286 lines
12 KiB
Python
286 lines
12 KiB
Python
# 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 .modeling import Sam
|
|
|
|
from typing import Optional, Tuple
|
|
|
|
|
|
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
|
|
from .utils.transforms import ResizeLongestSide
|
|
|
|
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 = 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
|
|
|
|
@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 = 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
|
|
|
|
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
|