From 0402ee383f37c6f8d7bc0cfeb8be48485e393f9e Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 01:05:36 -0800 Subject: [PATCH 01/18] Added hand landmarker Python API and tests --- mediapipe/tasks/python/test/vision/BUILD | 23 + .../test/vision/hand_landmarker_test.py | 436 ++++++++++++++++++ mediapipe/tasks/python/vision/BUILD | 23 + .../tasks/python/vision/hand_landmarker.py | 381 +++++++++++++++ 4 files changed, 863 insertions(+) create mode 100644 mediapipe/tasks/python/test/vision/hand_landmarker_test.py create mode 100644 mediapipe/tasks/python/vision/hand_landmarker.py diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD index c29648160..f99eb25a2 100644 --- a/mediapipe/tasks/python/test/vision/BUILD +++ b/mediapipe/tasks/python/test/vision/BUILD @@ -73,3 +73,26 @@ py_test( "//mediapipe/tasks/python/vision/core:vision_task_running_mode", ], ) + +py_test( + name = "hand_landmarker_test", + srcs = ["hand_landmarker_test.py"], + data = [ + "//mediapipe/tasks/testdata/vision:test_images", + "//mediapipe/tasks/testdata/vision:test_models", + "//mediapipe/tasks/testdata/vision:test_protos", + ], + deps = [ + "//mediapipe/python:_framework_bindings", + "//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_py_pb2", + "//mediapipe/tasks/python/components/containers:rect", + "//mediapipe/tasks/python/components/containers:landmark", + "//mediapipe/tasks/python/components/containers:landmark_detection_result", + "//mediapipe/tasks/python/core:base_options", + "//mediapipe/tasks/python/test:test_utils", + "//mediapipe/tasks/python/vision:hand_landmarker", + "//mediapipe/tasks/python/vision/core:vision_task_running_mode", + "//mediapipe/tasks/python/vision/core:image_processing_options", + "@com_google_protobuf//:protobuf_python" + ], +) diff --git a/mediapipe/tasks/python/test/vision/hand_landmarker_test.py b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py new file mode 100644 index 000000000..9d311e210 --- /dev/null +++ b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py @@ -0,0 +1,436 @@ +# Copyright 2022 The MediaPipe Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for hand landmarker.""" + +import enum +from unittest import mock + +import numpy as np +from google.protobuf import text_format +from absl.testing import absltest +from absl.testing import parameterized + +from mediapipe.python._framework_bindings import image as image_module +from mediapipe.tasks.cc.components.containers.proto import landmarks_detection_result_pb2 +from mediapipe.tasks.python.components.containers import rect as rect_module +from mediapipe.tasks.python.components.containers import landmark as landmark_module +from mediapipe.tasks.python.components.containers import landmark_detection_result as landmark_detection_result_module +from mediapipe.tasks.python.core import base_options as base_options_module +from mediapipe.tasks.python.test import test_utils +from mediapipe.tasks.python.vision import hand_landmarker +from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module +from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module + +_LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult +_BaseOptions = base_options_module.BaseOptions +_Rect = rect_module.Rect +_Landmark = landmark_module.Landmark +_NormalizedLandmark = landmark_module.NormalizedLandmark +_LandmarksDetectionResult = landmark_detection_result_module.LandmarksDetectionResult +_Image = image_module.Image +_HandLandmarker = hand_landmarker.HandLandmarker +_HandLandmarkerOptions = hand_landmarker.HandLandmarkerOptions +_HandLandmarksDetectionResult = hand_landmarker.HandLandmarksDetectionResult +_RUNNING_MODE = running_mode_module.VisionTaskRunningMode +_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions + +_HAND_LANDMARKER_BUNDLE_ASSET_FILE = 'hand_landmarker.task' +_NO_HANDS_IMAGE = 'cats_and_dogs.jpg' +_TWO_HANDS_IMAGE = 'right_hands.jpg' +_THUMB_UP_IMAGE = 'thumb_up.jpg' +_THUMB_UP_LANDMARKS = 'thumb_up_landmarks.pbtxt' +_POINTING_UP_IMAGE = 'pointing_up.jpg' +_POINTING_UP_LANDMARKS = 'pointing_up_landmarks.pbtxt' +_POINTING_UP_ROTATED_IMAGE = 'pointing_up_rotated.jpg' +_POINTING_UP_ROTATED_LANDMARKS = 'pointing_up_rotated_landmarks.pbtxt' +_LANDMARKS_ERROR_TOLERANCE = 0.03 +_HANDEDNESS_MARGIN = 0.05 + + +def _get_expected_hand_landmarks_detection_result( + file_path: str) -> _HandLandmarksDetectionResult: + landmarks_detection_result_file_path = test_utils.get_test_data_path( + file_path) + with open(landmarks_detection_result_file_path, "rb") as f: + landmarks_detection_result_proto = _LandmarksDetectionResultProto() + # Use this if a .pb file is available. + # landmarks_detection_result_proto.ParseFromString(f.read()) + text_format.Parse(f.read(), landmarks_detection_result_proto) + landmarks_detection_result = _LandmarksDetectionResult.create_from_pb2( + landmarks_detection_result_proto) + return _HandLandmarksDetectionResult( + handedness=[landmarks_detection_result.categories], + hand_landmarks=[landmarks_detection_result.landmarks], + hand_world_landmarks=[landmarks_detection_result.world_landmarks]) + + +class ModelFileType(enum.Enum): + FILE_CONTENT = 1 + FILE_NAME = 2 + + +class GestureRecognizerTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.test_image = _Image.create_from_file( + test_utils.get_test_data_path(_THUMB_UP_IMAGE)) + self.model_path = test_utils.get_test_data_path( + _HAND_LANDMARKER_BUNDLE_ASSET_FILE) + + def _assert_actual_result_approximately_matches_expected_result( + self, + actual_result: _HandLandmarksDetectionResult, + expected_result: _HandLandmarksDetectionResult + ): + # Expects to have the same number of hands detected. + self.assertLen(actual_result.hand_landmarks, + len(expected_result.hand_landmarks)) + self.assertLen(actual_result.hand_world_landmarks, + len(expected_result.hand_world_landmarks)) + self.assertLen(actual_result.handedness, len(expected_result.handedness)) + # Actual landmarks match expected landmarks. + self.assertLen(actual_result.hand_landmarks[0], + len(expected_result.hand_landmarks[0])) + actual_landmarks = actual_result.hand_landmarks[0] + expected_landmarks = expected_result.hand_landmarks[0] + for i in range(len(actual_landmarks)): + self.assertAlmostEqual(actual_landmarks[i].x, expected_landmarks[i].x, + delta=_LANDMARKS_ERROR_TOLERANCE) + self.assertAlmostEqual(actual_landmarks[i].y, expected_landmarks[i].y, + delta=_LANDMARKS_ERROR_TOLERANCE) + # Actual handedness matches expected handedness. + actual_top_handedness = actual_result.handedness[0][0] + expected_top_handedness = expected_result.handedness[0][0] + self.assertEqual(actual_top_handedness.index, expected_top_handedness.index) + self.assertEqual(actual_top_handedness.category_name, + expected_top_handedness.category_name) + self.assertAlmostEqual(actual_top_handedness.score, + expected_top_handedness.score, + delta=_HANDEDNESS_MARGIN) + + def test_create_from_file_succeeds_with_valid_model_path(self): + # Creates with default option and valid model file successfully. + with _HandLandmarker.create_from_model_path(self.model_path) as landmarker: + self.assertIsInstance(landmarker, _HandLandmarker) + + def test_create_from_options_succeeds_with_valid_model_path(self): + # Creates with options containing model file successfully. + base_options = _BaseOptions(model_asset_path=self.model_path) + options = _HandLandmarkerOptions(base_options=base_options) + with _HandLandmarker.create_from_options(options) as landmarker: + self.assertIsInstance(landmarker, _HandLandmarker) + + def test_create_from_options_fails_with_invalid_model_path(self): + # Invalid empty model path. + with self.assertRaisesRegex( + ValueError, + r"ExternalFile must specify at least one of 'file_content', " + r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."): + base_options = _BaseOptions(model_asset_path='') + options = _HandLandmarkerOptions(base_options=base_options) + _HandLandmarker.create_from_options(options) + + def test_create_from_options_succeeds_with_valid_model_content(self): + # Creates with options containing model content successfully. + with open(self.model_path, 'rb') as f: + base_options = _BaseOptions(model_asset_buffer=f.read()) + options = _HandLandmarkerOptions(base_options=base_options) + landmarker = _HandLandmarker.create_from_options(options) + self.assertIsInstance(landmarker, _HandLandmarker) + + @parameterized.parameters( + (ModelFileType.FILE_NAME, _get_expected_hand_landmarks_detection_result( + _THUMB_UP_LANDMARKS + )), + (ModelFileType.FILE_CONTENT, _get_expected_hand_landmarks_detection_result( + _THUMB_UP_LANDMARKS + ))) + def test_detect(self, model_file_type, expected_detection_result): + # Creates hand landmarker. + 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_content = f.read() + base_options = _BaseOptions(model_asset_buffer=model_content) + else: + # Should never happen + raise ValueError('model_file_type is invalid.') + + options = _HandLandmarkerOptions(base_options=base_options) + landmarker = _HandLandmarker.create_from_options(options) + + # Performs hand landmarks detection on the input. + detection_result = landmarker.detect(self.test_image) + # Comparing results. + self._assert_actual_result_approximately_matches_expected_result( + detection_result, expected_detection_result) + # Closes the hand landmarker explicitly when the hand landmarker is not used + # in a context. + landmarker.close() + + @parameterized.parameters( + (ModelFileType.FILE_NAME, _get_expected_hand_landmarks_detection_result( + _THUMB_UP_LANDMARKS + )), + (ModelFileType.FILE_CONTENT, _get_expected_hand_landmarks_detection_result( + _THUMB_UP_LANDMARKS + ))) + def test_detect_in_context(self, model_file_type, expected_detection_result): + # Creates hand landmarker. + 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_content = f.read() + base_options = _BaseOptions(model_asset_buffer=model_content) + else: + # Should never happen + raise ValueError('model_file_type is invalid.') + + options = _HandLandmarkerOptions(base_options=base_options) + with _HandLandmarker.create_from_options(options) as landmarker: + # Performs hand landmarks detection on the input. + detection_result = landmarker.detect(self.test_image) + # Comparing results. + self._assert_actual_result_approximately_matches_expected_result( + detection_result, expected_detection_result) + + def test_detect_succeeds_with_num_hands(self): + # Creates hand landmarker. + base_options = _BaseOptions(model_asset_path=self.model_path) + options = _HandLandmarkerOptions(base_options=base_options, num_hands=2) + with _HandLandmarker.create_from_options(options) as landmarker: + # Load the two hands image. + test_image = _Image.create_from_file( + test_utils.get_test_data_path(_TWO_HANDS_IMAGE)) + # Performs hand landmarks detection on the input. + detection_result = landmarker.detect(test_image) + # Comparing results. + self.assertLen(detection_result.handedness, 2) + + def test_detect_succeeds_with_rotation(self): + # Creates hand landmarker. + base_options = _BaseOptions(model_asset_path=self.model_path) + options = _HandLandmarkerOptions(base_options=base_options) + with _HandLandmarker.create_from_options(options) as landmarker: + # Load the pointing up rotated image. + test_image = _Image.create_from_file( + test_utils.get_test_data_path(_POINTING_UP_ROTATED_IMAGE)) + # Set rotation parameters using ImageProcessingOptions. + image_processing_options = _ImageProcessingOptions(rotation_degrees=-90) + # Performs hand landmarks detection on the input. + detection_result = landmarker.detect(test_image, + image_processing_options) + expected_detection_result = _get_expected_hand_landmarks_detection_result( + _POINTING_UP_ROTATED_LANDMARKS) + # Comparing results. + self._assert_actual_result_approximately_matches_expected_result( + detection_result, expected_detection_result) + + def test_detect_fails_with_region_of_interest(self): + # Creates hand landmarker. + base_options = _BaseOptions(model_asset_path=self.model_path) + options = _HandLandmarkerOptions(base_options=base_options) + with self.assertRaisesRegex( + ValueError, "This task doesn't support region-of-interest."): + with _HandLandmarker.create_from_options(options) as landmarker: + # Set the `region_of_interest` parameter using `ImageProcessingOptions`. + image_processing_options = _ImageProcessingOptions( + region_of_interest=_Rect(0, 0, 1, 1)) + # Attempt to perform hand landmarks detection on the cropped input. + landmarker.detect(self.test_image, image_processing_options) + + def test_empty_detection_outputs(self): + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path)) + with _HandLandmarker.create_from_options(options) as landmarker: + # Load the image with no hands. + no_hands_test_image = _Image.create_from_file( + test_utils.get_test_data_path(_NO_HANDS_IMAGE)) + # Performs hand landmarks detection on the input. + detection_result = landmarker.detect(no_hands_test_image) + self.assertEmpty(detection_result.hand_landmarks) + self.assertEmpty(detection_result.hand_world_landmarks) + self.assertEmpty(detection_result.handedness) + + def test_missing_result_callback(self): + options = _HandLandmarkerOptions( + 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'): + with _HandLandmarker.create_from_options(options) as unused_landmarker: + pass + + @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO)) + def test_illegal_result_callback(self, running_mode): + options = _HandLandmarkerOptions( + 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'): + with _HandLandmarker.create_from_options(options) as unused_landmarker: + pass + + def test_calling_detect_for_video_in_image_mode(self): + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE) + with _HandLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex(ValueError, + r'not initialized with the video mode'): + landmarker.detect_for_video(self.test_image, 0) + + def test_calling_detect_async_in_image_mode(self): + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE) + with _HandLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex(ValueError, + r'not initialized with the live stream mode'): + landmarker.detect_async(self.test_image, 0) + + def test_calling_detect_in_video_mode(self): + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _HandLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex(ValueError, + r'not initialized with the image mode'): + landmarker.detect(self.test_image) + + def test_calling_detect_async_in_video_mode(self): + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _HandLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex(ValueError, + r'not initialized with the live stream mode'): + landmarker.detect_async(self.test_image, 0) + + def test_detect_for_video_with_out_of_order_timestamp(self): + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _HandLandmarker.create_from_options(options) as landmarker: + unused_result = landmarker.detect_for_video(self.test_image, 1) + with self.assertRaisesRegex( + ValueError, r'Input timestamp must be monotonically increasing'): + landmarker.detect_for_video(self.test_image, 0) + + @parameterized.parameters( + (_THUMB_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result( + _THUMB_UP_LANDMARKS)), + (_POINTING_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result( + _POINTING_UP_LANDMARKS)), + (_POINTING_UP_ROTATED_IMAGE, -90, + _get_expected_hand_landmarks_detection_result( + _POINTING_UP_ROTATED_LANDMARKS)), + (_NO_HANDS_IMAGE, 0, _HandLandmarksDetectionResult([], [], []))) + def test_detect_for_video(self, image_path, rotation, expected_result): + test_image = _Image.create_from_file( + test_utils.get_test_data_path(image_path)) + # Set rotation parameters using ImageProcessingOptions. + image_processing_options = _ImageProcessingOptions(rotation_degrees=rotation) + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO) + with _HandLandmarker.create_from_options(options) as landmarker: + for timestamp in range(0, 300, 30): + result = landmarker.detect_for_video(test_image, + timestamp, + image_processing_options) + if result.hand_landmarks and result.hand_world_landmarks and \ + result.handedness: + self._assert_actual_result_approximately_matches_expected_result( + result, expected_result) + else: + self.assertEqual(result, expected_result) + + def test_calling_detect_in_live_stream_mode(self): + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock()) + with _HandLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex(ValueError, + r'not initialized with the image mode'): + landmarker.detect(self.test_image) + + def test_calling_detect_for_video_in_live_stream_mode(self): + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock()) + with _HandLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex(ValueError, + r'not initialized with the video mode'): + landmarker.detect_for_video(self.test_image, 0) + + def test_detect_async_calls_with_illegal_timestamp(self): + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock()) + with _HandLandmarker.create_from_options(options) as landmarker: + landmarker.detect_async(self.test_image, 100) + with self.assertRaisesRegex( + ValueError, r'Input timestamp must be monotonically increasing'): + landmarker.detect_async(self.test_image, 0) + + @parameterized.parameters( + (_THUMB_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result( + _THUMB_UP_LANDMARKS)), + (_POINTING_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result( + _POINTING_UP_LANDMARKS)), + (_POINTING_UP_ROTATED_IMAGE, -90, + _get_expected_hand_landmarks_detection_result( + _POINTING_UP_ROTATED_LANDMARKS)), + (_NO_HANDS_IMAGE, 0, _HandLandmarksDetectionResult([], [], []))) + def test_detect_async_calls(self, image_path, rotation, expected_result): + test_image = _Image.create_from_file( + test_utils.get_test_data_path(image_path)) + # Set rotation parameters using ImageProcessingOptions. + image_processing_options = _ImageProcessingOptions(rotation_degrees=rotation) + observed_timestamp_ms = -1 + + def check_result(result: _HandLandmarksDetectionResult, + output_image: _Image, + timestamp_ms: int): + if result.hand_landmarks and result.hand_world_landmarks and \ + result.handedness: + self._assert_actual_result_approximately_matches_expected_result( + result, expected_result) + else: + self.assertEqual(result, expected_result) + self.assertTrue( + np.array_equal(output_image.numpy_view(), + test_image.numpy_view())) + self.assertLess(observed_timestamp_ms, timestamp_ms) + self.observed_timestamp_ms = timestamp_ms + + options = _HandLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=check_result) + with _HandLandmarker.create_from_options(options) as landmarker: + for timestamp in range(0, 300, 30): + landmarker.detect_async(test_image, timestamp, image_processing_options) + + +if __name__ == '__main__': + absltest.main() diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD index 527c6d883..79d01c6d0 100644 --- a/mediapipe/tasks/python/vision/BUILD +++ b/mediapipe/tasks/python/vision/BUILD @@ -102,3 +102,26 @@ py_library( "//mediapipe/tasks/python/vision/core:vision_task_running_mode", ], ) + +py_library( + name = "hand_landmarker", + srcs = [ + "hand_landmarker.py", + ], + deps = [ + "//mediapipe/framework/formats:classification_py_pb2", + "//mediapipe/framework/formats:landmark_py_pb2", + "//mediapipe/python:_framework_bindings", + "//mediapipe/python:packet_creator", + "//mediapipe/python:packet_getter", + "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarker_graph_options_py_pb2", + "//mediapipe/tasks/python/components/containers:category", + "//mediapipe/tasks/python/components/containers:landmark", + "//mediapipe/tasks/python/core:base_options", + "//mediapipe/tasks/python/core:optional_dependencies", + "//mediapipe/tasks/python/core:task_info", + "//mediapipe/tasks/python/vision/core:base_vision_task_api", + "//mediapipe/tasks/python/vision/core:image_processing_options", + "//mediapipe/tasks/python/vision/core:vision_task_running_mode", + ], +) diff --git a/mediapipe/tasks/python/vision/hand_landmarker.py b/mediapipe/tasks/python/vision/hand_landmarker.py new file mode 100644 index 000000000..e063bdafb --- /dev/null +++ b/mediapipe/tasks/python/vision/hand_landmarker.py @@ -0,0 +1,381 @@ +# Copyright 2022 The MediaPipe Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""MediaPipe hand landmarker task.""" + +import dataclasses +from typing import Callable, Mapping, Optional, List + +from mediapipe.framework.formats import classification_pb2 +from mediapipe.framework.formats import landmark_pb2 +from mediapipe.python import packet_creator +from mediapipe.python import packet_getter +from mediapipe.python._framework_bindings import image as image_module +from mediapipe.python._framework_bindings import packet as packet_module +from mediapipe.tasks.cc.vision.hand_landmarker.proto import hand_landmarker_graph_options_pb2 +from mediapipe.tasks.python.components.containers import category as category_module +from mediapipe.tasks.python.components.containers import landmark as landmark_module +from mediapipe.tasks.python.core import base_options as base_options_module +from mediapipe.tasks.python.core import task_info as task_info_module +from mediapipe.tasks.python.core.optional_dependencies import doc_controls +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 as running_mode_module + +_BaseOptions = base_options_module.BaseOptions +_HandLandmarkerGraphOptionsProto = hand_landmarker_graph_options_pb2.HandLandmarkerGraphOptions +_RunningMode = running_mode_module.VisionTaskRunningMode +_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions +_TaskInfo = task_info_module.TaskInfo + +_IMAGE_IN_STREAM_NAME = 'image_in' +_IMAGE_OUT_STREAM_NAME = 'image_out' +_IMAGE_TAG = 'IMAGE' +_NORM_RECT_STREAM_NAME = 'norm_rect_in' +_NORM_RECT_TAG = 'NORM_RECT' +_HANDEDNESS_STREAM_NAME = 'handedness' +_HANDEDNESS_TAG = 'HANDEDNESS' +_HAND_LANDMARKS_STREAM_NAME = 'landmarks' +_HAND_LANDMARKS_TAG = 'LANDMARKS' +_HAND_WORLD_LANDMARKS_STREAM_NAME = 'world_landmarks' +_HAND_WORLD_LANDMARKS_TAG = 'WORLD_LANDMARKS' +_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.hand_landmarker.HandLandmarkerGraph' +_MICRO_SECONDS_PER_MILLISECOND = 1000 + + +@dataclasses.dataclass +class HandLandmarksDetectionResult: + """The hand landmarks detection result from HandLandmarker, where each vector + element represents a single hand detected in the image. + + Attributes: + handedness: Classification of handedness. + hand_landmarks: Detected hand landmarks in normalized image coordinates. + hand_world_landmarks: Detected hand landmarks in world coordinates. + """ + + handedness: List[List[category_module.Category]] + hand_landmarks: List[List[landmark_module.NormalizedLandmark]] + hand_world_landmarks: List[List[landmark_module.Landmark]] + + +def _build_detection_result( + output_packets: Mapping[str,packet_module.Packet] +) -> HandLandmarksDetectionResult: + """Constructs a `HandLandmarksDetectionResult` from output packets.""" + handedness_proto_list = packet_getter.get_proto_list( + output_packets[_HANDEDNESS_STREAM_NAME]) + hand_landmarks_proto_list = packet_getter.get_proto_list( + output_packets[_HAND_LANDMARKS_STREAM_NAME]) + hand_world_landmarks_proto_list = packet_getter.get_proto_list( + output_packets[_HAND_WORLD_LANDMARKS_STREAM_NAME]) + + handedness_results = [] + for proto in handedness_proto_list: + handedness_categories = [] + handedness_classifications = classification_pb2.ClassificationList() + handedness_classifications.MergeFrom(proto) + for handedness in handedness_classifications.classification: + handedness_categories.append( + category_module.Category( + index=handedness.index, + score=handedness.score, + display_name=handedness.display_name, + category_name=handedness.label)) + handedness_results.append(handedness_categories) + + hand_landmarks_results = [] + for proto in hand_landmarks_proto_list: + hand_landmarks = landmark_pb2.NormalizedLandmarkList() + hand_landmarks.MergeFrom(proto) + hand_landmarks_results.append([ + landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark) + for hand_landmark in hand_landmarks.landmark + ]) + + hand_world_landmarks_results = [] + for proto in hand_world_landmarks_proto_list: + hand_world_landmarks = landmark_pb2.LandmarkList() + hand_world_landmarks.MergeFrom(proto) + hand_world_landmarks_results.append([ + landmark_module.Landmark.create_from_pb2(hand_world_landmark) + for hand_world_landmark in hand_world_landmarks.landmark + ]) + + return HandLandmarksDetectionResult(handedness_results, + hand_landmarks_results, + hand_world_landmarks_results) + + +@dataclasses.dataclass +class HandLandmarkerOptions: + """Options for the hand landmarker task. + + Attributes: + base_options: Base options for the hand landmarker task. + running_mode: The running mode of the task. Default to the image mode. + HandLandmarker has three running modes: 1) The image mode for detecting + hand landmarks on single image inputs. 2) The video mode for detecting + hand landmarks on the decoded frames of a video. 3) The live stream mode + for detecting hand landmarks on the live stream of input data, such as + from camera. In this mode, the "result_callback" below must be specified + to receive the detection results asynchronously. + num_hands: The maximum number of hands can be detected by the hand + landmarker. + min_hand_detection_confidence: The minimum confidence score for the hand + detection to be considered successful. + min_hand_presence_confidence: The minimum confidence score of hand presence + score in the hand landmark detection. + min_tracking_confidence: The minimum confidence score for the hand tracking + to be considered successful. + 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. + """ + base_options: _BaseOptions + running_mode: _RunningMode = _RunningMode.IMAGE + num_hands: Optional[int] = 1 + min_hand_detection_confidence: Optional[float] = 0.5 + min_hand_presence_confidence: Optional[float] = 0.5 + min_tracking_confidence: Optional[float] = 0.5 + result_callback: Optional[Callable[ + [HandLandmarksDetectionResult, image_module.Image, int], None]] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _HandLandmarkerGraphOptionsProto: + """Generates an HandLandmarkerGraphOptions protobuf object.""" + base_options_proto = self.base_options.to_pb2() + base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True + + # Initialize the hand landmarker options from base options. + hand_landmarker_options_proto = _HandLandmarkerGraphOptionsProto( + base_options=base_options_proto) + hand_landmarker_options_proto.min_tracking_confidence = self.min_tracking_confidence + hand_landmarker_options_proto.hand_detector_graph_options.num_hands = self.num_hands + hand_landmarker_options_proto.hand_detector_graph_options.min_detection_confidence = self.min_hand_detection_confidence + hand_landmarker_options_proto.hand_landmarks_detector_graph_options.min_detection_confidence = self.min_hand_presence_confidence + return hand_landmarker_options_proto + + +class HandLandmarker(base_vision_task_api.BaseVisionTaskApi): + """Class that performs hand landmarks detection on images.""" + + @classmethod + def create_from_model_path(cls, model_path: str) -> 'HandLandmarker': + """Creates an `HandLandmarker` object from a TensorFlow Lite model and the default `HandLandmarkerOptions`. + + Note that the created `HandLandmarker` instance is in image mode, for + detecting hand landmarks on single image inputs. + + Args: + model_path: Path to the model. + + Returns: + `HandLandmarker` object that's created from the model file and the + default `HandLandmarkerOptions`. + + Raises: + ValueError: If failed to create `HandLandmarker` object from the + provided file such as invalid file path. + RuntimeError: If other types of error occurred. + """ + base_options = _BaseOptions(model_asset_path=model_path) + options = HandLandmarkerOptions( + base_options=base_options, running_mode=_RunningMode.IMAGE) + return cls.create_from_options(options) + + @classmethod + def create_from_options( + cls, options: HandLandmarkerOptions) -> 'HandLandmarker': + """Creates the `HandLandmarker` object from hand landmarker options. + + Args: + options: Options for the hand landmarker task. + + Returns: + `HandLandmarker` object that's created from `options`. + + Raises: + ValueError: If failed to create `HandLandmarker` object from + `HandLandmarkerOptions` such as missing the model. + RuntimeError: If other types of error occurred. + """ + + def packets_callback(output_packets: Mapping[str, packet_module.Packet]): + if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty(): + return + + image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME]) + + if output_packets[_HAND_LANDMARKS_STREAM_NAME].is_empty(): + empty_packet = output_packets[_HAND_LANDMARKS_STREAM_NAME] + options.result_callback( + HandLandmarksDetectionResult([], [], []), image, + empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) + return + + hand_landmarks_detection_result = _build_detection_result(output_packets) + timestamp = output_packets[_HAND_LANDMARKS_STREAM_NAME].timestamp + options.result_callback(hand_landmarks_detection_result, image, + timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) + + 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([_HANDEDNESS_TAG, _HANDEDNESS_STREAM_NAME]), + ':'.join([_HAND_LANDMARKS_TAG, + _HAND_LANDMARKS_STREAM_NAME]), ':'.join([ + _HAND_WORLD_LANDMARKS_TAG, + _HAND_WORLD_LANDMARKS_STREAM_NAME + ]), ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]) + ], + task_options=options) + return cls( + task_info.generate_graph_config( + enable_flow_limiting=options.running_mode == + _RunningMode.LIVE_STREAM), options.running_mode, + packets_callback if options.result_callback else None) + + def detect( + self, + image: image_module.Image, + image_processing_options: Optional[_ImageProcessingOptions] = None + ) -> HandLandmarksDetectionResult: + """Performs hand landmarks detection on the given image. + + Only use this method when the HandLandmarker is created with the image + running mode. + + The image can be of any size with format RGB or RGBA. + TODO: Describes how the input image will be preprocessed after the yuv + support is implemented. + + Args: + image: MediaPipe Image. + image_processing_options: Options for image processing. + + Returns: + The hand landmarks detection results. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If hand landmarker detection failed to run. + """ + normalized_rect = self.convert_to_normalized_rect( + image_processing_options, roi_allowed=False) + output_packets = self._process_image_data({ + _IMAGE_IN_STREAM_NAME: + packet_creator.create_image(image), + _NORM_RECT_STREAM_NAME: + packet_creator.create_proto(normalized_rect.to_pb2()) + }) + + if output_packets[_HAND_LANDMARKS_STREAM_NAME].is_empty(): + return HandLandmarksDetectionResult([], [], []) + + return _build_detection_result(output_packets) + + def detect_for_video( + self, + image: image_module.Image, + timestamp_ms: int, + image_processing_options: Optional[_ImageProcessingOptions] = None + ) -> HandLandmarksDetectionResult: + """Performs hand landmarks detection on the provided video frame. + + Only use this method when the HandLandmarker is created with the video + running mode. + + Only use this method when the HandLandmarker is created with the video + running mode. It's required to provide the video frame's timestamp (in + milliseconds) along with the video frame. The input timestamps should be + monotonically increasing for adjacent calls of this method. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input video frame in milliseconds. + image_processing_options: Options for image processing. + + Returns: + The hand landmarks detection results. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If hand landmarker detection failed to run. + """ + normalized_rect = self.convert_to_normalized_rect( + image_processing_options, roi_allowed=False) + output_packets = self._process_video_data({ + _IMAGE_IN_STREAM_NAME: + packet_creator.create_image(image).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND), + _NORM_RECT_STREAM_NAME: + packet_creator.create_proto(normalized_rect.to_pb2()).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND) + }) + + if output_packets[_HAND_LANDMARKS_STREAM_NAME].is_empty(): + return HandLandmarksDetectionResult([], [], []) + + return _build_detection_result(output_packets) + + def detect_async( + self, + image: image_module.Image, + timestamp_ms: int, + image_processing_options: Optional[_ImageProcessingOptions] = None + ) -> None: + """Sends live image data to perform hand landmarks detection. + + The results will be available via the "result_callback" provided in the + HandLandmarkerOptions. Only use this method when the HandLandmarker is + created with the live stream running mode. + + Only use this method when the HandLandmarker is created with the live + stream running mode. The input timestamps should be monotonically increasing + for adjacent calls of this method. This method will return immediately after + the input image is accepted. The results will be available via the + `result_callback` provided in the `HandLandmarkerOptions`. The + `detect_async` method is designed to process live stream data such as + camera input. To lower the overall latency, hand landmarker may drop the + input images if needed. In other words, it's not guaranteed to have output + per input image. + + The `result_callback` provides: + - The hand landmarks detection results. + - The input image that the hand landmarker runs on. + - The input timestamp in milliseconds. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input image in milliseconds. + image_processing_options: Options for image processing. + + Raises: + ValueError: If the current input timestamp is smaller than what the + hand landmarker has already processed. + """ + normalized_rect = self.convert_to_normalized_rect( + image_processing_options, roi_allowed=False) + self._send_live_stream_data({ + _IMAGE_IN_STREAM_NAME: + packet_creator.create_image(image).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND), + _NORM_RECT_STREAM_NAME: + packet_creator.create_proto(normalized_rect.to_pb2()).at( + timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND) + }) From 9c9519eeb81728de0fd1e528457768af4a0171a5 Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 02:55:19 -0800 Subject: [PATCH 02/18] Fixed a to_pb2() bug in the landmark_detection_result container --- .../containers/landmark_detection_result.py | 28 +++++++++++++------ 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/mediapipe/tasks/python/components/containers/landmark_detection_result.py b/mediapipe/tasks/python/components/containers/landmark_detection_result.py index e058f84a8..317290811 100644 --- a/mediapipe/tasks/python/components/containers/landmark_detection_result.py +++ b/mediapipe/tasks/python/components/containers/landmark_detection_result.py @@ -53,19 +53,29 @@ class LandmarksDetectionResult: def to_pb2(self) -> _LandmarksDetectionResultProto: """Generates a LandmarksDetectionResult protobuf object.""" + landmarks = _NormalizedLandmarkListProto() + landmarks.landmark.extend([ + landmark.to_pb2() for landmark in self.landmarks + ]) + classifications = _ClassificationListProto() - for category in self.categories: - classifications.classification.append( - _ClassificationProto( - index=category.index, - score=category.score, - label=category.category_name, - display_name=category.display_name)) + classifications.classification.extend([ + _ClassificationProto( + index=category.index, + score=category.score, + label=category.category_name, + display_name=category.display_name) for category in self.categories + ]) + + world_landmarks = _LandmarkListProto() + world_landmarks.landmark.extend([ + world_landmark.to_pb2() for world_landmark in self.world_landmarks + ]) return _LandmarksDetectionResultProto( - landmarks=_NormalizedLandmarkListProto(self.landmarks), + landmarks=landmarks, classifications=classifications, - world_landmarks=_LandmarkListProto(self.world_landmarks), + world_landmarks=world_landmarks, rect=self.rect.to_pb2()) @classmethod From e8d771baf3d37b044b76fe1e8ff286317c1319e2 Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 10:37:37 -0800 Subject: [PATCH 03/18] Updated landmark_detection_result --- .../containers/landmark_detection_result.py | 46 +++++++++---------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/mediapipe/tasks/python/components/containers/landmark_detection_result.py b/mediapipe/tasks/python/components/containers/landmark_detection_result.py index 317290811..5e68efda1 100644 --- a/mediapipe/tasks/python/components/containers/landmark_detection_result.py +++ b/mediapipe/tasks/python/components/containers/landmark_detection_result.py @@ -54,23 +54,19 @@ class LandmarksDetectionResult: """Generates a LandmarksDetectionResult protobuf object.""" landmarks = _NormalizedLandmarkListProto() - landmarks.landmark.extend([ - landmark.to_pb2() for landmark in self.landmarks - ]) - classifications = _ClassificationListProto() - classifications.classification.extend([ - _ClassificationProto( - index=category.index, - score=category.score, - label=category.category_name, - display_name=category.display_name) for category in self.categories - ]) - world_landmarks = _LandmarkListProto() - world_landmarks.landmark.extend([ - world_landmark.to_pb2() for world_landmark in self.world_landmarks - ]) + + for landmark in self.landmarks: + landmarks.landmark.append(landmark.to_pb2()) + + for category in self.categories: + classifications.classification.append( + _ClassificationProto( + index=category.index, + score=category.score, + label=category.category_name, + display_name=category.display_name)) return _LandmarksDetectionResultProto( landmarks=landmarks, @@ -86,6 +82,9 @@ class LandmarksDetectionResult: """Creates a `LandmarksDetectionResult` object from the given protobuf object. """ categories = [] + landmarks = [] + world_landmarks = [] + for classification in pb2_obj.classifications.classification: categories.append( category_module.Category( @@ -93,14 +92,15 @@ class LandmarksDetectionResult: index=classification.index, category_name=classification.label, display_name=classification.display_name)) + + for landmark in pb2_obj.landmarks.landmark: + landmarks.append(_NormalizedLandmark.create_from_pb2(landmark)) + + for landmark in pb2_obj.world_landmarks.landmark: + world_landmarks.append(_Landmark.create_from_pb2(landmark) + ) return LandmarksDetectionResult( - landmarks=[ - _NormalizedLandmark.create_from_pb2(landmark) - for landmark in pb2_obj.landmarks.landmark - ], + landmarks=landmarks, categories=categories, - world_landmarks=[ - _Landmark.create_from_pb2(landmark) - for landmark in pb2_obj.world_landmarks.landmark - ], + world_landmarks=world_landmarks, rect=_NormalizedRect.create_from_pb2(pb2_obj.rect)) From c0265ca2ace336dc1e2cae310e76e57979306730 Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 10:44:21 -0800 Subject: [PATCH 04/18] Fixed some typos --- mediapipe/tasks/python/test/vision/hand_landmarker_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mediapipe/tasks/python/test/vision/hand_landmarker_test.py b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py index 9d311e210..b387267a2 100644 --- a/mediapipe/tasks/python/test/vision/hand_landmarker_test.py +++ b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py @@ -80,7 +80,7 @@ class ModelFileType(enum.Enum): FILE_NAME = 2 -class GestureRecognizerTest(parameterized.TestCase): +class HandLandmarkerTest(parameterized.TestCase): def setUp(self): super().setUp() From 88ab9d4922beb652b8b52b6aebaba2ce7b6bb47d Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 10:52:26 -0800 Subject: [PATCH 05/18] Used loops instead of complex list comprehensions --- .../tasks/python/vision/gesture_recognizer.py | 18 ++++++++++-------- .../tasks/python/vision/hand_landmarker.py | 18 ++++++++++-------- 2 files changed, 20 insertions(+), 16 deletions(-) diff --git a/mediapipe/tasks/python/vision/gesture_recognizer.py b/mediapipe/tasks/python/vision/gesture_recognizer.py index 081ecc429..4c25aff33 100644 --- a/mediapipe/tasks/python/vision/gesture_recognizer.py +++ b/mediapipe/tasks/python/vision/gesture_recognizer.py @@ -122,19 +122,21 @@ def _build_recognition_result( for proto in hand_landmarks_proto_list: hand_landmarks = landmark_pb2.NormalizedLandmarkList() hand_landmarks.MergeFrom(proto) - hand_landmarks_results.append([ - landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark) - for hand_landmark in hand_landmarks.landmark - ]) + hand_landmarks_list = [] + for hand_landmark in hand_landmarks.landmark: + hand_landmarks_list.append( + landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)) + hand_landmarks_results.append(hand_landmarks_list) hand_world_landmarks_results = [] for proto in hand_world_landmarks_proto_list: hand_world_landmarks = landmark_pb2.LandmarkList() hand_world_landmarks.MergeFrom(proto) - hand_world_landmarks_results.append([ - landmark_module.Landmark.create_from_pb2(hand_world_landmark) - for hand_world_landmark in hand_world_landmarks.landmark - ]) + hand_world_landmarks_list = [] + for hand_world_landmark in hand_world_landmarks.landmark: + hand_world_landmarks_list.append( + landmark_module.Landmark.create_from_pb2(hand_world_landmark)) + hand_world_landmarks_results.append(hand_world_landmarks_list) return GestureRecognitionResult(gesture_results, handedness_results, hand_landmarks_results, diff --git a/mediapipe/tasks/python/vision/hand_landmarker.py b/mediapipe/tasks/python/vision/hand_landmarker.py index e063bdafb..9dea08d02 100644 --- a/mediapipe/tasks/python/vision/hand_landmarker.py +++ b/mediapipe/tasks/python/vision/hand_landmarker.py @@ -98,19 +98,21 @@ def _build_detection_result( for proto in hand_landmarks_proto_list: hand_landmarks = landmark_pb2.NormalizedLandmarkList() hand_landmarks.MergeFrom(proto) - hand_landmarks_results.append([ - landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark) - for hand_landmark in hand_landmarks.landmark - ]) + hand_landmarks_list = [] + for hand_landmark in hand_landmarks.landmark: + hand_landmarks_list.append( + landmark_module.NormalizedLandmark.create_from_pb2(hand_landmark)) + hand_landmarks_results.append(hand_landmarks_list) hand_world_landmarks_results = [] for proto in hand_world_landmarks_proto_list: hand_world_landmarks = landmark_pb2.LandmarkList() hand_world_landmarks.MergeFrom(proto) - hand_world_landmarks_results.append([ - landmark_module.Landmark.create_from_pb2(hand_world_landmark) - for hand_world_landmark in hand_world_landmarks.landmark - ]) + hand_world_landmarks_list = [] + for hand_world_landmark in hand_world_landmarks.landmark: + hand_world_landmarks_list.append( + landmark_module.Landmark.create_from_pb2(hand_world_landmark)) + hand_world_landmarks_results.append(hand_world_landmarks_list) return HandLandmarksDetectionResult(handedness_results, hand_landmarks_results, From 647b94240d043bf3a176142c945a57af67ef20f2 Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 10:54:44 -0800 Subject: [PATCH 06/18] Updated docstring --- mediapipe/tasks/python/vision/hand_landmarker.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mediapipe/tasks/python/vision/hand_landmarker.py b/mediapipe/tasks/python/vision/hand_landmarker.py index 9dea08d02..d30c0b37b 100644 --- a/mediapipe/tasks/python/vision/hand_landmarker.py +++ b/mediapipe/tasks/python/vision/hand_landmarker.py @@ -176,8 +176,7 @@ class HandLandmarker(base_vision_task_api.BaseVisionTaskApi): def create_from_model_path(cls, model_path: str) -> 'HandLandmarker': """Creates an `HandLandmarker` object from a TensorFlow Lite model and the default `HandLandmarkerOptions`. - Note that the created `HandLandmarker` instance is in image mode, for - detecting hand landmarks on single image inputs. + Note that the created `HandLandmarker` instance is in image mode, for detecting hand landmarks on single image inputs. Args: model_path: Path to the model. From 77cc4c53ab7aa5e65f80e5549faafe30d0dca91d Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 11:26:02 -0800 Subject: [PATCH 07/18] Updated the 'test_create_from_options_fails_with_invalid_model_path' test case --- mediapipe/tasks/python/test/vision/hand_landmarker_test.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/mediapipe/tasks/python/test/vision/hand_landmarker_test.py b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py index b387267a2..7e364556a 100644 --- a/mediapipe/tasks/python/test/vision/hand_landmarker_test.py +++ b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py @@ -135,10 +135,9 @@ class HandLandmarkerTest(parameterized.TestCase): def test_create_from_options_fails_with_invalid_model_path(self): # Invalid empty model path. with self.assertRaisesRegex( - ValueError, - r"ExternalFile must specify at least one of 'file_content', " - r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."): - base_options = _BaseOptions(model_asset_path='') + RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'): + base_options = _BaseOptions( + model_asset_path='/path/to/invalid/model.tflite') options = _HandLandmarkerOptions(base_options=base_options) _HandLandmarker.create_from_options(options) From 37930609ffd27d1aca55dd705290b400cf1b0d59 Mon Sep 17 00:00:00 2001 From: Kinar R <42828719+kinaryml@users.noreply.github.com> Date: Wed, 9 Nov 2022 02:58:38 +0530 Subject: [PATCH 08/18] Update landmark_detection_result.py --- .../python/components/containers/landmark_detection_result.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/mediapipe/tasks/python/components/containers/landmark_detection_result.py b/mediapipe/tasks/python/components/containers/landmark_detection_result.py index 5e68efda1..d115cb52f 100644 --- a/mediapipe/tasks/python/components/containers/landmark_detection_result.py +++ b/mediapipe/tasks/python/components/containers/landmark_detection_result.py @@ -97,8 +97,7 @@ class LandmarksDetectionResult: landmarks.append(_NormalizedLandmark.create_from_pb2(landmark)) for landmark in pb2_obj.world_landmarks.landmark: - world_landmarks.append(_Landmark.create_from_pb2(landmark) - ) + world_landmarks.append(_Landmark.create_from_pb2(landmark)) return LandmarksDetectionResult( landmarks=landmarks, categories=categories, From 46f135e54d1f67c3949e9055a3d926d15a356594 Mon Sep 17 00:00:00 2001 From: kinaryml Date: Tue, 8 Nov 2022 23:06:28 -0800 Subject: [PATCH 09/18] Renamed HandLandmarksDetectionResult to HandLandmarkerResult --- .../test/vision/hand_landmarker_test.py | 40 +++++++++---------- .../tasks/python/vision/hand_landmarker.py | 32 +++++++-------- 2 files changed, 36 insertions(+), 36 deletions(-) diff --git a/mediapipe/tasks/python/test/vision/hand_landmarker_test.py b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py index 7e364556a..0ccff5b24 100644 --- a/mediapipe/tasks/python/test/vision/hand_landmarker_test.py +++ b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py @@ -41,7 +41,7 @@ _LandmarksDetectionResult = landmark_detection_result_module.LandmarksDetectionR _Image = image_module.Image _HandLandmarker = hand_landmarker.HandLandmarker _HandLandmarkerOptions = hand_landmarker.HandLandmarkerOptions -_HandLandmarksDetectionResult = hand_landmarker.HandLandmarksDetectionResult +_HandLandmarkerResult = hand_landmarker.HandLandmarkerResult _RUNNING_MODE = running_mode_module.VisionTaskRunningMode _ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions @@ -58,8 +58,8 @@ _LANDMARKS_ERROR_TOLERANCE = 0.03 _HANDEDNESS_MARGIN = 0.05 -def _get_expected_hand_landmarks_detection_result( - file_path: str) -> _HandLandmarksDetectionResult: +def _get_expected_hand_landmarker_result( + file_path: str) -> _HandLandmarkerResult: landmarks_detection_result_file_path = test_utils.get_test_data_path( file_path) with open(landmarks_detection_result_file_path, "rb") as f: @@ -69,7 +69,7 @@ def _get_expected_hand_landmarks_detection_result( text_format.Parse(f.read(), landmarks_detection_result_proto) landmarks_detection_result = _LandmarksDetectionResult.create_from_pb2( landmarks_detection_result_proto) - return _HandLandmarksDetectionResult( + return _HandLandmarkerResult( handedness=[landmarks_detection_result.categories], hand_landmarks=[landmarks_detection_result.landmarks], hand_world_landmarks=[landmarks_detection_result.world_landmarks]) @@ -91,8 +91,8 @@ class HandLandmarkerTest(parameterized.TestCase): def _assert_actual_result_approximately_matches_expected_result( self, - actual_result: _HandLandmarksDetectionResult, - expected_result: _HandLandmarksDetectionResult + actual_result: _HandLandmarkerResult, + expected_result: _HandLandmarkerResult ): # Expects to have the same number of hands detected. self.assertLen(actual_result.hand_landmarks, @@ -150,10 +150,10 @@ class HandLandmarkerTest(parameterized.TestCase): self.assertIsInstance(landmarker, _HandLandmarker) @parameterized.parameters( - (ModelFileType.FILE_NAME, _get_expected_hand_landmarks_detection_result( + (ModelFileType.FILE_NAME, _get_expected_hand_landmarker_result( _THUMB_UP_LANDMARKS )), - (ModelFileType.FILE_CONTENT, _get_expected_hand_landmarks_detection_result( + (ModelFileType.FILE_CONTENT, _get_expected_hand_landmarker_result( _THUMB_UP_LANDMARKS ))) def test_detect(self, model_file_type, expected_detection_result): @@ -181,10 +181,10 @@ class HandLandmarkerTest(parameterized.TestCase): landmarker.close() @parameterized.parameters( - (ModelFileType.FILE_NAME, _get_expected_hand_landmarks_detection_result( + (ModelFileType.FILE_NAME, _get_expected_hand_landmarker_result( _THUMB_UP_LANDMARKS )), - (ModelFileType.FILE_CONTENT, _get_expected_hand_landmarks_detection_result( + (ModelFileType.FILE_CONTENT, _get_expected_hand_landmarker_result( _THUMB_UP_LANDMARKS ))) def test_detect_in_context(self, model_file_type, expected_detection_result): @@ -233,7 +233,7 @@ class HandLandmarkerTest(parameterized.TestCase): # Performs hand landmarks detection on the input. detection_result = landmarker.detect(test_image, image_processing_options) - expected_detection_result = _get_expected_hand_landmarks_detection_result( + expected_detection_result = _get_expected_hand_landmarker_result( _POINTING_UP_ROTATED_LANDMARKS) # Comparing results. self._assert_actual_result_approximately_matches_expected_result( @@ -332,14 +332,14 @@ class HandLandmarkerTest(parameterized.TestCase): landmarker.detect_for_video(self.test_image, 0) @parameterized.parameters( - (_THUMB_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result( + (_THUMB_UP_IMAGE, 0, _get_expected_hand_landmarker_result( _THUMB_UP_LANDMARKS)), - (_POINTING_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result( + (_POINTING_UP_IMAGE, 0, _get_expected_hand_landmarker_result( _POINTING_UP_LANDMARKS)), (_POINTING_UP_ROTATED_IMAGE, -90, - _get_expected_hand_landmarks_detection_result( + _get_expected_hand_landmarker_result( _POINTING_UP_ROTATED_LANDMARKS)), - (_NO_HANDS_IMAGE, 0, _HandLandmarksDetectionResult([], [], []))) + (_NO_HANDS_IMAGE, 0, _HandLandmarkerResult([], [], []))) def test_detect_for_video(self, image_path, rotation, expected_result): test_image = _Image.create_from_file( test_utils.get_test_data_path(image_path)) @@ -392,14 +392,14 @@ class HandLandmarkerTest(parameterized.TestCase): landmarker.detect_async(self.test_image, 0) @parameterized.parameters( - (_THUMB_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result( + (_THUMB_UP_IMAGE, 0, _get_expected_hand_landmarker_result( _THUMB_UP_LANDMARKS)), - (_POINTING_UP_IMAGE, 0, _get_expected_hand_landmarks_detection_result( + (_POINTING_UP_IMAGE, 0, _get_expected_hand_landmarker_result( _POINTING_UP_LANDMARKS)), (_POINTING_UP_ROTATED_IMAGE, -90, - _get_expected_hand_landmarks_detection_result( + _get_expected_hand_landmarker_result( _POINTING_UP_ROTATED_LANDMARKS)), - (_NO_HANDS_IMAGE, 0, _HandLandmarksDetectionResult([], [], []))) + (_NO_HANDS_IMAGE, 0, _HandLandmarkerResult([], [], []))) def test_detect_async_calls(self, image_path, rotation, expected_result): test_image = _Image.create_from_file( test_utils.get_test_data_path(image_path)) @@ -407,7 +407,7 @@ class HandLandmarkerTest(parameterized.TestCase): image_processing_options = _ImageProcessingOptions(rotation_degrees=rotation) observed_timestamp_ms = -1 - def check_result(result: _HandLandmarksDetectionResult, + def check_result(result: _HandLandmarkerResult, output_image: _Image, timestamp_ms: int): if result.hand_landmarks and result.hand_world_landmarks and \ diff --git a/mediapipe/tasks/python/vision/hand_landmarker.py b/mediapipe/tasks/python/vision/hand_landmarker.py index d30c0b37b..532eaf568 100644 --- a/mediapipe/tasks/python/vision/hand_landmarker.py +++ b/mediapipe/tasks/python/vision/hand_landmarker.py @@ -54,8 +54,8 @@ _MICRO_SECONDS_PER_MILLISECOND = 1000 @dataclasses.dataclass -class HandLandmarksDetectionResult: - """The hand landmarks detection result from HandLandmarker, where each vector +class HandLandmarkerResult: + """The hand landmarks result from HandLandmarker, where each vector element represents a single hand detected in the image. Attributes: @@ -69,9 +69,9 @@ class HandLandmarksDetectionResult: hand_world_landmarks: List[List[landmark_module.Landmark]] -def _build_detection_result( +def _build_landmarker_result( output_packets: Mapping[str,packet_module.Packet] -) -> HandLandmarksDetectionResult: +) -> HandLandmarkerResult: """Constructs a `HandLandmarksDetectionResult` from output packets.""" handedness_proto_list = packet_getter.get_proto_list( output_packets[_HANDEDNESS_STREAM_NAME]) @@ -114,9 +114,9 @@ def _build_detection_result( landmark_module.Landmark.create_from_pb2(hand_world_landmark)) hand_world_landmarks_results.append(hand_world_landmarks_list) - return HandLandmarksDetectionResult(handedness_results, - hand_landmarks_results, - hand_world_landmarks_results) + return HandLandmarkerResult(handedness_results, + hand_landmarks_results, + hand_world_landmarks_results) @dataclasses.dataclass @@ -151,7 +151,7 @@ class HandLandmarkerOptions: min_hand_presence_confidence: Optional[float] = 0.5 min_tracking_confidence: Optional[float] = 0.5 result_callback: Optional[Callable[ - [HandLandmarksDetectionResult, image_module.Image, int], None]] = None + [HandLandmarkerResult, image_module.Image, int], None]] = None @doc_controls.do_not_generate_docs def to_pb2(self) -> _HandLandmarkerGraphOptionsProto: @@ -221,11 +221,11 @@ class HandLandmarker(base_vision_task_api.BaseVisionTaskApi): if output_packets[_HAND_LANDMARKS_STREAM_NAME].is_empty(): empty_packet = output_packets[_HAND_LANDMARKS_STREAM_NAME] options.result_callback( - HandLandmarksDetectionResult([], [], []), image, + HandLandmarkerResult([], [], []), image, empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) return - hand_landmarks_detection_result = _build_detection_result(output_packets) + hand_landmarks_detection_result = _build_landmarker_result(output_packets) timestamp = output_packets[_HAND_LANDMARKS_STREAM_NAME].timestamp options.result_callback(hand_landmarks_detection_result, image, timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) @@ -255,7 +255,7 @@ class HandLandmarker(base_vision_task_api.BaseVisionTaskApi): self, image: image_module.Image, image_processing_options: Optional[_ImageProcessingOptions] = None - ) -> HandLandmarksDetectionResult: + ) -> HandLandmarkerResult: """Performs hand landmarks detection on the given image. Only use this method when the HandLandmarker is created with the image @@ -286,16 +286,16 @@ class HandLandmarker(base_vision_task_api.BaseVisionTaskApi): }) if output_packets[_HAND_LANDMARKS_STREAM_NAME].is_empty(): - return HandLandmarksDetectionResult([], [], []) + return HandLandmarkerResult([], [], []) - return _build_detection_result(output_packets) + return _build_landmarker_result(output_packets) def detect_for_video( self, image: image_module.Image, timestamp_ms: int, image_processing_options: Optional[_ImageProcessingOptions] = None - ) -> HandLandmarksDetectionResult: + ) -> HandLandmarkerResult: """Performs hand landmarks detection on the provided video frame. Only use this method when the HandLandmarker is created with the video @@ -330,9 +330,9 @@ class HandLandmarker(base_vision_task_api.BaseVisionTaskApi): }) if output_packets[_HAND_LANDMARKS_STREAM_NAME].is_empty(): - return HandLandmarksDetectionResult([], [], []) + return HandLandmarkerResult([], [], []) - return _build_detection_result(output_packets) + return _build_landmarker_result(output_packets) def detect_async( self, From 84895316d2ff9ff0c56532ded453941c00511ff4 Mon Sep 17 00:00:00 2001 From: Sebastian Schmidt Date: Wed, 9 Nov 2022 10:26:25 -0800 Subject: [PATCH 10/18] Add pre-compiled WASM files to NPM packages PiperOrigin-RevId: 487283112 --- WORKSPACE | 3 ++ mediapipe/tasks/web/BUILD | 48 +++++++++++++++++++++++--------- mediapipe/tasks/web/package.json | 11 ++++++-- third_party/wasm_files.bzl | 47 +++++++++++++++++++++++++++++++ 4 files changed, 93 insertions(+), 16 deletions(-) create mode 100644 third_party/wasm_files.bzl diff --git a/WORKSPACE b/WORKSPACE index d2d7d8ea7..f5594a524 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -546,3 +546,6 @@ rules_proto_toolchains() load("//third_party:external_files.bzl", "external_files") external_files() + +load("//third_party:wasm_files.bzl", "wasm_files") +wasm_files() diff --git a/mediapipe/tasks/web/BUILD b/mediapipe/tasks/web/BUILD index 9e4b52417..8fe5f7fdd 100644 --- a/mediapipe/tasks/web/BUILD +++ b/mediapipe/tasks/web/BUILD @@ -3,9 +3,22 @@ load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library") load("@build_bazel_rules_nodejs//:index.bzl", "pkg_npm") load("@npm//@bazel/rollup:index.bzl", "rollup_bundle") +load( + "//mediapipe/framework/tool:mediapipe_files.bzl", + "mediapipe_files", +) package(default_visibility = ["//mediapipe/tasks:internal"]) +mediapipe_files(srcs = [ + "wasm/audio_wasm_internal.js", + "wasm/audio_wasm_internal.wasm", + "wasm/text_wasm_internal.js", + "wasm/text_wasm_internal.wasm", + "wasm/vision_wasm_internal.js", + "wasm/vision_wasm_internal.wasm", +]) + # Audio mediapipe_ts_library( @@ -28,15 +41,18 @@ rollup_bundle( pkg_npm( name = "audio_pkg", - package_name = "__PACKAGE_NAME__", + package_name = "@mediapipe/tasks-__NAME__", srcs = ["package.json"], substitutions = { - "__PACKAGE_NAME__": "@mediapipe/tasks-audio", + "__NAME__": "audio", "__DESCRIPTION__": "MediaPipe Audio Tasks", - "__BUNDLE__": "audio_bundle.js", }, tgz = "audio.tgz", - deps = [":audio_bundle"], + deps = [ + "wasm/audio_wasm_internal.js", + "wasm/audio_wasm_internal.wasm", + ":audio_bundle", + ], ) # Text @@ -61,15 +77,18 @@ rollup_bundle( pkg_npm( name = "text_pkg", - package_name = "__PACKAGE_NAME__", + package_name = "@mediapipe/tasks-__NAME__", srcs = ["package.json"], substitutions = { - "__PACKAGE_NAME__": "@mediapipe/tasks-text", + "__NAME__": "text", "__DESCRIPTION__": "MediaPipe Text Tasks", - "__BUNDLE__": "text_bundle.js", }, tgz = "text.tgz", - deps = [":text_bundle"], + deps = [ + "wasm/text_wasm_internal.js", + "wasm/text_wasm_internal.wasm", + ":text_bundle", + ], ) # Vision @@ -94,13 +113,16 @@ rollup_bundle( pkg_npm( name = "vision_pkg", - package_name = "__PACKAGE_NAME__", + package_name = "@mediapipe/tasks-__NAME__", srcs = ["package.json"], substitutions = { - "__PACKAGE_NAME__": "@mediapipe/tasks-vision", + "__NAME__": "vision", "__DESCRIPTION__": "MediaPipe Vision Tasks", - "__BUNDLE__": "vision_bundle.js", }, - tgz = "vision.tgz", - deps = [":vision_bundle"], + tgz = "vision_pkg.tgz", + deps = [ + "wasm/vision_wasm_internal.js", + "wasm/vision_wasm_internal.wasm", + ":vision_bundle", + ], ) diff --git a/mediapipe/tasks/web/package.json b/mediapipe/tasks/web/package.json index d3bd8b669..7726f1cb3 100644 --- a/mediapipe/tasks/web/package.json +++ b/mediapipe/tasks/web/package.json @@ -1,9 +1,14 @@ { - "name": "__PACKAGE_NAME__", + "name": "@mediapipe/tasks-__NAME__", "version": "__VERSION__", "description": "__DESCRIPTION__", - "main": "__BUNDLE__", - "module": "__BUNDLE__", + "main": "__NAME__bundle.js", + "module": "__NAME__bundle.js", + "exports": { + ".": "./__NAME__bundle.js", + "./loader": "./wasm/__NAME__wasm_internal.js", + "./wasm": "./wasm/__NAME__wasm_internal.wasm" + }, "author": "mediapipe@google.com", "license": "Apache-2.0", "type": "module", diff --git a/third_party/wasm_files.bzl b/third_party/wasm_files.bzl new file mode 100644 index 000000000..6bfde21ba --- /dev/null +++ b/third_party/wasm_files.bzl @@ -0,0 +1,47 @@ +""" +WASM dependencies for MediaPipe. + +This file is auto-generated. +""" + +load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_file") + +# buildifier: disable=unnamed-macro +def wasm_files(): + """WASM dependencies for MediaPipe.""" + + http_file( + name = "com_google_mediapipe_wasm_audio_wasm_internal_js", + sha256 = "9419766229f24790388805d891af907cf11fe8e2cdacabcf016feb054b720c82", + urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.js?generation=1667934266184984"], + ) + + http_file( + name = "com_google_mediapipe_wasm_text_wasm_internal_js", + sha256 = "39d9445ab3b90f625a3332251fe82e59b40cd0501a5657475f3b115b7c6122c8", + urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.js?generation=1667934268229056"], + ) + + http_file( + name = "com_google_mediapipe_wasm_vision_wasm_internal_js", + sha256 = "b43c7078fe5da72990394af4fefd798bd844b4ac47849a49067bd68c3c910a3d", + urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.js?generation=1667934270239845"], + ) + + http_file( + name = "com_google_mediapipe_wasm_audio_wasm_internal_wasm", + sha256 = "9f2abe2a51d1ebc854859f620759cec1cc643773f3748d0d19e0868578c3d746", + urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/audio_wasm_internal.wasm?generation=1667934272818542"], + ) + + http_file( + name = "com_google_mediapipe_wasm_text_wasm_internal_wasm", + sha256 = "8334caec5fb10cd1f936f6ee41f8853771c7bf3a421f5c15c39ee41aa503ca54", + urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/text_wasm_internal.wasm?generation=1667934275451198"], + ) + + http_file( + name = "com_google_mediapipe_wasm_vision_wasm_internal_wasm", + sha256 = "b996eaa324da151359ad8e16edad27d9768505f1fd073625bc50dbb0f252e098", + urls = ["https://storage.googleapis.com/mediapipe-assets/wasm/vision_wasm_internal.wasm?generation=1667934277855507"], + ) From 4e568a1c3df97d0cec345f48ff15b3090a8d7de5 Mon Sep 17 00:00:00 2001 From: Jiuqiang Tang Date: Wed, 9 Nov 2022 11:04:46 -0800 Subject: [PATCH 11/18] Expose HandLandmarker API in PyPI packages. PiperOrigin-RevId: 487294501 --- mediapipe/tasks/python/vision/__init__.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/mediapipe/tasks/python/vision/__init__.py b/mediapipe/tasks/python/vision/__init__.py index 276433a49..4ff367f2b 100644 --- a/mediapipe/tasks/python/vision/__init__.py +++ b/mediapipe/tasks/python/vision/__init__.py @@ -16,12 +16,16 @@ import mediapipe.tasks.python.vision.core import mediapipe.tasks.python.vision.gesture_recognizer +import mediapipe.tasks.python.vision.hand_landmarker import mediapipe.tasks.python.vision.image_classifier import mediapipe.tasks.python.vision.image_segmenter import mediapipe.tasks.python.vision.object_detector GestureRecognizer = gesture_recognizer.GestureRecognizer GestureRecognizerOptions = gesture_recognizer.GestureRecognizerOptions +HandLandmarker = hand_landmarker.HandLandmarker +HandLandmarkerOptions = hand_landmarker.HandLandmarkerOptions +HandLandmarkerResult = hand_landmarker.HandLandmarkerResult ImageClassifier = image_classifier.ImageClassifier ImageClassifierOptions = image_classifier.ImageClassifierOptions ImageSegmenter = image_segmenter.ImageSegmenter @@ -33,6 +37,7 @@ RunningMode = core.vision_task_running_mode.VisionTaskRunningMode # Remove unnecessary modules to avoid duplication in API docs. del core del gesture_recognizer +del hand_landmarker del image_classifier del image_segmenter del object_detector From 6f66a7fde4abaa4e072e8c2d87912328c12e7f6a Mon Sep 17 00:00:00 2001 From: Jiuqiang Tang Date: Wed, 9 Nov 2022 11:47:35 -0800 Subject: [PATCH 12/18] Rename GestureRecognitionResult to GestureRecognizerResult. PiperOrigin-RevId: 487306292 --- mediapipe/tasks/python/vision/__init__.py | 1 + .../tasks/python/vision/gesture_recognizer.py | 24 +++++++++---------- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/mediapipe/tasks/python/vision/__init__.py b/mediapipe/tasks/python/vision/__init__.py index 4ff367f2b..17593357b 100644 --- a/mediapipe/tasks/python/vision/__init__.py +++ b/mediapipe/tasks/python/vision/__init__.py @@ -23,6 +23,7 @@ import mediapipe.tasks.python.vision.object_detector GestureRecognizer = gesture_recognizer.GestureRecognizer GestureRecognizerOptions = gesture_recognizer.GestureRecognizerOptions +GestureRecognizerResult = gesture_recognizer.GestureRecognizerResult HandLandmarker = hand_landmarker.HandLandmarker HandLandmarkerOptions = hand_landmarker.HandLandmarkerOptions HandLandmarkerResult = hand_landmarker.HandLandmarkerResult diff --git a/mediapipe/tasks/python/vision/gesture_recognizer.py b/mediapipe/tasks/python/vision/gesture_recognizer.py index 2e61e36eb..d118a315a 100644 --- a/mediapipe/tasks/python/vision/gesture_recognizer.py +++ b/mediapipe/tasks/python/vision/gesture_recognizer.py @@ -59,7 +59,7 @@ _GESTURE_DEFAULT_INDEX = -1 @dataclasses.dataclass -class GestureRecognitionResult: +class GestureRecognizerResult: """The gesture recognition result from GestureRecognizer, where each vector element represents a single hand detected in the image. Attributes: @@ -79,8 +79,8 @@ class GestureRecognitionResult: def _build_recognition_result( output_packets: Mapping[str, - packet_module.Packet]) -> GestureRecognitionResult: - """Consturcts a `GestureRecognitionResult` from output packets.""" + packet_module.Packet]) -> GestureRecognizerResult: + """Consturcts a `GestureRecognizerResult` from output packets.""" gestures_proto_list = packet_getter.get_proto_list( output_packets[_HAND_GESTURE_STREAM_NAME]) handedness_proto_list = packet_getter.get_proto_list( @@ -138,9 +138,9 @@ def _build_recognition_result( landmark_module.Landmark.create_from_pb2(hand_world_landmark)) hand_world_landmarks_results.append(hand_world_landmarks_list) - return GestureRecognitionResult(gesture_results, handedness_results, - hand_landmarks_results, - hand_world_landmarks_results) + return GestureRecognizerResult(gesture_results, handedness_results, + hand_landmarks_results, + hand_world_landmarks_results) @dataclasses.dataclass @@ -185,7 +185,7 @@ class GestureRecognizerOptions: custom_gesture_classifier_options: Optional[ _ClassifierOptions] = _ClassifierOptions() result_callback: Optional[Callable[ - [GestureRecognitionResult, image_module.Image, int], None]] = None + [GestureRecognizerResult, image_module.Image, int], None]] = None @doc_controls.do_not_generate_docs def to_pb2(self) -> _GestureRecognizerGraphOptionsProto: @@ -266,7 +266,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi): if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty(): empty_packet = output_packets[_HAND_GESTURE_STREAM_NAME] options.result_callback( - GestureRecognitionResult([], [], [], []), image, + GestureRecognizerResult([], [], [], []), image, empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND) return @@ -301,7 +301,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi): self, image: image_module.Image, image_processing_options: Optional[_ImageProcessingOptions] = None - ) -> GestureRecognitionResult: + ) -> GestureRecognizerResult: """Performs hand gesture recognition on the given image. Only use this method when the GestureRecognizer is created with the image @@ -332,7 +332,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi): }) if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty(): - return GestureRecognitionResult([], [], [], []) + return GestureRecognizerResult([], [], [], []) return _build_recognition_result(output_packets) @@ -341,7 +341,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi): image: image_module.Image, timestamp_ms: int, image_processing_options: Optional[_ImageProcessingOptions] = None - ) -> GestureRecognitionResult: + ) -> GestureRecognizerResult: """Performs gesture recognition on the provided video frame. Only use this method when the GestureRecognizer is created with the video @@ -376,7 +376,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi): }) if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty(): - return GestureRecognitionResult([], [], [], []) + return GestureRecognizerResult([], [], [], []) return _build_recognition_result(output_packets) From 7066ee231e2c68eb6ea281afa62588e9263306d1 Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Wed, 9 Nov 2022 11:53:37 -0800 Subject: [PATCH 13/18] Hand Landmarker Java API and unit test. PiperOrigin-RevId: 487307858 --- .../com/google/mediapipe/tasks/vision/BUILD | 30 ++ .../vision/handlandmarker/AndroidManifest.xml | 8 + .../vision/handlandmarker/HandLandmarker.java | 501 ++++++++++++++++++ .../handlandmarker/HandLandmarkerResult.java | 109 ++++ .../vision/handlandmarker/AndroidManifest.xml | 24 + .../tasks/vision/handlandmarker/BUILD | 19 + .../handlandmarker/HandLandmarkerTest.java | 424 +++++++++++++++ 7 files changed, 1115 insertions(+) create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/AndroidManifest.xml create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java create mode 100644 mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarkerResult.java create mode 100644 mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/AndroidManifest.xml create mode 100644 mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/BUILD create mode 100644 mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarkerTest.java diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/BUILD b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/BUILD index 146097bbd..2b71dcdd8 100644 --- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/BUILD +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/BUILD @@ -142,6 +142,36 @@ android_library( ], ) +android_library( + name = "handlandmarker", + srcs = [ + "handlandmarker/HandLandmarker.java", + "handlandmarker/HandLandmarkerResult.java", + ], + javacopts = [ + "-Xep:AndroidJdkLibsChecker:OFF", + ], + manifest = "handlandmarker/AndroidManifest.xml", + deps = [ + ":core", + "//mediapipe/framework:calculator_options_java_proto_lite", + "//mediapipe/framework/formats:classification_java_proto_lite", + "//mediapipe/framework/formats:landmark_java_proto_lite", + "//mediapipe/java/com/google/mediapipe/framework:android_framework", + "//mediapipe/java/com/google/mediapipe/framework/image", + "//mediapipe/tasks/cc/components/processors/proto:classifier_options_java_proto_lite", + "//mediapipe/tasks/cc/core/proto:base_options_java_proto_lite", + "//mediapipe/tasks/cc/vision/hand_detector/proto:hand_detector_graph_options_java_proto_lite", + "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarker_graph_options_java_proto_lite", + "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarks_detector_graph_options_java_proto_lite", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:category", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/components/containers:landmark", + "//mediapipe/tasks/java/com/google/mediapipe/tasks/core", + "//third_party:autovalue", + "@maven//:com_google_guava_guava", + ], +) + load("//mediapipe/tasks/java/com/google/mediapipe/tasks:mediapipe_tasks_aar.bzl", "mediapipe_tasks_vision_aar") mediapipe_tasks_vision_aar( diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/AndroidManifest.xml b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/AndroidManifest.xml new file mode 100644 index 000000000..d933570b5 --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/AndroidManifest.xml @@ -0,0 +1,8 @@ + + + + + + diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java new file mode 100644 index 000000000..91ec07f07 --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarker.java @@ -0,0 +1,501 @@ +// Copyright 2022 The MediaPipe Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +package com.google.mediapipe.tasks.vision.handlandmarker; + +import android.content.Context; +import android.os.ParcelFileDescriptor; +import com.google.auto.value.AutoValue; +import com.google.mediapipe.formats.proto.LandmarkProto.LandmarkList; +import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmarkList; +import com.google.mediapipe.proto.CalculatorOptionsProto.CalculatorOptions; +import com.google.mediapipe.formats.proto.ClassificationProto.ClassificationList; +import com.google.mediapipe.framework.AndroidPacketGetter; +import com.google.mediapipe.framework.Packet; +import com.google.mediapipe.framework.PacketGetter; +import com.google.mediapipe.framework.image.BitmapImageBuilder; +import com.google.mediapipe.framework.image.MPImage; +import com.google.mediapipe.tasks.core.BaseOptions; +import com.google.mediapipe.tasks.core.ErrorListener; +import com.google.mediapipe.tasks.core.OutputHandler; +import com.google.mediapipe.tasks.core.OutputHandler.ResultListener; +import com.google.mediapipe.tasks.core.TaskInfo; +import com.google.mediapipe.tasks.core.TaskOptions; +import com.google.mediapipe.tasks.core.TaskRunner; +import com.google.mediapipe.tasks.core.proto.BaseOptionsProto; +import com.google.mediapipe.tasks.vision.core.BaseVisionTaskApi; +import com.google.mediapipe.tasks.vision.core.ImageProcessingOptions; +import com.google.mediapipe.tasks.vision.core.RunningMode; +import com.google.mediapipe.tasks.vision.handdetector.proto.HandDetectorGraphOptionsProto; +import com.google.mediapipe.tasks.vision.handlandmarker.proto.HandLandmarkerGraphOptionsProto; +import com.google.mediapipe.tasks.vision.handlandmarker.proto.HandLandmarksDetectorGraphOptionsProto; +import java.io.File; +import java.io.IOException; +import java.nio.ByteBuffer; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.Collections; +import java.util.List; +import java.util.Optional; + +/** + * Performs hand landmarks detection on images. + * + *

This API expects a pre-trained hand landmarks model asset bundle. See . + * + *

    + *
  • Input image {@link MPImage} + *
      + *
    • The image that hand landmarks detection runs on. + *
    + *
  • Output HandLandmarkerResult {@link HandLandmarkerResult} + *
      + *
    • A HandLandmarkerResult containing hand landmarks. + *
    + *
+ */ +public final class HandLandmarker extends BaseVisionTaskApi { + private static final String TAG = HandLandmarker.class.getSimpleName(); + private static final String IMAGE_IN_STREAM_NAME = "image_in"; + private static final String NORM_RECT_IN_STREAM_NAME = "norm_rect_in"; + private static final List INPUT_STREAMS = + Collections.unmodifiableList( + Arrays.asList("IMAGE:" + IMAGE_IN_STREAM_NAME, "NORM_RECT:" + NORM_RECT_IN_STREAM_NAME)); + private static final List OUTPUT_STREAMS = + Collections.unmodifiableList( + Arrays.asList( + "LANDMARKS:hand_landmarks", + "WORLD_LANDMARKS:world_hand_landmarks", + "HANDEDNESS:handedness", + "IMAGE:image_out")); + private static final int LANDMARKS_OUT_STREAM_INDEX = 0; + private static final int WORLD_LANDMARKS_OUT_STREAM_INDEX = 1; + private static final int HANDEDNESS_OUT_STREAM_INDEX = 2; + private static final int IMAGE_OUT_STREAM_INDEX = 3; + private static final String TASK_GRAPH_NAME = + "mediapipe.tasks.vision.hand_landmarker.HandLandmarkerGraph"; + + /** + * Creates a {@link HandLandmarker} instance from a model file and the default {@link + * HandLandmarkerOptions}. + * + * @param context an Android {@link Context}. + * @param modelPath path to the hand landmarks model with metadata in the assets. + * @throws MediaPipeException if there is an error during {@link HandLandmarker} creation. + */ + public static HandLandmarker createFromFile(Context context, String modelPath) { + BaseOptions baseOptions = BaseOptions.builder().setModelAssetPath(modelPath).build(); + return createFromOptions( + context, HandLandmarkerOptions.builder().setBaseOptions(baseOptions).build()); + } + + /** + * Creates a {@link HandLandmarker} instance from a model file and the default {@link + * HandLandmarkerOptions}. + * + * @param context an Android {@link Context}. + * @param modelFile the hand landmarks model {@link File} instance. + * @throws IOException if an I/O error occurs when opening the tflite model file. + * @throws MediaPipeException if there is an error during {@link HandLandmarker} creation. + */ + public static HandLandmarker createFromFile(Context context, File modelFile) throws IOException { + try (ParcelFileDescriptor descriptor = + ParcelFileDescriptor.open(modelFile, ParcelFileDescriptor.MODE_READ_ONLY)) { + BaseOptions baseOptions = + BaseOptions.builder().setModelAssetFileDescriptor(descriptor.getFd()).build(); + return createFromOptions( + context, HandLandmarkerOptions.builder().setBaseOptions(baseOptions).build()); + } + } + + /** + * Creates a {@link HandLandmarker} instance from a model buffer and the default {@link + * HandLandmarkerOptions}. + * + * @param context an Android {@link Context}. + * @param modelBuffer a direct {@link ByteBuffer} or a {@link MappedByteBuffer} of the detection + * model. + * @throws MediaPipeException if there is an error during {@link HandLandmarker} creation. + */ + public static HandLandmarker createFromBuffer(Context context, final ByteBuffer modelBuffer) { + BaseOptions baseOptions = BaseOptions.builder().setModelAssetBuffer(modelBuffer).build(); + return createFromOptions( + context, HandLandmarkerOptions.builder().setBaseOptions(baseOptions).build()); + } + + /** + * Creates a {@link HandLandmarker} instance from a {@link HandLandmarkerOptions}. + * + * @param context an Android {@link Context}. + * @param landmarkerOptions a {@link HandLandmarkerOptions} instance. + * @throws MediaPipeException if there is an error during {@link HandLandmarker} creation. + */ + public static HandLandmarker createFromOptions( + Context context, HandLandmarkerOptions landmarkerOptions) { + // TODO: Consolidate OutputHandler and TaskRunner. + OutputHandler handler = new OutputHandler<>(); + handler.setOutputPacketConverter( + new OutputHandler.OutputPacketConverter() { + @Override + public HandLandmarkerResult convertToTaskResult(List packets) { + // If there is no hands detected in the image, just returns empty lists. + if (packets.get(LANDMARKS_OUT_STREAM_INDEX).isEmpty()) { + return HandLandmarkerResult.create( + new ArrayList<>(), + new ArrayList<>(), + new ArrayList<>(), + packets.get(LANDMARKS_OUT_STREAM_INDEX).getTimestamp()); + } + return HandLandmarkerResult.create( + PacketGetter.getProtoVector( + packets.get(LANDMARKS_OUT_STREAM_INDEX), NormalizedLandmarkList.parser()), + PacketGetter.getProtoVector( + packets.get(WORLD_LANDMARKS_OUT_STREAM_INDEX), LandmarkList.parser()), + PacketGetter.getProtoVector( + packets.get(HANDEDNESS_OUT_STREAM_INDEX), ClassificationList.parser()), + packets.get(LANDMARKS_OUT_STREAM_INDEX).getTimestamp()); + } + + @Override + public MPImage convertToTaskInput(List packets) { + return new BitmapImageBuilder( + AndroidPacketGetter.getBitmapFromRgb(packets.get(IMAGE_OUT_STREAM_INDEX))) + .build(); + } + }); + landmarkerOptions.resultListener().ifPresent(handler::setResultListener); + landmarkerOptions.errorListener().ifPresent(handler::setErrorListener); + TaskRunner runner = + TaskRunner.create( + context, + TaskInfo.builder() + .setTaskGraphName(TASK_GRAPH_NAME) + .setInputStreams(INPUT_STREAMS) + .setOutputStreams(OUTPUT_STREAMS) + .setTaskOptions(landmarkerOptions) + .setEnableFlowLimiting(landmarkerOptions.runningMode() == RunningMode.LIVE_STREAM) + .build(), + handler); + return new HandLandmarker(runner, landmarkerOptions.runningMode()); + } + + /** + * Constructor to initialize an {@link HandLandmarker} from a {@link TaskRunner} and a {@link + * RunningMode}. + * + * @param taskRunner a {@link TaskRunner}. + * @param runningMode a mediapipe vision task {@link RunningMode}. + */ + private HandLandmarker(TaskRunner taskRunner, RunningMode runningMode) { + super(taskRunner, runningMode, IMAGE_IN_STREAM_NAME, NORM_RECT_IN_STREAM_NAME); + } + + /** + * Performs hand landmarks detection on the provided single image with default image processing + * options, i.e. without any rotation applied. Only use this method when the {@link + * HandLandmarker} is created with {@link RunningMode.IMAGE}. TODO update java doc + * for input image format. + * + *

{@link HandLandmarker} supports the following color space types: + * + *

    + *
  • {@link Bitmap.Config.ARGB_8888} + *
+ * + * @param image a MediaPipe {@link MPImage} object for processing. + * @throws MediaPipeException if there is an internal error. + */ + public HandLandmarkerResult detect(MPImage image) { + return detect(image, ImageProcessingOptions.builder().build()); + } + + /** + * Performs hand landmarks detection on the provided single image. Only use this method when the + * {@link HandLandmarker} is created with {@link RunningMode.IMAGE}. TODO update java + * doc for input image format. + * + *

{@link HandLandmarker} supports the following color space types: + * + *

    + *
  • {@link Bitmap.Config.ARGB_8888} + *
+ * + * @param image a MediaPipe {@link MPImage} object for processing. + * @param imageProcessingOptions the {@link ImageProcessingOptions} specifying how to process the + * input image before running inference. Note that region-of-interest is not supported + * by this task: specifying {@link ImageProcessingOptions#regionOfInterest()} will result in + * this method throwing an IllegalArgumentException. + * @throws IllegalArgumentException if the {@link ImageProcessingOptions} specify a + * region-of-interest. + * @throws MediaPipeException if there is an internal error. + */ + public HandLandmarkerResult detect( + MPImage image, ImageProcessingOptions imageProcessingOptions) { + validateImageProcessingOptions(imageProcessingOptions); + return (HandLandmarkerResult) processImageData(image, imageProcessingOptions); + } + + /** + * Performs hand landmarks detection on the provided video frame with default image processing + * options, i.e. without any rotation applied. Only use this method when the {@link + * HandLandmarker} is created with {@link RunningMode.VIDEO}. + * + *

It's required to provide the video frame's timestamp (in milliseconds). The input timestamps + * must be monotonically increasing. + * + *

{@link HandLandmarker} supports the following color space types: + * + *

    + *
  • {@link Bitmap.Config.ARGB_8888} + *
+ * + * @param image a MediaPipe {@link MPImage} object for processing. + * @param timestampMs the input timestamp (in milliseconds). + * @throws MediaPipeException if there is an internal error. + */ + public HandLandmarkerResult detectForVideo(MPImage image, long timestampMs) { + return detectForVideo(image, ImageProcessingOptions.builder().build(), timestampMs); + } + + /** + * Performs hand landmarks detection on the provided video frame. Only use this method when the + * {@link HandLandmarker} is created with {@link RunningMode.VIDEO}. + * + *

It's required to provide the video frame's timestamp (in milliseconds). The input timestamps + * must be monotonically increasing. + * + *

{@link HandLandmarker} supports the following color space types: + * + *

    + *
  • {@link Bitmap.Config.ARGB_8888} + *
+ * + * @param image a MediaPipe {@link MPImage} object for processing. + * @param imageProcessingOptions the {@link ImageProcessingOptions} specifying how to process the + * input image before running inference. Note that region-of-interest is not supported + * by this task: specifying {@link ImageProcessingOptions#regionOfInterest()} will result in + * this method throwing an IllegalArgumentException. + * @param timestampMs the input timestamp (in milliseconds). + * @throws IllegalArgumentException if the {@link ImageProcessingOptions} specify a + * region-of-interest. + * @throws MediaPipeException if there is an internal error. + */ + public HandLandmarkerResult detectForVideo( + MPImage image, ImageProcessingOptions imageProcessingOptions, long timestampMs) { + validateImageProcessingOptions(imageProcessingOptions); + return (HandLandmarkerResult) + processVideoData(image, imageProcessingOptions, timestampMs); + } + + /** + * Sends live image data to perform hand landmarks detection with default image processing + * options, i.e. without any rotation applied, and the results will be available via the {@link + * ResultListener} provided in the {@link HandLandmarkerOptions}. Only use this method when the + * {@link HandLandmarker } is created with {@link RunningMode.LIVE_STREAM}. + * + *

It's required to provide a timestamp (in milliseconds) to indicate when the input image is + * sent to the hand landmarker. The input timestamps must be monotonically increasing. + * + *

{@link HandLandmarker} supports the following color space types: + * + *

    + *
  • {@link Bitmap.Config.ARGB_8888} + *
+ * + * @param image a MediaPipe {@link MPImage} object for processing. + * @param timestampMs the input timestamp (in milliseconds). + * @throws MediaPipeException if there is an internal error. + */ + public void detectAsync(MPImage image, long timestampMs) { + detectAsync(image, ImageProcessingOptions.builder().build(), timestampMs); + } + + /** + * Sends live image data to perform hand landmarks detection, and the results will be available + * via the {@link ResultListener} provided in the {@link HandLandmarkerOptions}. Only use this + * method when the {@link HandLandmarker} is created with {@link RunningMode.LIVE_STREAM}. + * + *

It's required to provide a timestamp (in milliseconds) to indicate when the input image is + * sent to the hand landmarker. The input timestamps must be monotonically increasing. + * + *

{@link HandLandmarker} supports the following color space types: + * + *

    + *
  • {@link Bitmap.Config.ARGB_8888} + *
+ * + * @param image a MediaPipe {@link MPImage} object for processing. + * @param imageProcessingOptions the {@link ImageProcessingOptions} specifying how to process the + * input image before running inference. Note that region-of-interest is not supported + * by this task: specifying {@link ImageProcessingOptions#regionOfInterest()} will result in + * this method throwing an IllegalArgumentException. + * @param timestampMs the input timestamp (in milliseconds). + * @throws IllegalArgumentException if the {@link ImageProcessingOptions} specify a + * region-of-interest. + * @throws MediaPipeException if there is an internal error. + */ + public void detectAsync( + MPImage image, ImageProcessingOptions imageProcessingOptions, long timestampMs) { + validateImageProcessingOptions(imageProcessingOptions); + sendLiveStreamData(image, imageProcessingOptions, timestampMs); + } + + /** Options for setting up an {@link HandLandmarker}. */ + @AutoValue + public abstract static class HandLandmarkerOptions extends TaskOptions { + + /** Builder for {@link HandLandmarkerOptions}. */ + @AutoValue.Builder + public abstract static class Builder { + /** Sets the base options for the hand landmarker task. */ + public abstract Builder setBaseOptions(BaseOptions value); + + /** + * Sets the running mode for the hand landmarker task. Default to the image mode. Hand + * landmarker has three modes: + * + *
    + *
  • IMAGE: The mode for detecting hand landmarks on single image inputs. + *
  • VIDEO: The mode for detecting hand landmarks on the decoded frames of a video. + *
  • LIVE_STREAM: The mode for for detecting hand landmarks on a live stream of input + * data, such as from camera. In this mode, {@code setResultListener} must be called to + * set up a listener to receive the detection results asynchronously. + *
+ */ + public abstract Builder setRunningMode(RunningMode value); + + /** Sets the maximum number of hands can be detected by the HandLandmarker. */ + public abstract Builder setNumHands(Integer value); + + /** Sets minimum confidence score for the hand detection to be considered successful */ + public abstract Builder setMinHandDetectionConfidence(Float value); + + /** Sets minimum confidence score of hand presence score in the hand landmark detection. */ + public abstract Builder setMinHandPresenceConfidence(Float value); + + /** Sets the minimum confidence score for the hand tracking to be considered successful. */ + public abstract Builder setMinTrackingConfidence(Float value); + + /** + * Sets the result listener to receive the detection results asynchronously when the hand + * landmarker is in the live stream mode. + */ + public abstract Builder setResultListener( + ResultListener value); + + /** Sets an optional error listener. */ + public abstract Builder setErrorListener(ErrorListener value); + + abstract HandLandmarkerOptions autoBuild(); + + /** + * Validates and builds the {@link HandLandmarkerOptions} instance. + * + * @throws IllegalArgumentException if the result listener and the running mode are not + * properly configured. The result listener should only be set when the hand landmarker is + * in the live stream mode. + */ + public final HandLandmarkerOptions build() { + HandLandmarkerOptions options = autoBuild(); + if (options.runningMode() == RunningMode.LIVE_STREAM) { + if (!options.resultListener().isPresent()) { + throw new IllegalArgumentException( + "The hand landmarker is in the live stream mode, a user-defined result listener" + + " must be provided in HandLandmarkerOptions."); + } + } else if (options.resultListener().isPresent()) { + throw new IllegalArgumentException( + "The hand landmarker is in the image or the video mode, a user-defined result" + + " listener shouldn't be provided in HandLandmarkerOptions."); + } + return options; + } + } + + abstract BaseOptions baseOptions(); + + abstract RunningMode runningMode(); + + abstract Optional numHands(); + + abstract Optional minHandDetectionConfidence(); + + abstract Optional minHandPresenceConfidence(); + + abstract Optional minTrackingConfidence(); + + abstract Optional> resultListener(); + + abstract Optional errorListener(); + + public static Builder builder() { + return new AutoValue_HandLandmarker_HandLandmarkerOptions.Builder() + .setRunningMode(RunningMode.IMAGE) + .setNumHands(1) + .setMinHandDetectionConfidence(0.5f) + .setMinHandPresenceConfidence(0.5f) + .setMinTrackingConfidence(0.5f); + } + + /** Converts a {@link HandLandmarkerOptions} to a {@link CalculatorOptions} protobuf message. */ + @Override + public CalculatorOptions convertToCalculatorOptionsProto() { + HandLandmarkerGraphOptionsProto.HandLandmarkerGraphOptions.Builder taskOptionsBuilder = + HandLandmarkerGraphOptionsProto.HandLandmarkerGraphOptions.newBuilder() + .setBaseOptions( + BaseOptionsProto.BaseOptions.newBuilder() + .setUseStreamMode(runningMode() != RunningMode.IMAGE) + .mergeFrom(convertBaseOptionsToProto(baseOptions())) + .build()); + + // Setup HandDetectorGraphOptions. + HandDetectorGraphOptionsProto.HandDetectorGraphOptions.Builder + handDetectorGraphOptionsBuilder = + HandDetectorGraphOptionsProto.HandDetectorGraphOptions.newBuilder(); + numHands().ifPresent(handDetectorGraphOptionsBuilder::setNumHands); + minHandDetectionConfidence() + .ifPresent(handDetectorGraphOptionsBuilder::setMinDetectionConfidence); + + // Setup HandLandmarkerGraphOptions. + HandLandmarksDetectorGraphOptionsProto.HandLandmarksDetectorGraphOptions.Builder + handLandmarksDetectorGraphOptionsBuilder = + HandLandmarksDetectorGraphOptionsProto.HandLandmarksDetectorGraphOptions.newBuilder(); + minHandPresenceConfidence() + .ifPresent(handLandmarksDetectorGraphOptionsBuilder::setMinDetectionConfidence); + minTrackingConfidence().ifPresent(taskOptionsBuilder::setMinTrackingConfidence); + + taskOptionsBuilder + .setHandDetectorGraphOptions(handDetectorGraphOptionsBuilder.build()) + .setHandLandmarksDetectorGraphOptions(handLandmarksDetectorGraphOptionsBuilder.build()); + + return CalculatorOptions.newBuilder() + .setExtension( + HandLandmarkerGraphOptionsProto.HandLandmarkerGraphOptions.ext, + taskOptionsBuilder.build()) + .build(); + } + } + + /** + * Validates that the provided {@link ImageProcessingOptions} doesn't contain a + * region-of-interest. + */ + private static void validateImageProcessingOptions( + ImageProcessingOptions imageProcessingOptions) { + if (imageProcessingOptions.regionOfInterest().isPresent()) { + throw new IllegalArgumentException("HandLandmarker doesn't support region-of-interest."); + } + } +} diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarkerResult.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarkerResult.java new file mode 100644 index 000000000..2889b0e0b --- /dev/null +++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarkerResult.java @@ -0,0 +1,109 @@ +// Copyright 2022 The MediaPipe Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +package com.google.mediapipe.tasks.vision.handlandmarker; + +import com.google.auto.value.AutoValue; +import com.google.mediapipe.formats.proto.LandmarkProto.Landmark; +import com.google.mediapipe.formats.proto.LandmarkProto.LandmarkList; +import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmark; +import com.google.mediapipe.formats.proto.LandmarkProto.NormalizedLandmarkList; +import com.google.mediapipe.formats.proto.ClassificationProto.Classification; +import com.google.mediapipe.formats.proto.ClassificationProto.ClassificationList; +import com.google.mediapipe.tasks.components.containers.Category; +import com.google.mediapipe.tasks.core.TaskResult; +import java.util.ArrayList; +import java.util.Collections; +import java.util.List; + +/** Represents the hand landmarks deection results generated by {@link HandLandmarker}. */ +@AutoValue +public abstract class HandLandmarkerResult implements TaskResult { + + /** + * Creates a {@link HandLandmarkerResult} instance from the lists of landmarks and + * handedness protobuf messages. + * + * @param landmarksProto a List of {@link NormalizedLandmarkList} + * @param worldLandmarksProto a List of {@link LandmarkList} + * @param handednessesProto a List of {@link ClassificationList} + */ + static HandLandmarkerResult create( + List landmarksProto, + List worldLandmarksProto, + List handednessesProto, + long timestampMs) { + List> multiHandLandmarks = + new ArrayList<>(); + List> multiHandWorldLandmarks = + new ArrayList<>(); + List> multiHandHandednesses = new ArrayList<>(); + for (NormalizedLandmarkList handLandmarksProto : landmarksProto) { + List handLandmarks = + new ArrayList<>(); + multiHandLandmarks.add(handLandmarks); + for (NormalizedLandmark handLandmarkProto : handLandmarksProto.getLandmarkList()) { + handLandmarks.add( + com.google.mediapipe.tasks.components.containers.Landmark.create( + handLandmarkProto.getX(), + handLandmarkProto.getY(), + handLandmarkProto.getZ(), + true)); + } + } + for (LandmarkList handWorldLandmarksProto : worldLandmarksProto) { + List handWorldLandmarks = + new ArrayList<>(); + multiHandWorldLandmarks.add(handWorldLandmarks); + for (Landmark handWorldLandmarkProto : handWorldLandmarksProto.getLandmarkList()) { + handWorldLandmarks.add( + com.google.mediapipe.tasks.components.containers.Landmark.create( + handWorldLandmarkProto.getX(), + handWorldLandmarkProto.getY(), + handWorldLandmarkProto.getZ(), + false)); + } + } + for (ClassificationList handednessProto : handednessesProto) { + List handedness = new ArrayList<>(); + multiHandHandednesses.add(handedness); + for (Classification classification : handednessProto.getClassificationList()) { + handedness.add( + Category.create( + classification.getScore(), + classification.getIndex(), + classification.getLabel(), + classification.getDisplayName())); + } + } + return new AutoValue_HandLandmarkerResult( + timestampMs, + Collections.unmodifiableList(multiHandLandmarks), + Collections.unmodifiableList(multiHandWorldLandmarks), + Collections.unmodifiableList(multiHandHandednesses)); + } + + @Override + public abstract long timestampMs(); + + /** Hand landmarks of detected hands. */ + public abstract List> landmarks(); + + /** Hand landmarks in world coordniates of detected hands. */ + public abstract List> + worldLandmarks(); + + /** Handedness of detected hands. */ + public abstract List> handednesses(); +} diff --git a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/AndroidManifest.xml b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/AndroidManifest.xml new file mode 100644 index 000000000..0f33c7475 --- /dev/null +++ b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/AndroidManifest.xml @@ -0,0 +1,24 @@ + + + + + + + + + + + + + + + diff --git a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/BUILD b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/BUILD new file mode 100644 index 000000000..a7f804c64 --- /dev/null +++ b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/BUILD @@ -0,0 +1,19 @@ +# Copyright 2022 The MediaPipe Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +package(default_visibility = ["//mediapipe/tasks:internal"]) + +licenses(["notice"]) + +# TODO: Enable this in OSS diff --git a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarkerTest.java b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarkerTest.java new file mode 100644 index 000000000..9e12d210f --- /dev/null +++ b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/handlandmarker/HandLandmarkerTest.java @@ -0,0 +1,424 @@ +// Copyright 2022 The MediaPipe Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +package com.google.mediapipe.tasks.vision.handlandmarker; + +import static com.google.common.truth.Truth.assertThat; +import static org.junit.Assert.assertThrows; + +import android.content.res.AssetManager; +import android.graphics.BitmapFactory; +import android.graphics.RectF; +import androidx.test.core.app.ApplicationProvider; +import androidx.test.ext.junit.runners.AndroidJUnit4; +import com.google.common.truth.Correspondence; +import com.google.mediapipe.framework.MediaPipeException; +import com.google.mediapipe.framework.image.BitmapImageBuilder; +import com.google.mediapipe.framework.image.MPImage; +import com.google.mediapipe.tasks.components.containers.Category; +import com.google.mediapipe.tasks.components.containers.Landmark; +import com.google.mediapipe.tasks.components.containers.proto.LandmarksDetectionResultProto.LandmarksDetectionResult; +import com.google.mediapipe.tasks.core.BaseOptions; +import com.google.mediapipe.tasks.vision.core.ImageProcessingOptions; +import com.google.mediapipe.tasks.vision.core.RunningMode; +import com.google.mediapipe.tasks.vision.handlandmarker.HandLandmarker.HandLandmarkerOptions; +import java.io.InputStream; +import java.util.Arrays; +import org.junit.Test; +import org.junit.runner.RunWith; +import org.junit.runners.Suite; +import org.junit.runners.Suite.SuiteClasses; + +/** Test for {@link HandLandmarker}. */ +@RunWith(Suite.class) +@SuiteClasses({HandLandmarkerTest.General.class, HandLandmarkerTest.RunningModeTest.class}) +public class HandLandmarkerTest { + private static final String HAND_LANDMARKER_BUNDLE_ASSET_FILE = "hand_landmarker.task"; + private static final String TWO_HANDS_IMAGE = "right_hands.jpg"; + private static final String THUMB_UP_IMAGE = "thumb_up.jpg"; + private static final String POINTING_UP_ROTATED_IMAGE = "pointing_up_rotated.jpg"; + private static final String NO_HANDS_IMAGE = "cats_and_dogs.jpg"; + private static final String THUMB_UP_LANDMARKS = "thumb_up_landmarks.pb"; + private static final String POINTING_UP_ROTATED_LANDMARKS = "pointing_up_rotated_landmarks.pb"; + private static final String TAG = "Hand Landmarker Test"; + private static final float LANDMARKS_ERROR_TOLERANCE = 0.03f; + private static final int IMAGE_WIDTH = 382; + private static final int IMAGE_HEIGHT = 406; + + @RunWith(AndroidJUnit4.class) + public static final class General extends HandLandmarkerTest { + + @Test + public void detect_successWithValidModels() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder() + .setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE) + .build()) + .build(); + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + HandLandmarkerResult actualResult = + handLandmarker.detect(getImageFromAsset(THUMB_UP_IMAGE)); + HandLandmarkerResult expectedResult = + getExpectedHandLandmarkerResult(THUMB_UP_LANDMARKS); + assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); + } + + @Test + public void detect_successWithEmptyResult() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder() + .setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE) + .build()) + .build(); + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + HandLandmarkerResult actualResult = + handLandmarker.detect(getImageFromAsset(NO_HANDS_IMAGE)); + assertThat(actualResult.landmarks()).isEmpty(); + assertThat(actualResult.worldLandmarks()).isEmpty(); + assertThat(actualResult.handednesses()).isEmpty(); + } + + @Test + public void detect_successWithNumHands() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder() + .setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE) + .build()) + .setNumHands(2) + .build(); + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + HandLandmarkerResult actualResult = + handLandmarker.detect(getImageFromAsset(TWO_HANDS_IMAGE)); + assertThat(actualResult.handednesses()).hasSize(2); + } + + @Test + public void recognize_successWithRotation() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder() + .setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE) + .build()) + .setNumHands(1) + .build(); + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + ImageProcessingOptions imageProcessingOptions = + ImageProcessingOptions.builder().setRotationDegrees(-90).build(); + HandLandmarkerResult actualResult = + handLandmarker.detect( + getImageFromAsset(POINTING_UP_ROTATED_IMAGE), imageProcessingOptions); + HandLandmarkerResult expectedResult = + getExpectedHandLandmarkerResult(POINTING_UP_ROTATED_LANDMARKS); + assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); + } + + @Test + public void recognize_failsWithRegionOfInterest() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder() + .setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE) + .build()) + .setNumHands(1) + .build(); + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + ImageProcessingOptions imageProcessingOptions = + ImageProcessingOptions.builder().setRegionOfInterest(new RectF(0, 0, 1, 1)).build(); + IllegalArgumentException exception = + assertThrows( + IllegalArgumentException.class, + () -> + handLandmarker.detect(getImageFromAsset(THUMB_UP_IMAGE), imageProcessingOptions)); + assertThat(exception) + .hasMessageThat() + .contains("HandLandmarker doesn't support region-of-interest"); + } + } + + @RunWith(AndroidJUnit4.class) + public static final class RunningModeTest extends HandLandmarkerTest { + @Test + public void create_failsWithIllegalResultListenerInNonLiveStreamMode() throws Exception { + for (RunningMode mode : new RunningMode[] {RunningMode.IMAGE, RunningMode.VIDEO}) { + IllegalArgumentException exception = + assertThrows( + IllegalArgumentException.class, + () -> + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder() + .setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE) + .build()) + .setRunningMode(mode) + .setResultListener((HandLandmarkerResults, inputImage) -> {}) + .build()); + assertThat(exception) + .hasMessageThat() + .contains("a user-defined result listener shouldn't be provided"); + } + } + } + + @Test + public void create_failsWithMissingResultListenerInLiveSteamMode() throws Exception { + IllegalArgumentException exception = + assertThrows( + IllegalArgumentException.class, + () -> + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder() + .setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE) + .build()) + .setRunningMode(RunningMode.LIVE_STREAM) + .build()); + assertThat(exception) + .hasMessageThat() + .contains("a user-defined result listener must be provided"); + } + + @Test + public void recognize_failsWithCallingWrongApiInImageMode() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE).build()) + .setRunningMode(RunningMode.IMAGE) + .build(); + + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + MediaPipeException exception = + assertThrows( + MediaPipeException.class, + () -> + handLandmarker.detectForVideo( + getImageFromAsset(THUMB_UP_IMAGE), /*timestampsMs=*/ 0)); + assertThat(exception).hasMessageThat().contains("not initialized with the video mode"); + exception = + assertThrows( + MediaPipeException.class, + () -> + handLandmarker.detectAsync(getImageFromAsset(THUMB_UP_IMAGE), /*timestampsMs=*/ 0)); + assertThat(exception).hasMessageThat().contains("not initialized with the live stream mode"); + } + + @Test + public void recognize_failsWithCallingWrongApiInVideoMode() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE).build()) + .setRunningMode(RunningMode.VIDEO) + .build(); + + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + MediaPipeException exception = + assertThrows( + MediaPipeException.class, + () -> handLandmarker.detect(getImageFromAsset(THUMB_UP_IMAGE))); + assertThat(exception).hasMessageThat().contains("not initialized with the image mode"); + exception = + assertThrows( + MediaPipeException.class, + () -> + handLandmarker.detectAsync(getImageFromAsset(THUMB_UP_IMAGE), /*timestampsMs=*/ 0)); + assertThat(exception).hasMessageThat().contains("not initialized with the live stream mode"); + } + + @Test + public void recognize_failsWithCallingWrongApiInLiveSteamMode() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE).build()) + .setRunningMode(RunningMode.LIVE_STREAM) + .setResultListener((HandLandmarkerResults, inputImage) -> {}) + .build(); + + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + MediaPipeException exception = + assertThrows( + MediaPipeException.class, + () -> handLandmarker.detect(getImageFromAsset(THUMB_UP_IMAGE))); + assertThat(exception).hasMessageThat().contains("not initialized with the image mode"); + exception = + assertThrows( + MediaPipeException.class, + () -> + handLandmarker.detectForVideo( + getImageFromAsset(THUMB_UP_IMAGE), /*timestampsMs=*/ 0)); + assertThat(exception).hasMessageThat().contains("not initialized with the video mode"); + } + + @Test + public void recognize_successWithImageMode() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE).build()) + .setRunningMode(RunningMode.IMAGE) + .build(); + + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + HandLandmarkerResult actualResult = + handLandmarker.detect(getImageFromAsset(THUMB_UP_IMAGE)); + HandLandmarkerResult expectedResult = + getExpectedHandLandmarkerResult(THUMB_UP_LANDMARKS); + assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); + } + + @Test + public void recognize_successWithVideoMode() throws Exception { + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE).build()) + .setRunningMode(RunningMode.VIDEO) + .build(); + HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options); + HandLandmarkerResult expectedResult = + getExpectedHandLandmarkerResult(THUMB_UP_LANDMARKS); + for (int i = 0; i < 3; i++) { + HandLandmarkerResult actualResult = + handLandmarker.detectForVideo(getImageFromAsset(THUMB_UP_IMAGE), /*timestampsMs=*/ i); + assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult); + } + } + + @Test + public void recognize_failsWithOutOfOrderInputTimestamps() throws Exception { + MPImage image = getImageFromAsset(THUMB_UP_IMAGE); + HandLandmarkerResult expectedResult = + getExpectedHandLandmarkerResult(THUMB_UP_LANDMARKS); + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE).build()) + .setRunningMode(RunningMode.LIVE_STREAM) + .setResultListener( + (actualResult, inputImage) -> { + assertActualResultApproximatelyEqualsToExpectedResult( + actualResult, expectedResult); + assertImageSizeIsExpected(inputImage); + }) + .build(); + try (HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options)) { + handLandmarker.detectAsync(image, /*timestampsMs=*/ 1); + MediaPipeException exception = + assertThrows( + MediaPipeException.class, + () -> handLandmarker.detectAsync(image, /*timestampsMs=*/ 0)); + assertThat(exception) + .hasMessageThat() + .contains("having a smaller timestamp than the processed timestamp"); + } + } + + @Test + public void recognize_successWithLiveSteamMode() throws Exception { + MPImage image = getImageFromAsset(THUMB_UP_IMAGE); + HandLandmarkerResult expectedResult = + getExpectedHandLandmarkerResult(THUMB_UP_LANDMARKS); + HandLandmarkerOptions options = + HandLandmarkerOptions.builder() + .setBaseOptions( + BaseOptions.builder().setModelAssetPath(HAND_LANDMARKER_BUNDLE_ASSET_FILE).build()) + .setRunningMode(RunningMode.LIVE_STREAM) + .setResultListener( + (actualResult, inputImage) -> { + assertActualResultApproximatelyEqualsToExpectedResult( + actualResult, expectedResult); + assertImageSizeIsExpected(inputImage); + }) + .build(); + try (HandLandmarker handLandmarker = + HandLandmarker.createFromOptions(ApplicationProvider.getApplicationContext(), options)) { + for (int i = 0; i < 3; i++) { + handLandmarker.detectAsync(image, /*timestampsMs=*/ i); + } + } + } + + private static MPImage getImageFromAsset(String filePath) throws Exception { + AssetManager assetManager = ApplicationProvider.getApplicationContext().getAssets(); + InputStream istr = assetManager.open(filePath); + return new BitmapImageBuilder(BitmapFactory.decodeStream(istr)).build(); + } + + private static HandLandmarkerResult getExpectedHandLandmarkerResult( + String filePath) throws Exception { + AssetManager assetManager = ApplicationProvider.getApplicationContext().getAssets(); + InputStream istr = assetManager.open(filePath); + LandmarksDetectionResult landmarksDetectionResultProto = + LandmarksDetectionResult.parser().parseFrom(istr); + return HandLandmarkerResult.create( + Arrays.asList(landmarksDetectionResultProto.getLandmarks()), + Arrays.asList(landmarksDetectionResultProto.getWorldLandmarks()), + Arrays.asList(landmarksDetectionResultProto.getClassifications()), + /*timestampMs=*/ 0); + } + + private static void assertActualResultApproximatelyEqualsToExpectedResult( + HandLandmarkerResult actualResult, HandLandmarkerResult expectedResult) { + // Expects to have the same number of hands detected. + assertThat(actualResult.landmarks()).hasSize(expectedResult.landmarks().size()); + assertThat(actualResult.worldLandmarks()).hasSize(expectedResult.worldLandmarks().size()); + assertThat(actualResult.handednesses()).hasSize(expectedResult.handednesses().size()); + + // Actual landmarks match expected landmarks. + assertThat(actualResult.landmarks().get(0)) + .comparingElementsUsing( + Correspondence.from( + (Correspondence.BinaryPredicate) + (actual, expected) -> { + return Correspondence.tolerance(LANDMARKS_ERROR_TOLERANCE) + .compare(actual.x(), expected.x()) + && Correspondence.tolerance(LANDMARKS_ERROR_TOLERANCE) + .compare(actual.y(), expected.y()); + }, + "landmarks approximately equal to")) + .containsExactlyElementsIn(expectedResult.landmarks().get(0)); + + // Actual handedness matches expected handedness. + Category actualTopHandedness = actualResult.handednesses().get(0).get(0); + Category expectedTopHandedness = expectedResult.handednesses().get(0).get(0); + assertThat(actualTopHandedness.index()).isEqualTo(expectedTopHandedness.index()); + assertThat(actualTopHandedness.categoryName()).isEqualTo(expectedTopHandedness.categoryName()); + } + + private static void assertImageSizeIsExpected(MPImage inputImage) { + assertThat(inputImage).isNotNull(); + assertThat(inputImage.getWidth()).isEqualTo(IMAGE_WIDTH); + assertThat(inputImage.getHeight()).isEqualTo(IMAGE_HEIGHT); + } +} From 116b4bb6c43d28d09121159b99ace6f8673767d3 Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Wed, 9 Nov 2022 13:55:45 -0800 Subject: [PATCH 14/18] Internal change PiperOrigin-RevId: 487338991 --- mediapipe/tasks/testdata/vision/BUILD | 14 -------------- third_party/external_files.bzl | 8 ++++---- 2 files changed, 4 insertions(+), 18 deletions(-) diff --git a/mediapipe/tasks/testdata/vision/BUILD b/mediapipe/tasks/testdata/vision/BUILD index 55d386185..ad8072b87 100644 --- a/mediapipe/tasks/testdata/vision/BUILD +++ b/mediapipe/tasks/testdata/vision/BUILD @@ -143,20 +143,6 @@ filegroup( ], ) -# Gestures related models. Visible to model_maker. -# TODO: Upload canned gesture model and gesture embedding model to GCS after Model Card approval -filegroup( - name = "test_gesture_models", - srcs = [ - "hand_landmark_full.tflite", - "palm_detection_full.tflite", - ], - visibility = [ - "//mediapipe/model_maker:__subpackages__", - "//mediapipe/tasks:internal", - ], -) - filegroup( name = "test_protos", srcs = [ diff --git a/third_party/external_files.bzl b/third_party/external_files.bzl index d5ddc8d78..dec8342cf 100644 --- a/third_party/external_files.bzl +++ b/third_party/external_files.bzl @@ -403,7 +403,7 @@ def external_files(): http_file( name = "com_google_mediapipe_labels_txt", sha256 = "536feacc519de3d418de26b2effb4d75694a8c4c0063e36499a46fa8061e2da9", - urls = ["https://storage.googleapis.com/mediapipe-assets/labels.txt?generation=1667888034706429"], + urls = ["https://storage.googleapis.com/mediapipe-assets/labels.txt?generation=1667892497527642"], ) http_file( @@ -553,13 +553,13 @@ def external_files(): http_file( name = "com_google_mediapipe_movie_review_json", sha256 = "c09b88af05844cad5133b49744fed3a0bd514d4a1c75b9d2f23e9a40bd7bc04e", - urls = ["https://storage.googleapis.com/mediapipe-assets/movie_review.json?generation=1667888039053188"], + urls = ["https://storage.googleapis.com/mediapipe-assets/movie_review.json?generation=1667892501695336"], ) http_file( name = "com_google_mediapipe_movie_review_labels_txt", sha256 = "4b9b26392f765e7a872372131cd4cee8ad7c02e496b5a1228279619b138c4b7a", - urls = ["https://storage.googleapis.com/mediapipe-assets/movie_review_labels.txt?generation=1667888041670721"], + urls = ["https://storage.googleapis.com/mediapipe-assets/movie_review_labels.txt?generation=1667892504334882"], ) http_file( @@ -703,7 +703,7 @@ def external_files(): http_file( name = "com_google_mediapipe_regex_vocab_txt", sha256 = "b1134b10927a53ce4224bbc30ccf075c9969c94ebf40c368966d1dcf445ca923", - urls = ["https://storage.googleapis.com/mediapipe-assets/regex_vocab.txt?generation=1667888047885461"], + urls = ["https://storage.googleapis.com/mediapipe-assets/regex_vocab.txt?generation=1667892507770551"], ) http_file( From d2142e86a9c86ce726f759f830ac5b5ad298e8c1 Mon Sep 17 00:00:00 2001 From: Yuqi Li Date: Wed, 9 Nov 2022 14:51:46 -0800 Subject: [PATCH 15/18] Metadata Writer: Add Bert metadata writer in Text Classifier. PiperOrigin-RevId: 487354439 --- .../python/metadata/metadata_writers/BUILD | 2 +- .../metadata_writers/metadata_info.py | 223 +- .../metadata_writers/metadata_writer.py | 105 +- .../metadata_writers/text_classifier.py | 55 + .../metadata_writers/metadata_info_test.py | 36 + .../metadata_writers/text_classifier_test.py | 50 +- mediapipe/tasks/testdata/metadata/BUILD | 14 + ...t_text_classifier_with_bert_tokenizer.json | 84 + ...t_text_classifier_with_sentence_piece.json | 83 + .../metadata/bert_tokenizer_meta.json | 20 + .../testdata/metadata/mobilebert_vocab.txt | 30522 ++++++++++++++++ .../sentence_piece_tokenizer_meta.json | 26 + third_party/external_files.bzl | 30 + 13 files changed, 31235 insertions(+), 15 deletions(-) create mode 100644 mediapipe/tasks/testdata/metadata/bert_text_classifier_with_bert_tokenizer.json create mode 100644 mediapipe/tasks/testdata/metadata/bert_text_classifier_with_sentence_piece.json create mode 100644 mediapipe/tasks/testdata/metadata/bert_tokenizer_meta.json create mode 100644 mediapipe/tasks/testdata/metadata/mobilebert_vocab.txt create mode 100644 mediapipe/tasks/testdata/metadata/sentence_piece_tokenizer_meta.json diff --git a/mediapipe/tasks/python/metadata/metadata_writers/BUILD b/mediapipe/tasks/python/metadata/metadata_writers/BUILD index 4f8a675f4..1177939bd 100644 --- a/mediapipe/tasks/python/metadata/metadata_writers/BUILD +++ b/mediapipe/tasks/python/metadata/metadata_writers/BUILD @@ -12,9 +12,9 @@ py_library( srcs = [ "metadata_info.py", ], - srcs_version = "PY3", visibility = ["//visibility:public"], deps = [ + ":writer_utils", "//mediapipe/tasks/metadata:metadata_schema_py", "//mediapipe/tasks/metadata:schema_py", ], diff --git a/mediapipe/tasks/python/metadata/metadata_writers/metadata_info.py b/mediapipe/tasks/python/metadata/metadata_writers/metadata_info.py index 9300370b7..9ddf818b6 100644 --- a/mediapipe/tasks/python/metadata/metadata_writers/metadata_info.py +++ b/mediapipe/tasks/python/metadata/metadata_writers/metadata_info.py @@ -14,12 +14,14 @@ # ============================================================================== """Helper classes for common model metadata information.""" +import collections import csv import os -from typing import List, Optional, Type +from typing import List, Optional, Type, Union from mediapipe.tasks.metadata import metadata_schema_py_generated as _metadata_fb from mediapipe.tasks.metadata import schema_py_generated as _schema_fb +from mediapipe.tasks.python.metadata.metadata_writers import writer_utils # Min and max values for UINT8 tensors. _MIN_UINT8 = 0 @@ -267,6 +269,86 @@ class RegexTokenizerMd: return tokenizer +class BertTokenizerMd: + """A container for the Bert tokenizer [1] metadata information. + + [1]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L477 + """ + + def __init__(self, vocab_file_path: str): + """Initializes a BertTokenizerMd object. + + Args: + vocab_file_path: path to the vocabulary file. + """ + self._vocab_file_path = vocab_file_path + + def create_metadata(self) -> _metadata_fb.ProcessUnitT: + """Creates the Bert tokenizer metadata based on the information. + + Returns: + A Flatbuffers Python object of the Bert tokenizer metadata. + """ + vocab = _metadata_fb.AssociatedFileT() + vocab.name = self._vocab_file_path + vocab.description = _VOCAB_FILE_DESCRIPTION + vocab.type = _metadata_fb.AssociatedFileType.VOCABULARY + tokenizer = _metadata_fb.ProcessUnitT() + tokenizer.optionsType = _metadata_fb.ProcessUnitOptions.BertTokenizerOptions + tokenizer.options = _metadata_fb.BertTokenizerOptionsT() + tokenizer.options.vocabFile = [vocab] + return tokenizer + + +class SentencePieceTokenizerMd: + """A container for the sentence piece tokenizer [1] metadata information. + + [1]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L485 + """ + + _SP_MODEL_DESCRIPTION = "The sentence piece model file." + _SP_VOCAB_FILE_DESCRIPTION = _VOCAB_FILE_DESCRIPTION + ( + " This file is optional during tokenization, while the sentence piece " + "model is mandatory.") + + def __init__(self, + sentence_piece_model_path: str, + vocab_file_path: Optional[str] = None): + """Initializes a SentencePieceTokenizerMd object. + + Args: + sentence_piece_model_path: path to the sentence piece model file. + vocab_file_path: path to the vocabulary file. + """ + self._sentence_piece_model_path = sentence_piece_model_path + self._vocab_file_path = vocab_file_path + + def create_metadata(self) -> _metadata_fb.ProcessUnitT: + """Creates the sentence piece tokenizer metadata based on the information. + + Returns: + A Flatbuffers Python object of the sentence piece tokenizer metadata. + """ + tokenizer = _metadata_fb.ProcessUnitT() + tokenizer.optionsType = ( + _metadata_fb.ProcessUnitOptions.SentencePieceTokenizerOptions) + tokenizer.options = _metadata_fb.SentencePieceTokenizerOptionsT() + + sp_model = _metadata_fb.AssociatedFileT() + sp_model.name = self._sentence_piece_model_path + sp_model.description = self._SP_MODEL_DESCRIPTION + tokenizer.options.sentencePieceModel = [sp_model] + if self._vocab_file_path: + vocab = _metadata_fb.AssociatedFileT() + vocab.name = self._vocab_file_path + vocab.description = self._SP_VOCAB_FILE_DESCRIPTION + vocab.type = _metadata_fb.AssociatedFileType.VOCABULARY + tokenizer.options.vocabFile = [vocab] + return tokenizer + + class TensorMd: """A container for common tensor metadata information. @@ -486,6 +568,145 @@ class InputTextTensorMd(TensorMd): return tensor_metadata +def _get_file_paths(files: List[_metadata_fb.AssociatedFileT]) -> List[str]: + """Gets file paths from a list of associated files.""" + if not files: + return [] + return [file.name for file in files] + + +def _get_tokenizer_associated_files( + tokenizer_options: Optional[ + Union[_metadata_fb.BertTokenizerOptionsT, + _metadata_fb.SentencePieceTokenizerOptionsT]] +) -> List[str]: + """Gets a list of associated files packed in the tokenizer_options. + + Args: + tokenizer_options: a tokenizer metadata object. Support the following + tokenizer types: + 1. BertTokenizerOptions: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L477 + 2. SentencePieceTokenizerOptions: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L485 + + Returns: + A list of associated files included in tokenizer_options. + """ + + if not tokenizer_options: + return [] + + if isinstance(tokenizer_options, _metadata_fb.BertTokenizerOptionsT): + return _get_file_paths(tokenizer_options.vocabFile) + elif isinstance(tokenizer_options, + _metadata_fb.SentencePieceTokenizerOptionsT): + return _get_file_paths(tokenizer_options.vocabFile) + _get_file_paths( + tokenizer_options.sentencePieceModel) + else: + return [] + + +class BertInputTensorsMd: + """A container for the input tensor metadata information of Bert models.""" + + _IDS_NAME = "ids" + _IDS_DESCRIPTION = "Tokenized ids of the input text." + _MASK_NAME = "mask" + _MASK_DESCRIPTION = ("Mask with 1 for real tokens and 0 for padding " + "tokens.") + _SEGMENT_IDS_NAME = "segment_ids" + _SEGMENT_IDS_DESCRIPTION = ( + "0 for the first sequence, 1 for the second sequence if exists.") + + def __init__(self, + model_buffer: bytearray, + ids_name: str, + mask_name: str, + segment_name: str, + tokenizer_md: Union[None, BertTokenizerMd, + SentencePieceTokenizerMd] = None): + """Initializes a BertInputTensorsMd object. + + `ids_name`, `mask_name`, and `segment_name` correspond to the `Tensor.name` + in the TFLite schema, which help to determine the tensor order when + populating metadata. + + Args: + model_buffer: valid buffer of the model file. + ids_name: name of the ids tensor, which represents the tokenized ids of + the input text. + mask_name: name of the mask tensor, which represents the mask with `1` for + real tokens and `0` for padding tokens. + segment_name: name of the segment ids tensor, where `0` stands for the + first sequence, and `1` stands for the second sequence if exists. + tokenizer_md: information of the tokenizer used to process the input + string, if any. Supported tokenizers are: `BertTokenizer` [1] and + `SentencePieceTokenizer` [2]. If the tokenizer is `RegexTokenizer` [3], + refer to `InputTensorsMd`. + [1]: + https://github.com/tensorflow/tflite-support/blob/b80289c4cd1224d0e1836c7654e82f070f9eefaa/tensorflow_lite_support/metadata/metadata_schema.fbs#L436 + [2]: + https://github.com/tensorflow/tflite-support/blob/b80289c4cd1224d0e1836c7654e82f070f9eefaa/tensorflow_lite_support/metadata/metadata_schema.fbs#L473 + [3]: + https://github.com/tensorflow/tflite-support/blob/b80289c4cd1224d0e1836c7654e82f070f9eefaa/tensorflow_lite_support/metadata/metadata_schema.fbs#L475 + """ + # Verify that tflite_input_names (read from the model) and + # input_name (collected from users) are aligned. + tflite_input_names = writer_utils.get_input_tensor_names(model_buffer) + input_names = [ids_name, mask_name, segment_name] + if collections.Counter(tflite_input_names) != collections.Counter( + input_names): + raise ValueError( + f"The input tensor names ({input_names}) do not match the tensor " + f"names read from the model ({tflite_input_names}).") + + ids_md = TensorMd( + name=self._IDS_NAME, + description=self._IDS_DESCRIPTION, + tensor_name=ids_name) + + mask_md = TensorMd( + name=self._MASK_NAME, + description=self._MASK_DESCRIPTION, + tensor_name=mask_name) + + segment_ids_md = TensorMd( + name=self._SEGMENT_IDS_NAME, + description=self._SEGMENT_IDS_DESCRIPTION, + tensor_name=segment_name) + + self._input_md = [ids_md, mask_md, segment_ids_md] + + if not isinstance(tokenizer_md, + (type(None), BertTokenizerMd, SentencePieceTokenizerMd)): + raise ValueError( + f"The type of tokenizer_options, {type(tokenizer_md)}, is unsupported" + ) + + self._tokenizer_md = tokenizer_md + + def create_input_process_unit_metadata( + self) -> List[_metadata_fb.ProcessUnitT]: + """Creates the input process unit metadata.""" + if self._tokenizer_md: + return [self._tokenizer_md.create_metadata()] + else: + return [] + + def get_tokenizer_associated_files(self) -> List[str]: + """Gets the associated files that are packed in the tokenizer.""" + if self._tokenizer_md: + return _get_tokenizer_associated_files( + self._tokenizer_md.create_metadata().options) + else: + return [] + + @property + def input_md(self) -> List[TensorMd]: + return self._input_md + + class ClassificationTensorMd(TensorMd): """A container for the classification tensor metadata information. diff --git a/mediapipe/tasks/python/metadata/metadata_writers/metadata_writer.py b/mediapipe/tasks/python/metadata/metadata_writers/metadata_writer.py index 49563a73f..06a45645e 100644 --- a/mediapipe/tasks/python/metadata/metadata_writers/metadata_writer.py +++ b/mediapipe/tasks/python/metadata/metadata_writers/metadata_writer.py @@ -19,7 +19,7 @@ import csv import dataclasses import os import tempfile -from typing import List, Optional, Tuple +from typing import List, Optional, Tuple, Union import flatbuffers from mediapipe.tasks.metadata import metadata_schema_py_generated as metadata_fb @@ -101,6 +101,34 @@ class RegexTokenizer: vocab_file_path: str +@dataclasses.dataclass +class BertTokenizer: + """Parameters of the Bert tokenizer [1] metadata information. + + [1]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L477 + + Attributes: + vocab_file_path: path to the vocabulary file. + """ + vocab_file_path: str + + +@dataclasses.dataclass +class SentencePieceTokenizer: + """Parameters of the sentence piece tokenizer tokenizer [1] metadata information. + + [1]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L485 + + Attributes: + sentence_piece_model_path: path to the sentence piece model file. + vocab_file_path: path to the vocabulary file. + """ + sentence_piece_model_path: str + vocab_file_path: Optional[str] = None + + class Labels(object): """Simple container holding classification labels of a particular tensor. @@ -282,7 +310,9 @@ def _create_metadata_buffer( model_buffer: bytearray, general_md: Optional[metadata_info.GeneralMd] = None, input_md: Optional[List[metadata_info.TensorMd]] = None, - output_md: Optional[List[metadata_info.TensorMd]] = None) -> bytearray: + output_md: Optional[List[metadata_info.TensorMd]] = None, + input_process_units: Optional[List[metadata_fb.ProcessUnitT]] = None +) -> bytearray: """Creates a buffer of the metadata. Args: @@ -290,7 +320,9 @@ def _create_metadata_buffer( general_md: general information about the model. input_md: metadata information of the input tensors. output_md: metadata information of the output tensors. - + input_process_units: a lists of metadata of the input process units [1]. + [1]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L655 Returns: A buffer of the metadata. @@ -325,6 +357,8 @@ def _create_metadata_buffer( subgraph_metadata = metadata_fb.SubGraphMetadataT() subgraph_metadata.inputTensorMetadata = input_metadata subgraph_metadata.outputTensorMetadata = output_metadata + if input_process_units: + subgraph_metadata.inputProcessUnits = input_process_units # Create the whole model metadata. if general_md is None: @@ -366,6 +400,7 @@ class MetadataWriter(object): self._model_buffer = model_buffer self._general_md = None self._input_mds = [] + self._input_process_units = [] self._output_mds = [] self._associated_files = [] self._temp_folder = tempfile.TemporaryDirectory() @@ -416,7 +451,7 @@ class MetadataWriter(object): description: Description of the input tensor. Returns: - The MetaWriter instance, can be used for chained operation. + The MetadataWriter instance, can be used for chained operation. [1]: https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L389 @@ -448,7 +483,7 @@ class MetadataWriter(object): description: Description of the input tensor. Returns: - The MetaWriter instance, can be used for chained operation. + The MetadataWriter instance, can be used for chained operation. [1]: https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L500 @@ -462,6 +497,63 @@ class MetadataWriter(object): self._associated_files.append(regex_tokenizer.vocab_file_path) return self + def add_bert_text_input(self, tokenizer: Union[BertTokenizer, + SentencePieceTokenizer], + ids_name: str, mask_name: str, + segment_name: str) -> 'MetadataWriter': + """Adds an metadata for the text input with bert / sentencepiece tokenizer. + + `ids_name`, `mask_name`, and `segment_name` correspond to the `Tensor.name` + in the TFLite schema, which help to determine the tensor order when + populating metadata. + + Args: + tokenizer: information of the tokenizer used to process the input string, + if any. Supported tokenziers are: `BertTokenizer` [1] and + `SentencePieceTokenizer` [2]. + ids_name: name of the ids tensor, which represents the tokenized ids of + the input text. + mask_name: name of the mask tensor, which represents the mask with `1` for + real tokens and `0` for padding tokens. + segment_name: name of the segment ids tensor, where `0` stands for the + first sequence, and `1` stands for the second sequence if exists. + + Returns: + The MetadataWriter instance, can be used for chained operation. + + Raises: + ValueError: if the type tokenizer is not BertTokenizer or + SentencePieceTokenizer. + + [1]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L477 + [2]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L485 + """ + if isinstance(tokenizer, BertTokenizer): + tokenizer_md = metadata_info.BertTokenizerMd( + vocab_file_path=tokenizer.vocab_file_path) + elif isinstance(tokenizer, SentencePieceTokenizer): + tokenizer_md = metadata_info.SentencePieceTokenizerMd( + sentence_piece_model_path=tokenizer.sentence_piece_model_path, + vocab_file_path=tokenizer.vocab_file_path) + else: + raise ValueError( + f'The type of tokenizer, {type(tokenizer)}, is unsupported') + bert_input_md = metadata_info.BertInputTensorsMd( + self._model_buffer, + ids_name, + mask_name, + segment_name, + tokenizer_md=tokenizer_md) + + self._input_mds.extend(bert_input_md.input_md) + self._associated_files.extend( + bert_input_md.get_tokenizer_associated_files()) + self._input_process_units.extend( + bert_input_md.create_input_process_unit_metadata()) + return self + def add_classification_output( self, labels: Optional[Labels] = None, @@ -546,7 +638,8 @@ class MetadataWriter(object): model_buffer=self._model_buffer, general_md=self._general_md, input_md=self._input_mds, - output_md=self._output_mds) + output_md=self._output_mds, + input_process_units=self._input_process_units) populator.load_metadata_buffer(metadata_buffer) if self._associated_files: populator.load_associated_files(self._associated_files) diff --git a/mediapipe/tasks/python/metadata/metadata_writers/text_classifier.py b/mediapipe/tasks/python/metadata/metadata_writers/text_classifier.py index ef3df58b1..b9abb6b5d 100644 --- a/mediapipe/tasks/python/metadata/metadata_writers/text_classifier.py +++ b/mediapipe/tasks/python/metadata/metadata_writers/text_classifier.py @@ -14,11 +14,18 @@ # ============================================================================== """Writes metadata and label file to the Text classifier models.""" +from typing import Union + from mediapipe.tasks.python.metadata.metadata_writers import metadata_writer _MODEL_NAME = "TextClassifier" _MODEL_DESCRIPTION = ("Classify the input text into a set of known categories.") +# The input tensor names of models created by Model Maker. +_DEFAULT_ID_NAME = "serving_default_input_word_ids:0" +_DEFAULT_MASK_NAME = "serving_default_input_mask:0" +_DEFAULT_SEGMENT_ID_NAME = "serving_default_input_type_ids:0" + class MetadataWriter(metadata_writer.MetadataWriterBase): """MetadataWriter to write the metadata into the text classifier.""" @@ -62,3 +69,51 @@ class MetadataWriter(metadata_writer.MetadataWriterBase): writer.add_regex_text_input(regex_tokenizer) writer.add_classification_output(labels) return cls(writer) + + @classmethod + def create_for_bert_model( + cls, + model_buffer: bytearray, + tokenizer: Union[metadata_writer.BertTokenizer, + metadata_writer.SentencePieceTokenizer], + labels: metadata_writer.Labels, + ids_name: str = _DEFAULT_ID_NAME, + mask_name: str = _DEFAULT_MASK_NAME, + segment_name: str = _DEFAULT_SEGMENT_ID_NAME, + ) -> "MetadataWriter": + """Creates MetadataWriter for models with {Bert/SentencePiece}Tokenizer. + + `ids_name`, `mask_name`, and `segment_name` correspond to the `Tensor.name` + in the TFLite schema, which help to determine the tensor order when + populating metadata. The default values come from Model Maker. + + Args: + model_buffer: valid buffer of the model file. + tokenizer: information of the tokenizer used to process the input string, + if any. Supported tokenziers are: `BertTokenizer` [1] and + `SentencePieceTokenizer` [2]. If the tokenizer is `RegexTokenizer` [3], + refer to `create_for_regex_model`. + labels: an instance of Labels helper class used in the output + classification tensor [4]. + ids_name: name of the ids tensor, which represents the tokenized ids of + the input text. + mask_name: name of the mask tensor, which represents the mask with `1` for + real tokens and `0` for padding tokens. + segment_name: name of the segment ids tensor, where `0` stands for the + first sequence, and `1` stands for the second sequence if exists. [1]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L477 + [2]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L485 + [3]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L500 + [4]: + https://github.com/google/mediapipe/blob/f8af41b1eb49ff4bdad756ff19d1d36f486be614/mediapipe/tasks/metadata/metadata_schema.fbs#L99 + + Returns: + A MetadataWriter object. + """ + writer = metadata_writer.MetadataWriter(model_buffer) + writer.add_general_info(_MODEL_NAME, _MODEL_DESCRIPTION) + writer.add_bert_text_input(tokenizer, ids_name, mask_name, segment_name) + writer.add_classification_output(labels) + return cls(writer) diff --git a/mediapipe/tasks/python/test/metadata/metadata_writers/metadata_info_test.py b/mediapipe/tasks/python/test/metadata/metadata_writers/metadata_info_test.py index a30f22a08..57e5e3033 100644 --- a/mediapipe/tasks/python/test/metadata/metadata_writers/metadata_info_test.py +++ b/mediapipe/tasks/python/test/metadata/metadata_writers/metadata_info_test.py @@ -367,6 +367,42 @@ class ScoreThresholdingMdTest(absltest.TestCase): self.assertEqual(metadata_json, expected_json) +class BertTokenizerMdTest(absltest.TestCase): + + _VOCAB_FILE = "vocab.txt" + _EXPECTED_TENSOR_JSON = test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, "bert_tokenizer_meta.json")) + + def test_create_metadata_should_succeed(self): + tokenizer_md = metadata_info.BertTokenizerMd(self._VOCAB_FILE) + tokenizer_metadata = tokenizer_md.create_metadata() + + metadata_json = _metadata.convert_to_json( + _create_dummy_model_metadata_with_process_uint(tokenizer_metadata)) + with open(self._EXPECTED_TENSOR_JSON, "r") as f: + expected_json = f.read() + self.assertEqual(metadata_json, expected_json) + + +class SentencePieceTokenizerMdTest(absltest.TestCase): + + _VOCAB_FILE = "vocab.txt" + _SP_MODEL = "sp.model" + _EXPECTED_TENSOR_JSON = test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, "sentence_piece_tokenizer_meta.json")) + + def test_create_metadata_should_succeed(self): + tokenizer_md = metadata_info.SentencePieceTokenizerMd( + self._SP_MODEL, self._VOCAB_FILE) + tokenizer_metadata = tokenizer_md.create_metadata() + + metadata_json = _metadata.convert_to_json( + _create_dummy_model_metadata_with_process_uint(tokenizer_metadata)) + with open(self._EXPECTED_TENSOR_JSON, "r") as f: + expected_json = f.read() + self.assertEqual(metadata_json, expected_json) + + def _create_dummy_model_metadata_with_tensor( tensor_metadata: _metadata_fb.TensorMetadataT) -> bytes: # Create a dummy model using the tensor metadata. diff --git a/mediapipe/tasks/python/test/metadata/metadata_writers/text_classifier_test.py b/mediapipe/tasks/python/test/metadata/metadata_writers/text_classifier_test.py index e0391085e..b8793d965 100644 --- a/mediapipe/tasks/python/test/metadata/metadata_writers/text_classifier_test.py +++ b/mediapipe/tasks/python/test/metadata/metadata_writers/text_classifier_test.py @@ -21,28 +21,64 @@ from mediapipe.tasks.python.metadata.metadata_writers import text_classifier from mediapipe.tasks.python.test import test_utils _TEST_DIR = "mediapipe/tasks/testdata/metadata/" -_MODEL = test_utils.get_test_data_path(_TEST_DIR + "movie_review.tflite") +_REGEX_MODEL = test_utils.get_test_data_path(_TEST_DIR + "movie_review.tflite") _LABEL_FILE = test_utils.get_test_data_path(_TEST_DIR + "movie_review_labels.txt") -_VOCAB_FILE = test_utils.get_test_data_path(_TEST_DIR + "regex_vocab.txt") +_REGEX_VOCAB_FILE = test_utils.get_test_data_path(_TEST_DIR + "regex_vocab.txt") _DELIM_REGEX_PATTERN = r"[^\w\']+" -_JSON_FILE = test_utils.get_test_data_path("movie_review.json") +_REGEX_JSON_FILE = test_utils.get_test_data_path("movie_review.json") + +_BERT_MODEL = test_utils.get_test_data_path( + _TEST_DIR + "bert_text_classifier_no_metadata.tflite") +_BERT_VOCAB_FILE = test_utils.get_test_data_path(_TEST_DIR + + "mobilebert_vocab.txt") +_SP_MODEL_FILE = test_utils.get_test_data_path(_TEST_DIR + "30k-clean.model") +_BERT_JSON_FILE = test_utils.get_test_data_path( + _TEST_DIR + "bert_text_classifier_with_bert_tokenizer.json") +_SENTENCE_PIECE_JSON_FILE = test_utils.get_test_data_path( + _TEST_DIR + "bert_text_classifier_with_sentence_piece.json") class TextClassifierTest(absltest.TestCase): - def test_write_metadata(self,): - with open(_MODEL, "rb") as f: + def test_write_metadata_for_regex_model(self): + with open(_REGEX_MODEL, "rb") as f: model_buffer = f.read() writer = text_classifier.MetadataWriter.create_for_regex_model( model_buffer, regex_tokenizer=metadata_writer.RegexTokenizer( delim_regex_pattern=_DELIM_REGEX_PATTERN, - vocab_file_path=_VOCAB_FILE), + vocab_file_path=_REGEX_VOCAB_FILE), labels=metadata_writer.Labels().add_from_file(_LABEL_FILE)) _, metadata_json = writer.populate() - with open(_JSON_FILE, "r") as f: + with open(_REGEX_JSON_FILE, "r") as f: + expected_json = f.read() + self.assertEqual(metadata_json, expected_json) + + def test_write_metadata_for_bert(self): + with open(_BERT_MODEL, "rb") as f: + model_buffer = f.read() + writer = text_classifier.MetadataWriter.create_for_bert_model( + model_buffer, + tokenizer=metadata_writer.BertTokenizer(_BERT_VOCAB_FILE), + labels=metadata_writer.Labels().add_from_file(_LABEL_FILE)) + _, metadata_json = writer.populate() + + with open(_BERT_JSON_FILE, "r") as f: + expected_json = f.read() + self.assertEqual(metadata_json, expected_json) + + def test_write_metadata_for_sentence_piece(self): + with open(_BERT_MODEL, "rb") as f: + model_buffer = f.read() + writer = text_classifier.MetadataWriter.create_for_bert_model( + model_buffer, + tokenizer=metadata_writer.SentencePieceTokenizer(_SP_MODEL_FILE), + labels=metadata_writer.Labels().add_from_file(_LABEL_FILE)) + _, metadata_json = writer.populate() + + with open(_SENTENCE_PIECE_JSON_FILE, "r") as f: expected_json = f.read() self.assertEqual(metadata_json, expected_json) diff --git a/mediapipe/tasks/testdata/metadata/BUILD b/mediapipe/tasks/testdata/metadata/BUILD index 899e552c2..7905fbadb 100644 --- a/mediapipe/tasks/testdata/metadata/BUILD +++ b/mediapipe/tasks/testdata/metadata/BUILD @@ -23,10 +23,13 @@ package( ) mediapipe_files(srcs = [ + "30k-clean.model", + "bert_text_classifier_no_metadata.tflite", "mobile_ica_8bit-with-metadata.tflite", "mobile_ica_8bit-with-unsupported-metadata-version.tflite", "mobile_ica_8bit-without-model-metadata.tflite", "mobile_object_classifier_v0_2_3-metadata-no-name.tflite", + "mobilebert_vocab.txt", "mobilenet_v1_0.25_224_1_default_1.tflite", "mobilenet_v2_1.0_224_quant.tflite", "mobilenet_v2_1.0_224_quant_without_metadata.tflite", @@ -60,11 +63,17 @@ exports_files([ "movie_review_labels.txt", "regex_vocab.txt", "movie_review.json", + "bert_tokenizer_meta.json", + "bert_text_classifier_with_sentence_piece.json", + "sentence_piece_tokenizer_meta.json", + "bert_text_classifier_with_bert_tokenizer.json", ]) filegroup( name = "model_files", srcs = [ + "30k-clean.model", + "bert_text_classifier_no_metadata.tflite", "mobile_ica_8bit-with-metadata.tflite", "mobile_ica_8bit-with-unsupported-metadata-version.tflite", "mobile_ica_8bit-without-model-metadata.tflite", @@ -81,6 +90,9 @@ filegroup( name = "data_files", srcs = [ "associated_file_meta.json", + "bert_text_classifier_with_bert_tokenizer.json", + "bert_text_classifier_with_sentence_piece.json", + "bert_tokenizer_meta.json", "bounding_box_tensor_meta.json", "classification_tensor_float_meta.json", "classification_tensor_uint8_meta.json", @@ -96,6 +108,7 @@ filegroup( "input_text_tensor_default_meta.json", "input_text_tensor_meta.json", "labels.txt", + "mobilebert_vocab.txt", "mobilenet_v2_1.0_224.json", "mobilenet_v2_1.0_224_quant.json", "movie_review.json", @@ -105,5 +118,6 @@ filegroup( "score_calibration_file_meta.json", "score_calibration_tensor_meta.json", "score_thresholding_meta.json", + "sentence_piece_tokenizer_meta.json", ], ) diff --git a/mediapipe/tasks/testdata/metadata/bert_text_classifier_with_bert_tokenizer.json b/mediapipe/tasks/testdata/metadata/bert_text_classifier_with_bert_tokenizer.json new file mode 100644 index 000000000..9714fee34 --- /dev/null +++ b/mediapipe/tasks/testdata/metadata/bert_text_classifier_with_bert_tokenizer.json @@ -0,0 +1,84 @@ +{ + "name": "TextClassifier", + "description": "Classify the input text into a set of known categories.", + "subgraph_metadata": [ + { + "input_tensor_metadata": [ + { + "name": "ids", + "description": "Tokenized ids of the input text.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "stats": { + } + }, + { + "name": "segment_ids", + "description": "0 for the first sequence, 1 for the second sequence if exists.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "stats": { + } + }, + { + "name": "mask", + "description": "Mask with 1 for real tokens and 0 for padding tokens.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "stats": { + } + } + ], + "output_tensor_metadata": [ + { + "name": "score", + "description": "Score of the labels respectively.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "stats": { + "max": [ + 1.0 + ], + "min": [ + 0.0 + ] + }, + "associated_files": [ + { + "name": "labels.txt", + "description": "Labels for categories that the model can recognize.", + "type": "TENSOR_AXIS_LABELS" + } + ] + } + ], + "input_process_units": [ + { + "options_type": "BertTokenizerOptions", + "options": { + "vocab_file": [ + { + "name": "mobilebert_vocab.txt", + "description": "Vocabulary file to convert natural language words to embedding vectors.", + "type": "VOCABULARY" + } + ] + } + } + ] + } + ], + "min_parser_version": "1.1.0" +} diff --git a/mediapipe/tasks/testdata/metadata/bert_text_classifier_with_sentence_piece.json b/mediapipe/tasks/testdata/metadata/bert_text_classifier_with_sentence_piece.json new file mode 100644 index 000000000..0ca741ba3 --- /dev/null +++ b/mediapipe/tasks/testdata/metadata/bert_text_classifier_with_sentence_piece.json @@ -0,0 +1,83 @@ +{ + "name": "TextClassifier", + "description": "Classify the input text into a set of known categories.", + "subgraph_metadata": [ + { + "input_tensor_metadata": [ + { + "name": "ids", + "description": "Tokenized ids of the input text.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "stats": { + } + }, + { + "name": "segment_ids", + "description": "0 for the first sequence, 1 for the second sequence if exists.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "stats": { + } + }, + { + "name": "mask", + "description": "Mask with 1 for real tokens and 0 for padding tokens.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "stats": { + } + } + ], + "output_tensor_metadata": [ + { + "name": "score", + "description": "Score of the labels respectively.", + "content": { + "content_properties_type": "FeatureProperties", + "content_properties": { + } + }, + "stats": { + "max": [ + 1.0 + ], + "min": [ + 0.0 + ] + }, + "associated_files": [ + { + "name": "labels.txt", + "description": "Labels for categories that the model can recognize.", + "type": "TENSOR_AXIS_LABELS" + } + ] + } + ], + "input_process_units": [ + { + "options_type": "SentencePieceTokenizerOptions", + "options": { + "sentencePiece_model": [ + { + "name": "30k-clean.model", + "description": "The sentence piece model file." + } + ] + } + } + ] + } + ], + "min_parser_version": "1.1.0" +} diff --git a/mediapipe/tasks/testdata/metadata/bert_tokenizer_meta.json b/mediapipe/tasks/testdata/metadata/bert_tokenizer_meta.json new file mode 100644 index 000000000..c90e7df7e --- /dev/null +++ b/mediapipe/tasks/testdata/metadata/bert_tokenizer_meta.json @@ -0,0 +1,20 @@ +{ + "subgraph_metadata": [ + { + "input_process_units": [ + { + "options_type": "BertTokenizerOptions", + "options": { + "vocab_file": [ + { + "name": "vocab.txt", + "description": "Vocabulary file to convert natural language words to embedding vectors.", + "type": "VOCABULARY" + } + ] + } + } + ] + } + ] +} diff --git a/mediapipe/tasks/testdata/metadata/mobilebert_vocab.txt b/mediapipe/tasks/testdata/metadata/mobilebert_vocab.txt new file mode 100644 index 000000000..a8aca822b --- /dev/null +++ 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"sentencePiece_model": [ + { + "name": "sp.model", + "description": "The sentence piece model file." + } + ], + "vocab_file": [ + { + "name": "vocab.txt", + "description": "Vocabulary file to convert natural language words to embedding vectors. This file is optional during tokenization, while the sentence piece model is mandatory.", + "type": "VOCABULARY" + } + ] + } + } + ] + } + ] +} diff --git a/third_party/external_files.bzl b/third_party/external_files.bzl index dec8342cf..e55b88131 100644 --- a/third_party/external_files.bzl +++ b/third_party/external_files.bzl @@ -28,12 +28,36 @@ def external_files(): urls = ["https://storage.googleapis.com/mediapipe-assets/associated_file_meta.json?generation=1665422792304395"], ) + http_file( + name = "com_google_mediapipe_bert_text_classifier_no_metadata_tflite", + sha256 = "9b4554f6e28a72a3f40511964eed1ccf4e74cc074f81543cacca4faf169a173e", + urls = ["https://storage.googleapis.com/mediapipe-assets/bert_text_classifier_no_metadata.tflite?generation=1667948360250899"], + ) + http_file( name = "com_google_mediapipe_bert_text_classifier_tflite", sha256 = "1e5a550c09bff0a13e61858bcfac7654d7fcc6d42106b4f15e11117695069600", urls = ["https://storage.googleapis.com/mediapipe-assets/bert_text_classifier.tflite?generation=1666144699858747"], ) + http_file( + name = "com_google_mediapipe_bert_text_classifier_with_bert_tokenizer_json", + sha256 = "49f148a13a4e3b486b1d3c2400e46e5ebd0d375674c0154278b835760e873a95", + urls = ["https://storage.googleapis.com/mediapipe-assets/bert_text_classifier_with_bert_tokenizer.json?generation=1667948363241334"], + ) + + http_file( + name = "com_google_mediapipe_bert_text_classifier_with_sentence_piece_json", + sha256 = "113091f3892691de57e379387256b2ce0cc18a1b5185af866220a46da8221f26", + urls = ["https://storage.googleapis.com/mediapipe-assets/bert_text_classifier_with_sentence_piece.json?generation=1667948366009530"], + ) + + http_file( + name = "com_google_mediapipe_bert_tokenizer_meta_json", + sha256 = "116d70c7c3ef413a8bff54ab758f9ed3d6e51fdc5621d8c920ad2f0035831804", + urls = ["https://storage.googleapis.com/mediapipe-assets/bert_tokenizer_meta.json?generation=1667948368809108"], + ) + http_file( name = "com_google_mediapipe_bounding_box_tensor_meta_json", sha256 = "cc019cee86529955a24a3d43ca3d778fa366bcb90d67c8eaf55696789833841a", @@ -790,6 +814,12 @@ def external_files(): urls = ["https://storage.googleapis.com/mediapipe-assets/selfie_segmentation.tflite?generation=1661875931201364"], ) + http_file( + name = "com_google_mediapipe_sentence_piece_tokenizer_meta_json", + sha256 = "416bfe231710502e4a93e1b1950c0c6e5db49cffb256d241ef3d3f2d0d57718b", + urls = ["https://storage.googleapis.com/mediapipe-assets/sentence_piece_tokenizer_meta.json?generation=1667948375508564"], + ) + http_file( name = "com_google_mediapipe_speech_16000_hz_mono_wav", sha256 = "71caf50b8757d6ab9cad5eae4d36669d3c20c225a51660afd7fe0dc44cdb74f6", From f11c75762996bf0dc066ae7d39ba6cb355c82637 Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Wed, 9 Nov 2022 23:18:02 -0800 Subject: [PATCH 16/18] Update C++ HandLandmarksDetectionResult to HandLandmarkerResult. PiperOrigin-RevId: 487443827 --- .../tasks/cc/components/containers/BUILD | 9 -- .../tasks/cc/vision/hand_landmarker/BUILD | 11 ++- .../vision/hand_landmarker/hand_landmarker.cc | 14 ++-- .../vision/hand_landmarker/hand_landmarker.h | 20 ++--- .../hand_landmarker/hand_landmarker_result.h} | 16 ++-- .../hand_landmarker/hand_landmarker_test.cc | 82 +++++++++---------- 6 files changed, 70 insertions(+), 82 deletions(-) rename mediapipe/tasks/cc/{components/containers/hand_landmarks_detection_result.h => vision/hand_landmarker/hand_landmarker_result.h} (77%) diff --git a/mediapipe/tasks/cc/components/containers/BUILD b/mediapipe/tasks/cc/components/containers/BUILD index b5c9427a3..5004383d2 100644 --- a/mediapipe/tasks/cc/components/containers/BUILD +++ b/mediapipe/tasks/cc/components/containers/BUILD @@ -30,15 +30,6 @@ cc_library( ], ) -cc_library( - name = "hand_landmarks_detection_result", - hdrs = ["hand_landmarks_detection_result.h"], - deps = [ - "//mediapipe/framework/formats:classification_cc_proto", - "//mediapipe/framework/formats:landmark_cc_proto", - ], -) - cc_library( name = "category", srcs = ["category.cc"], diff --git a/mediapipe/tasks/cc/vision/hand_landmarker/BUILD b/mediapipe/tasks/cc/vision/hand_landmarker/BUILD index 084934286..5c5073fc2 100644 --- a/mediapipe/tasks/cc/vision/hand_landmarker/BUILD +++ b/mediapipe/tasks/cc/vision/hand_landmarker/BUILD @@ -110,12 +110,22 @@ cc_library( alwayslink = 1, ) +cc_library( + name = "hand_landmarker_result", + hdrs = ["hand_landmarker_result.h"], + deps = [ + "//mediapipe/framework/formats:classification_cc_proto", + "//mediapipe/framework/formats:landmark_cc_proto", + ], +) + cc_library( name = "hand_landmarker", srcs = ["hand_landmarker.cc"], hdrs = ["hand_landmarker.h"], deps = [ ":hand_landmarker_graph", + ":hand_landmarker_result", "//mediapipe/framework/api2:builder", "//mediapipe/framework/api2:port", "//mediapipe/framework/formats:classification_cc_proto", @@ -124,7 +134,6 @@ cc_library( "//mediapipe/framework/formats:rect_cc_proto", "//mediapipe/tasks/cc:common", "//mediapipe/tasks/cc/components:image_preprocessing", - "//mediapipe/tasks/cc/components/containers:hand_landmarks_detection_result", "//mediapipe/tasks/cc/components/processors:classifier_options", "//mediapipe/tasks/cc/components/processors/proto:classifier_options_cc_proto", "//mediapipe/tasks/cc/core:base_options", diff --git a/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker.cc b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker.cc index ec9790c30..3a9ed5bc2 100644 --- a/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker.cc +++ b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker.cc @@ -22,7 +22,6 @@ limitations under the License. #include "mediapipe/framework/formats/landmark.pb.h" #include "mediapipe/framework/formats/rect.pb.h" #include "mediapipe/tasks/cc/common.h" -#include "mediapipe/tasks/cc/components/containers/hand_landmarks_detection_result.h" #include "mediapipe/tasks/cc/components/image_preprocessing.h" #include "mediapipe/tasks/cc/components/processors/proto/classifier_options.pb.h" #include "mediapipe/tasks/cc/core/base_task_api.h" @@ -34,6 +33,7 @@ limitations under the License. #include "mediapipe/tasks/cc/vision/core/image_processing_options.h" #include "mediapipe/tasks/cc/vision/core/vision_task_api_factory.h" #include "mediapipe/tasks/cc/vision/hand_detector/proto/hand_detector_graph_options.pb.h" +#include "mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_result.h" #include "mediapipe/tasks/cc/vision/hand_landmarker/proto/hand_landmarker_graph_options.pb.h" #include "mediapipe/tasks/cc/vision/hand_landmarker/proto/hand_landmarks_detector_graph_options.pb.h" @@ -47,8 +47,6 @@ namespace { using HandLandmarkerGraphOptionsProto = ::mediapipe::tasks::vision:: hand_landmarker::proto::HandLandmarkerGraphOptions; -using ::mediapipe::tasks::components::containers::HandLandmarksDetectionResult; - constexpr char kHandLandmarkerGraphTypeName[] = "mediapipe.tasks.vision.hand_landmarker.HandLandmarkerGraph"; @@ -145,7 +143,7 @@ absl::StatusOr> HandLandmarker::Create( Packet empty_packet = status_or_packets.value()[kHandLandmarksStreamName]; result_callback( - {HandLandmarksDetectionResult()}, image_packet.Get(), + {HandLandmarkerResult()}, image_packet.Get(), empty_packet.Timestamp().Value() / kMicroSecondsPerMilliSecond); return; } @@ -173,7 +171,7 @@ absl::StatusOr> HandLandmarker::Create( std::move(packets_callback)); } -absl::StatusOr HandLandmarker::Detect( +absl::StatusOr HandLandmarker::Detect( mediapipe::Image image, std::optional image_processing_options) { if (image.UsesGpu()) { @@ -192,7 +190,7 @@ absl::StatusOr HandLandmarker::Detect( {kNormRectStreamName, MakePacket(std::move(norm_rect))}})); if (output_packets[kHandLandmarksStreamName].IsEmpty()) { - return {HandLandmarksDetectionResult()}; + return {HandLandmarkerResult()}; } return {{/* handedness= */ {output_packets[kHandednessStreamName] @@ -205,7 +203,7 @@ absl::StatusOr HandLandmarker::Detect( .Get>()}}}; } -absl::StatusOr HandLandmarker::DetectForVideo( +absl::StatusOr HandLandmarker::DetectForVideo( mediapipe::Image image, int64 timestamp_ms, std::optional image_processing_options) { if (image.UsesGpu()) { @@ -227,7 +225,7 @@ absl::StatusOr HandLandmarker::DetectForVideo( MakePacket(std::move(norm_rect)) .At(Timestamp(timestamp_ms * kMicroSecondsPerMilliSecond))}})); if (output_packets[kHandLandmarksStreamName].IsEmpty()) { - return {HandLandmarksDetectionResult()}; + return {HandLandmarkerResult()}; } return { {/* handedness= */ diff --git a/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker.h b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker.h index 3538ab3f5..6f96fc68e 100644 --- a/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker.h +++ b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker.h @@ -24,12 +24,12 @@ limitations under the License. #include "mediapipe/framework/formats/classification.pb.h" #include "mediapipe/framework/formats/image.h" #include "mediapipe/framework/formats/landmark.pb.h" -#include "mediapipe/tasks/cc/components/containers/hand_landmarks_detection_result.h" #include "mediapipe/tasks/cc/components/processors/classifier_options.h" #include "mediapipe/tasks/cc/core/base_options.h" #include "mediapipe/tasks/cc/vision/core/base_vision_task_api.h" #include "mediapipe/tasks/cc/vision/core/image_processing_options.h" #include "mediapipe/tasks/cc/vision/core/running_mode.h" +#include "mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_result.h" namespace mediapipe { namespace tasks { @@ -70,9 +70,7 @@ struct HandLandmarkerOptions { // The user-defined result callback for processing live stream data. // The result callback should only be specified when the running mode is set // to RunningMode::LIVE_STREAM. - std::function, - const Image&, int64)> + std::function, const Image&, int64)> result_callback = nullptr; }; @@ -92,7 +90,7 @@ struct HandLandmarkerOptions { // 'y_center', 'width' and 'height' fields is NOT supported and will // result in an invalid argument error being returned. // Outputs: -// HandLandmarksDetectionResult +// HandLandmarkerResult // - The hand landmarks detection results. class HandLandmarker : tasks::vision::core::BaseVisionTaskApi { public: @@ -129,7 +127,7 @@ class HandLandmarker : tasks::vision::core::BaseVisionTaskApi { // The image can be of any size with format RGB or RGBA. // TODO: Describes how the input image will be preprocessed // after the yuv support is implemented. - absl::StatusOr Detect( + absl::StatusOr Detect( Image image, std::optional image_processing_options = std::nullopt); @@ -147,10 +145,10 @@ class HandLandmarker : tasks::vision::core::BaseVisionTaskApi { // The image can be of any size with format RGB or RGBA. It's required to // provide the video frame's timestamp (in milliseconds). The input timestamps // must be monotonically increasing. - absl::StatusOr - DetectForVideo(Image image, int64 timestamp_ms, - std::optional - image_processing_options = std::nullopt); + absl::StatusOr DetectForVideo( + Image image, int64 timestamp_ms, + std::optional image_processing_options = + std::nullopt); // Sends live image data to perform hand landmarks detection, and the results // will be available via the "result_callback" provided in the @@ -169,7 +167,7 @@ class HandLandmarker : tasks::vision::core::BaseVisionTaskApi { // invalid argument error being returned. // // The "result_callback" provides - // - A vector of HandLandmarksDetectionResult, each is the detected results + // - A vector of HandLandmarkerResult, each is the detected results // for a input frame. // - The const reference to the corresponding input image that the hand // landmarker runs on. Note that the const reference to the image will no diff --git a/mediapipe/tasks/cc/components/containers/hand_landmarks_detection_result.h b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_result.h similarity index 77% rename from mediapipe/tasks/cc/components/containers/hand_landmarks_detection_result.h rename to mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_result.h index b341cd8d3..5e51c244e 100644 --- a/mediapipe/tasks/cc/components/containers/hand_landmarks_detection_result.h +++ b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_result.h @@ -13,20 +13,20 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef MEDIAPIPE_TASKS_CC_COMPONENTS_CONTAINERS_HAND_LANDMARKS_DETECTION_RESULT_H_ -#define MEDIAPIPE_TASKS_CC_COMPONENTS_CONTAINERS_HAND_LANDMARKS_DETECTION_RESULT_H_ +#ifndef MEDIAPIPE_TASKS_CC_VISION_HAND_LANDMARKER_HAND_LANDMARKER_RESULT_H_ +#define MEDIAPIPE_TASKS_CC_VISION_HAND_LANDMARKER_HAND_LANDMARKER_RESULT_H_ #include "mediapipe/framework/formats/classification.pb.h" #include "mediapipe/framework/formats/landmark.pb.h" namespace mediapipe { namespace tasks { -namespace components { -namespace containers { +namespace vision { +namespace hand_landmarker { // The hand landmarks detection result from HandLandmarker, where each vector // element represents a single hand detected in the image. -struct HandLandmarksDetectionResult { +struct HandLandmarkerResult { // Classification of handedness. std::vector handedness; // Detected hand landmarks in normalized image coordinates. @@ -35,9 +35,9 @@ struct HandLandmarksDetectionResult { std::vector hand_world_landmarks; }; -} // namespace containers -} // namespace components +} // namespace hand_landmarker +} // namespace vision } // namespace tasks } // namespace mediapipe -#endif // MEDIAPIPE_TASKS_CC_COMPONENTS_CONTAINERS_HAND_LANDMARKS_DETECTION_RESULT_H_ +#endif // MEDIAPIPE_TASKS_CC_VISION_HAND_LANDMARKER_HAND_LANDMARKER_RESULT_H_ diff --git a/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_test.cc b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_test.cc index ee8c3c10d..fa49a4c1f 100644 --- a/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_test.cc +++ b/mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_test.cc @@ -32,12 +32,12 @@ limitations under the License. #include "mediapipe/framework/port/gmock.h" #include "mediapipe/framework/port/gtest.h" #include "mediapipe/tasks/cc/common.h" -#include "mediapipe/tasks/cc/components/containers/hand_landmarks_detection_result.h" #include "mediapipe/tasks/cc/components/containers/proto/landmarks_detection_result.pb.h" #include "mediapipe/tasks/cc/components/containers/rect.h" #include "mediapipe/tasks/cc/components/processors/proto/classifier_options.pb.h" #include "mediapipe/tasks/cc/core/base_options.h" #include "mediapipe/tasks/cc/vision/core/image_processing_options.h" +#include "mediapipe/tasks/cc/vision/hand_landmarker/hand_landmarker_result.h" #include "mediapipe/tasks/cc/vision/utils/image_utils.h" #include "tensorflow/lite/core/shims/cc/shims_test_util.h" @@ -50,7 +50,6 @@ namespace { using ::file::Defaults; using ::mediapipe::file::JoinPath; -using ::mediapipe::tasks::components::containers::HandLandmarksDetectionResult; using ::mediapipe::tasks::components::containers::Rect; using ::mediapipe::tasks::containers::proto::LandmarksDetectionResult; using ::mediapipe::tasks::vision::core::ImageProcessingOptions; @@ -95,9 +94,9 @@ LandmarksDetectionResult GetLandmarksDetectionResult( return result; } -HandLandmarksDetectionResult GetExpectedHandLandmarksDetectionResult( +HandLandmarkerResult GetExpectedHandLandmarkerResult( const std::vector& landmarks_file_names) { - HandLandmarksDetectionResult expected_results; + HandLandmarkerResult expected_results; for (const auto& file_name : landmarks_file_names) { const auto landmarks_detection_result = GetLandmarksDetectionResult(file_name); @@ -109,9 +108,9 @@ HandLandmarksDetectionResult GetExpectedHandLandmarksDetectionResult( return expected_results; } -void ExpectHandLandmarksDetectionResultsCorrect( - const HandLandmarksDetectionResult& actual_results, - const HandLandmarksDetectionResult& expected_results) { +void ExpectHandLandmarkerResultsCorrect( + const HandLandmarkerResult& actual_results, + const HandLandmarkerResult& expected_results) { const auto& actual_landmarks = actual_results.hand_landmarks; const auto& actual_handedness = actual_results.handedness; @@ -145,7 +144,7 @@ struct TestParams { // clockwise. int rotation; // Expected results from the hand landmarker model output. - HandLandmarksDetectionResult expected_results; + HandLandmarkerResult expected_results; }; class ImageModeTest : public testing::TestWithParam {}; @@ -213,7 +212,7 @@ TEST_P(ImageModeTest, Succeeds) { MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr hand_landmarker, HandLandmarker::Create(std::move(options))); - HandLandmarksDetectionResult hand_landmarker_results; + HandLandmarkerResult hand_landmarker_results; if (GetParam().rotation != 0) { ImageProcessingOptions image_processing_options; image_processing_options.rotation_degrees = GetParam().rotation; @@ -224,8 +223,8 @@ TEST_P(ImageModeTest, Succeeds) { MP_ASSERT_OK_AND_ASSIGN(hand_landmarker_results, hand_landmarker->Detect(image)); } - ExpectHandLandmarksDetectionResultsCorrect(hand_landmarker_results, - GetParam().expected_results); + ExpectHandLandmarkerResultsCorrect(hand_landmarker_results, + GetParam().expected_results); MP_ASSERT_OK(hand_landmarker->Close()); } @@ -237,8 +236,7 @@ INSTANTIATE_TEST_SUITE_P( /* test_model_file= */ kHandLandmarkerBundleAsset, /* rotation= */ 0, /* expected_results = */ - GetExpectedHandLandmarksDetectionResult( - {kThumbUpLandmarksFilename}), + GetExpectedHandLandmarkerResult({kThumbUpLandmarksFilename}), }, TestParams{ /* test_name= */ "LandmarksPointingUp", @@ -246,8 +244,7 @@ INSTANTIATE_TEST_SUITE_P( /* test_model_file= */ kHandLandmarkerBundleAsset, /* rotation= */ 0, /* expected_results = */ - GetExpectedHandLandmarksDetectionResult( - {kPointingUpLandmarksFilename}), + GetExpectedHandLandmarkerResult({kPointingUpLandmarksFilename}), }, TestParams{ /* test_name= */ "LandmarksPointingUpRotated", @@ -255,7 +252,7 @@ INSTANTIATE_TEST_SUITE_P( /* test_model_file= */ kHandLandmarkerBundleAsset, /* rotation= */ -90, /* expected_results = */ - GetExpectedHandLandmarksDetectionResult( + GetExpectedHandLandmarkerResult( {kPointingUpRotatedLandmarksFilename}), }, TestParams{ @@ -315,7 +312,7 @@ TEST_P(VideoModeTest, Succeeds) { HandLandmarker::Create(std::move(options))); const auto expected_results = GetParam().expected_results; for (int i = 0; i < iterations; ++i) { - HandLandmarksDetectionResult hand_landmarker_results; + HandLandmarkerResult hand_landmarker_results; if (GetParam().rotation != 0) { ImageProcessingOptions image_processing_options; image_processing_options.rotation_degrees = GetParam().rotation; @@ -326,8 +323,8 @@ TEST_P(VideoModeTest, Succeeds) { MP_ASSERT_OK_AND_ASSIGN(hand_landmarker_results, hand_landmarker->DetectForVideo(image, i)); } - ExpectHandLandmarksDetectionResultsCorrect(hand_landmarker_results, - expected_results); + ExpectHandLandmarkerResultsCorrect(hand_landmarker_results, + expected_results); } MP_ASSERT_OK(hand_landmarker->Close()); } @@ -340,8 +337,7 @@ INSTANTIATE_TEST_SUITE_P( /* test_model_file= */ kHandLandmarkerBundleAsset, /* rotation= */ 0, /* expected_results = */ - GetExpectedHandLandmarksDetectionResult( - {kThumbUpLandmarksFilename}), + GetExpectedHandLandmarkerResult({kThumbUpLandmarksFilename}), }, TestParams{ /* test_name= */ "LandmarksPointingUp", @@ -349,8 +345,7 @@ INSTANTIATE_TEST_SUITE_P( /* test_model_file= */ kHandLandmarkerBundleAsset, /* rotation= */ 0, /* expected_results = */ - GetExpectedHandLandmarksDetectionResult( - {kPointingUpLandmarksFilename}), + GetExpectedHandLandmarkerResult({kPointingUpLandmarksFilename}), }, TestParams{ /* test_name= */ "LandmarksPointingUpRotated", @@ -358,7 +353,7 @@ INSTANTIATE_TEST_SUITE_P( /* test_model_file= */ kHandLandmarkerBundleAsset, /* rotation= */ -90, /* expected_results = */ - GetExpectedHandLandmarksDetectionResult( + GetExpectedHandLandmarkerResult( {kPointingUpRotatedLandmarksFilename}), }, TestParams{ @@ -383,9 +378,8 @@ TEST_F(LiveStreamModeTest, FailsWithCallingWrongMethod) { options->base_options.model_asset_path = JoinPath("./", kTestDataDirectory, kHandLandmarkerBundleAsset); options->running_mode = core::RunningMode::LIVE_STREAM; - options->result_callback = - [](absl::StatusOr results, - const Image& image, int64 timestamp_ms) {}; + options->result_callback = [](absl::StatusOr results, + const Image& image, int64 timestamp_ms) {}; MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr hand_landmarker, HandLandmarker::Create(std::move(options))); @@ -416,23 +410,23 @@ TEST_P(LiveStreamModeTest, Succeeds) { options->base_options.model_asset_path = JoinPath("./", kTestDataDirectory, GetParam().test_model_file); options->running_mode = core::RunningMode::LIVE_STREAM; - std::vector hand_landmarker_results; + std::vector hand_landmarker_results; std::vector> image_sizes; std::vector timestamps; - options->result_callback = - [&hand_landmarker_results, &image_sizes, ×tamps]( - absl::StatusOr results, - const Image& image, int64 timestamp_ms) { - MP_ASSERT_OK(results.status()); - hand_landmarker_results.push_back(std::move(results.value())); - image_sizes.push_back({image.width(), image.height()}); - timestamps.push_back(timestamp_ms); - }; + options->result_callback = [&hand_landmarker_results, &image_sizes, + ×tamps]( + absl::StatusOr results, + const Image& image, int64 timestamp_ms) { + MP_ASSERT_OK(results.status()); + hand_landmarker_results.push_back(std::move(results.value())); + image_sizes.push_back({image.width(), image.height()}); + timestamps.push_back(timestamp_ms); + }; MP_ASSERT_OK_AND_ASSIGN(std::unique_ptr hand_landmarker, HandLandmarker::Create(std::move(options))); for (int i = 0; i < iterations; ++i) { - HandLandmarksDetectionResult hand_landmarker_results; + HandLandmarkerResult hand_landmarker_results; if (GetParam().rotation != 0) { ImageProcessingOptions image_processing_options; image_processing_options.rotation_degrees = GetParam().rotation; @@ -450,8 +444,8 @@ TEST_P(LiveStreamModeTest, Succeeds) { const auto expected_results = GetParam().expected_results; for (int i = 0; i < hand_landmarker_results.size(); ++i) { - ExpectHandLandmarksDetectionResultsCorrect(hand_landmarker_results[i], - expected_results); + ExpectHandLandmarkerResultsCorrect(hand_landmarker_results[i], + expected_results); } for (const auto& image_size : image_sizes) { EXPECT_EQ(image_size.first, image.width()); @@ -472,8 +466,7 @@ INSTANTIATE_TEST_SUITE_P( /* test_model_file= */ kHandLandmarkerBundleAsset, /* rotation= */ 0, /* expected_results = */ - GetExpectedHandLandmarksDetectionResult( - {kThumbUpLandmarksFilename}), + GetExpectedHandLandmarkerResult({kThumbUpLandmarksFilename}), }, TestParams{ /* test_name= */ "LandmarksPointingUp", @@ -481,8 +474,7 @@ INSTANTIATE_TEST_SUITE_P( /* test_model_file= */ kHandLandmarkerBundleAsset, /* rotation= */ 0, /* expected_results = */ - GetExpectedHandLandmarksDetectionResult( - {kPointingUpLandmarksFilename}), + GetExpectedHandLandmarkerResult({kPointingUpLandmarksFilename}), }, TestParams{ /* test_name= */ "LandmarksPointingUpRotated", @@ -490,7 +482,7 @@ INSTANTIATE_TEST_SUITE_P( /* test_model_file= */ kHandLandmarkerBundleAsset, /* rotation= */ -90, /* expected_results = */ - GetExpectedHandLandmarksDetectionResult( + GetExpectedHandLandmarkerResult( {kPointingUpRotatedLandmarksFilename}), }, TestParams{ From 0ac604d507978b0e44f420c43c800a3d52d2d235 Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Thu, 10 Nov 2022 01:19:00 -0800 Subject: [PATCH 17/18] Add timestamp aggregation to EmbeddingPostprocessingGraph. PiperOrigin-RevId: 487463848 --- .../tasks/cc/components/calculators/BUILD | 35 ++++ .../embedding_aggregation_calculator.cc | 132 ++++++++++++ .../embedding_aggregation_calculator_test.cc | 158 +++++++++++++++ .../tasks/cc/components/processors/BUILD | 1 + .../embedding_postprocessing_graph.cc | 59 +++++- .../embedding_postprocessing_graph.h | 18 +- .../embedding_postprocessing_graph_test.cc | 191 +++++++++++++++++- ...bedding_postprocessing_graph_options.proto | 3 - 8 files changed, 576 insertions(+), 21 deletions(-) create mode 100644 mediapipe/tasks/cc/components/calculators/embedding_aggregation_calculator.cc create mode 100644 mediapipe/tasks/cc/components/calculators/embedding_aggregation_calculator_test.cc diff --git a/mediapipe/tasks/cc/components/calculators/BUILD b/mediapipe/tasks/cc/components/calculators/BUILD index 061875272..1f726a018 100644 --- a/mediapipe/tasks/cc/components/calculators/BUILD +++ b/mediapipe/tasks/cc/components/calculators/BUILD @@ -200,3 +200,38 @@ cc_test( "@com_google_absl//absl/status", ], ) + +cc_library( + name = "embedding_aggregation_calculator", + srcs = ["embedding_aggregation_calculator.cc"], + deps = [ + "//mediapipe/framework:calculator_framework", + "//mediapipe/framework/api2:node", + "//mediapipe/framework/api2:packet", + "//mediapipe/framework/api2:port", + "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto", + "@com_google_absl//absl/status", + ], + alwayslink = 1, +) + +cc_test( + name = "embedding_aggregation_calculator_test", + srcs = ["embedding_aggregation_calculator_test.cc"], + deps = [ + ":embedding_aggregation_calculator", + "//mediapipe/framework:calculator_framework", + "//mediapipe/framework:output_stream_poller", + "//mediapipe/framework:packet", + "//mediapipe/framework:timestamp", + "//mediapipe/framework/api2:builder", + "//mediapipe/framework/api2:port", + "//mediapipe/framework/port:gtest_main", + "//mediapipe/framework/port:parse_text_proto", + "//mediapipe/framework/port:status", + "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto", + "@com_google_absl//absl/status", + "@com_google_absl//absl/status:statusor", + "@org_tensorflow//tensorflow/lite/core/shims:cc_shims_test_util", + ], +) diff --git a/mediapipe/tasks/cc/components/calculators/embedding_aggregation_calculator.cc b/mediapipe/tasks/cc/components/calculators/embedding_aggregation_calculator.cc new file mode 100644 index 000000000..bae926b76 --- /dev/null +++ b/mediapipe/tasks/cc/components/calculators/embedding_aggregation_calculator.cc @@ -0,0 +1,132 @@ +// Copyright 2022 The MediaPipe Authors. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include +#include + +#include "absl/status/status.h" +#include "mediapipe/framework/api2/node.h" +#include "mediapipe/framework/api2/packet.h" +#include "mediapipe/framework/api2/port.h" +#include "mediapipe/framework/calculator_framework.h" +#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h" + +namespace mediapipe { +namespace api2 { + +using ::mediapipe::tasks::components::containers::proto::EmbeddingResult; + +// Aggregates EmbeddingResult packets into a vector of timestamped +// EmbeddingResult. Acts as a pass-through if no timestamp aggregation is +// needed. +// +// Inputs: +// EMBEDDINGS: EmbeddingResult +// The EmbeddingResult packets to aggregate. +// TIMESTAMPS: std::vector @Optional. +// The collection of timestamps that this calculator should aggregate. This +// stream is optional: if provided then the TIMESTAMPED_EMBEDDINGS output +// will contain the aggregated results. Otherwise as no timestamp +// aggregation is required the EMBEDDINGS output is used to pass the inputs +// EmbeddingResults unchanged. +// +// Outputs: +// EMBEDDINGS: EmbeddingResult @Optional +// The input EmbeddingResult, unchanged. Must be connected if the TIMESTAMPS +// input is not connected, as it signals that timestamp aggregation is not +// required. +// TIMESTAMPED_EMBEDDINGS: std::vector @Optional +// The embedding results aggregated by timestamp. Must be connected if the +// TIMESTAMPS input is connected as it signals that timestamp aggregation is +// required. +// +// Example without timestamp aggregation (pass-through): +// node { +// calculator: "EmbeddingAggregationCalculator" +// input_stream: "EMBEDDINGS:embeddings_in" +// output_stream: "EMBEDDINGS:embeddings_out" +// } +// +// Example with timestamp aggregation: +// node { +// calculator: "EmbeddingAggregationCalculator" +// input_stream: "EMBEDDINGS:embeddings_in" +// input_stream: "TIMESTAMPS:timestamps_in" +// output_stream: "TIMESTAMPED_EMBEDDINGS:timestamped_embeddings_out" +// } +class EmbeddingAggregationCalculator : public Node { + public: + static constexpr Input kEmbeddingsIn{"EMBEDDINGS"}; + static constexpr Input>::Optional kTimestampsIn{ + "TIMESTAMPS"}; + static constexpr Output::Optional kEmbeddingsOut{ + "EMBEDDINGS"}; + static constexpr Output>::Optional + kTimestampedEmbeddingsOut{"TIMESTAMPED_EMBEDDINGS"}; + MEDIAPIPE_NODE_CONTRACT(kEmbeddingsIn, kTimestampsIn, kEmbeddingsOut, + kTimestampedEmbeddingsOut); + + static absl::Status UpdateContract(CalculatorContract* cc); + absl::Status Open(CalculatorContext* cc); + absl::Status Process(CalculatorContext* cc); + + private: + bool time_aggregation_enabled_; + std::unordered_map cached_embeddings_; +}; + +absl::Status EmbeddingAggregationCalculator::UpdateContract( + CalculatorContract* cc) { + if (kTimestampsIn(cc).IsConnected()) { + RET_CHECK(kTimestampedEmbeddingsOut(cc).IsConnected()); + } else { + RET_CHECK(kEmbeddingsOut(cc).IsConnected()); + } + return absl::OkStatus(); +} + +absl::Status EmbeddingAggregationCalculator::Open(CalculatorContext* cc) { + time_aggregation_enabled_ = kTimestampsIn(cc).IsConnected(); + return absl::OkStatus(); +} + +absl::Status EmbeddingAggregationCalculator::Process(CalculatorContext* cc) { + if (time_aggregation_enabled_) { + cached_embeddings_[cc->InputTimestamp().Value()] = + std::move(*kEmbeddingsIn(cc)); + if (kTimestampsIn(cc).IsEmpty()) { + return absl::OkStatus(); + } + auto timestamps = kTimestampsIn(cc).Get(); + std::vector results; + results.reserve(timestamps.size()); + for (const auto& timestamp : timestamps) { + auto& result = cached_embeddings_[timestamp.Value()]; + result.set_timestamp_ms((timestamp.Value() - timestamps[0].Value()) / + 1000); + results.push_back(std::move(result)); + cached_embeddings_.erase(timestamp.Value()); + } + kTimestampedEmbeddingsOut(cc).Send(std::move(results)); + } else { + kEmbeddingsOut(cc).Send(kEmbeddingsIn(cc)); + } + RET_CHECK(cached_embeddings_.empty()); + return absl::OkStatus(); +} + +MEDIAPIPE_REGISTER_NODE(EmbeddingAggregationCalculator); + +} // namespace api2 +} // namespace mediapipe diff --git a/mediapipe/tasks/cc/components/calculators/embedding_aggregation_calculator_test.cc b/mediapipe/tasks/cc/components/calculators/embedding_aggregation_calculator_test.cc new file mode 100644 index 000000000..ebb4d8880 --- /dev/null +++ b/mediapipe/tasks/cc/components/calculators/embedding_aggregation_calculator_test.cc @@ -0,0 +1,158 @@ +/* Copyright 2022 The MediaPipe Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +==============================================================================*/ + +#include +#include +#include + +#include "absl/status/status.h" +#include "absl/status/statusor.h" +#include "mediapipe/framework/api2/builder.h" +#include "mediapipe/framework/api2/port.h" +#include "mediapipe/framework/calculator_framework.h" +#include "mediapipe/framework/output_stream_poller.h" +#include "mediapipe/framework/packet.h" +#include "mediapipe/framework/port/gmock.h" +#include "mediapipe/framework/port/gtest.h" +#include "mediapipe/framework/port/parse_text_proto.h" +#include "mediapipe/framework/port/status_macros.h" +#include "mediapipe/framework/port/status_matchers.h" +#include "mediapipe/framework/timestamp.h" +#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h" +#include "tensorflow/lite/core/shims/cc/shims_test_util.h" + +namespace mediapipe { +namespace { + +using ::mediapipe::ParseTextProtoOrDie; +using ::mediapipe::api2::Input; +using ::mediapipe::api2::Output; +using ::mediapipe::api2::builder::Graph; +using ::mediapipe::api2::builder::Source; +using ::mediapipe::tasks::components::containers::proto::EmbeddingResult; +using ::testing::Pointwise; + +constexpr char kEmbeddingsTag[] = "EMBEDDINGS"; +constexpr char kEmbeddingsInName[] = "embeddings_in"; +constexpr char kEmbeddingsOutName[] = "embeddings_out"; +constexpr char kTimestampsTag[] = "TIMESTAMPS"; +constexpr char kTimestampsName[] = "timestamps_in"; +constexpr char kTimestampedEmbeddingsTag[] = "TIMESTAMPED_EMBEDDINGS"; +constexpr char kTimestampedEmbeddingsName[] = "timestamped_embeddings_out"; + +class EmbeddingAggregationCalculatorTest : public tflite_shims::testing::Test { + protected: + absl::StatusOr BuildGraph(bool connect_timestamps) { + Graph graph; + auto& calculator = graph.AddNode("EmbeddingAggregationCalculator"); + graph[Input(kEmbeddingsTag)].SetName(kEmbeddingsInName) >> + calculator.In(kEmbeddingsTag); + if (connect_timestamps) { + graph[Input>(kTimestampsTag)].SetName( + kTimestampsName) >> + calculator.In(kTimestampsTag); + calculator.Out(kTimestampedEmbeddingsTag) + .SetName(kTimestampedEmbeddingsName) >> + graph[Output>( + kTimestampedEmbeddingsTag)]; + } else { + calculator.Out(kEmbeddingsTag).SetName(kEmbeddingsOutName) >> + graph[Output(kEmbeddingsTag)]; + } + + MP_RETURN_IF_ERROR(calculator_graph_.Initialize(graph.GetConfig())); + if (connect_timestamps) { + ASSIGN_OR_RETURN(auto poller, calculator_graph_.AddOutputStreamPoller( + kTimestampedEmbeddingsName)); + MP_RETURN_IF_ERROR(calculator_graph_.StartRun(/*extra_side_packets=*/{})); + return poller; + } + ASSIGN_OR_RETURN(auto poller, calculator_graph_.AddOutputStreamPoller( + kEmbeddingsOutName)); + MP_RETURN_IF_ERROR(calculator_graph_.StartRun(/*extra_side_packets=*/{})); + return poller; + } + + absl::Status Send( + const EmbeddingResult& embeddings, int timestamp = 0, + std::optional> aggregation_timestamps = std::nullopt) { + MP_RETURN_IF_ERROR(calculator_graph_.AddPacketToInputStream( + kEmbeddingsInName, MakePacket(std::move(embeddings)) + .At(Timestamp(timestamp)))); + if (aggregation_timestamps.has_value()) { + auto packet = std::make_unique>(); + for (const auto& timestamp : *aggregation_timestamps) { + packet->emplace_back(Timestamp(timestamp)); + } + MP_RETURN_IF_ERROR(calculator_graph_.AddPacketToInputStream( + kTimestampsName, Adopt(packet.release()).At(Timestamp(timestamp)))); + } + return absl::OkStatus(); + } + + template + absl::StatusOr GetResult(OutputStreamPoller& poller) { + MP_RETURN_IF_ERROR(calculator_graph_.WaitUntilIdle()); + MP_RETURN_IF_ERROR(calculator_graph_.CloseAllInputStreams()); + + Packet packet; + if (!poller.Next(&packet)) { + return absl::InternalError("Unable to get output packet"); + } + auto result = packet.Get(); + MP_RETURN_IF_ERROR(calculator_graph_.WaitUntilDone()); + return result; + } + + private: + CalculatorGraph calculator_graph_; +}; + +TEST_F(EmbeddingAggregationCalculatorTest, SucceedsWithoutTimestamps) { + EmbeddingResult embedding = ParseTextProtoOrDie( + R"pb(embeddings { head_index: 0 })pb"); + + MP_ASSERT_OK_AND_ASSIGN(auto poller, + BuildGraph(/*connect_timestamps=*/false)); + MP_ASSERT_OK(Send(embedding)); + MP_ASSERT_OK_AND_ASSIGN(auto result, GetResult(poller)); + + EXPECT_THAT(result, EqualsProto(embedding)); +} + +TEST_F(EmbeddingAggregationCalculatorTest, SucceedsWithTimestamps) { + MP_ASSERT_OK_AND_ASSIGN(auto poller, BuildGraph(/*connect_timestamps=*/true)); + MP_ASSERT_OK(Send(ParseTextProtoOrDie(R"pb(embeddings { + head_index: 0 + })pb"))); + MP_ASSERT_OK(Send( + ParseTextProtoOrDie( + R"pb(embeddings { head_index: 1 })pb"), + /*timestamp=*/1000, + /*aggregation_timestamps=*/std::optional>({0, 1000}))); + MP_ASSERT_OK_AND_ASSIGN(auto results, + GetResult>(poller)); + + EXPECT_THAT(results, + Pointwise(EqualsProto(), {ParseTextProtoOrDie( + R"pb(embeddings { head_index: 0 } + timestamp_ms: 0)pb"), + ParseTextProtoOrDie( + R"pb(embeddings { head_index: 1 } + timestamp_ms: 1)pb")})); +} + +} // namespace +} // namespace mediapipe diff --git a/mediapipe/tasks/cc/components/processors/BUILD b/mediapipe/tasks/cc/components/processors/BUILD index 12af55ed9..7845a3dae 100644 --- a/mediapipe/tasks/cc/components/processors/BUILD +++ b/mediapipe/tasks/cc/components/processors/BUILD @@ -82,6 +82,7 @@ cc_library( "//mediapipe/framework/formats:tensor", "//mediapipe/framework/tool:options_map", "//mediapipe/tasks/cc:common", + "//mediapipe/tasks/cc/components/calculators:embedding_aggregation_calculator", "//mediapipe/tasks/cc/components/calculators:tensors_to_embeddings_calculator", "//mediapipe/tasks/cc/components/calculators:tensors_to_embeddings_calculator_cc_proto", "//mediapipe/tasks/cc/components/containers/proto:embeddings_cc_proto", diff --git a/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.cc b/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.cc index 3a3884689..880aec5d7 100644 --- a/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.cc +++ b/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.cc @@ -56,6 +56,14 @@ using TensorsSource = constexpr char kTensorsTag[] = "TENSORS"; constexpr char kEmbeddingsTag[] = "EMBEDDINGS"; +constexpr char kTimestampedEmbeddingsTag[] = "TIMESTAMPED_EMBEDDINGS"; +constexpr char kTimestampsTag[] = "TIMESTAMPS"; + +// Struct holding the different output streams produced by the graph. +struct EmbeddingPostprocessingOutputStreams { + Source embeddings; + Source> timestamped_embeddings; +}; // Identifies whether or not the model has quantized outputs, and performs // sanity checks. @@ -168,27 +176,39 @@ absl::Status ConfigureEmbeddingPostprocessing( // TENSORS - std::vector // The output tensors of an InferenceCalculator, to convert into // EmbeddingResult objects. Expected to be of type kFloat32 or kUInt8. +// TIMESTAMPS - std::vector @Optional +// The collection of the timestamps that this calculator should aggregate. +// This stream is optional: if provided then the TIMESTAMPED_EMBEDDINGS +// output is used for results. Otherwise as no timestamp aggregation is +// required the EMBEDDINGS output is used for results. +// // Outputs: -// EMBEDDING_RESULT - EmbeddingResult -// The output EmbeddingResult. +// EMBEDDINGS - EmbeddingResult @Optional +// The embedding results aggregated by head. Must be connected if the +// TIMESTAMPS input is not connected, as it signals that timestamp +// aggregation is not required. +// TIMESTAMPED_EMBEDDINGS - std::vector @Optional +// The embedding result aggregated by timestamp, then by head. Must be +// connected if the TIMESTAMPS input is connected, as it signals that +// timestamp aggregation is required. // // The recommended way of using this graph is through the GraphBuilder API using // the 'ConfigureEmbeddingPostprocessing()' function. See header file for more // details. -// -// TODO: add support for additional optional "TIMESTAMPS" input for -// embeddings aggregation. class EmbeddingPostprocessingGraph : public mediapipe::Subgraph { public: absl::StatusOr GetConfig( mediapipe::SubgraphContext* sc) override { Graph graph; ASSIGN_OR_RETURN( - auto embedding_result_out, + auto output_streams, BuildEmbeddingPostprocessing( sc->Options(), - graph[Input>(kTensorsTag)], graph)); - embedding_result_out >> graph[Output(kEmbeddingsTag)]; + graph[Input>(kTensorsTag)], + graph[Input>(kTimestampsTag)], graph)); + output_streams.embeddings >> graph[Output(kEmbeddingsTag)]; + output_streams.timestamped_embeddings >> + graph[Output>(kTimestampedEmbeddingsTag)]; return graph.GetConfig(); } @@ -200,10 +220,14 @@ class EmbeddingPostprocessingGraph : public mediapipe::Subgraph { // // options: the on-device EmbeddingPostprocessingGraphOptions // tensors_in: (std::vector) tensors to postprocess. + // timestamps_in: (std::vector) optional collection of + // timestamps that should be used to aggregate embedding results. // graph: the mediapipe builder::Graph instance to be updated. - absl::StatusOr> BuildEmbeddingPostprocessing( + absl::StatusOr + BuildEmbeddingPostprocessing( const proto::EmbeddingPostprocessingGraphOptions options, - Source> tensors_in, Graph& graph) { + Source> tensors_in, + Source> timestamps_in, Graph& graph) { // If output tensors are quantized, they must be dequantized first. TensorsSource dequantized_tensors(&tensors_in); if (options.has_quantized_outputs()) { @@ -220,7 +244,20 @@ class EmbeddingPostprocessingGraph : public mediapipe::Subgraph { .GetOptions() .CopyFrom(options.tensors_to_embeddings_options()); dequantized_tensors >> tensors_to_embeddings_node.In(kTensorsTag); - return tensors_to_embeddings_node[Output(kEmbeddingsTag)]; + + // Adds EmbeddingAggregationCalculator. + GenericNode& aggregation_node = + graph.AddNode("EmbeddingAggregationCalculator"); + tensors_to_embeddings_node[Output(kEmbeddingsTag)] >> + aggregation_node.In(kEmbeddingsTag); + timestamps_in >> aggregation_node.In(kTimestampsTag); + + // Connects outputs. + return EmbeddingPostprocessingOutputStreams{ + /*embeddings=*/aggregation_node[Output( + kEmbeddingsTag)], + /*timestamped_embeddings=*/aggregation_node + [Output>(kTimestampedEmbeddingsTag)]}; } }; REGISTER_MEDIAPIPE_GRAPH( diff --git a/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h b/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h index 5e8f2c084..58606ed80 100644 --- a/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h +++ b/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph.h @@ -44,12 +44,20 @@ namespace processors { // TENSORS - std::vector // The output tensors of an InferenceCalculator, to convert into // EmbeddingResult objects. Expected to be of type kFloat32 or kUInt8. +// TIMESTAMPS - std::vector @Optional +// The collection of the timestamps that this calculator should aggregate. +// This stream is optional: if provided then the TIMESTAMPED_EMBEDDINGS +// output is used for results. Otherwise as no timestamp aggregation is +// required the EMBEDDINGS output is used for results. // Outputs: -// EMBEDDINGS - EmbeddingResult -// The output EmbeddingResult. -// -// TODO: add support for additional optional "TIMESTAMPS" input for -// embeddings aggregation. +// EMBEDDINGS - EmbeddingResult @Optional +// The embedding results aggregated by head. Must be connected if the +// TIMESTAMPS input is not connected, as it signals that timestamp +// aggregation is not required. +// TIMESTAMPED_EMBEDDINGS - std::vector @Optional +// The embedding result aggregated by timestamp, then by head. Must be +// connected if the TIMESTAMPS input is connected, as it signals that +// timestamp aggregation is required. absl::Status ConfigureEmbeddingPostprocessing( const tasks::core::ModelResources& model_resources, const proto::EmbedderOptions& embedder_options, diff --git a/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph_test.cc b/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph_test.cc index 62fab8f7e..84d84d648 100644 --- a/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph_test.cc +++ b/mediapipe/tasks/cc/components/processors/embedding_postprocessing_graph_test.cc @@ -20,11 +20,20 @@ limitations under the License. #include "absl/flags/flag.h" #include "absl/status/statusor.h" #include "absl/strings/string_view.h" +#include "mediapipe/framework/api2/builder.h" +#include "mediapipe/framework/api2/port.h" +#include "mediapipe/framework/calculator_framework.h" +#include "mediapipe/framework/calculator_runner.h" #include "mediapipe/framework/deps/file_path.h" +#include "mediapipe/framework/formats/tensor.h" +#include "mediapipe/framework/graph_runner.h" +#include "mediapipe/framework/output_stream_poller.h" #include "mediapipe/framework/port/gmock.h" #include "mediapipe/framework/port/gtest.h" #include "mediapipe/framework/port/parse_text_proto.h" #include "mediapipe/framework/port/status_matchers.h" +#include "mediapipe/framework/timestamp.h" +#include "mediapipe/tasks/cc/components/containers/proto/embeddings.pb.h" #include "mediapipe/tasks/cc/components/processors/proto/embedder_options.pb.h" #include "mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.pb.h" #include "mediapipe/tasks/cc/core/model_resources.h" @@ -37,7 +46,12 @@ namespace components { namespace processors { namespace { +using ::mediapipe::api2::Input; +using ::mediapipe::api2::Output; +using ::mediapipe::api2::builder::Graph; +using ::mediapipe::api2::builder::Source; using ::mediapipe::file::JoinPath; +using ::mediapipe::tasks::components::containers::proto::EmbeddingResult; using ::mediapipe::tasks::core::ModelResources; constexpr char kTestDataDirectory[] = "/mediapipe/tasks/testdata/"; @@ -51,6 +65,16 @@ constexpr char kQuantizedImageClassifierWithoutMetadata[] = "vision/mobilenet_v1_0.25_192_quantized_1_default_1.tflite"; constexpr char kTestModelResourcesTag[] = "test_model_resources"; +constexpr int kMobileNetV3EmbedderEmbeddingSize = 1024; + +constexpr char kTensorsTag[] = "TENSORS"; +constexpr char kTensorsName[] = "tensors"; +constexpr char kTimestampsTag[] = "TIMESTAMPS"; +constexpr char kTimestampsName[] = "timestamps"; +constexpr char kEmbeddingsTag[] = "EMBEDDINGS"; +constexpr char kEmbeddingsName[] = "embeddings"; +constexpr char kTimestampedEmbeddingsTag[] = "TIMESTAMPED_EMBEDDINGS"; +constexpr char kTimestampedEmbeddingsName[] = "timestamped_embeddings"; // Helper function to get ModelResources. absl::StatusOr> CreateModelResourcesForModel( @@ -128,8 +152,171 @@ TEST_F(ConfigureTest, SucceedsWithFloatModelWithMetadata) { has_quantized_outputs: false)pb"))); } -// TODO: add E2E Postprocessing tests once timestamp aggregation is -// supported. +class PostprocessingTest : public tflite_shims::testing::Test { + protected: + absl::StatusOr BuildGraph( + absl::string_view model_name, const proto::EmbedderOptions& options, + bool connect_timestamps = false) { + ASSIGN_OR_RETURN(auto model_resources, + CreateModelResourcesForModel(model_name)); + + Graph graph; + auto& postprocessing = graph.AddNode( + "mediapipe.tasks.components.processors." + "EmbeddingPostprocessingGraph"); + MP_RETURN_IF_ERROR(ConfigureEmbeddingPostprocessing( + *model_resources, options, + &postprocessing + .GetOptions())); + graph[Input>(kTensorsTag)].SetName(kTensorsName) >> + postprocessing.In(kTensorsTag); + if (connect_timestamps) { + graph[Input>(kTimestampsTag)].SetName( + kTimestampsName) >> + postprocessing.In(kTimestampsTag); + postprocessing.Out(kTimestampedEmbeddingsTag) + .SetName(kTimestampedEmbeddingsName) >> + graph[Output>( + kTimestampedEmbeddingsTag)]; + } else { + postprocessing.Out(kEmbeddingsTag).SetName(kEmbeddingsName) >> + graph[Output(kEmbeddingsTag)]; + } + + MP_RETURN_IF_ERROR(calculator_graph_.Initialize(graph.GetConfig())); + if (connect_timestamps) { + ASSIGN_OR_RETURN(auto poller, calculator_graph_.AddOutputStreamPoller( + kTimestampedEmbeddingsName)); + MP_RETURN_IF_ERROR(calculator_graph_.StartRun(/*extra_side_packets=*/{})); + return poller; + } + ASSIGN_OR_RETURN(auto poller, + calculator_graph_.AddOutputStreamPoller(kEmbeddingsName)); + MP_RETURN_IF_ERROR(calculator_graph_.StartRun(/*extra_side_packets=*/{})); + return poller; + } + + template + void AddTensor( + const std::vector& tensor, const Tensor::ElementType& element_type, + const Tensor::QuantizationParameters& quantization_parameters = {}) { + tensors_->emplace_back(element_type, + Tensor::Shape{1, static_cast(tensor.size())}, + quantization_parameters); + auto view = tensors_->back().GetCpuWriteView(); + T* buffer = view.buffer(); + std::copy(tensor.begin(), tensor.end(), buffer); + } + + absl::Status Run( + std::optional> aggregation_timestamps = std::nullopt, + int timestamp = 0) { + MP_RETURN_IF_ERROR(calculator_graph_.AddPacketToInputStream( + kTensorsName, Adopt(tensors_.release()).At(Timestamp(timestamp)))); + // Reset tensors for future calls. + tensors_ = absl::make_unique>(); + if (aggregation_timestamps.has_value()) { + auto packet = absl::make_unique>(); + for (const auto& timestamp : *aggregation_timestamps) { + packet->emplace_back(Timestamp(timestamp)); + } + MP_RETURN_IF_ERROR(calculator_graph_.AddPacketToInputStream( + kTimestampsName, Adopt(packet.release()).At(Timestamp(timestamp)))); + } + return absl::OkStatus(); + } + + template + absl::StatusOr GetResult(OutputStreamPoller& poller) { + MP_RETURN_IF_ERROR(calculator_graph_.WaitUntilIdle()); + MP_RETURN_IF_ERROR(calculator_graph_.CloseAllInputStreams()); + + Packet packet; + if (!poller.Next(&packet)) { + return absl::InternalError("Unable to get output packet"); + } + auto result = packet.Get(); + MP_RETURN_IF_ERROR(calculator_graph_.WaitUntilDone()); + return result; + } + + private: + CalculatorGraph calculator_graph_; + std::unique_ptr> tensors_ = + absl::make_unique>(); +}; + +TEST_F(PostprocessingTest, SucceedsWithoutTimestamps) { + // Build graph. + proto::EmbedderOptions options; + MP_ASSERT_OK_AND_ASSIGN(auto poller, + BuildGraph(kMobileNetV3Embedder, options)); + // Build input tensor. + std::vector tensor(kMobileNetV3EmbedderEmbeddingSize, 0); + tensor[0] = 1.0; + + // Send tensor and get results. + AddTensor(tensor, Tensor::ElementType::kFloat32); + MP_ASSERT_OK(Run()); + MP_ASSERT_OK_AND_ASSIGN(auto results, GetResult(poller)); + + // Validate results. + EXPECT_FALSE(results.has_timestamp_ms()); + EXPECT_EQ(results.embeddings_size(), 1); + EXPECT_EQ(results.embeddings(0).head_index(), 0); + EXPECT_EQ(results.embeddings(0).head_name(), "feature"); + EXPECT_EQ(results.embeddings(0).float_embedding().values_size(), + kMobileNetV3EmbedderEmbeddingSize); + EXPECT_FLOAT_EQ(results.embeddings(0).float_embedding().values(0), 1.0); + for (int i = 1; i < kMobileNetV3EmbedderEmbeddingSize; ++i) { + EXPECT_FLOAT_EQ(results.embeddings(0).float_embedding().values(i), 0.0); + } +} + +TEST_F(PostprocessingTest, SucceedsWithTimestamps) { + // Build graph. + proto::EmbedderOptions options; + MP_ASSERT_OK_AND_ASSIGN(auto poller, BuildGraph(kMobileNetV3Embedder, options, + /*connect_timestamps=*/true)); + // Build input tensors. + std::vector tensor_0(kMobileNetV3EmbedderEmbeddingSize, 0); + tensor_0[0] = 1.0; + std::vector tensor_1(kMobileNetV3EmbedderEmbeddingSize, 0); + tensor_1[0] = 2.0; + + // Send tensors and get results. + AddTensor(tensor_0, Tensor::ElementType::kFloat32); + MP_ASSERT_OK(Run()); + AddTensor(tensor_1, Tensor::ElementType::kFloat32); + MP_ASSERT_OK(Run( + /*aggregation_timestamps=*/std::optional>({0, 1000}), + /*timestamp=*/1000)); + MP_ASSERT_OK_AND_ASSIGN(auto results, + GetResult>(poller)); + + // Validate results. + EXPECT_EQ(results.size(), 2); + // First timestamp. + EXPECT_EQ(results[0].timestamp_ms(), 0); + EXPECT_EQ(results[0].embeddings(0).head_index(), 0); + EXPECT_EQ(results[0].embeddings(0).head_name(), "feature"); + EXPECT_EQ(results[0].embeddings(0).float_embedding().values_size(), + kMobileNetV3EmbedderEmbeddingSize); + EXPECT_FLOAT_EQ(results[0].embeddings(0).float_embedding().values(0), 1.0); + for (int i = 1; i < kMobileNetV3EmbedderEmbeddingSize; ++i) { + EXPECT_FLOAT_EQ(results[0].embeddings(0).float_embedding().values(i), 0.0); + } + // Second timestamp. + EXPECT_EQ(results[1].timestamp_ms(), 1); + EXPECT_EQ(results[1].embeddings(0).head_index(), 0); + EXPECT_EQ(results[1].embeddings(0).head_name(), "feature"); + EXPECT_EQ(results[1].embeddings(0).float_embedding().values_size(), + kMobileNetV3EmbedderEmbeddingSize); + EXPECT_FLOAT_EQ(results[1].embeddings(0).float_embedding().values(0), 2.0); + for (int i = 1; i < kMobileNetV3EmbedderEmbeddingSize; ++i) { + EXPECT_FLOAT_EQ(results[1].embeddings(0).float_embedding().values(i), 0.0); + } +} } // namespace } // namespace processors diff --git a/mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.proto b/mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.proto index f8dbf59f0..3a50818f6 100644 --- a/mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.proto +++ b/mediapipe/tasks/cc/components/processors/proto/embedding_postprocessing_graph_options.proto @@ -32,7 +32,4 @@ message EmbeddingPostprocessingGraphOptions { // Whether output tensors are quantized (kTfLiteUint8) or not (kFloat32). optional bool has_quantized_outputs = 2; - - // TODO: add options to control whether timestamp aggregation - // should be used or not. } From 0b12aa943585a39ea8cc4a7edb5d84f78d4b2ded Mon Sep 17 00:00:00 2001 From: MediaPipe Team Date: Thu, 10 Nov 2022 01:30:55 -0800 Subject: [PATCH 18/18] Internal change PiperOrigin-RevId: 487466061 --- .../tasks/web/audio/audio_classifier/BUILD | 2 +- .../audio_classifier/audio_classifier.ts | 40 +++++++------- .../audio_classifier_result.ts | 2 +- .../tasks/web/components/containers/BUILD | 4 +- ...ations.d.ts => classification_result.d.ts} | 36 +++++++------ .../tasks/web/components/processors/BUILD | 3 +- .../processors/classifier_result.ts | 54 +++++++++---------- .../tasks/web/text/text_classifier/BUILD | 2 +- .../text/text_classifier/text_classifier.ts | 34 +++++------- .../text_classifier/text_classifier_result.ts | 2 +- .../tasks/web/vision/image_classifier/BUILD | 2 +- .../image_classifier/image_classifier.ts | 36 +++++-------- .../image_classifier_result.ts | 2 +- 13 files changed, 103 insertions(+), 116 deletions(-) rename mediapipe/tasks/web/components/containers/{classifications.d.ts => classification_result.d.ts} (61%) diff --git a/mediapipe/tasks/web/audio/audio_classifier/BUILD b/mediapipe/tasks/web/audio/audio_classifier/BUILD index bc3048df1..1bc4af309 100644 --- a/mediapipe/tasks/web/audio/audio_classifier/BUILD +++ b/mediapipe/tasks/web/audio/audio_classifier/BUILD @@ -21,7 +21,7 @@ mediapipe_ts_library( "//mediapipe/tasks/cc/audio/audio_classifier/proto:audio_classifier_graph_options_jspb_proto", "//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto", "//mediapipe/tasks/web/components/containers:category", - "//mediapipe/tasks/web/components/containers:classifications", + "//mediapipe/tasks/web/components/containers:classification_result", "//mediapipe/tasks/web/components/processors:base_options", "//mediapipe/tasks/web/components/processors:classifier_options", "//mediapipe/tasks/web/components/processors:classifier_result", diff --git a/mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts b/mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts index 3d3ca5ae7..e3700cd7a 100644 --- a/mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts +++ b/mediapipe/tasks/web/audio/audio_classifier/audio_classifier.ts @@ -27,7 +27,7 @@ import {createMediaPipeLib, FileLocator} from '../../../../web/graph_runner/wasm // Placeholder for internal dependency on trusted resource url import {AudioClassifierOptions} from './audio_classifier_options'; -import {Classifications} from './audio_classifier_result'; +import {AudioClassifierResult} from './audio_classifier_result'; const MEDIAPIPE_GRAPH = 'mediapipe.tasks.audio.audio_classifier.AudioClassifierGraph'; @@ -38,14 +38,14 @@ const MEDIAPIPE_GRAPH = // implementation const AUDIO_STREAM = 'input_audio'; const SAMPLE_RATE_STREAM = 'sample_rate'; -const CLASSIFICATION_RESULT_STREAM = 'classification_result'; +const TIMESTAMPED_CLASSIFICATIONS_STREAM = 'timestamped_classifications'; // The OSS JS API does not support the builder pattern. // tslint:disable:jspb-use-builder-pattern /** Performs audio classification. */ export class AudioClassifier extends TaskRunner { - private classifications: Classifications[] = []; + private classificationResults: AudioClassifierResult[] = []; private defaultSampleRate = 48000; private readonly options = new AudioClassifierGraphOptions(); @@ -150,7 +150,8 @@ export class AudioClassifier extends TaskRunner { * `48000` if no custom default was set. * @return The classification result of the audio datas */ - classify(audioData: Float32Array, sampleRate?: number): Classifications[] { + classify(audioData: Float32Array, sampleRate?: number): + AudioClassifierResult[] { sampleRate = sampleRate ?? this.defaultSampleRate; // Configures the number of samples in the WASM layer. We re-configure the @@ -164,20 +165,22 @@ export class AudioClassifier extends TaskRunner { this.addDoubleToStream(sampleRate, SAMPLE_RATE_STREAM, timestamp); this.addAudioToStream(audioData, timestamp); - this.classifications = []; + this.classificationResults = []; this.finishProcessing(); - return [...this.classifications]; + return [...this.classificationResults]; } /** - * Internal function for converting raw data into a classification, and - * adding it to our classfications list. + * Internal function for converting raw data into classification results, and + * adding them to our classfication results list. **/ - private addJsAudioClassification(binaryProto: Uint8Array): void { - const classificationResult = - ClassificationResult.deserializeBinary(binaryProto); - this.classifications.push( - ...convertFromClassificationResultProto(classificationResult)); + private addJsAudioClassificationResults(binaryProtos: Uint8Array[]): void { + binaryProtos.forEach(binaryProto => { + const classificationResult = + ClassificationResult.deserializeBinary(binaryProto); + this.classificationResults.push( + convertFromClassificationResultProto(classificationResult)); + }); } /** Updates the MediaPipe graph configuration. */ @@ -185,7 +188,7 @@ export class AudioClassifier extends TaskRunner { const graphConfig = new CalculatorGraphConfig(); graphConfig.addInputStream(AUDIO_STREAM); graphConfig.addInputStream(SAMPLE_RATE_STREAM); - graphConfig.addOutputStream(CLASSIFICATION_RESULT_STREAM); + graphConfig.addOutputStream(TIMESTAMPED_CLASSIFICATIONS_STREAM); const calculatorOptions = new CalculatorOptions(); calculatorOptions.setExtension( @@ -198,14 +201,15 @@ export class AudioClassifier extends TaskRunner { classifierNode.addInputStream('AUDIO:' + AUDIO_STREAM); classifierNode.addInputStream('SAMPLE_RATE:' + SAMPLE_RATE_STREAM); classifierNode.addOutputStream( - 'CLASSIFICATIONS:' + CLASSIFICATION_RESULT_STREAM); + 'TIMESTAMPED_CLASSIFICATIONS:' + TIMESTAMPED_CLASSIFICATIONS_STREAM); classifierNode.setOptions(calculatorOptions); graphConfig.addNode(classifierNode); - this.attachProtoListener(CLASSIFICATION_RESULT_STREAM, binaryProto => { - this.addJsAudioClassification(binaryProto); - }); + this.attachProtoVectorListener( + TIMESTAMPED_CLASSIFICATIONS_STREAM, binaryProtos => { + this.addJsAudioClassificationResults(binaryProtos); + }); const binaryGraph = graphConfig.serializeBinary(); this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true); diff --git a/mediapipe/tasks/web/audio/audio_classifier/audio_classifier_result.ts b/mediapipe/tasks/web/audio/audio_classifier/audio_classifier_result.ts index 0a51dee04..0b616126a 100644 --- a/mediapipe/tasks/web/audio/audio_classifier/audio_classifier_result.ts +++ b/mediapipe/tasks/web/audio/audio_classifier/audio_classifier_result.ts @@ -15,4 +15,4 @@ */ export {Category} from '../../../../tasks/web/components/containers/category'; -export {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications'; +export {ClassificationResult as AudioClassifierResult, Classifications} from '../../../../tasks/web/components/containers/classification_result'; diff --git a/mediapipe/tasks/web/components/containers/BUILD b/mediapipe/tasks/web/components/containers/BUILD index 7d13fadcb..1b0e403ff 100644 --- a/mediapipe/tasks/web/components/containers/BUILD +++ b/mediapipe/tasks/web/components/containers/BUILD @@ -10,8 +10,8 @@ mediapipe_ts_library( ) mediapipe_ts_library( - name = "classifications", - srcs = ["classifications.d.ts"], + name = "classification_result", + srcs = ["classification_result.d.ts"], deps = [":category"], ) diff --git a/mediapipe/tasks/web/components/containers/classifications.d.ts b/mediapipe/tasks/web/components/containers/classification_result.d.ts similarity index 61% rename from mediapipe/tasks/web/components/containers/classifications.d.ts rename to mediapipe/tasks/web/components/containers/classification_result.d.ts index 67a259bbe..33632f925 100644 --- a/mediapipe/tasks/web/components/containers/classifications.d.ts +++ b/mediapipe/tasks/web/components/containers/classification_result.d.ts @@ -16,27 +16,14 @@ import {Category} from '../../../../tasks/web/components/containers/category'; -/** List of predicted categories with an optional timestamp. */ -export interface ClassificationEntry { +/** Classification results for a given classifier head. */ +export interface Classifications { /** * The array of predicted categories, usually sorted by descending scores, * e.g., from high to low probability. */ categories: Category[]; - /** - * The optional timestamp (in milliseconds) associated to the classification - * entry. This is useful for time series use cases, e.g., audio - * classification. - */ - timestampMs?: number; -} - -/** Classifications for a given classifier head. */ -export interface Classifications { - /** A list of classification entries. */ - entries: ClassificationEntry[]; - /** * The index of the classifier head these categories refer to. This is * useful for multi-head models. @@ -45,7 +32,24 @@ export interface Classifications { /** * The name of the classifier head, which is the corresponding tensor - * metadata name. + * metadata name. Defaults to an empty string if there is no such metadata. */ headName: string; } + +/** Classification results of a model. */ +export interface ClassificationResult { + /** The classification results for each head of the model. */ + classifications: Classifications[]; + + /** + * The optional timestamp (in milliseconds) of the start of the chunk of data + * corresponding to these results. + * + * This is only used for classification on time series (e.g. audio + * classification). In these use cases, the amount of data to process might + * exceed the maximum size that the model can process: to solve this, the + * input data is split into multiple chunks starting at different timestamps. + */ + timestampMs?: number; +} diff --git a/mediapipe/tasks/web/components/processors/BUILD b/mediapipe/tasks/web/components/processors/BUILD index cd7190dd9..e0d84b632 100644 --- a/mediapipe/tasks/web/components/processors/BUILD +++ b/mediapipe/tasks/web/components/processors/BUILD @@ -17,8 +17,9 @@ mediapipe_ts_library( name = "classifier_result", srcs = ["classifier_result.ts"], deps = [ + "//mediapipe/framework/formats:classification_jspb_proto", "//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto", - "//mediapipe/tasks/web/components/containers:classifications", + "//mediapipe/tasks/web/components/containers:classification_result", ], ) diff --git a/mediapipe/tasks/web/components/processors/classifier_result.ts b/mediapipe/tasks/web/components/processors/classifier_result.ts index ade967932..90d10b84d 100644 --- a/mediapipe/tasks/web/components/processors/classifier_result.ts +++ b/mediapipe/tasks/web/components/processors/classifier_result.ts @@ -14,48 +14,46 @@ * limitations under the License. */ -import {ClassificationEntry as ClassificationEntryProto, ClassificationResult} from '../../../../tasks/cc/components/containers/proto/classifications_pb'; -import {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications'; +import {ClassificationResult as ClassificationResultProto, Classifications as ClassificationsProto} from '../../../../tasks/cc/components/containers/proto/classifications_pb'; +import {ClassificationResult, Classifications} from '../../../../tasks/web/components/containers/classification_result'; const DEFAULT_INDEX = -1; const DEFAULT_SCORE = 0.0; /** - * Converts a ClassificationEntry proto to the ClassificationEntry result - * type. + * Converts a Classifications proto to a Classifications object. */ -function convertFromClassificationEntryProto(source: ClassificationEntryProto): - ClassificationEntry { - const categories = source.getCategoriesList().map(category => { - return { - index: category.getIndex() ?? DEFAULT_INDEX, - score: category.getScore() ?? DEFAULT_SCORE, - displayName: category.getDisplayName() ?? '', - categoryName: category.getCategoryName() ?? '', - }; - }); - +function convertFromClassificationsProto(source: ClassificationsProto): + Classifications { + const categories = + source.getClassificationList()?.getClassificationList().map( + classification => { + return { + index: classification.getIndex() ?? DEFAULT_INDEX, + score: classification.getScore() ?? DEFAULT_SCORE, + categoryName: classification.getLabel() ?? '', + displayName: classification.getDisplayName() ?? '', + }; + }) ?? + []; return { categories, - timestampMs: source.getTimestampMs(), + headIndex: source.getHeadIndex() ?? DEFAULT_INDEX, + headName: source.getHeadName() ?? '', }; } /** - * Converts a ClassificationResult proto to a list of classifications. + * Converts a ClassificationResult proto to a ClassificationResult object. */ export function convertFromClassificationResultProto( - classificationResult: ClassificationResult) : Classifications[] { - const result: Classifications[] = []; - for (const classificationsProto of - classificationResult.getClassificationsList()) { - const classifications: Classifications = { - entries: classificationsProto.getEntriesList().map( - entry => convertFromClassificationEntryProto(entry)), - headIndex: classificationsProto.getHeadIndex() ?? DEFAULT_INDEX, - headName: classificationsProto.getHeadName() ?? '', - }; - result.push(classifications); + source: ClassificationResultProto): ClassificationResult { + const result: ClassificationResult = { + classifications: source.getClassificationsList().map( + classififications => convertFromClassificationsProto(classififications)) + }; + if (source.hasTimestampMs()) { + result.timestampMs = source.getTimestampMs(); } return result; } diff --git a/mediapipe/tasks/web/text/text_classifier/BUILD b/mediapipe/tasks/web/text/text_classifier/BUILD index 25a8817d4..4ebdce18a 100644 --- a/mediapipe/tasks/web/text/text_classifier/BUILD +++ b/mediapipe/tasks/web/text/text_classifier/BUILD @@ -22,7 +22,7 @@ mediapipe_ts_library( "//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto", "//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_jspb_proto", "//mediapipe/tasks/web/components/containers:category", - "//mediapipe/tasks/web/components/containers:classifications", + "//mediapipe/tasks/web/components/containers:classification_result", "//mediapipe/tasks/web/components/processors:base_options", "//mediapipe/tasks/web/components/processors:classifier_options", "//mediapipe/tasks/web/components/processors:classifier_result", diff --git a/mediapipe/tasks/web/text/text_classifier/text_classifier.ts b/mediapipe/tasks/web/text/text_classifier/text_classifier.ts index d92248b80..e1d0c9601 100644 --- a/mediapipe/tasks/web/text/text_classifier/text_classifier.ts +++ b/mediapipe/tasks/web/text/text_classifier/text_classifier.ts @@ -27,10 +27,10 @@ import {createMediaPipeLib, FileLocator} from '../../../../web/graph_runner/wasm // Placeholder for internal dependency on trusted resource url import {TextClassifierOptions} from './text_classifier_options'; -import {Classifications} from './text_classifier_result'; +import {TextClassifierResult} from './text_classifier_result'; const INPUT_STREAM = 'text_in'; -const CLASSIFICATION_RESULT_STREAM = 'classification_result_out'; +const CLASSIFICATIONS_STREAM = 'classifications_out'; const TEXT_CLASSIFIER_GRAPH = 'mediapipe.tasks.text.text_classifier.TextClassifierGraph'; @@ -39,7 +39,7 @@ const TEXT_CLASSIFIER_GRAPH = /** Performs Natural Language classification. */ export class TextClassifier extends TaskRunner { - private classifications: Classifications[] = []; + private classificationResult: TextClassifierResult = {classifications: []}; private readonly options = new TextClassifierGraphOptions(); /** @@ -129,30 +129,20 @@ export class TextClassifier extends TaskRunner { * @param text The text to process. * @return The classification result of the text */ - classify(text: string): Classifications[] { - // Get classification classes by running our MediaPipe graph. - this.classifications = []; + classify(text: string): TextClassifierResult { + // Get classification result by running our MediaPipe graph. + this.classificationResult = {classifications: []}; this.addStringToStream( text, INPUT_STREAM, /* timestamp= */ performance.now()); this.finishProcessing(); - return [...this.classifications]; - } - - // Internal function for converting raw data into a classification, and - // adding it to our classifications list. - private addJsTextClassification(binaryProto: Uint8Array): void { - const classificationResult = - ClassificationResult.deserializeBinary(binaryProto); - console.log(classificationResult.toObject()); - this.classifications.push( - ...convertFromClassificationResultProto(classificationResult)); + return this.classificationResult; } /** Updates the MediaPipe graph configuration. */ private refreshGraph(): void { const graphConfig = new CalculatorGraphConfig(); graphConfig.addInputStream(INPUT_STREAM); - graphConfig.addOutputStream(CLASSIFICATION_RESULT_STREAM); + graphConfig.addOutputStream(CLASSIFICATIONS_STREAM); const calculatorOptions = new CalculatorOptions(); calculatorOptions.setExtension( @@ -161,14 +151,14 @@ export class TextClassifier extends TaskRunner { const classifierNode = new CalculatorGraphConfig.Node(); classifierNode.setCalculator(TEXT_CLASSIFIER_GRAPH); classifierNode.addInputStream('TEXT:' + INPUT_STREAM); - classifierNode.addOutputStream( - 'CLASSIFICATION_RESULT:' + CLASSIFICATION_RESULT_STREAM); + classifierNode.addOutputStream('CLASSIFICATIONS:' + CLASSIFICATIONS_STREAM); classifierNode.setOptions(calculatorOptions); graphConfig.addNode(classifierNode); - this.attachProtoListener(CLASSIFICATION_RESULT_STREAM, binaryProto => { - this.addJsTextClassification(binaryProto); + this.attachProtoListener(CLASSIFICATIONS_STREAM, binaryProto => { + this.classificationResult = convertFromClassificationResultProto( + ClassificationResult.deserializeBinary(binaryProto)); }); const binaryGraph = graphConfig.serializeBinary(); diff --git a/mediapipe/tasks/web/text/text_classifier/text_classifier_result.ts b/mediapipe/tasks/web/text/text_classifier/text_classifier_result.ts index 0a51dee04..707ba5da2 100644 --- a/mediapipe/tasks/web/text/text_classifier/text_classifier_result.ts +++ b/mediapipe/tasks/web/text/text_classifier/text_classifier_result.ts @@ -15,4 +15,4 @@ */ export {Category} from '../../../../tasks/web/components/containers/category'; -export {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications'; +export {ClassificationResult as TextClassifierResult, Classifications} from '../../../../tasks/web/components/containers/classification_result'; diff --git a/mediapipe/tasks/web/vision/image_classifier/BUILD b/mediapipe/tasks/web/vision/image_classifier/BUILD index 6937dc4f3..e96d6a8e3 100644 --- a/mediapipe/tasks/web/vision/image_classifier/BUILD +++ b/mediapipe/tasks/web/vision/image_classifier/BUILD @@ -21,7 +21,7 @@ mediapipe_ts_library( "//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto", "//mediapipe/tasks/cc/vision/image_classifier/proto:image_classifier_graph_options_jspb_proto", "//mediapipe/tasks/web/components/containers:category", - "//mediapipe/tasks/web/components/containers:classifications", + "//mediapipe/tasks/web/components/containers:classification_result", "//mediapipe/tasks/web/components/processors:base_options", "//mediapipe/tasks/web/components/processors:classifier_options", "//mediapipe/tasks/web/components/processors:classifier_result", diff --git a/mediapipe/tasks/web/vision/image_classifier/image_classifier.ts b/mediapipe/tasks/web/vision/image_classifier/image_classifier.ts index cb63874c4..ba4b6c907 100644 --- a/mediapipe/tasks/web/vision/image_classifier/image_classifier.ts +++ b/mediapipe/tasks/web/vision/image_classifier/image_classifier.ts @@ -27,12 +27,12 @@ import {createMediaPipeLib, FileLocator, ImageSource} from '../../../../web/grap // Placeholder for internal dependency on trusted resource url import {ImageClassifierOptions} from './image_classifier_options'; -import {Classifications} from './image_classifier_result'; +import {ImageClassifierResult} from './image_classifier_result'; const IMAGE_CLASSIFIER_GRAPH = 'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph'; const INPUT_STREAM = 'input_image'; -const CLASSIFICATION_RESULT_STREAM = 'classification_result'; +const CLASSIFICATIONS_STREAM = 'classifications'; export {ImageSource}; // Used in the public API @@ -41,7 +41,7 @@ export {ImageSource}; // Used in the public API /** Performs classification on images. */ export class ImageClassifier extends TaskRunner { - private classifications: Classifications[] = []; + private classificationResult: ImageClassifierResult = {classifications: []}; private readonly options = new ImageClassifierGraphOptions(); /** @@ -133,31 +133,21 @@ export class ImageClassifier extends TaskRunner { * provided, defaults to `performance.now()`. * @return The classification result of the image */ - classify(imageSource: ImageSource, timestamp?: number): Classifications[] { - // Get classification classes by running our MediaPipe graph. - this.classifications = []; + classify(imageSource: ImageSource, timestamp?: number): + ImageClassifierResult { + // Get classification result by running our MediaPipe graph. + this.classificationResult = {classifications: []}; this.addGpuBufferAsImageToStream( imageSource, INPUT_STREAM, timestamp ?? performance.now()); this.finishProcessing(); - return [...this.classifications]; - } - - /** - * Internal function for converting raw data into a classification, and - * adding it to our classfications list. - **/ - private addJsImageClassification(binaryProto: Uint8Array): void { - const classificationResult = - ClassificationResult.deserializeBinary(binaryProto); - this.classifications.push( - ...convertFromClassificationResultProto(classificationResult)); + return this.classificationResult; } /** Updates the MediaPipe graph configuration. */ private refreshGraph(): void { const graphConfig = new CalculatorGraphConfig(); graphConfig.addInputStream(INPUT_STREAM); - graphConfig.addOutputStream(CLASSIFICATION_RESULT_STREAM); + graphConfig.addOutputStream(CLASSIFICATIONS_STREAM); const calculatorOptions = new CalculatorOptions(); calculatorOptions.setExtension( @@ -168,14 +158,14 @@ export class ImageClassifier extends TaskRunner { const classifierNode = new CalculatorGraphConfig.Node(); classifierNode.setCalculator(IMAGE_CLASSIFIER_GRAPH); classifierNode.addInputStream('IMAGE:' + INPUT_STREAM); - classifierNode.addOutputStream( - 'CLASSIFICATION_RESULT:' + CLASSIFICATION_RESULT_STREAM); + classifierNode.addOutputStream('CLASSIFICATIONS:' + CLASSIFICATIONS_STREAM); classifierNode.setOptions(calculatorOptions); graphConfig.addNode(classifierNode); - this.attachProtoListener(CLASSIFICATION_RESULT_STREAM, binaryProto => { - this.addJsImageClassification(binaryProto); + this.attachProtoListener(CLASSIFICATIONS_STREAM, binaryProto => { + this.classificationResult = convertFromClassificationResultProto( + ClassificationResult.deserializeBinary(binaryProto)); }); const binaryGraph = graphConfig.serializeBinary(); diff --git a/mediapipe/tasks/web/vision/image_classifier/image_classifier_result.ts b/mediapipe/tasks/web/vision/image_classifier/image_classifier_result.ts index 0a51dee04..44032234c 100644 --- a/mediapipe/tasks/web/vision/image_classifier/image_classifier_result.ts +++ b/mediapipe/tasks/web/vision/image_classifier/image_classifier_result.ts @@ -15,4 +15,4 @@ */ export {Category} from '../../../../tasks/web/components/containers/category'; -export {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications'; +export {ClassificationResult as ImageClassifierResult, Classifications} from '../../../../tasks/web/components/containers/classification_result';