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| # 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. | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torchvision.transforms import Normalize, Resize, ToTensor | |
| class SAM2Transforms(nn.Module): | |
| def __init__( | |
| self, resolution, mask_threshold, max_hole_area=0.0, max_sprinkle_area=0.0 | |
| ): | |
| """ | |
| Transforms for SAM2. | |
| """ | |
| super().__init__() | |
| self.resolution = resolution | |
| self.mask_threshold = mask_threshold | |
| self.max_hole_area = max_hole_area | |
| self.max_sprinkle_area = max_sprinkle_area | |
| self.mean = [0.485, 0.456, 0.406] | |
| self.std = [0.229, 0.224, 0.225] | |
| self.to_tensor = ToTensor() | |
| self.transforms = nn.Sequential( | |
| Resize((self.resolution, self.resolution), antialias=True), | |
| Normalize(self.mean, self.std), | |
| ) | |
| def __call__(self, x): | |
| x = self.to_tensor(x) | |
| return self.transforms(x) | |
| def forward_batch(self, img_list): | |
| img_batch = [self.transforms(self.to_tensor(img)) for img in img_list] | |
| img_batch = torch.stack(img_batch, dim=0) | |
| return img_batch | |
| def transform_coords( | |
| self, coords: torch.Tensor, normalize=False, orig_hw=None | |
| ) -> torch.Tensor: | |
| """ | |
| Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates, | |
| If the coords are in absolute image coordinates, normalize should be set to True and original image size is required. | |
| Returns | |
| Un-normalized coordinates in the range of [0, 1] which is expected by the SAM2 model. | |
| """ | |
| if normalize: | |
| assert orig_hw is not None | |
| h, w = orig_hw | |
| coords = coords.clone() | |
| coords[..., 0] = coords[..., 0] / w | |
| coords[..., 1] = coords[..., 1] / h | |
| coords = coords * self.resolution # unnormalize coords | |
| return coords | |
| def transform_boxes( | |
| self, boxes: torch.Tensor, normalize=False, orig_hw=None | |
| ) -> torch.Tensor: | |
| """ | |
| Expects a tensor of shape Bx4. The coordinates can be in absolute image or normalized coordinates, | |
| if the coords are in absolute image coordinates, normalize should be set to True and original image size is required. | |
| """ | |
| boxes = self.transform_coords(boxes.reshape(-1, 2, 2), normalize, orig_hw) | |
| return boxes | |
| def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor: | |
| """ | |
| Perform PostProcessing on output masks. | |
| """ | |
| from sam2.utils.misc import get_connected_components | |
| masks = masks.float() | |
| if self.max_hole_area > 0: | |
| # Holes are those connected components in background with area <= self.fill_hole_area | |
| # (background regions are those with mask scores <= self.mask_threshold) | |
| mask_flat = masks.flatten(0, 1).unsqueeze(1) # flatten as 1-channel image | |
| labels, areas = get_connected_components(mask_flat <= self.mask_threshold) | |
| is_hole = (labels > 0) & (areas <= self.max_hole_area) | |
| is_hole = is_hole.reshape_as(masks) | |
| # We fill holes with a small positive mask score (10.0) to change them to foreground. | |
| masks = torch.where(is_hole, self.mask_threshold + 10.0, masks) | |
| if self.max_sprinkle_area > 0: | |
| labels, areas = get_connected_components(mask_flat > self.mask_threshold) | |
| is_hole = (labels > 0) & (areas <= self.max_sprinkle_area) | |
| is_hole = is_hole.reshape_as(masks) | |
| # We fill holes with negative mask score (-10.0) to change them to background. | |
| masks = torch.where(is_hole, self.mask_threshold - 10.0, masks) | |
| masks = F.interpolate(masks, orig_hw, mode="bilinear", align_corners=False) | |
| return masks | |