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import numpy as np
import torch
from typing import Optional, Tuple
from torch.nn import functional as F
from copy import deepcopy
from albumentations.pytorch import ToTensorV2
import albumentations as A
import cv2
class SammedPredictor:
def __init__(self, sam_model):
super().__init__()
self.model = sam_model
self.devices = sam_model.device
self.reset_image()
def set_image(self,image: np.ndarray, image_format: str = "RGB") -> None:
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
if self.model.pixel_mean.device.type == 'cuda':
pixel_mean, pixel_std = self.model.pixel_mean.squeeze().cpu().numpy(), self.model.pixel_std.squeeze().cpu().numpy()
input_image = (image - pixel_mean) / pixel_std
else:
pixel_mean, pixel_std = self.model.pixel_mean.squeeze().numpy(), self.model.pixel_std.squeeze().numpy()
input_image = (image - pixel_mean) / pixel_std
ori_h, ori_w, _ = input_image.shape
self.original_size = (ori_h, ori_w)
self.new_size = (self.model.image_encoder.img_size, self.model.image_encoder.img_size)
transforms = self.transforms(self.new_size)
augments = transforms(image=input_image)
input_image = augments['image'][None, :, :, :]
assert (
len(input_image.shape) == 4
and input_image.shape[1] == 3
and max(*input_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.features = self.model.image_encoder(input_image.to(self.device))
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]:
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.apply_coords(point_coords, self.original_size, self.new_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.apply_boxes(box, self.original_size, self.new_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]:
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
if boxes is not None and boxes.shape[0] > 1:
mask_list = []
# Embed prompts
for i in range(boxes.shape[0]):
pre_boxes = boxes[i:i+1,...]
sparse_embeddings, dense_embeddings = self.model.prompt_encoder(
points=points,
boxes=pre_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,
)
if multimask_output:
max_values, max_indexs = torch.max(iou_predictions, dim=1)
max_values = max_values.unsqueeze(1)
iou_predictions = max_values
low_res_masks = low_res_masks[:, max_indexs]
# Upscale the masks to the original image resolution
pre_masks = self.postprocess_masks(low_res_masks, self.model.image_encoder.img_size, self.original_size)
mask_list.append(pre_masks)
masks = torch.cat(mask_list, dim=0)
else:
# 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,
)
if multimask_output:
max_values, max_indexs = torch.max(iou_predictions, dim=1)
max_values = max_values.unsqueeze(1)
iou_predictions = max_values
low_res_masks = low_res_masks[:, max_indexs]
# Upscale the masks to the original image resolution
masks = self.postprocess_masks(low_res_masks, self.model.image_encoder.img_size, self.original_size)
if not return_logits:
sigmoid_output = torch.sigmoid(masks)
masks = (sigmoid_output > 0.5).float()
return masks, iou_predictions, low_res_masks
def postprocess_masks(self, low_res_masks, image_size, original_size):
ori_h, ori_w = original_size
masks = F.interpolate(low_res_masks,(image_size, image_size), mode="bilinear", align_corners=False)
masks = F.interpolate(masks, original_size, mode="bilinear", align_corners=False)
return masks
def apply_coords(self, coords, original_size, new_size):
old_h, old_w = original_size
new_h, new_w = new_size
coords = deepcopy(coords).astype(float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes(self, boxes, original_size, new_size):
boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size, new_size)
return boxes.reshape(-1, 4)
def apply_coords_torch(self, coords, original_size, new_size):
old_h, old_w = original_size
new_h, new_w = new_size
coords = deepcopy(coords).to(torch.float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes_torch(self, boxes, original_size, new_size):
boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size, new_size)
return boxes.reshape(-1, 4)
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
def transforms(self, new_size):
Transforms = []
new_h, new_w = new_size
Transforms.append(A.Resize(int(new_h), int(new_w), interpolation=cv2.INTER_NEAREST))
Transforms.append(ToTensorV2(p=1.0))
return A.Compose(Transforms, p=1.)
@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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