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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 numpy as np
import torch
from segment_anything.modeling import Sam
from typing import Optional, Tuple
from .utils.transforms import ResizeLongestSide
class SamPredictor:
def __init__(
self,
sam_model: Sam,
) -> None:
"""
Uses SAM to calculate the image embedding for an image, and then
allow repeated, efficient mask prediction given prompts.
Arguments:
sam_model (Sam): The model to use for mask prediction.
"""
super().__init__()
self.model = sam_model
self.transform = ResizeLongestSide(sam_model.image_encoder.img_size)
self.reset_image()
def set_image(
self,
image: np.ndarray,
image_format: str = "RGB",
) -> None:
"""
Calculates the image embeddings for the provided image, allowing
masks to be predicted with the 'predict' method.
Arguments:
image (np.ndarray): The image for calculating masks. Expects an
image in HWC uint8 format, with pixel values in [0, 255].
image_format (str): The color format of the image, in ['RGB', 'BGR'].
"""
assert image_format in [
"RGB",
"BGR",
], f"image_format must be in ['RGB', 'BGR'], is {image_format}."
if image_format != self.model.image_format:
image = image[..., ::-1]
# Transform the image to the form expected by the model
input_image = self.transform.apply_image(image)
input_image_torch = torch.as_tensor(input_image, device=self.device)
input_image_torch = input_image_torch.permute(2, 0, 1).contiguous()[None, :, :, :]
self.set_torch_image(input_image_torch, image.shape[:2])
@torch.no_grad()
def set_torch_image(
self,
transformed_image: torch.Tensor,
original_image_size: Tuple[int, ...],
) -> None:
"""
Calculates the image embeddings for the provided image, allowing
masks to be predicted with the 'predict' method. Expects the input
image to be already transformed to the format expected by the model.
Arguments:
transformed_image (torch.Tensor): The input image, with shape
1x3xHxW, which has been transformed with ResizeLongestSide.
original_image_size (tuple(int, int)): The size of the image
before transformation, in (H, W) format.
"""
assert (
len(transformed_image.shape) == 4
and transformed_image.shape[1] == 3
and max(*transformed_image.shape[2:]) == self.model.image_encoder.img_size
), f"set_torch_image input must be BCHW with long side {self.model.image_encoder.img_size}."
self.reset_image()
self.original_size = original_image_size
self.input_size = tuple(transformed_image.shape[-2:])
input_image = self.model.preprocess(transformed_image)
self.features = self.model.image_encoder(input_image)
self.is_image_set = True
def predict(
self,
point_coords: Optional[np.ndarray] = None,
point_labels: Optional[np.ndarray] = None,
box: Optional[np.ndarray] = None,
mask_input: Optional[np.ndarray] = None,
multimask_output: bool = True,
return_logits: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Predict masks for the given input prompts, using the currently set image.
Arguments:
point_coords (np.ndarray or None): A Nx2 array of point prompts to the
model. Each point is in (X,Y) in pixels.
point_labels (np.ndarray or None): A length N array of labels for the
point prompts. 1 indicates a foreground point and 0 indicates a
background point.
box (np.ndarray or None): A length 4 array given a box prompt to the
model, in XYXY format.
mask_input (np.ndarray): A low resolution mask input to the model, typically
coming from a previous prediction iteration. Has form 1xHxW, where
for SAM, H=W=256.
multimask_output (bool): If true, the model will return three masks.
For ambiguous input prompts (such as a single click), this will often
produce better masks than a single prediction. If only a single
mask is needed, the model's predicted quality score can be used
to select the best mask. For non-ambiguous prompts, such as multiple
input prompts, multimask_output=False can give better results.
return_logits (bool): If true, returns un-thresholded masks logits
instead of a binary mask.
Returns:
(np.ndarray): The output masks in CxHxW format, where C is the
number of masks, and (H, W) is the original image size.
(np.ndarray): An array of length C containing the model's
predictions for the quality of each mask.
(np.ndarray): An array of shape CxHxW, where C is the number
of masks and H=W=256. These low resolution logits can be passed to
a subsequent iteration as mask input.
"""
if not self.is_image_set:
raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
# Transform input prompts
coords_torch, labels_torch, box_torch, mask_input_torch = None, None, None, None
if point_coords is not None:
assert (
point_labels is not None
), "point_labels must be supplied if point_coords is supplied."
point_coords = self.transform.apply_coords(point_coords, self.original_size)
coords_torch = torch.as_tensor(point_coords, dtype=torch.float, device=self.device)
labels_torch = torch.as_tensor(point_labels, dtype=torch.int, device=self.device)
coords_torch, labels_torch = coords_torch[None, :, :], labels_torch[None, :]
if box is not None:
box = self.transform.apply_boxes(box, self.original_size)
box_torch = torch.as_tensor(box, dtype=torch.float, device=self.device)
box_torch = box_torch[None, :]
if mask_input is not None:
mask_input_torch = torch.as_tensor(mask_input, dtype=torch.float, device=self.device)
mask_input_torch = mask_input_torch[None, :, :, :]
masks, iou_predictions, low_res_masks, mask_tokens = self.predict_torch(
coords_torch,
labels_torch,
box_torch,
mask_input_torch,
multimask_output,
return_logits=return_logits,
)
masks = masks[0].detach().cpu().numpy()
iou_predictions = iou_predictions[0].detach().cpu().numpy()
low_res_masks = low_res_masks[0].detach().cpu().numpy()
return masks, iou_predictions, low_res_masks, mask_tokens
@torch.no_grad()
def predict_torch(
self,
point_coords: Optional[torch.Tensor],
point_labels: Optional[torch.Tensor],
boxes: Optional[torch.Tensor] = None,
mask_input: Optional[torch.Tensor] = None,
multimask_output: bool = True,
return_logits: bool = False,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Predict masks for the given input prompts, using the currently set image.
Input prompts are batched torch tensors and are expected to already be
transformed to the input frame using ResizeLongestSide.
Arguments:
point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the
model. Each point is in (X,Y) in pixels.
point_labels (torch.Tensor or None): A BxN array of labels for the
point prompts. 1 indicates a foreground point and 0 indicates a
background point.
box (np.ndarray or None): A Bx4 array given a box prompt to the
model, in XYXY format.
mask_input (np.ndarray): A low resolution mask input to the model, typically
coming from a previous prediction iteration. Has form Bx1xHxW, where
for SAM, H=W=256. Masks returned by a previous iteration of the
predict method do not need further transformation.
multimask_output (bool): If true, the model will return three masks.
For ambiguous input prompts (such as a single click), this will often
produce better masks than a single prediction. If only a single
mask is needed, the model's predicted quality score can be used
to select the best mask. For non-ambiguous prompts, such as multiple
input prompts, multimask_output=False can give better results.
return_logits (bool): If true, returns un-thresholded masks logits
instead of a binary mask.
Returns:
(torch.Tensor): The output masks in BxCxHxW format, where C is the
number of masks, and (H, W) is the original image size.
(torch.Tensor): An array of shape BxC containing the model's
predictions for the quality of each mask.
(torch.Tensor): An array of shape BxCxHxW, where C is the number
of masks and H=W=256. These low res logits can be passed to
a subsequent iteration as mask input.
"""
if not self.is_image_set:
raise RuntimeError("An image must be set with .set_image(...) before mask prediction.")
if point_coords is not None:
points = (point_coords, point_labels)
else:
points = None
# Embed prompts
sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
points=points,
boxes=boxes,
masks=mask_input,
)
# Predict masks
low_res_masks, iou_predictions, mask_tokens = self.model.mask_decoder(
image_embeddings=self.features,
image_pe=self.model.prompt_encoder.get_dense_pe(),
sparse_prompt_embeddings=sparse_embeddings,
dense_prompt_embeddings=dense_embeddings,
multimask_output=multimask_output,
)
# Upscale the masks to the original image resolution
masks = self.model.postprocess_masks(low_res_masks, self.input_size, self.original_size)
if not return_logits:
masks = masks > self.model.mask_threshold
return masks, iou_predictions, low_res_masks, mask_tokens
def get_image_embedding(self) -> torch.Tensor:
"""
Returns the image embeddings for the currently set image, with
shape 1xCxHxW, where C is the embedding dimension and (H,W) are
the embedding spatial dimension of SAM (typically C=256, H=W=64).
"""
if not self.is_image_set:
raise RuntimeError(
"An image must be set with .set_image(...) to generate an embedding."
)
assert self.features is not None, "Features must exist if an image has been set."
return self.features
@property
def device(self) -> torch.device:
return self.model.device
def reset_image(self) -> None:
"""Resets the currently set image."""
self.is_image_set = False
self.features = None
self.orig_h = None
self.orig_w = None
self.input_h = None
self.input_w = None
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