license | |
| import torch | |
| from PIL import Image | |
| from ultralytics.models.yolo.segment import SegmentationPredictor | |
| from ultralytics.utils import DEFAULT_CFG, checks | |
| from ultralytics.utils.metrics import box_iou | |
| from ultralytics.utils.ops import scale_masks | |
| from .utils import adjust_bboxes_to_image_border | |
| class FastSAMPredictor(SegmentationPredictor): | |
| """ | |
| FastSAMPredictor is specialized for fast SAM (Segment Anything Model) segmentation prediction tasks in Ultralytics | |
| YOLO framework. | |
| This class extends the SegmentationPredictor, customizing the prediction pipeline specifically for fast SAM. It | |
| adjusts post-processing steps to incorporate mask prediction and non-max suppression while optimizing for single- | |
| class segmentation. | |
| """ | |
| def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): | |
| """Initializes a FastSAMPredictor for fast SAM segmentation tasks in Ultralytics YOLO framework.""" | |
| super().__init__(cfg, overrides, _callbacks) | |
| self.prompts = {} | |
| def postprocess(self, preds, img, orig_imgs): | |
| """Applies box postprocess for FastSAM predictions.""" | |
| bboxes = self.prompts.pop("bboxes", None) | |
| points = self.prompts.pop("points", None) | |
| labels = self.prompts.pop("labels", None) | |
| texts = self.prompts.pop("texts", None) | |
| results = super().postprocess(preds, img, orig_imgs) | |
| for result in results: | |
| full_box = torch.tensor( | |
| [0, 0, result.orig_shape[1], result.orig_shape[0]], device=preds[0].device, dtype=torch.float32 | |
| ) | |
| boxes = adjust_bboxes_to_image_border(result.boxes.xyxy, result.orig_shape) | |
| idx = torch.nonzero(box_iou(full_box[None], boxes) > 0.9).flatten() | |
| if idx.numel() != 0: | |
| result.boxes.xyxy[idx] = full_box | |
| return self.prompt(results, bboxes=bboxes, points=points, labels=labels, texts=texts) | |
| def prompt(self, results, bboxes=None, points=None, labels=None, texts=None): | |
| """ | |
| Internal function for image segmentation inference based on cues like bounding boxes, points, and masks. | |
| Leverages SAM's specialized architecture for prompt-based, real-time segmentation. | |
| Args: | |
| results (Results | List[Results]): The original inference results from FastSAM models without any prompts. | |
| bboxes (np.ndarray | List, optional): Bounding boxes with shape (N, 4), in XYXY format. | |
| points (np.ndarray | List, optional): Points indicating object locations with shape (N, 2), in pixels. | |
| labels (np.ndarray | List, optional): Labels for point prompts, shape (N, ). 1 = foreground, 0 = background. | |
| texts (str | List[str], optional): Textual prompts, a list contains string objects. | |
| Returns: | |
| (List[Results]): The output results determined by prompts. | |
| """ | |
| if bboxes is None and points is None and texts is None: | |
| return results | |
| prompt_results = [] | |
| if not isinstance(results, list): | |
| results = [results] | |
| for result in results: | |
| if len(result) == 0: | |
| prompt_results.append(result) | |
| continue | |
| masks = result.masks.data | |
| if masks.shape[1:] != result.orig_shape: | |
| masks = scale_masks(masks[None], result.orig_shape)[0] | |
| # bboxes prompt | |
| idx = torch.zeros(len(result), dtype=torch.bool, device=self.device) | |
| if bboxes is not None: | |
| bboxes = torch.as_tensor(bboxes, dtype=torch.int32, device=self.device) | |
| bboxes = bboxes[None] if bboxes.ndim == 1 else bboxes | |
| bbox_areas = (bboxes[:, 3] - bboxes[:, 1]) * (bboxes[:, 2] - bboxes[:, 0]) | |
| mask_areas = torch.stack([masks[:, b[1] : b[3], b[0] : b[2]].sum(dim=(1, 2)) for b in bboxes]) | |
| full_mask_areas = torch.sum(masks, dim=(1, 2)) | |
| union = bbox_areas[:, None] + full_mask_areas - mask_areas | |
| idx[torch.argmax(mask_areas / union, dim=1)] = True | |
| if points is not None: | |
| points = torch.as_tensor(points, dtype=torch.int32, device=self.device) | |
| points = points[None] if points.ndim == 1 else points | |
| if labels is None: | |
| labels = torch.ones(points.shape[0]) | |
| labels = torch.as_tensor(labels, dtype=torch.int32, device=self.device) | |
| assert len(labels) == len(points), ( | |
| f"Excepted `labels` got same size as `point`, but got {len(labels)} and {len(points)}" | |
| ) | |
| point_idx = ( | |
| torch.ones(len(result), dtype=torch.bool, device=self.device) | |
| if labels.sum() == 0 # all negative points | |
| else torch.zeros(len(result), dtype=torch.bool, device=self.device) | |
| ) | |
| for point, label in zip(points, labels): | |
| point_idx[torch.nonzero(masks[:, point[1], point[0]], as_tuple=True)[0]] = bool(label) | |
| idx |= point_idx | |
| if texts is not None: | |
| if isinstance(texts, str): | |
| texts = [texts] | |
| crop_ims, filter_idx = [], [] | |
| for i, b in enumerate(result.boxes.xyxy.tolist()): | |
| x1, y1, x2, y2 = (int(x) for x in b) | |
| if masks[i].sum() <= 100: | |
| filter_idx.append(i) | |
| continue | |
| crop_ims.append(Image.fromarray(result.orig_img[y1:y2, x1:x2, ::-1])) | |
| similarity = self._clip_inference(crop_ims, texts) | |
| text_idx = torch.argmax(similarity, dim=-1) # (M, ) | |
| if len(filter_idx): | |
| text_idx += (torch.tensor(filter_idx, device=self.device)[None] <= int(text_idx)).sum(0) | |
| idx[text_idx] = True | |
| prompt_results.append(result[idx]) | |
| return prompt_results | |
| def _clip_inference(self, images, texts): | |
| """ | |
| CLIP Inference process. | |
| Args: | |
| images (List[PIL.Image]): A list of source images and each of them should be PIL.Image type with RGB channel order. | |
| texts (List[str]): A list of prompt texts and each of them should be string object. | |
| Returns: | |
| (torch.Tensor): The similarity between given images and texts. | |
| """ | |
| try: | |
| import clip | |
| except ImportError: | |
| checks.check_requirements("git+https://github.com/ultralytics/CLIP.git") | |
| import clip | |
| if (not hasattr(self, "clip_model")) or (not hasattr(self, "clip_preprocess")): | |
| self.clip_model, self.clip_preprocess = clip.load("ViT-B/32", device=self.device) | |
| images = torch.stack([self.clip_preprocess(image).to(self.device) for image in images]) | |
| tokenized_text = clip.tokenize(texts).to(self.device) | |
| image_features = self.clip_model.encode_image(images) | |
| text_features = self.clip_model.encode_text(tokenized_text) | |
| image_features /= image_features.norm(dim=-1, keepdim=True) # (N, 512) | |
| text_features /= text_features.norm(dim=-1, keepdim=True) # (M, 512) | |
| return (image_features * text_features[:, None]).sum(-1) # (M, N) | |
| def set_prompts(self, prompts): | |
| """Set prompts in advance.""" | |
| self.prompts = prompts | |