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"""
machine learning segment anything class.
Modified from https://github.com/vietanhdev/samexporter/
Copyright (c) 2023 Viet Anh Nguyen
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
from copy import deepcopy
import cv2
import numpy as np
import onnxruntime
from src import app_logger
class SegmentAnythingONNX:
"""Segmentation model using SegmentAnything"""
def __init__(self, encoder_model_path, decoder_model_path) -> None:
self.target_size = 1024
self.input_size = (684, 1024)
# Load models
providers = onnxruntime.get_available_providers()
# Pop TensorRT Runtime due to crashing issues
# TODO: Add back when TensorRT backend is stable
providers = [p for p in providers if p != "TensorrtExecutionProvider"]
if providers:
app_logger.info(
"Available providers for ONNXRuntime: %s", ", ".join(providers)
)
else:
app_logger.warning("No available providers for ONNXRuntime")
self.encoder_session = onnxruntime.InferenceSession(
encoder_model_path, providers=providers
)
self.encoder_input_name = self.encoder_session.get_inputs()[0].name
self.decoder_session = onnxruntime.InferenceSession(
decoder_model_path, providers=providers
)
@staticmethod
def get_input_points(prompt):
"""Get input points"""
points = []
labels = []
for mark in prompt:
if mark["type"] == "point":
points.append(mark["data"])
labels.append(mark["label"])
elif mark["type"] == "rectangle":
points.append([mark["data"][0], mark["data"][1]]) # top left
points.append(
[mark["data"][2], mark["data"][3]]
) # bottom right
labels.append(2)
labels.append(3)
points, labels = np.array(points), np.array(labels)
return points, labels
def run_encoder(self, encoder_inputs):
"""Run encoder"""
output = self.encoder_session.run(None, encoder_inputs)
image_embedding = output[0]
return image_embedding
@staticmethod
def get_preprocess_shape(old_h: int, old_w: int, long_side_length: int):
"""
Compute the output size given input size and target long side length.
"""
scale = long_side_length * 1.0 / max(old_h, old_w)
new_h, new_w = old_h * scale, old_w * scale
new_w = int(new_w + 0.5)
new_h = int(new_h + 0.5)
return new_h, new_w
def apply_coords(self, coords: np.ndarray, original_size, target_length):
"""
Expects a numpy array of length 2 in the final dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], target_length
)
coords = deepcopy(coords).astype(float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def run_decoder(
self, image_embedding, original_size, transform_matrix, prompt
):
"""Run decoder"""
input_points, input_labels = self.get_input_points(prompt)
# Add a batch index, concatenate a padding point, and transform.
onnx_coord = np.concatenate(
[input_points, np.array([[0.0, 0.0]])], axis=0
)[None, :, :]
onnx_label = np.concatenate([input_labels, np.array([-1])], axis=0)[
None, :
].astype(np.float32)
onnx_coord = self.apply_coords(
onnx_coord, self.input_size, self.target_size
).astype(np.float32)
# Apply the transformation matrix to the coordinates.
onnx_coord = np.concatenate(
[
onnx_coord,
np.ones((1, onnx_coord.shape[1], 1), dtype=np.float32),
],
axis=2,
)
onnx_coord = np.matmul(onnx_coord, transform_matrix.T)
onnx_coord = onnx_coord[:, :, :2].astype(np.float32)
# Create an empty mask input and an indicator for no mask.
onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)
onnx_has_mask_input = np.zeros(1, dtype=np.float32)
decoder_inputs = {
"image_embeddings": image_embedding,
"point_coords": onnx_coord,
"point_labels": onnx_label,
"mask_input": onnx_mask_input,
"has_mask_input": onnx_has_mask_input,
"orig_im_size": np.array(self.input_size, dtype=np.float32),
}
masks, _, _ = self.decoder_session.run(None, decoder_inputs)
# Transform the masks back to the original image size.
inv_transform_matrix = np.linalg.inv(transform_matrix)
transformed_masks = self.transform_masks(
masks, original_size, inv_transform_matrix
)
return transformed_masks
@staticmethod
def transform_masks(masks, original_size, transform_matrix):
"""Transform masks
Transform the masks back to the original image size.
"""
output_masks = []
for batch in range(masks.shape[0]):
batch_masks = []
for mask_id in range(masks.shape[1]):
mask = masks[batch, mask_id]
try:
try:
app_logger.debug(f"mask_shape transform_masks:{mask.shape}, dtype:{mask.dtype}.")
except Exception as e_mask_shape_transform_masks:
app_logger.error(f"e_mask_shape_transform_masks:{e_mask_shape_transform_masks}.")
# raise e_mask_shape_transform_masks
mask = cv2.warpAffine(
mask,
transform_matrix[:2],
(original_size[1], original_size[0]),
flags=cv2.INTER_LINEAR,
)
except Exception as e_warp_affine1:
app_logger.error(f"e_warp_affine1 mask shape:{mask.shape}, dtype:{mask.dtype}.")
app_logger.error(f"e_warp_affine1 transform_matrix:{transform_matrix}, [:2] {transform_matrix[:2]}.")
app_logger.error(f"e_warp_affine1 original_size:{original_size}.")
raise e_warp_affine1
batch_masks.append(mask)
output_masks.append(batch_masks)
return np.array(output_masks)
def encode(self, cv_image):
"""
Calculate embedding and metadata for a single image.
"""
original_size = cv_image.shape[:2]
# Calculate a transformation matrix to convert to self.input_size
scale_x = self.input_size[1] / cv_image.shape[1]
scale_y = self.input_size[0] / cv_image.shape[0]
scale = min(scale_x, scale_y)
transform_matrix = np.array(
[
[scale, 0, 0],
[0, scale, 0],
[0, 0, 1],
]
)
try:
cv_image = cv2.warpAffine(
cv_image,
transform_matrix[:2],
(self.input_size[1], self.input_size[0]),
flags=cv2.INTER_LINEAR,
)
except Exception as e_warp_affine2:
app_logger.error(f"e_warp_affine2:{e_warp_affine2}.")
np_cv_image = np.array(cv_image)
app_logger.error(f"e_warp_affine2 cv_image shape:{np_cv_image.shape}, dtype:{np_cv_image.dtype}.")
app_logger.error(f"e_warp_affine2 transform_matrix:{transform_matrix}, [:2] {transform_matrix[:2]}")
app_logger.error(f"e_warp_affine2 self.input_size:{self.input_size}.")
raise e_warp_affine2
encoder_inputs = {
self.encoder_input_name: cv_image.astype(np.float32),
}
image_embedding = self.run_encoder(encoder_inputs)
return {
"image_embedding": image_embedding,
"original_size": original_size,
"transform_matrix": transform_matrix,
}
def predict_masks(self, embedding, prompt):
"""
Predict masks for a single image.
"""
masks = self.run_decoder(
embedding["image_embedding"],
embedding["original_size"],
embedding["transform_matrix"],
prompt,
)
return masks
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