cassdlcm/mediapipe
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'google:master' into segmenter-python-add-labels

Browse Source
This commit is contained in:
Kinar R 2023-04-19 09:51:42 +05:30 committed by GitHub
commit d621df8046
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
10 changed files with 459 additions and 229 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
mediapipe/gpu/gl_texture_buffer.cc
View File

@ -64,7 +64,7 @@ std::unique_ptr<GlTextureBuffer> GlTextureBuffer::Create(
int actual_ws = image_frame.WidthStep();
int alignment = 0;
std::unique_ptr<ImageFrame> temp;
const uint8* data = image_frame.PixelData();
const uint8_t* data = image_frame.PixelData();
// Let's see if the pixel data is tightly aligned to one of the alignments
// supported by OpenGL, preferring 4 if possible since it's the default.

6
mediapipe/model_maker/python/vision/object_detector/BUILD
View File

@ -175,11 +175,7 @@ py_test(
data = [":testdata"],
tags = ["requires-net:external"],
deps = [
":dataset",
":hyperparameters",
":model_spec",
":object_detector",
":object_detector_options",
":object_detector_import",
"//mediapipe/tasks/python/test:test_utils",
],
)

17
mediapipe/model_maker/python/vision/object_detector/object_detector_test.py
View File

@ -19,11 +19,7 @@ from unittest import mock as unittest_mock
from absl.testing import parameterized
import tensorflow as tf
from mediapipe.model_maker.python.vision.object_detector import dataset
from mediapipe.model_maker.python.vision.object_detector import hyperparameters
from mediapipe.model_maker.python.vision.object_detector import model_spec as ms
from mediapipe.model_maker.python.vision.object_detector import object_detector
from mediapipe.model_maker.python.vision.object_detector import object_detector_options
from mediapipe.model_maker.python.vision import object_detector
from mediapipe.tasks.python.test import test_utils as task_test_utils
@ -33,7 +29,7 @@ class ObjectDetectorTest(tf.test.TestCase, parameterized.TestCase):
super().setUp()
dataset_folder = task_test_utils.get_test_data_path('coco_data')
cache_dir = self.create_tempdir()
self.data = dataset.Dataset.from_coco_folder(
self.data = object_detector.Dataset.from_coco_folder(
dataset_folder, cache_dir=cache_dir
)
# Mock tempfile.gettempdir() to be unique for each test to avoid race
@ -48,15 +44,16 @@ class ObjectDetectorTest(tf.test.TestCase, parameterized.TestCase):
self.addCleanup(mock_gettempdir.stop)
def test_object_detector(self):
hparams = hyperparameters.HParams(
hparams = object_detector.HParams(
epochs=1,
batch_size=2,
learning_rate=0.9,
shuffle=False,
export_dir=self.create_tempdir(),
)
options = object_detector_options.ObjectDetectorOptions(
supported_model=ms.SupportedModels.MOBILENET_V2, hparams=hparams
options = object_detector.ObjectDetectorOptions(
supported_model=object_detector.SupportedModels.MOBILENET_V2,
hparams=hparams,
)
# Test `create``
model = object_detector.ObjectDetector.create(
@ -79,7 +76,7 @@ class ObjectDetectorTest(tf.test.TestCase, parameterized.TestCase):
self.assertGreater(os.path.getsize(output_metadata_file), 0)
# Test `quantization_aware_training`
qat_hparams = hyperparameters.QATHParams(
qat_hparams = object_detector.QATHParams(
learning_rate=0.9,
batch_size=2,
epochs=1,

6
mediapipe/modules/objectron/calculators/frame_annotation_tracker.cc
View File

@ -24,8 +24,8 @@ namespace mediapipe {
void FrameAnnotationTracker::AddDetectionResult(
const FrameAnnotation& frame_annotation) {
const int64 time_us =
static_cast<int64>(std::round(frame_annotation.timestamp()));
const int64_t time_us =
static_cast<int64_t>(std::round(frame_annotation.timestamp()));
for (const auto& object_annotation : frame_annotation.annotations()) {
detected_objects_[time_us + object_annotation.object_id()] =
object_annotation;
@ -37,7 +37,7 @@ FrameAnnotation FrameAnnotationTracker::ConsolidateTrackingResult(
absl::flat_hash_set<int>* cancel_object_ids) {
CHECK(cancel_object_ids != nullptr);
FrameAnnotation frame_annotation;
std::vector<int64> keys_to_be_deleted;
std::vector<int64_t> keys_to_be_deleted;
for (const auto& detected_obj : detected_objects_) {
const int object_id = detected_obj.second.object_id();
if (cancel_object_ids->contains(object_id)) {

38
mediapipe/tasks/python/test/text/text_embedder_test.py
View File

@ -32,6 +32,7 @@ _TextEmbedderOptions = text_embedder.TextEmbedderOptions
_BERT_MODEL_FILE = 'mobilebert_embedding_with_metadata.tflite'
_REGEX_MODEL_FILE = 'regex_one_embedding_with_metadata.tflite'
_USE_MODEL_FILE = 'universal_sentence_encoder_qa_with_metadata.tflite'
_TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
# Tolerance for embedding vector coordinate values.
_EPSILON = 1e-4
@ -138,6 +139,24 @@ class TextEmbedderTest(parameterized.TestCase):
16,
(0.549632, 0.552879),
),
(
False,
False,
_USE_MODEL_FILE,
ModelFileType.FILE_NAME,
0.851961,
100,
(1.422951, 1.404664),
),
(
True,
False,
_USE_MODEL_FILE,
ModelFileType.FILE_CONTENT,
0.851961,
100,
(0.127049, 0.125416),
),
)
def test_embed(self, l2_normalize, quantize, model_name, model_file_type,
expected_similarity, expected_size, expected_first_values):
@ -213,6 +232,24 @@ class TextEmbedderTest(parameterized.TestCase):
16,
(0.549632, 0.552879),
),
(
False,
False,
_USE_MODEL_FILE,
ModelFileType.FILE_NAME,
0.851961,
100,
(1.422951, 1.404664),
),
(
True,
False,
_USE_MODEL_FILE,
ModelFileType.FILE_CONTENT,
0.851961,
100,
(0.127049, 0.125416),
),
)
def test_embed_in_context(self, l2_normalize, quantize, model_name,
model_file_type, expected_similarity, expected_size,
@ -251,6 +288,7 @@ class TextEmbedderTest(parameterized.TestCase):
@parameterized.parameters(
# TODO: The similarity should likely be lower
(_BERT_MODEL_FILE, 0.980880),
(_USE_MODEL_FILE, 0.780334),
)
def test_embed_with_different_themes(self, model_file, expected_similarity):
# Creates embedder.

354
mediapipe/tasks/python/test/vision/image_segmenter_test.py
View File

@ -15,7 +15,6 @@
import enum
import os
from typing import List
from unittest import mock
from absl.testing import absltest
@ -30,11 +29,10 @@ from mediapipe.tasks.python.test import test_utils
from mediapipe.tasks.python.vision import image_segmenter
from mediapipe.tasks.python.vision.core import vision_task_running_mode
ImageSegmenterResult = image_segmenter.ImageSegmenterResult
_BaseOptions = base_options_module.BaseOptions
_Image = image_module.Image
_ImageFormat = image_frame.ImageFormat
_OutputType = image_segmenter.ImageSegmenterOptions.OutputType
_Activation = image_segmenter.ImageSegmenterOptions.Activation
_ImageSegmenter = image_segmenter.ImageSegmenter
_ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
_RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
@ -42,6 +40,8 @@ _RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
_MODEL_FILE = 'deeplabv3.tflite'
_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
_SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
_CAT_IMAGE = 'cat.jpg'
_CAT_MASK = 'cat_mask.jpg'
_MASK_MAGNIFICATION_FACTOR = 10
_MASK_SIMILARITY_THRESHOLD = 0.98
_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
@ -70,6 +70,26 @@ _EXPECTED_LABELS = [
]
def _calculate_soft_iou(m1, m2):
intersection_sum = np.sum(m1 * m2)
union_sum = np.sum(m1 * m1) + np.sum(m2 * m2) - intersection_sum
if union_sum > 0:
return intersection_sum / union_sum
else:
return 0
def _similar_to_float_mask(actual_mask, expected_mask, similarity_threshold):
actual_mask = actual_mask.numpy_view()
expected_mask = expected_mask.numpy_view() / 255.0
return (
actual_mask.shape == expected_mask.shape
and _calculate_soft_iou(actual_mask, expected_mask) > similarity_threshold
)
def _similar_to_uint8_mask(actual_mask, expected_mask):
actual_mask_pixels = actual_mask.numpy_view().flatten()
expected_mask_pixels = expected_mask.numpy_view().flatten()
@ -79,8 +99,9 @@ def _similar_to_uint8_mask(actual_mask, expected_mask):
for index in range(num_pixels):
consistent_pixels += (
actual_mask_pixels[index] *
_MASK_MAGNIFICATION_FACTOR == expected_mask_pixels[index])
actual_mask_pixels[index] * _MASK_MAGNIFICATION_FACTOR
== expected_mask_pixels[index]
)
return consistent_pixels / num_pixels >= _MASK_SIMILARITY_THRESHOLD
@ -96,16 +117,27 @@ class ImageSegmenterTest(parameterized.TestCase):
super().setUp()
# Load the test input image.
self.test_image = _Image.create_from_file(
test_utils.get_test_data_path(
os.path.join(_TEST_DATA_DIR, _IMAGE_FILE)))
test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _IMAGE_FILE))
)
# Loads ground truth segmentation file.
gt_segmentation_data = cv2.imread(
test_utils.get_test_data_path(
os.path.join(_TEST_DATA_DIR, _SEGMENTATION_FILE)),
cv2.IMREAD_GRAYSCALE)
os.path.join(_TEST_DATA_DIR, _SEGMENTATION_FILE)
),
cv2.IMREAD_GRAYSCALE,
)
self.test_seg_image = _Image(_ImageFormat.GRAY8, gt_segmentation_data)
self.model_path = test_utils.get_test_data_path(
os.path.join(_TEST_DATA_DIR, _MODEL_FILE))
os.path.join(_TEST_DATA_DIR, _MODEL_FILE)
)
def _load_segmentation_mask(self, file_path: str):
# Loads ground truth segmentation file.
gt_segmentation_data = cv2.imread(
test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, file_path)),
cv2.IMREAD_GRAYSCALE,
)
return _Image(_ImageFormat.GRAY8, gt_segmentation_data)
def test_create_from_file_succeeds_with_valid_model_path(self):
# Creates with default option and valid model file successfully.
@ -121,9 +153,11 @@ class ImageSegmenterTest(parameterized.TestCase):
def test_create_from_options_fails_with_invalid_model_path(self):
with self.assertRaisesRegex(
RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'
):
base_options = _BaseOptions(
model_asset_path='/path/to/invalid/model.tflite')
model_asset_path='/path/to/invalid/model.tflite'
)
options = _ImageSegmenterOptions(base_options=base_options)
_ImageSegmenter.create_from_options(options)
@ -135,8 +169,9 @@ class ImageSegmenterTest(parameterized.TestCase):
segmenter = _ImageSegmenter.create_from_options(options)
self.assertIsInstance(segmenter, _ImageSegmenter)
@parameterized.parameters((ModelFileType.FILE_NAME,),
(ModelFileType.FILE_CONTENT,))
@parameterized.parameters(
(ModelFileType.FILE_NAME,), (ModelFileType.FILE_CONTENT,)
)
def test_segment_succeeds_with_category_mask(self, model_file_type):
# Creates segmenter.
if model_file_type is ModelFileType.FILE_NAME:
@ -150,22 +185,27 @@ class ImageSegmenterTest(parameterized.TestCase):
raise ValueError('model_file_type is invalid.')
options = _ImageSegmenterOptions(
base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
base_options=base_options,
output_category_mask=True,
output_confidence_masks=False,
)
segmenter = _ImageSegmenter.create_from_options(options)
# Performs image segmentation on the input.
category_masks = segmenter.segment(self.test_image)
self.assertLen(category_masks, 1)
category_mask = category_masks[0]
segmentation_result = segmenter.segment(self.test_image)
category_mask = segmentation_result.category_mask
result_pixels = category_mask.numpy_view().flatten()
# Check if data type of `category_mask` is correct.
self.assertEqual(result_pixels.dtype, np.uint8)
self.assertTrue(
_similar_to_uint8_mask(category_masks[0], self.test_seg_image),
f'Number of pixels in the candidate mask differing from that of the '
f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
_similar_to_uint8_mask(category_mask, self.test_seg_image),
(
'Number of pixels in the candidate mask differing from that of the'
f' ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
),
)
# Closes the segmenter explicitly when the segmenter is not used in
# a context.
@ -175,67 +215,37 @@ class ImageSegmenterTest(parameterized.TestCase):
# Creates segmenter.
base_options = _BaseOptions(model_asset_path=self.model_path)
# Run segmentation on the model in CATEGORY_MASK mode.
options = _ImageSegmenterOptions(
base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
segmenter = _ImageSegmenter.create_from_options(options)
category_masks = segmenter.segment(self.test_image)
category_mask = category_masks[0].numpy_view()
# Load the cat image.
test_image = _Image.create_from_file(
test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
)
# Run segmentation on the model in CONFIDENCE_MASK mode.
options = _ImageSegmenterOptions(
base_options=base_options,
output_type=_OutputType.CONFIDENCE_MASK,
activation=_Activation.SOFTMAX)
segmenter = _ImageSegmenter.create_from_options(options)
confidence_masks = segmenter.segment(self.test_image)
output_category_mask=False,
output_confidence_masks=True,
)
# Check if confidence mask shape is correct.
self.assertLen(
confidence_masks, 21,
'Number of confidence masks must match with number of categories.')
# Gather the confidence masks in a single array `confidence_mask_array`.
confidence_mask_array = np.array(
[confidence_mask.numpy_view() for confidence_mask in confidence_masks])
# Check if data type of `confidence_masks` are correct.
self.assertEqual(confidence_mask_array.dtype, np.float32)
# Compute the category mask from the created confidence mask.
calculated_category_mask = np.argmax(confidence_mask_array, axis=0)
self.assertListEqual(
calculated_category_mask.tolist(), category_mask.tolist(),
'Confidence mask does not match with the category mask.')
# Closes the segmenter explicitly when the segmenter is not used in
# a context.
segmenter.close()
@parameterized.parameters((ModelFileType.FILE_NAME),
(ModelFileType.FILE_CONTENT))
def test_segment_in_context(self, model_file_type):
if model_file_type is ModelFileType.FILE_NAME:
base_options = _BaseOptions(model_asset_path=self.model_path)
elif model_file_type is ModelFileType.FILE_CONTENT:
with open(self.model_path, 'rb') as f:
model_contents = f.read()
base_options = _BaseOptions(model_asset_buffer=model_contents)
else:
# Should never happen
raise ValueError('model_file_type is invalid.')
options = _ImageSegmenterOptions(
base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
with _ImageSegmenter.create_from_options(options) as segmenter:
# Performs image segmentation on the input.
category_masks = segmenter.segment(self.test_image)
self.assertLen(category_masks, 1)
segmentation_result = segmenter.segment(test_image)
confidence_masks = segmentation_result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
confidence_masks,
21,
'Number of confidence masks must match with number of categories.',
)
# Loads ground truth segmentation file.
expected_mask = self._load_segmentation_mask(_CAT_MASK)
self.assertTrue(
_similar_to_uint8_mask(category_masks[0], self.test_seg_image),
f'Number of pixels in the candidate mask differing from that of the '
f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
_similar_to_float_mask(
confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
)
)
def test_get_labels_succeeds(self):
expected_labels = _EXPECTED_LABELS
@ -250,9 +260,11 @@ class ImageSegmenterTest(parameterized.TestCase):
def test_missing_result_callback(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.LIVE_STREAM)
with self.assertRaisesRegex(ValueError,
r'result callback must be provided'):
running_mode=_RUNNING_MODE.LIVE_STREAM,
)
with self.assertRaisesRegex(
ValueError, r'result callback must be provided'
):
with _ImageSegmenter.create_from_options(options) as unused_segmenter:
pass
@ -261,130 +273,236 @@ class ImageSegmenterTest(parameterized.TestCase):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=running_mode,
result_callback=mock.MagicMock())
with self.assertRaisesRegex(ValueError,
r'result callback should not be provided'):
result_callback=mock.MagicMock(),
)
with self.assertRaisesRegex(
ValueError, r'result callback should not be provided'
):
with _ImageSegmenter.create_from_options(options) as unused_segmenter:
pass
def test_calling_segment_for_video_in_image_mode(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.IMAGE)
running_mode=_RUNNING_MODE.IMAGE,
)
with _ImageSegmenter.create_from_options(options) as segmenter:
with self.assertRaisesRegex(ValueError,
r'not initialized with the video mode'):
with self.assertRaisesRegex(
ValueError, r'not initialized with the video mode'
):
segmenter.segment_for_video(self.test_image, 0)
def test_calling_segment_async_in_image_mode(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.IMAGE)
running_mode=_RUNNING_MODE.IMAGE,
)
with _ImageSegmenter.create_from_options(options) as segmenter:
with self.assertRaisesRegex(ValueError,
r'not initialized with the live stream mode'):
with self.assertRaisesRegex(
ValueError, r'not initialized with the live stream mode'
):
segmenter.segment_async(self.test_image, 0)
def test_calling_segment_in_video_mode(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.VIDEO)
running_mode=_RUNNING_MODE.VIDEO,
)
with _ImageSegmenter.create_from_options(options) as segmenter:
with self.assertRaisesRegex(ValueError,
r'not initialized with the image mode'):
with self.assertRaisesRegex(
ValueError, r'not initialized with the image mode'
):
segmenter.segment(self.test_image)
def test_calling_segment_async_in_video_mode(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.VIDEO)
running_mode=_RUNNING_MODE.VIDEO,
)
with _ImageSegmenter.create_from_options(options) as segmenter:
with self.assertRaisesRegex(ValueError,
r'not initialized with the live stream mode'):
with self.assertRaisesRegex(
ValueError, r'not initialized with the live stream mode'
):
segmenter.segment_async(self.test_image, 0)
def test_segment_for_video_with_out_of_order_timestamp(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.VIDEO)
running_mode=_RUNNING_MODE.VIDEO,
)
with _ImageSegmenter.create_from_options(options) as segmenter:
unused_result = segmenter.segment_for_video(self.test_image, 1)
with self.assertRaisesRegex(
ValueError, r'Input timestamp must be monotonically increasing'):
ValueError, r'Input timestamp must be monotonically increasing'
):
segmenter.segment_for_video(self.test_image, 0)
def test_segment_for_video(self):
def test_segment_for_video_in_category_mask_mode(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
output_type=_OutputType.CATEGORY_MASK,
running_mode=_RUNNING_MODE.VIDEO)
output_category_mask=True,
output_confidence_masks=False,
running_mode=_RUNNING_MODE.VIDEO,
)
with _ImageSegmenter.create_from_options(options) as segmenter:
for timestamp in range(0, 300, 30):
category_masks = segmenter.segment_for_video(self.test_image, timestamp)
self.assertLen(category_masks, 1)
segmentation_result = segmenter.segment_for_video(
self.test_image, timestamp
)
category_mask = segmentation_result.category_mask
self.assertTrue(
_similar_to_uint8_mask(category_masks[0], self.test_seg_image),
f'Number of pixels in the candidate mask differing from that of the '
f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
_similar_to_uint8_mask(category_mask, self.test_seg_image),
(
'Number of pixels in the candidate mask differing from that of'
f' the ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
),
)
def test_segment_for_video_in_confidence_mask_mode(self):
# Load the cat image.
test_image = _Image.create_from_file(
test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
)
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.VIDEO,
output_category_mask=False,
output_confidence_masks=True,
)
with _ImageSegmenter.create_from_options(options) as segmenter:
for timestamp in range(0, 300, 30):
segmentation_result = segmenter.segment_for_video(test_image, timestamp)
confidence_masks = segmentation_result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
confidence_masks,
21,
'Number of confidence masks must match with number of categories.',
)
# Loads ground truth segmentation file.
expected_mask = self._load_segmentation_mask(_CAT_MASK)
self.assertTrue(
_similar_to_float_mask(
confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
)
)
def test_calling_segment_in_live_stream_mode(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.LIVE_STREAM,
result_callback=mock.MagicMock())
result_callback=mock.MagicMock(),
)
with _ImageSegmenter.create_from_options(options) as segmenter:
with self.assertRaisesRegex(ValueError,
r'not initialized with the image mode'):
with self.assertRaisesRegex(
ValueError, r'not initialized with the image mode'
):
segmenter.segment(self.test_image)
def test_calling_segment_for_video_in_live_stream_mode(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.LIVE_STREAM,
result_callback=mock.MagicMock())
result_callback=mock.MagicMock(),
)
with _ImageSegmenter.create_from_options(options) as segmenter:
with self.assertRaisesRegex(ValueError,
r'not initialized with the video mode'):
with self.assertRaisesRegex(
ValueError, r'not initialized with the video mode'
):
segmenter.segment_for_video(self.test_image, 0)
def test_segment_async_calls_with_illegal_timestamp(self):
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.LIVE_STREAM,
result_callback=mock.MagicMock())
result_callback=mock.MagicMock(),
)
with _ImageSegmenter.create_from_options(options) as segmenter:
segmenter.segment_async(self.test_image, 100)
with self.assertRaisesRegex(
ValueError, r'Input timestamp must be monotonically increasing'):
ValueError, r'Input timestamp must be monotonically increasing'
):
segmenter.segment_async(self.test_image, 0)
def test_segment_async_calls(self):
def test_segment_async_calls_in_category_mask_mode(self):
observed_timestamp_ms = -1
def check_result(result: List[image_module.Image], output_image: _Image,
timestamp_ms: int):
def check_result(
result: ImageSegmenterResult, output_image: _Image, timestamp_ms: int
):
# Get the output category mask.
category_mask = result[0]
category_mask = result.category_mask
self.assertEqual(output_image.width, self.test_image.width)
self.assertEqual(output_image.height, self.test_image.height)
self.assertEqual(output_image.width, self.test_seg_image.width)
self.assertEqual(output_image.height, self.test_seg_image.height)
self.assertTrue(
_similar_to_uint8_mask(category_mask, self.test_seg_image),
f'Number of pixels in the candidate mask differing from that of the '
f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
(
'Number of pixels in the candidate mask differing from that of'
f' the ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.'
),
)
self.assertLess(observed_timestamp_ms, timestamp_ms)
self.observed_timestamp_ms = timestamp_ms
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
output_type=_OutputType.CATEGORY_MASK,
output_category_mask=True,
output_confidence_masks=False,
running_mode=_RUNNING_MODE.LIVE_STREAM,
result_callback=check_result)
result_callback=check_result,
)
with _ImageSegmenter.create_from_options(options) as segmenter:
for timestamp in range(0, 300, 30):
segmenter.segment_async(self.test_image, timestamp)
def test_segment_async_calls_in_confidence_mask_mode(self):
# Load the cat image.
test_image = _Image.create_from_file(
test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _CAT_IMAGE))
)
# Loads ground truth segmentation file.
expected_mask = self._load_segmentation_mask(_CAT_MASK)
observed_timestamp_ms = -1
def check_result(
result: ImageSegmenterResult, output_image: _Image, timestamp_ms: int
):
# Get the output category mask.
confidence_masks = result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
confidence_masks,
21,
'Number of confidence masks must match with number of categories.',
)
self.assertEqual(output_image.width, test_image.width)
self.assertEqual(output_image.height, test_image.height)
self.assertTrue(
_similar_to_float_mask(
confidence_masks[8], expected_mask, _MASK_SIMILARITY_THRESHOLD
)
)
self.assertLess(observed_timestamp_ms, timestamp_ms)
self.observed_timestamp_ms = timestamp_ms
options = _ImageSegmenterOptions(
base_options=_BaseOptions(model_asset_path=self.model_path),
running_mode=_RUNNING_MODE.LIVE_STREAM,
output_category_mask=False,
output_confidence_masks=True,
result_callback=check_result,
)
with _ImageSegmenter.create_from_options(options) as segmenter:
for timestamp in range(0, 300, 30):
segmenter.segment_async(test_image, timestamp)
if __name__ == '__main__':
absltest.main()

31
mediapipe/tasks/python/test/vision/interactive_segmenter_test.py
View File

@ -30,12 +30,12 @@ from mediapipe.tasks.python.test import test_utils
from mediapipe.tasks.python.vision import interactive_segmenter
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
InteractiveSegmenterResult = interactive_segmenter.InteractiveSegmenterResult
_BaseOptions = base_options_module.BaseOptions
_Image = image_module.Image
_ImageFormat = image_frame.ImageFormat
_NormalizedKeypoint = keypoint_module.NormalizedKeypoint
_Rect = rect.Rect
_OutputType = interactive_segmenter.InteractiveSegmenterOptions.OutputType
_InteractiveSegmenter = interactive_segmenter.InteractiveSegmenter
_InteractiveSegmenterOptions = interactive_segmenter.InteractiveSegmenterOptions
_RegionOfInterest = interactive_segmenter.RegionOfInterest
@ -200,15 +200,16 @@ class InteractiveSegmenterTest(parameterized.TestCase):
raise ValueError('model_file_type is invalid.')
options = _InteractiveSegmenterOptions(
base_options=base_options, output_type=_OutputType.CATEGORY_MASK
base_options=base_options,
output_category_mask=True,
output_confidence_masks=False,
)
segmenter = _InteractiveSegmenter.create_from_options(options)
# Performs image segmentation on the input.
roi = _RegionOfInterest(format=roi_format, keypoint=keypoint)
category_masks = segmenter.segment(self.test_image, roi)
self.assertLen(category_masks, 1)
category_mask = category_masks[0]
segmentation_result = segmenter.segment(self.test_image, roi)
category_mask = segmentation_result.category_mask
result_pixels = category_mask.numpy_view().flatten()
# Check if data type of `category_mask` is correct.
@ -219,7 +220,7 @@ class InteractiveSegmenterTest(parameterized.TestCase):
self.assertTrue(
_similar_to_uint8_mask(
category_masks[0], test_seg_image, similarity_threshold
category_mask, test_seg_image, similarity_threshold
),
(
'Number of pixels in the candidate mask differing from that of the'
@ -254,12 +255,15 @@ class InteractiveSegmenterTest(parameterized.TestCase):
# Run segmentation on the model in CONFIDENCE_MASK mode.
options = _InteractiveSegmenterOptions(
base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
base_options=base_options,
output_category_mask=False,
output_confidence_masks=True,
)
with _InteractiveSegmenter.create_from_options(options) as segmenter:
# Perform segmentation
confidence_masks = segmenter.segment(self.test_image, roi)
segmentation_result = segmenter.segment(self.test_image, roi)
confidence_masks = segmentation_result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
@ -287,15 +291,18 @@ class InteractiveSegmenterTest(parameterized.TestCase):
# Run segmentation on the model in CONFIDENCE_MASK mode.
options = _InteractiveSegmenterOptions(
base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
base_options=base_options,
output_category_mask=False,
output_confidence_masks=True,
)
with _InteractiveSegmenter.create_from_options(options) as segmenter:
# Perform segmentation
image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
confidence_masks = segmenter.segment(
segmentation_result = segmenter.segment(
self.test_image, roi, image_processing_options
)
confidence_masks = segmentation_result.confidence_masks
# Check if confidence mask shape is correct.
self.assertLen(
@ -314,7 +321,9 @@ class InteractiveSegmenterTest(parameterized.TestCase):
# Run segmentation on the model in CONFIDENCE_MASK mode.
options = _InteractiveSegmenterOptions(
base_options=base_options, output_type=_OutputType.CONFIDENCE_MASK
base_options=base_options,
output_category_mask=False,
output_confidence_masks=True,
)
with self.assertRaisesRegex(

113
mediapipe/tasks/python/vision/image_segmenter.py
View File

@ -14,7 +14,6 @@
"""MediaPipe image segmenter task."""
import dataclasses
import enum
from typing import Callable, List, Mapping, Optional
from mediapipe.python import packet_creator
@ -32,7 +31,6 @@ from mediapipe.tasks.python.vision.core import base_vision_task_api
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
from mediapipe.tasks.python.vision.core import vision_task_running_mode
ImageSegmenterResult = List[image_module.Image]
_NormalizedRect = rect.NormalizedRect
_BaseOptions = base_options_module.BaseOptions
_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
@ -46,8 +44,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
_TaskInfo = task_info_module.TaskInfo
_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
_CATEGORY_MASK_STREAM_NAME = 'category_mask'
_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
_IMAGE_IN_STREAM_NAME = 'image_in'
_IMAGE_OUT_STREAM_NAME = 'image_out'
_IMAGE_TAG = 'IMAGE'
@ -58,6 +58,21 @@ _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_segmenter.ImageSegmenterGraph'
_MICRO_SECONDS_PER_MILLISECOND = 1000
@dataclasses.dataclass
class ImageSegmenterResult:
"""Output result of ImageSegmenter.
confidence_masks: multiple masks of float image where, for each mask, each
pixel represents the prediction confidence, usually in the [0, 1] range.
category_mask: a category mask of uint8 image where each pixel represents the
class which the pixel in the original image was predicted to belong to.
"""
confidence_masks: Optional[List[image_module.Image]] = None
category_mask: Optional[image_module.Image] = None
@dataclasses.dataclass
class ImageSegmenterOptions:
"""Options for the image segmenter task.
@ -69,28 +84,17 @@ class ImageSegmenterOptions:
objects on single image inputs. 2) The video mode for segmenting objects
on the decoded frames of a video. 3) The live stream mode for segmenting
objects on a live stream of input data, such as from camera.
output_type: The output mask type allows specifying the type of
post-processing to perform on the raw model results.
activation: Activation function to apply to input tensor.
output_confidence_masks: Whether to output confidence masks.
output_category_mask: Whether to output category mask.
result_callback: The user-defined result callback for processing live stream
data. The result callback should only be specified when the running mode
is set to the live stream mode.
"""
class OutputType(enum.Enum):
UNSPECIFIED = 0
CATEGORY_MASK = 1
CONFIDENCE_MASK = 2
class Activation(enum.Enum):
NONE = 0
SIGMOID = 1
SOFTMAX = 2
base_options: _BaseOptions
running_mode: _RunningMode = _RunningMode.IMAGE
output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
activation: Optional[Activation] = Activation.NONE
output_confidence_masks: bool = True
output_category_mask: bool = False
result_callback: Optional[
Callable[[ImageSegmenterResult, image_module.Image, int], None]
] = None
@ -102,9 +106,7 @@ class ImageSegmenterOptions:
base_options_proto.use_stream_mode = (
False if self.running_mode == _RunningMode.IMAGE else True
)
segmenter_options_proto = _SegmenterOptionsProto(
output_type=self.output_type.value, activation=self.activation.value
)
segmenter_options_proto = _SegmenterOptionsProto()
return _ImageSegmenterGraphOptionsProto(
base_options=base_options_proto,
segmenter_options=segmenter_options_proto,
@ -216,27 +218,48 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
def packets_callback(output_packets: Mapping[str, packet.Packet]):
if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
return
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
)
segmentation_result = ImageSegmenterResult()
if options.output_confidence_masks:
segmentation_result.confidence_masks = packet_getter.get_image_list(
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
)
if options.output_category_mask:
segmentation_result.category_mask = packet_getter.get_image(
output_packets[_CATEGORY_MASK_STREAM_NAME]
)
image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
options.result_callback(
segmentation_result,
image,
timestamp.value // _MICRO_SECONDS_PER_MILLISECOND,
)
output_streams = [
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
]
if options.output_confidence_masks:
output_streams.append(
':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
)
if options.output_category_mask:
output_streams.append(
':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
)
task_info = _TaskInfo(
task_graph=_TASK_GRAPH_NAME,
input_streams=[
':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
],
output_streams=[
':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
],
output_streams=output_streams,
task_options=options,
)
return cls(
@ -292,9 +315,18 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
normalized_rect.to_pb2()
),
})
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
)
segmentation_result = ImageSegmenterResult()
if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
segmentation_result.confidence_masks = packet_getter.get_image_list(
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
)
if _CATEGORY_MASK_STREAM_NAME in output_packets:
segmentation_result.category_mask = packet_getter.get_image(
output_packets[_CATEGORY_MASK_STREAM_NAME]
)
return segmentation_result
def segment_for_video(
@ -337,9 +369,18 @@ class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
normalized_rect.to_pb2()
).at(timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
})
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
)
segmentation_result = ImageSegmenterResult()
if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
segmentation_result.confidence_masks = packet_getter.get_image_list(
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
)
if _CATEGORY_MASK_STREAM_NAME in output_packets:
segmentation_result.category_mask = packet_getter.get_image(
output_packets[_CATEGORY_MASK_STREAM_NAME]
)
return segmentation_result
def segment_async(

73
mediapipe/tasks/python/vision/interactive_segmenter.py
View File

@ -41,8 +41,10 @@ _RunningMode = vision_task_running_mode.VisionTaskRunningMode
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
_TaskInfo = task_info_module.TaskInfo
_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
_CONFIDENCE_MASKS_STREAM_NAME = 'confidence_masks'
_CONFIDENCE_MASKS_TAG = 'CONFIDENCE_MASKS'
_CATEGORY_MASK_STREAM_NAME = 'category_mask'
_CATEGORY_MASK_TAG = 'CATEGORY_MASK'
_IMAGE_IN_STREAM_NAME = 'image_in'
_IMAGE_OUT_STREAM_NAME = 'image_out'
_ROI_STREAM_NAME = 'roi_in'
@ -55,32 +57,41 @@ _TASK_GRAPH_NAME = (
)
@dataclasses.dataclass
class InteractiveSegmenterResult:
"""Output result of InteractiveSegmenter.
confidence_masks: multiple masks of float image where, for each mask, each
pixel represents the prediction confidence, usually in the [0, 1] range.
category_mask: a category mask of uint8 image where each pixel represents the
class which the pixel in the original image was predicted to belong to.
"""
confidence_masks: Optional[List[image_module.Image]] = None
category_mask: Optional[image_module.Image] = None
@dataclasses.dataclass
class InteractiveSegmenterOptions:
"""Options for the interactive segmenter task.
Attributes:
base_options: Base options for the interactive segmenter task.
output_type: The output mask type allows specifying the type of
post-processing to perform on the raw model results.
output_confidence_masks: Whether to output confidence masks.
output_category_mask: Whether to output category mask.
"""
class OutputType(enum.Enum):
UNSPECIFIED = 0
CATEGORY_MASK = 1
CONFIDENCE_MASK = 2
base_options: _BaseOptions
output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
output_confidence_masks: bool = True
output_category_mask: bool = False
@doc_controls.do_not_generate_docs
def to_pb2(self) -> _ImageSegmenterGraphOptionsProto:
"""Generates an InteractiveSegmenterOptions protobuf object."""
base_options_proto = self.base_options.to_pb2()
base_options_proto.use_stream_mode = False
segmenter_options_proto = _SegmenterOptionsProto(
output_type=self.output_type.value
)
segmenter_options_proto = _SegmenterOptionsProto()
return _ImageSegmenterGraphOptionsProto(
base_options=base_options_proto,
segmenter_options=segmenter_options_proto,
@ -192,6 +203,20 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
RuntimeError: If other types of error occurred.
"""
output_streams = [
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
]
if options.output_confidence_masks:
output_streams.append(
':'.join([_CONFIDENCE_MASKS_TAG, _CONFIDENCE_MASKS_STREAM_NAME])
)
if options.output_category_mask:
output_streams.append(
':'.join([_CATEGORY_MASK_TAG, _CATEGORY_MASK_STREAM_NAME])
)
task_info = _TaskInfo(
task_graph=_TASK_GRAPH_NAME,
input_streams=[
@ -199,10 +224,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
':'.join([_ROI_TAG, _ROI_STREAM_NAME]),
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
],
output_streams=[
':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]),
],
output_streams=output_streams,
task_options=options,
)
return cls(
@ -216,7 +238,7 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
image: image_module.Image,
roi: RegionOfInterest,
image_processing_options: Optional[_ImageProcessingOptions] = None,
) -> List[image_module.Image]:
) -> InteractiveSegmenterResult:
"""Performs the actual segmentation task on the provided MediaPipe Image.
The image can be of any size with format RGB.
@ -248,7 +270,16 @@ class InteractiveSegmenter(base_vision_task_api.BaseVisionTaskApi):
normalized_rect.to_pb2()
),
})
segmentation_result = packet_getter.get_image_list(
output_packets[_SEGMENTATION_OUT_STREAM_NAME]
)
segmentation_result = InteractiveSegmenterResult()
if _CONFIDENCE_MASKS_STREAM_NAME in output_packets:
segmentation_result.confidence_masks = packet_getter.get_image_list(
output_packets[_CONFIDENCE_MASKS_STREAM_NAME]
)
if _CATEGORY_MASK_STREAM_NAME in output_packets:
segmentation_result.category_mask = packet_getter.get_image(
output_packets[_CATEGORY_MASK_STREAM_NAME]
)
return segmentation_result

48
third_party/wasm_files.bzl vendored
View File

@ -12,72 +12,72 @@ def wasm_files():
http_file(
name = "com_google_mediapipe_wasm_audio_wasm_internal_js",
sha256 = "0eca68e2291a548b734bcab5db4c9e6b997e852ea7e19228003b9e2a78c7c646",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.js?generation=1681328323089931"],
sha256 = "b810de53d7ccf991b9c70fcdf7e88b5c3f2942ae766436f22be48159b6a7e687",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.js?generation=1681849488227617"],
)
http_file(
name = "com_google_mediapipe_wasm_audio_wasm_internal_wasm",
sha256 = "69bc95af5b783b510ec1842d6fb9594254907d8e1334799c5753164878a7dcac",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.wasm?generation=1681328325829340"],
sha256 = "26d91147e5c6c8a92e0a4ebf59599068a3cff6108847b793ef33ac23e98eddb9",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.wasm?generation=1681849491546937"],
)
http_file(
name = "com_google_mediapipe_wasm_audio_wasm_nosimd_internal_js",
sha256 = "88a0176cc80d6a1eb175a5105df705cf8b8684cf13f6db0a264af0b67b65a22a",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.js?generation=1681328328330829"],
sha256 = "b38e37b3024692558eaaba159921fedd3297d1a09bba1c16a06fed327845b0bd",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.js?generation=1681849494099698"],
)
http_file(
name = "com_google_mediapipe_wasm_audio_wasm_nosimd_internal_wasm",
sha256 = "1cc0c3db7d252801be4b090d8bbba61f308cc3dd5efe197319581d3af29495c7",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.wasm?generation=1681328331085637"],
sha256 = "6a8e73d2e926565046e16adf1748f0f8ec5135fafe7eb8b9c83892e64c1a449a",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_nosimd_internal.wasm?generation=1681849496451970"],
)
http_file(
name = "com_google_mediapipe_wasm_text_wasm_internal_js",
sha256 = "d9cd100b6d330d36f7749fe5fc64a2cdd0abb947a0376e6140784cfb0361a4e2",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.js?generation=1681328333442454"],
sha256 = "785cba67b623b1dc66dc3621e97fd6b30edccbb408184a3094d0aa68ddd5becb",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.js?generation=1681849498746265"],
)
http_file(
name = "com_google_mediapipe_wasm_text_wasm_internal_wasm",
sha256 = "30a2fcca630bdad6e99173ea7d0d8c5d7086aedf393d0159fa05bf9d08d4ff65",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.wasm?generation=1681328335803336"],
sha256 = "a858b8a2e8b40e9c936b66566c5aefd396536c4e936459ab9ae7e239621adc14",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.wasm?generation=1681849501370461"],
)
http_file(
name = "com_google_mediapipe_wasm_text_wasm_nosimd_internal_js",
sha256 = "70ca2bd15c56e0ce7bb10ff2188b4a1f9eafbb657eb9424e4cab8d7b29179871",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.js?generation=1681328338162884"],
sha256 = "5292f1442d5e5c037e7cffb78a8c2d71255348ca2c3bd759b314bdbedd5590c2",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.js?generation=1681849503379116"],
)
http_file(
name = "com_google_mediapipe_wasm_text_wasm_nosimd_internal_wasm",
sha256 = "8221b385905f36a769d7731a0adbe18b681bcb873561890429ca84278c67c3fd",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.wasm?generation=1681328340808115"],
sha256 = "e44b48ab29ee1d8befec804e9a63445c56266b679d19fb476d556ca621f0e493",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_nosimd_internal.wasm?generation=1681849505997020"],
)
http_file(
name = "com_google_mediapipe_wasm_vision_wasm_internal_js",
sha256 = "07692acd8202adafebd35dbcd7e2b8e88a76d4a0e6b9229cb3cad59503eeddc7",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.js?generation=1681328343147709"],
sha256 = "205855eba70464a92b9d00e90acac15c51a9f76192f900e697304ac6dea8f714",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.js?generation=1681849508414277"],
)
http_file(
name = "com_google_mediapipe_wasm_vision_wasm_internal_wasm",
sha256 = "03bf553fa6a768b0d70103a5e7d835b6b37371ff44e201c3392f22e0879737c3",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.wasm?generation=1681328345605574"],
sha256 = "c0cbd0df3adb2a9cd1331d14f522d2bae9f8adc9f1b35f92cbbc4b782b190cef",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.wasm?generation=1681849510936608"],
)
http_file(
name = "com_google_mediapipe_wasm_vision_wasm_nosimd_internal_js",
sha256 = "36697be14f921985eac15d1447ec8a260817b05ade1c9bb3ca7e906e0f047ec0",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.js?generation=1681328348025082"],
sha256 = "0969812de4d3573198fa2eba4f5b0a7e97e98f97bd4215d876543f4925e57b84",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.js?generation=1681849513292639"],
)
http_file(
name = "com_google_mediapipe_wasm_vision_wasm_nosimd_internal_wasm",
sha256 = "103fb145438d61cfecb2e8db3f06b43a5d77a7e3fcea940437fe272227cf2592",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.wasm?generation=1681328350709881"],
sha256 = "f2ab62c3f8dabab0a573dadf5c105ff81a03c29c70f091f8cf273ae030c0a86f",
urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_nosimd_internal.wasm?generation=1681849515999000"],
)