# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. from typing import Any, Dict, List, Optional, Tuple import numpy as np import torch from torchvision.ops.boxes import batched_nms, box_area # type: ignore from ..amg import (MaskData, area_from_rle, batch_iterator, batched_mask_to_box, box_xyxy_to_xywh, build_all_layer_point_grids, calculate_stability_score, coco_encode_rle, generate_crop_boxes, is_box_near_crop_edge, mask_to_rle_pytorch, remove_small_regions, rle_to_mask, uncrop_boxes_xyxy, uncrop_masks, uncrop_points) from .prompt_predictor import PromptPredictor from .sam import Sam class SamAutomaticMaskGenerator: def __init__( self, model: Sam, points_per_side: Optional[int] = 32, points_per_batch: int = 64, pred_iou_thresh: float = 0.88, stability_score_thresh: float = 0.95, stability_score_offset: float = 1.0, box_nms_thresh: float = 0.7, crop_n_layers: int = 0, crop_nms_thresh: float = 0.7, crop_overlap_ratio: float = 512 / 1500, crop_n_points_downscale_factor: int = 1, point_grids: Optional[List[np.ndarray]] = None, min_mask_region_area: int = 0, output_mode: str = 'binary_mask', ) -> None: """ Using a SAM model, generates masks for the entire image. Generates a grid of point prompts over the image, then filters low quality and duplicate masks. The default settings are chosen for SAM with a ViT-H backbone. Arguments: model (Sam): The SAM model to use for mask prediction. points_per_side (int, None): The number of points to be sampled along one side of the image. The total number of points is points_per_side**2. If None, 'point_grids' must provide explicit point sampling. points_per_batch (int): Sets the number of points run simultaneously by the model. Higher numbers may be faster but use more GPU memory. pred_iou_thresh (float): A filtering threshold in [0,1], using the model's predicted mask quality. stability_score_thresh (float): A filtering threshold in [0,1], using the stability of the mask under changes to the cutoff used to binarize the model's mask predictions. stability_score_offset (float): The amount to shift the cutoff when calculated the stability score. box_nms_thresh (float): The box IoU cutoff used by non-maximal suppression to filter duplicate masks. crop_n_layers (int): If >0, mask prediction will be run again on crops of the image. Sets the number of layers to run, where each layer has 2**i_layer number of image crops. crop_nms_thresh (float): The box IoU cutoff used by non-maximal suppression to filter duplicate masks between different crops. crop_overlap_ratio (float): Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the image length. Later layers with more crops scale down this overlap. crop_n_points_downscale_factor (int): The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n. point_grids (list(np.ndarray), None): A list over explicit grids of points used for sampling, normalized to [0,1]. The nth grid in the list is used in the nth crop layer. Exclusive with points_per_side. min_mask_region_area (int): If >0, postprocessing will be applied to remove disconnected regions and holes in masks with area smaller than min_mask_region_area. Requires opencv. output_mode (str): The form masks are returned in. Can be 'binary_mask', 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools. For large resolutions, 'binary_mask' may consume large amounts of memory. """ assert (points_per_side is None) != (point_grids is None), 'Exactly one of points_per_side or point_grid must be provided.' if points_per_side is not None: self.point_grids = build_all_layer_point_grids( points_per_side, crop_n_layers, crop_n_points_downscale_factor, ) elif point_grids is not None: self.point_grids = point_grids else: raise ValueError("Can't have both points_per_side and point_grid be None.") assert output_mode in {'binary_mask', 'uncompressed_rle', 'coco_rle'}, f'Unknown output_mode {output_mode}.' if output_mode == 'coco_rle': from pycocotools import mask as mask_utils # type: ignore # noqa: F401 if min_mask_region_area > 0: import cv2 # type: ignore # noqa: F401 self.predictor = PromptPredictor(model) self.points_per_batch = points_per_batch self.pred_iou_thresh = pred_iou_thresh self.stability_score_thresh = stability_score_thresh self.stability_score_offset = stability_score_offset self.box_nms_thresh = box_nms_thresh self.crop_n_layers = crop_n_layers self.crop_nms_thresh = crop_nms_thresh self.crop_overlap_ratio = crop_overlap_ratio self.crop_n_points_downscale_factor = crop_n_points_downscale_factor self.min_mask_region_area = min_mask_region_area self.output_mode = output_mode # TODO: Temporary implementation for compatibility def __call__(self, image: np.ndarray, augment=False, visualize=False) -> List[Dict[str, Any]]: return self.generate(image) @torch.no_grad() def generate(self, image: np.ndarray) -> List[Dict[str, Any]]: """ Generates masks for the given image. Arguments: image (np.ndarray): The image to generate masks for, in HWC uint8 format. Returns: list(dict(str, any)): A list over records for masks. Each record is a dict containing the following keys: segmentation (dict(str, any), np.ndarray): The mask. If output_mode='binary_mask', is an array of shape HW. Otherwise, is a dictionary containing the RLE. bbox (list(float)): The box around the mask, in XYWH format. area (int): The area in pixels of the mask. predicted_iou (float): The model's own prediction of the mask's quality. This is filtered by the pred_iou_thresh parameter. point_coords (list(list(float))): The point coordinates input to the model to generate this mask. stability_score (float): A measure of the mask's quality. This is filtered on using the stability_score_thresh parameter. crop_box (list(float)): The crop of the image used to generate the mask, given in XYWH format. """ # Generate masks mask_data = self._generate_masks(image) # Filter small disconnected regions and holes in masks if self.min_mask_region_area > 0: mask_data = self.postprocess_small_regions( mask_data, self.min_mask_region_area, max(self.box_nms_thresh, self.crop_nms_thresh), ) # Encode masks if self.output_mode == 'coco_rle': mask_data['segmentations'] = [coco_encode_rle(rle) for rle in mask_data['rles']] elif self.output_mode == 'binary_mask': mask_data['segmentations'] = [rle_to_mask(rle) for rle in mask_data['rles']] else: mask_data['segmentations'] = mask_data['rles'] # Write mask records curr_anns = [] for idx in range(len(mask_data['segmentations'])): ann = { 'segmentation': mask_data['segmentations'][idx], 'area': area_from_rle(mask_data['rles'][idx]), 'bbox': box_xyxy_to_xywh(mask_data['boxes'][idx]).tolist(), 'predicted_iou': mask_data['iou_preds'][idx].item(), 'point_coords': [mask_data['points'][idx].tolist()], 'stability_score': mask_data['stability_score'][idx].item(), 'crop_box': box_xyxy_to_xywh(mask_data['crop_boxes'][idx]).tolist(), } curr_anns.append(ann) return curr_anns def _generate_masks(self, image: np.ndarray) -> MaskData: orig_size = image.shape[:2] crop_boxes, layer_idxs = generate_crop_boxes(orig_size, self.crop_n_layers, self.crop_overlap_ratio) # Iterate over image crops data = MaskData() for crop_box, layer_idx in zip(crop_boxes, layer_idxs): crop_data = self._process_crop(image, crop_box, layer_idx, orig_size) data.cat(crop_data) # Remove duplicate masks between crops if len(crop_boxes) > 1: # Prefer masks from smaller crops scores = 1 / box_area(data['crop_boxes']) scores = scores.to(data['boxes'].device) keep_by_nms = batched_nms( data['boxes'].float(), scores, torch.zeros_like(data['boxes'][:, 0]), # categories iou_threshold=self.crop_nms_thresh, ) data.filter(keep_by_nms) data.to_numpy() return data def _process_crop( self, image: np.ndarray, crop_box: List[int], crop_layer_idx: int, orig_size: Tuple[int, ...], ) -> MaskData: # Crop the image and calculate embeddings x0, y0, x1, y1 = crop_box cropped_im = image[y0:y1, x0:x1, :] cropped_im_size = cropped_im.shape[:2] self.predictor.set_image(cropped_im) # Get points for this crop points_scale = np.array(cropped_im_size)[None, ::-1] points_for_image = self.point_grids[crop_layer_idx] * points_scale # Generate masks for this crop in batches data = MaskData() for (points, ) in batch_iterator(self.points_per_batch, points_for_image): batch_data = self._process_batch(points, cropped_im_size, crop_box, orig_size) data.cat(batch_data) del batch_data self.predictor.reset_image() # Remove duplicates within this crop. keep_by_nms = batched_nms( data['boxes'].float(), data['iou_preds'], torch.zeros_like(data['boxes'][:, 0]), # categories iou_threshold=self.box_nms_thresh, ) data.filter(keep_by_nms) # Return to the original image frame data['boxes'] = uncrop_boxes_xyxy(data['boxes'], crop_box) data['points'] = uncrop_points(data['points'], crop_box) data['crop_boxes'] = torch.tensor([crop_box for _ in range(len(data['rles']))]) return data def _process_batch( self, points: np.ndarray, im_size: Tuple[int, ...], crop_box: List[int], orig_size: Tuple[int, ...], ) -> MaskData: orig_h, orig_w = orig_size # Run model on this batch transformed_points = self.predictor.transform.apply_coords(points, im_size) in_points = torch.as_tensor(transformed_points, device=self.predictor.device) in_labels = torch.ones(in_points.shape[0], dtype=torch.int, device=in_points.device) masks, iou_preds, _ = self.predictor.predict_torch( in_points[:, None, :], in_labels[:, None], multimask_output=True, return_logits=True, ) # Serialize predictions and store in MaskData data = MaskData( masks=masks.flatten(0, 1), iou_preds=iou_preds.flatten(0, 1), points=torch.as_tensor(points.repeat(masks.shape[1], axis=0)), ) del masks # Filter by predicted IoU if self.pred_iou_thresh > 0.0: keep_mask = data['iou_preds'] > self.pred_iou_thresh data.filter(keep_mask) # Calculate stability score data['stability_score'] = calculate_stability_score(data['masks'], self.predictor.model.mask_threshold, self.stability_score_offset) if self.stability_score_thresh > 0.0: keep_mask = data['stability_score'] >= self.stability_score_thresh data.filter(keep_mask) # Threshold masks and calculate boxes data['masks'] = data['masks'] > self.predictor.model.mask_threshold data['boxes'] = batched_mask_to_box(data['masks']) # Filter boxes that touch crop boundaries keep_mask = ~is_box_near_crop_edge(data['boxes'], crop_box, [0, 0, orig_w, orig_h]) if not torch.all(keep_mask): data.filter(keep_mask) # Compress to RLE data['masks'] = uncrop_masks(data['masks'], crop_box, orig_h, orig_w) data['rles'] = mask_to_rle_pytorch(data['masks']) del data['masks'] return data @staticmethod def postprocess_small_regions(mask_data: MaskData, min_area: int, nms_thresh: float) -> MaskData: """ Removes small disconnected regions and holes in masks, then reruns box NMS to remove any new duplicates. Edits mask_data in place. Requires open-cv as a dependency. """ if len(mask_data['rles']) == 0: return mask_data # Filter small disconnected regions and holes new_masks = [] scores = [] for rle in mask_data['rles']: mask = rle_to_mask(rle) mask, changed = remove_small_regions(mask, min_area, mode='holes') unchanged = not changed mask, changed = remove_small_regions(mask, min_area, mode='islands') unchanged = unchanged and not changed new_masks.append(torch.as_tensor(mask).unsqueeze(0)) # Give score=0 to changed masks and score=1 to unchanged masks # so NMS will prefer ones that didn't need postprocessing scores.append(float(unchanged)) # Recalculate boxes and remove any new duplicates masks = torch.cat(new_masks, dim=0) boxes = batched_mask_to_box(masks) keep_by_nms = batched_nms( boxes.float(), torch.as_tensor(scores), torch.zeros_like(boxes[:, 0]), # categories iou_threshold=nms_thresh, ) # Only recalculate RLEs for masks that have changed for i_mask in keep_by_nms: if scores[i_mask] == 0.0: mask_torch = masks[i_mask].unsqueeze(0) mask_data['rles'][i_mask] = mask_to_rle_pytorch(mask_torch)[0] mask_data['boxes'][i_mask] = boxes[i_mask] # update res directly mask_data.filter(keep_by_nms) return mask_data