Merge pull request #3801 from kinaryml:gesture-recognizer-python
PiperOrigin-RevId: 485884796
This commit is contained in:
commit
716e59f90c
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@ -87,6 +87,7 @@ cc_library(
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cc_library(
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name = "builtin_task_graphs",
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deps = [
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"//mediapipe/tasks/cc/vision/gesture_recognizer:gesture_recognizer_graph",
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"//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
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"//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
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"//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
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|
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@ -36,6 +36,29 @@ py_library(
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],
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)
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py_library(
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name = "landmark",
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srcs = ["landmark.py"],
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deps = [
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"//mediapipe/framework/formats:landmark_py_pb2",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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)
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py_library(
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name = "landmark_detection_result",
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srcs = ["landmark_detection_result.py"],
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deps = [
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":landmark",
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":rect",
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"//mediapipe/framework/formats:classification_py_pb2",
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"//mediapipe/framework/formats:landmark_py_pb2",
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"//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_py_pb2",
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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)
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py_library(
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name = "category",
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srcs = ["category.py"],
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@ -14,7 +14,7 @@
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"""Category data class."""
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import dataclasses
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from typing import Any
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from typing import Any, Optional
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from mediapipe.tasks.cc.components.containers.proto import category_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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@ -39,10 +39,10 @@ class Category:
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category_name: The label of this category object.
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"""
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index: int
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score: float
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display_name: str
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category_name: str
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index: Optional[int] = None
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score: Optional[float] = None
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display_name: Optional[str] = None
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category_name: Optional[str] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _CategoryProto:
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122
mediapipe/tasks/python/components/containers/landmark.py
Normal file
122
mediapipe/tasks/python/components/containers/landmark.py
Normal file
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@ -0,0 +1,122 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Landmark data class."""
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import dataclasses
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from typing import Optional
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from mediapipe.framework.formats import landmark_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_LandmarkProto = landmark_pb2.Landmark
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_NormalizedLandmarkProto = landmark_pb2.NormalizedLandmark
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@dataclasses.dataclass
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class Landmark:
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"""A landmark that can have 1 to 3 dimensions.
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Use x for 1D points, (x, y) for 2D points and (x, y, z) for 3D points.
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Attributes:
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x: The x coordinate.
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y: The y coordinate.
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z: The z coordinate.
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visibility: Landmark visibility. Should stay unset if not supported. Float
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score of whether landmark is visible or occluded by other objects.
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Landmark considered as invisible also if it is not present on the screen
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(out of scene bounds). Depending on the model, visibility value is either
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a sigmoid or an argument of sigmoid.
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presence: Landmark presence. Should stay unset if not supported. Float score
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of whether landmark is present on the scene (located within scene bounds).
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Depending on the model, presence value is either a result of sigmoid or an
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argument of sigmoid function to get landmark presence probability.
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"""
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x: Optional[float] = None
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y: Optional[float] = None
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z: Optional[float] = None
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visibility: Optional[float] = None
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presence: Optional[float] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _LandmarkProto:
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"""Generates a Landmark protobuf object."""
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return _LandmarkProto(
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x=self.x,
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y=self.y,
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z=self.z,
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visibility=self.visibility,
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presence=self.presence)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(cls, pb2_obj: _LandmarkProto) -> 'Landmark':
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"""Creates a `Landmark` object from the given protobuf object."""
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return Landmark(
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x=pb2_obj.x,
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y=pb2_obj.y,
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z=pb2_obj.z,
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visibility=pb2_obj.visibility,
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presence=pb2_obj.presence)
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@dataclasses.dataclass
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class NormalizedLandmark:
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"""A normalized version of above Landmark proto.
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All coordinates should be within [0, 1].
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Attributes:
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x: The normalized x coordinate.
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y: The normalized y coordinate.
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z: The normalized z coordinate.
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visibility: Landmark visibility. Should stay unset if not supported. Float
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score of whether landmark is visible or occluded by other objects.
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Landmark considered as invisible also if it is not present on the screen
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(out of scene bounds). Depending on the model, visibility value is either
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a sigmoid or an argument of sigmoid.
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presence: Landmark presence. Should stay unset if not supported. Float score
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of whether landmark is present on the scene (located within scene bounds).
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Depending on the model, presence value is either a result of sigmoid or an
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argument of sigmoid function to get landmark presence probability.
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"""
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x: Optional[float] = None
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y: Optional[float] = None
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z: Optional[float] = None
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visibility: Optional[float] = None
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presence: Optional[float] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _NormalizedLandmarkProto:
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"""Generates a NormalizedLandmark protobuf object."""
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return _NormalizedLandmarkProto(
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x=self.x,
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y=self.y,
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z=self.z,
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visibility=self.visibility,
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presence=self.presence)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls, pb2_obj: _NormalizedLandmarkProto) -> 'NormalizedLandmark':
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"""Creates a `NormalizedLandmark` object from the given protobuf object."""
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return NormalizedLandmark(
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x=pb2_obj.x,
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y=pb2_obj.y,
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z=pb2_obj.z,
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visibility=pb2_obj.visibility,
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presence=pb2_obj.presence)
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@ -0,0 +1,96 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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||||
#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Landmarks Detection Result data class."""
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import dataclasses
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from typing import Optional, List
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from mediapipe.framework.formats import classification_pb2
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from mediapipe.framework.formats import landmark_pb2
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from mediapipe.tasks.cc.components.containers.proto import landmarks_detection_result_pb2
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from mediapipe.tasks.python.components.containers import category as category_module
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from mediapipe.tasks.python.components.containers import landmark as landmark_module
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from mediapipe.tasks.python.components.containers import rect as rect_module
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult
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_ClassificationProto = classification_pb2.Classification
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_ClassificationListProto = classification_pb2.ClassificationList
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_LandmarkListProto = landmark_pb2.LandmarkList
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_NormalizedLandmarkListProto = landmark_pb2.NormalizedLandmarkList
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_NormalizedRect = rect_module.NormalizedRect
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_Category = category_module.Category
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_NormalizedLandmark = landmark_module.NormalizedLandmark
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_Landmark = landmark_module.Landmark
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@dataclasses.dataclass
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class LandmarksDetectionResult:
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"""Represents the landmarks detection result.
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Attributes: landmarks : A list of `NormalizedLandmark` objects. categories : A
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list of `Category` objects. world_landmarks : A list of `Landmark` objects.
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rect : A `NormalizedRect` object.
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"""
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landmarks: Optional[List[_NormalizedLandmark]]
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categories: Optional[List[_Category]]
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world_landmarks: Optional[List[_Landmark]]
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rect: _NormalizedRect
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _LandmarksDetectionResultProto:
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"""Generates a LandmarksDetectionResult protobuf object."""
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classifications = _ClassificationListProto()
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for category in self.categories:
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classifications.classification.append(
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_ClassificationProto(
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index=category.index,
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score=category.score,
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label=category.category_name,
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display_name=category.display_name))
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return _LandmarksDetectionResultProto(
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landmarks=_NormalizedLandmarkListProto(self.landmarks),
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classifications=classifications,
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world_landmarks=_LandmarkListProto(self.world_landmarks),
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rect=self.rect.to_pb2())
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(
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cls,
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pb2_obj: _LandmarksDetectionResultProto) -> 'LandmarksDetectionResult':
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"""Creates a `LandmarksDetectionResult` object from the given protobuf object.
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"""
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categories = []
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for classification in pb2_obj.classifications.classification:
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categories.append(
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category_module.Category(
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score=classification.score,
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index=classification.index,
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category_name=classification.label,
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display_name=classification.display_name))
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return LandmarksDetectionResult(
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landmarks=[
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_NormalizedLandmark.create_from_pb2(landmark)
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for landmark in pb2_obj.landmarks.landmark
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],
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categories=categories,
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world_landmarks=[
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_Landmark.create_from_pb2(landmark)
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for landmark in pb2_obj.world_landmarks.landmark
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],
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rect=_NormalizedRect.create_from_pb2(pb2_obj.rect))
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@ -19,80 +19,49 @@ from typing import Any, Optional
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from mediapipe.framework.formats import rect_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_RectProto = rect_pb2.Rect
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_NormalizedRectProto = rect_pb2.NormalizedRect
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@dataclasses.dataclass
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class Rect:
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"""A rectangle with rotation in image coordinates.
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"""A rectangle, used as part of detection results or as input region-of-interest.
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Attributes: x_center : The X coordinate of the top-left corner, in pixels.
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y_center : The Y coordinate of the top-left corner, in pixels.
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width: The width of the rectangle, in pixels.
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height: The height of the rectangle, in pixels.
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rotation: Rotation angle is clockwise in radians.
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rect_id: Optional unique id to help associate different rectangles to each
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other.
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The coordinates are normalized wrt the image dimensions, i.e. generally in
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[0,1] but they may exceed these bounds if describing a region overlapping the
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image. The origin is on the top-left corner of the image.
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Attributes:
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left: The X coordinate of the left side of the rectangle.
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top: The Y coordinate of the top of the rectangle.
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right: The X coordinate of the right side of the rectangle.
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bottom: The Y coordinate of the bottom of the rectangle.
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"""
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x_center: int
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y_center: int
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width: int
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height: int
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rotation: Optional[float] = 0.0
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rect_id: Optional[int] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _RectProto:
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"""Generates a Rect protobuf object."""
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return _RectProto(
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x_center=self.x_center,
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y_center=self.y_center,
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width=self.width,
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height=self.height,
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)
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@classmethod
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@doc_controls.do_not_generate_docs
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def create_from_pb2(cls, pb2_obj: _RectProto) -> 'Rect':
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"""Creates a `Rect` object from the given protobuf object."""
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return Rect(
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x_center=pb2_obj.x_center,
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y_center=pb2_obj.y_center,
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width=pb2_obj.width,
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height=pb2_obj.height)
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def __eq__(self, other: Any) -> bool:
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"""Checks if this object is equal to the given object.
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Args:
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other: The object to be compared with.
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Returns:
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True if the objects are equal.
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"""
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if not isinstance(other, Rect):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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left: float
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top: float
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right: float
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bottom: float
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|
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|
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@dataclasses.dataclass
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class NormalizedRect:
|
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"""A rectangle with rotation in normalized coordinates.
|
||||
|
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The values of box
|
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Location of the center of the rectangle in image coordinates. The (0.0, 0.0)
|
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point is at the (top, left) corner.
|
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|
||||
center location and size are within [0, 1].
|
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The values of box center location and size are within [0, 1].
|
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|
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Attributes: x_center : The X normalized coordinate of the top-left corner.
|
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y_center : The Y normalized coordinate of the top-left corner.
|
||||
Attributes:
|
||||
x_center: The normalized X coordinate of the rectangle, in image
|
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coordinates.
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y_center: The normalized Y coordinate of the rectangle, in image
|
||||
coordinates.
|
||||
width: The width of the rectangle.
|
||||
height: The height of the rectangle.
|
||||
rotation: Rotation angle is clockwise in radians.
|
||||
rect_id: Optional unique id to help associate different rectangles to each
|
||||
other.
|
||||
rect_id: Optional unique id to help associate different rectangles to each
|
||||
other.
|
||||
"""
|
||||
|
||||
x_center: float
|
||||
|
|
|
@ -53,6 +53,7 @@ py_test(
|
|||
"//mediapipe/tasks/python/core:base_options",
|
||||
"//mediapipe/tasks/python/test:test_utils",
|
||||
"//mediapipe/tasks/python/vision:image_classifier",
|
||||
"//mediapipe/tasks/python/vision/core:image_processing_options",
|
||||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -30,9 +30,10 @@ from mediapipe.tasks.python.components.processors import classifier_options
|
|||
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 image_classifier
|
||||
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
|
||||
|
||||
_NormalizedRect = rect.NormalizedRect
|
||||
_Rect = rect.Rect
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_ClassifierOptions = classifier_options.ClassifierOptions
|
||||
_Category = category.Category
|
||||
|
@ -43,6 +44,7 @@ _Image = image.Image
|
|||
_ImageClassifier = image_classifier.ImageClassifier
|
||||
_ImageClassifierOptions = image_classifier.ImageClassifierOptions
|
||||
_RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
|
||||
_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
|
||||
_IMAGE_FILE = 'burger.jpg'
|
||||
|
@ -227,11 +229,11 @@ class ImageClassifierTest(parameterized.TestCase):
|
|||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')))
|
||||
# NormalizedRect around the soccer ball.
|
||||
roi = _NormalizedRect(
|
||||
x_center=0.532, y_center=0.521, width=0.164, height=0.427)
|
||||
# Region-of-interest around the soccer ball.
|
||||
roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
|
||||
image_processing_options = _ImageProcessingOptions(roi)
|
||||
# Performs image classification on the input.
|
||||
image_result = classifier.classify(test_image, roi)
|
||||
image_result = classifier.classify(test_image, image_processing_options)
|
||||
# Comparing results.
|
||||
test_utils.assert_proto_equals(self, image_result.to_pb2(),
|
||||
_generate_soccer_ball_results(0).to_pb2())
|
||||
|
@ -417,12 +419,12 @@ class ImageClassifierTest(parameterized.TestCase):
|
|||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')))
|
||||
# NormalizedRect around the soccer ball.
|
||||
roi = _NormalizedRect(
|
||||
x_center=0.532, y_center=0.521, width=0.164, height=0.427)
|
||||
# Region-of-interest around the soccer ball.
|
||||
roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
|
||||
image_processing_options = _ImageProcessingOptions(roi)
|
||||
for timestamp in range(0, 300, 30):
|
||||
classification_result = classifier.classify_for_video(
|
||||
test_image, timestamp, roi)
|
||||
test_image, timestamp, image_processing_options)
|
||||
test_utils.assert_proto_equals(
|
||||
self, classification_result.to_pb2(),
|
||||
_generate_soccer_ball_results(timestamp).to_pb2())
|
||||
|
@ -491,9 +493,9 @@ class ImageClassifierTest(parameterized.TestCase):
|
|||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')))
|
||||
# NormalizedRect around the soccer ball.
|
||||
roi = _NormalizedRect(
|
||||
x_center=0.532, y_center=0.521, width=0.164, height=0.427)
|
||||
# Region-of-interest around the soccer ball.
|
||||
roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
|
||||
image_processing_options = _ImageProcessingOptions(roi)
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(result: _ClassificationResult, output_image: _Image,
|
||||
|
@ -514,7 +516,8 @@ class ImageClassifierTest(parameterized.TestCase):
|
|||
result_callback=check_result)
|
||||
with _ImageClassifier.create_from_options(options) as classifier:
|
||||
for timestamp in range(0, 300, 30):
|
||||
classifier.classify_async(test_image, timestamp, roi)
|
||||
classifier.classify_async(test_image, timestamp,
|
||||
image_processing_options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
|
|
@ -55,6 +55,7 @@ py_library(
|
|||
"//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",
|
||||
],
|
||||
)
|
||||
|
@ -77,3 +78,27 @@ py_library(
|
|||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "gesture_recognizer",
|
||||
srcs = [
|
||||
"gesture_recognizer.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/gesture_recognizer/proto:gesture_recognizer_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/python/components/containers:category",
|
||||
"//mediapipe/tasks/python/components/containers:landmark",
|
||||
"//mediapipe/tasks/python/components/processors:classifier_options",
|
||||
"//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",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -23,15 +23,25 @@ py_library(
|
|||
srcs = ["vision_task_running_mode.py"],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "image_processing_options",
|
||||
srcs = ["image_processing_options.py"],
|
||||
deps = [
|
||||
"//mediapipe/tasks/python/components/containers:rect",
|
||||
],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "base_vision_task_api",
|
||||
srcs = [
|
||||
"base_vision_task_api.py",
|
||||
],
|
||||
deps = [
|
||||
":image_processing_options",
|
||||
":vision_task_running_mode",
|
||||
"//mediapipe/framework:calculator_py_pb2",
|
||||
"//mediapipe/python:_framework_bindings",
|
||||
"//mediapipe/tasks/python/components/containers:rect",
|
||||
"//mediapipe/tasks/python/core:optional_dependencies",
|
||||
],
|
||||
)
|
||||
|
|
|
@ -13,17 +13,22 @@
|
|||
# limitations under the License.
|
||||
"""MediaPipe vision task base api."""
|
||||
|
||||
import math
|
||||
from typing import Callable, Mapping, Optional
|
||||
|
||||
from mediapipe.framework import calculator_pb2
|
||||
from mediapipe.python._framework_bindings import packet as packet_module
|
||||
from mediapipe.python._framework_bindings import task_runner as task_runner_module
|
||||
from mediapipe.tasks.python.components.containers import rect as rect_module
|
||||
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
|
||||
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
|
||||
|
||||
_TaskRunner = task_runner_module.TaskRunner
|
||||
_Packet = packet_module.Packet
|
||||
_NormalizedRect = rect_module.NormalizedRect
|
||||
_RunningMode = running_mode_module.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
|
||||
|
||||
class BaseVisionTaskApi(object):
|
||||
|
@ -122,6 +127,49 @@ class BaseVisionTaskApi(object):
|
|||
+ self._running_mode.name)
|
||||
self._runner.send(inputs)
|
||||
|
||||
def convert_to_normalized_rect(self,
|
||||
options: _ImageProcessingOptions,
|
||||
roi_allowed: bool = True) -> _NormalizedRect:
|
||||
"""Converts from ImageProcessingOptions to NormalizedRect, performing sanity checks on-the-fly.
|
||||
|
||||
If the input ImageProcessingOptions is not present, returns a default
|
||||
NormalizedRect covering the whole image with rotation set to 0. If
|
||||
'roi_allowed' is false, an error will be returned if the input
|
||||
ImageProcessingOptions has its 'region_of_interest' field set.
|
||||
|
||||
Args:
|
||||
options: Options for image processing.
|
||||
roi_allowed: Indicates if the `region_of_interest` field is allowed to be
|
||||
set. By default, it's set to True.
|
||||
|
||||
Returns:
|
||||
A normalized rect proto that repesents the image processing options.
|
||||
"""
|
||||
normalized_rect = _NormalizedRect(
|
||||
rotation=0, x_center=0.5, y_center=0.5, width=1, height=1)
|
||||
if options is None:
|
||||
return normalized_rect
|
||||
|
||||
if options.rotation_degrees % 90 != 0:
|
||||
raise ValueError('Expected rotation to be a multiple of 90°.')
|
||||
|
||||
# Convert to radians counter-clockwise.
|
||||
normalized_rect.rotation = -options.rotation_degrees * math.pi / 180.0
|
||||
|
||||
if options.region_of_interest:
|
||||
if not roi_allowed:
|
||||
raise ValueError("This task doesn't support region-of-interest.")
|
||||
roi = options.region_of_interest
|
||||
if roi.left >= roi.right or roi.top >= roi.bottom:
|
||||
raise ValueError('Expected Rect with left < right and top < bottom.')
|
||||
if roi.left < 0 or roi.top < 0 or roi.right > 1 or roi.bottom > 1:
|
||||
raise ValueError('Expected Rect values to be in [0,1].')
|
||||
normalized_rect.x_center = (roi.left + roi.right) / 2.0
|
||||
normalized_rect.y_center = (roi.top + roi.bottom) / 2.0
|
||||
normalized_rect.width = roi.right - roi.left
|
||||
normalized_rect.height = roi.bottom - roi.top
|
||||
return normalized_rect
|
||||
|
||||
def close(self) -> None:
|
||||
"""Shuts down the mediapipe vision task instance.
|
||||
|
||||
|
|
|
@ -0,0 +1,39 @@
|
|||
# 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 vision options for image processing."""
|
||||
|
||||
import dataclasses
|
||||
from typing import Optional
|
||||
|
||||
from mediapipe.tasks.python.components.containers import rect as rect_module
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class ImageProcessingOptions:
|
||||
"""Options for image processing.
|
||||
|
||||
If both region-of-interest and rotation are specified, the crop around the
|
||||
region-of-interest is extracted first, then the specified rotation is applied
|
||||
to the crop.
|
||||
|
||||
Attributes:
|
||||
region_of_interest: The optional region-of-interest to crop from the image.
|
||||
If not specified, the full image is used. Coordinates must be in [0,1]
|
||||
with 'left' < 'right' and 'top' < 'bottom'.
|
||||
rotation_degrees: The rotation to apply to the image (or cropped
|
||||
region-of-interest), in degrees clockwise. The rotation must be a multiple
|
||||
(positive or negative) of 90°.
|
||||
"""
|
||||
region_of_interest: Optional[rect_module.Rect] = None
|
||||
rotation_degrees: int = 0
|
426
mediapipe/tasks/python/vision/gesture_recognizer.py
Normal file
426
mediapipe/tasks/python/vision/gesture_recognizer.py
Normal file
|
@ -0,0 +1,426 @@
|
|||
# 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 gesture recognizer 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.gesture_recognizer.proto import gesture_recognizer_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.components.processors import classifier_options
|
||||
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
|
||||
_GestureRecognizerGraphOptionsProto = gesture_recognizer_graph_options_pb2.GestureRecognizerGraphOptions
|
||||
_ClassifierOptions = classifier_options.ClassifierOptions
|
||||
_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'
|
||||
_HAND_GESTURE_STREAM_NAME = 'hand_gestures'
|
||||
_HAND_GESTURE_TAG = 'HAND_GESTURES'
|
||||
_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.gesture_recognizer.GestureRecognizerGraph'
|
||||
_MICRO_SECONDS_PER_MILLISECOND = 1000
|
||||
_GESTURE_DEFAULT_INDEX = -1
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class GestureRecognitionResult:
|
||||
"""The gesture recognition result from GestureRecognizer, where each vector element represents a single hand detected in the image.
|
||||
|
||||
Attributes:
|
||||
gestures: Recognized hand gestures of detected hands. Note that the index of
|
||||
the gesture is always -1, because the raw indices from multiple gesture
|
||||
classifiers cannot consolidate to a meaningful index.
|
||||
handedness: Classification of handedness.
|
||||
hand_landmarks: Detected hand landmarks in normalized image coordinates.
|
||||
hand_world_landmarks: Detected hand landmarks in world coordinates.
|
||||
"""
|
||||
|
||||
gestures: List[List[category_module.Category]]
|
||||
handedness: List[List[category_module.Category]]
|
||||
hand_landmarks: List[List[landmark_module.NormalizedLandmark]]
|
||||
hand_world_landmarks: List[List[landmark_module.Landmark]]
|
||||
|
||||
|
||||
def _build_recognition_result(
|
||||
output_packets: Mapping[str,
|
||||
packet_module.Packet]) -> GestureRecognitionResult:
|
||||
"""Consturcts a `GestureRecognitionResult` 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(
|
||||
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])
|
||||
|
||||
gesture_results = []
|
||||
for proto in gestures_proto_list:
|
||||
gesture_categories = []
|
||||
gesture_classifications = classification_pb2.ClassificationList()
|
||||
gesture_classifications.MergeFrom(proto)
|
||||
for gesture in gesture_classifications.classification:
|
||||
gesture_categories.append(
|
||||
category_module.Category(
|
||||
index=_GESTURE_DEFAULT_INDEX,
|
||||
score=gesture.score,
|
||||
display_name=gesture.display_name,
|
||||
category_name=gesture.label))
|
||||
gesture_results.append(gesture_categories)
|
||||
|
||||
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 GestureRecognitionResult(gesture_results, handedness_results,
|
||||
hand_landmarks_results,
|
||||
hand_world_landmarks_results)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class GestureRecognizerOptions:
|
||||
"""Options for the gesture recognizer task.
|
||||
|
||||
Attributes:
|
||||
base_options: Base options for the hand gesture recognizer task.
|
||||
running_mode: The running mode of the task. Default to the image mode.
|
||||
Gesture recognizer task has three running modes: 1) The image mode for
|
||||
recognizing hand gestures on single image inputs. 2) The video mode for
|
||||
recognizing hand gestures on the decoded frames of a video. 3) The live
|
||||
stream mode for recognizing hand gestures on a live stream of input data,
|
||||
such as from camera.
|
||||
num_hands: The maximum number of hands can be detected by the recognizer.
|
||||
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.
|
||||
canned_gesture_classifier_options: Options for configuring the canned
|
||||
gestures classifier, such as score threshold, allow list and deny list of
|
||||
gestures. The categories for canned gesture classifiers are: ["None",
|
||||
"Closed_Fist", "Open_Palm", "Pointing_Up", "Thumb_Down", "Thumb_Up",
|
||||
"Victory", "ILoveYou"]. Note this option is subject to change.
|
||||
custom_gesture_classifier_options: Options for configuring the custom
|
||||
gestures classifier, such as score threshold, allow list and deny list of
|
||||
gestures. Note this option is subject to change.
|
||||
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
|
||||
canned_gesture_classifier_options: Optional[
|
||||
_ClassifierOptions] = _ClassifierOptions()
|
||||
custom_gesture_classifier_options: Optional[
|
||||
_ClassifierOptions] = _ClassifierOptions()
|
||||
result_callback: Optional[Callable[
|
||||
[GestureRecognitionResult, image_module.Image, int], None]] = None
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _GestureRecognizerGraphOptionsProto:
|
||||
"""Generates an GestureRecognizerOptions 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 gesture recognizer options from base options.
|
||||
gesture_recognizer_options_proto = _GestureRecognizerGraphOptionsProto(
|
||||
base_options=base_options_proto)
|
||||
# Configure hand detector and hand landmarker options.
|
||||
hand_landmarker_options_proto = gesture_recognizer_options_proto.hand_landmarker_graph_options
|
||||
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
|
||||
|
||||
# Configure hand gesture recognizer options.
|
||||
hand_gesture_recognizer_options_proto = gesture_recognizer_options_proto.hand_gesture_recognizer_graph_options
|
||||
hand_gesture_recognizer_options_proto.canned_gesture_classifier_graph_options.classifier_options.CopyFrom(
|
||||
self.canned_gesture_classifier_options.to_pb2())
|
||||
hand_gesture_recognizer_options_proto.custom_gesture_classifier_graph_options.classifier_options.CopyFrom(
|
||||
self.custom_gesture_classifier_options.to_pb2())
|
||||
|
||||
return gesture_recognizer_options_proto
|
||||
|
||||
|
||||
class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
|
||||
"""Class that performs gesture recognition on images."""
|
||||
|
||||
@classmethod
|
||||
def create_from_model_path(cls, model_path: str) -> 'GestureRecognizer':
|
||||
"""Creates an `GestureRecognizer` object from a TensorFlow Lite model and the default `GestureRecognizerOptions`.
|
||||
|
||||
Note that the created `GestureRecognizer` instance is in image mode, for
|
||||
recognizing hand gestures on single image inputs.
|
||||
|
||||
Args:
|
||||
model_path: Path to the model.
|
||||
|
||||
Returns:
|
||||
`GestureRecognizer` object that's created from the model file and the
|
||||
default `GestureRecognizerOptions`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `GestureRecognizer` 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 = GestureRecognizerOptions(
|
||||
base_options=base_options, running_mode=_RunningMode.IMAGE)
|
||||
return cls.create_from_options(options)
|
||||
|
||||
@classmethod
|
||||
def create_from_options(
|
||||
cls, options: GestureRecognizerOptions) -> 'GestureRecognizer':
|
||||
"""Creates the `GestureRecognizer` object from gesture recognizer options.
|
||||
|
||||
Args:
|
||||
options: Options for the gesture recognizer task.
|
||||
|
||||
Returns:
|
||||
`GestureRecognizer` object that's created from `options`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `GestureRecognizer` object from
|
||||
`GestureRecognizerOptions` 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_GESTURE_STREAM_NAME].is_empty():
|
||||
empty_packet = output_packets[_HAND_GESTURE_STREAM_NAME]
|
||||
options.result_callback(
|
||||
GestureRecognitionResult([], [], [], []), image,
|
||||
empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
|
||||
return
|
||||
|
||||
gesture_recognition_result = _build_recognition_result(output_packets)
|
||||
timestamp = output_packets[_HAND_GESTURE_STREAM_NAME].timestamp
|
||||
options.result_callback(gesture_recognition_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([_HAND_GESTURE_TAG, _HAND_GESTURE_STREAM_NAME]),
|
||||
':'.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 recognize(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> GestureRecognitionResult:
|
||||
"""Performs hand gesture recognition on the given image.
|
||||
|
||||
Only use this method when the GestureRecognizer 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 gesture recognition results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If gesture recognition 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_GESTURE_STREAM_NAME].is_empty():
|
||||
return GestureRecognitionResult([], [], [], [])
|
||||
|
||||
return _build_recognition_result(output_packets)
|
||||
|
||||
def recognize_for_video(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> GestureRecognitionResult:
|
||||
"""Performs gesture recognition on the provided video frame.
|
||||
|
||||
Only use this method when the GestureRecognizer is created with the video
|
||||
running mode.
|
||||
|
||||
Only use this method when the GestureRecognizer 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 gesture recognition results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If gesture recognition 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_GESTURE_STREAM_NAME].is_empty():
|
||||
return GestureRecognitionResult([], [], [], [])
|
||||
|
||||
return _build_recognition_result(output_packets)
|
||||
|
||||
def recognize_async(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> None:
|
||||
"""Sends live image data to perform gesture recognition.
|
||||
|
||||
The results will be available via the "result_callback" provided in the
|
||||
GestureRecognizerOptions. Only use this method when the GestureRecognizer
|
||||
is created with the live stream running mode.
|
||||
|
||||
Only use this method when the GestureRecognizer 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 `GestureRecognizerOptions`. The
|
||||
`recognize_async` method is designed to process live stream data such as
|
||||
camera input. To lower the overall latency, gesture recognizer 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 gesture recognition results.
|
||||
- The input image that the gesture recognizer 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
|
||||
gesture recognizer 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)
|
||||
})
|
|
@ -30,6 +30,7 @@ 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
|
||||
|
||||
_NormalizedRect = rect.NormalizedRect
|
||||
|
@ -37,6 +38,7 @@ _BaseOptions = base_options_module.BaseOptions
|
|||
_ImageClassifierGraphOptionsProto = image_classifier_graph_options_pb2.ImageClassifierGraphOptions
|
||||
_ClassifierOptions = classifier_options.ClassifierOptions
|
||||
_RunningMode = vision_task_running_mode.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
_TaskInfo = task_info_module.TaskInfo
|
||||
|
||||
_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
|
||||
|
@ -44,17 +46,12 @@ _CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT'
|
|||
_IMAGE_IN_STREAM_NAME = 'image_in'
|
||||
_IMAGE_OUT_STREAM_NAME = 'image_out'
|
||||
_IMAGE_TAG = 'IMAGE'
|
||||
_NORM_RECT_NAME = 'norm_rect_in'
|
||||
_NORM_RECT_STREAM_NAME = 'norm_rect_in'
|
||||
_NORM_RECT_TAG = 'NORM_RECT'
|
||||
_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph'
|
||||
_MICRO_SECONDS_PER_MILLISECOND = 1000
|
||||
|
||||
|
||||
def _build_full_image_norm_rect() -> _NormalizedRect:
|
||||
# Builds a NormalizedRect covering the entire image.
|
||||
return _NormalizedRect(x_center=0.5, y_center=0.5, width=1, height=1)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class ImageClassifierOptions:
|
||||
"""Options for the image classifier task.
|
||||
|
@ -156,7 +153,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
task_graph=_TASK_GRAPH_NAME,
|
||||
input_streams=[
|
||||
':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
|
||||
':'.join([_NORM_RECT_TAG, _NORM_RECT_NAME]),
|
||||
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
|
||||
],
|
||||
output_streams=[
|
||||
':'.join([
|
||||
|
@ -171,17 +168,16 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
_RunningMode.LIVE_STREAM), options.running_mode,
|
||||
packets_callback if options.result_callback else None)
|
||||
|
||||
# TODO: Replace _NormalizedRect with ImageProcessingOption
|
||||
def classify(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
roi: Optional[_NormalizedRect] = None
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> classifications.ClassificationResult:
|
||||
"""Performs image classification on the provided MediaPipe Image.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
roi: The region of interest.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
A classification result object that contains a list of classifications.
|
||||
|
@ -190,10 +186,12 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If image classification failed to run.
|
||||
"""
|
||||
norm_rect = roi if roi is not None else _build_full_image_norm_rect()
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options)
|
||||
output_packets = self._process_image_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
|
||||
_NORM_RECT_NAME: packet_creator.create_proto(norm_rect.to_pb2())
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image),
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2())
|
||||
})
|
||||
|
||||
classification_result_proto = classifications_pb2.ClassificationResult()
|
||||
|
@ -210,7 +208,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
roi: Optional[_NormalizedRect] = None
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> classifications.ClassificationResult:
|
||||
"""Performs image classification on the provided video frames.
|
||||
|
||||
|
@ -222,7 +220,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input video frame in milliseconds.
|
||||
roi: The region of interest.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
A classification result object that contains a list of classifications.
|
||||
|
@ -231,13 +229,13 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If image classification failed to run.
|
||||
"""
|
||||
norm_rect = roi if roi is not None else _build_full_image_norm_rect()
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options)
|
||||
output_packets = self._process_video_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
_NORM_RECT_NAME:
|
||||
packet_creator.create_proto(norm_rect.to_pb2()).at(
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
|
||||
|
@ -251,10 +249,12 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
for classification in classification_result_proto.classifications
|
||||
])
|
||||
|
||||
def classify_async(self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
roi: Optional[_NormalizedRect] = None) -> None:
|
||||
def classify_async(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> None:
|
||||
"""Sends live image data (an Image with a unique timestamp) to perform image classification.
|
||||
|
||||
Only use this method when the ImageClassifier is created with the live
|
||||
|
@ -275,18 +275,18 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input image in milliseconds.
|
||||
roi: The region of interest.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Raises:
|
||||
ValueError: If the current input timestamp is smaller than what the image
|
||||
classifier has already processed.
|
||||
"""
|
||||
norm_rect = roi if roi is not None else _build_full_image_norm_rect()
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options)
|
||||
self._send_live_stream_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
_NORM_RECT_NAME:
|
||||
packet_creator.create_proto(norm_rect.to_pb2()).at(
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
|
|
Loading…
Reference in New Issue
Block a user