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Original file line number | Diff line number | Diff line change |
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import collections | ||
import threading | ||
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import imgviz | ||
import numpy as np | ||
import onnxruntime | ||
import skimage | ||
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from ..logger import logger | ||
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from . import _utils | ||
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class EfficientSam: | ||
def __init__(self, encoder_path, decoder_path): | ||
self._encoder_session = onnxruntime.InferenceSession(encoder_path) | ||
self._decoder_session = onnxruntime.InferenceSession(decoder_path) | ||
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self._lock = threading.Lock() | ||
self._image_embedding_cache = collections.OrderedDict() | ||
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self._thread = None | ||
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def set_image(self, image: np.ndarray): | ||
with self._lock: | ||
self._image = image | ||
self._image_embedding = self._image_embedding_cache.get( | ||
self._image.tobytes() | ||
) | ||
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if self._image_embedding is None: | ||
self._thread = threading.Thread( | ||
target=self._compute_and_cache_image_embedding | ||
) | ||
self._thread.start() | ||
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def _compute_and_cache_image_embedding(self): | ||
with self._lock: | ||
logger.debug("Computing image embedding...") | ||
image = imgviz.rgba2rgb(self._image) | ||
batched_images = ( | ||
image.transpose(2, 0, 1)[None].astype(np.float32) / 255.0 | ||
) | ||
(self._image_embedding,) = self._encoder_session.run( | ||
output_names=None, | ||
input_feed={"batched_images": batched_images}, | ||
) | ||
if len(self._image_embedding_cache) > 10: | ||
self._image_embedding_cache.popitem(last=False) | ||
self._image_embedding_cache[ | ||
self._image.tobytes() | ||
] = self._image_embedding | ||
logger.debug("Done computing image embedding.") | ||
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def _get_image_embedding(self): | ||
if self._thread is not None: | ||
self._thread.join() | ||
self._thread = None | ||
with self._lock: | ||
return self._image_embedding | ||
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def predict_mask_from_points(self, points, point_labels): | ||
return _compute_mask_from_points( | ||
decoder_session=self._decoder_session, | ||
image=self._image, | ||
image_embedding=self._get_image_embedding(), | ||
points=points, | ||
point_labels=point_labels, | ||
) | ||
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def predict_polygon_from_points(self, points, point_labels): | ||
mask = self.predict_mask_from_points( | ||
points=points, point_labels=point_labels | ||
) | ||
return _utils.compute_polygon_from_mask(mask=mask) | ||
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def _compute_mask_from_points( | ||
decoder_session, image, image_embedding, points, point_labels | ||
): | ||
input_point = np.array(points, dtype=np.float32) | ||
input_label = np.array(point_labels, dtype=np.float32) | ||
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# batch_size, num_queries, num_points, 2 | ||
batched_point_coords = input_point[None, None, :, :] | ||
# batch_size, num_queries, num_points | ||
batched_point_labels = input_label[None, None, :] | ||
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decoder_inputs = { | ||
"image_embeddings": image_embedding, | ||
"batched_point_coords": batched_point_coords, | ||
"batched_point_labels": batched_point_labels, | ||
"orig_im_size": np.array(image.shape[:2], dtype=np.int64), | ||
} | ||
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masks, _, _ = decoder_session.run(None, decoder_inputs) | ||
mask = masks[0, 0, 0, :, :] # (1, 1, 3, H, W) -> (H, W) | ||
mask = mask > 0.0 | ||
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MIN_SIZE_RATIO = 0.05 | ||
skimage.morphology.remove_small_objects( | ||
mask, min_size=mask.sum() * MIN_SIZE_RATIO, out=mask | ||
) | ||
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if 0: | ||
imgviz.io.imsave( | ||
"mask.jpg", imgviz.label2rgb(mask, imgviz.rgb2gray(image)) | ||
) | ||
return mask |
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