Added a simple test to verify gesture recognition results
This commit is contained in:
kinaryml
parent
9a1a9d4c13
commit
18eb089d39
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@ -55,11 +55,13 @@ py_library(
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)
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py_library(
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name = "gesture",
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srcs = ["gesture.py"],
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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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":rect",
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":classification",
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":landmark",
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"//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_py_pb2",
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"//mediapipe/tasks/python/core:optional_dependencies",
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],
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)
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@ -14,14 +14,13 @@
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"""Classification data class."""
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import dataclasses
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from typing import Any, List
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from typing import Any, List, Optional
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from mediapipe.framework.formats import classification_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_ClassificationProto = classification_pb2.Classification
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_ClassificationListProto = classification_pb2.ClassificationList
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_ClassificationListCollectionProto = classification_pb2.ClassificationListCollection
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@dataclasses.dataclass
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@ -35,10 +34,10 @@ class Classification:
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display_name: Optional human-readable string for display purposes.
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"""
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index: int
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score: float
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label_name: str
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display_name: str
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index: Optional[int] = None
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score: Optional[float] = None
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label: Optional[str] = None
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display_name: Optional[str] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _ClassificationProto:
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@ -46,7 +45,7 @@ class Classification:
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return _ClassificationProto(
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index=self.index,
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score=self.score,
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label_name=self.label_name,
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label=self.label,
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display_name=self.display_name)
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@classmethod
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@ -56,7 +55,7 @@ class Classification:
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return Classification(
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index=pb2_obj.index,
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score=pb2_obj.score,
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label_name=pb2_obj.label_name,
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label=pb2_obj.label,
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display_name=pb2_obj.display_name)
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def __eq__(self, other: Any) -> bool:
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@ -86,8 +85,8 @@ class ClassificationList:
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"""
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classifications: List[Classification]
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tensor_index: int
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tensor_name: str
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tensor_index: Optional[int] = None
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tensor_name: Optional[str] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _ClassificationListProto:
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@ -1,138 +0,0 @@
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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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"""Gesture data class."""
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import dataclasses
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from typing import Any, List
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from mediapipe.tasks.python.components.containers import classification
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from mediapipe.tasks.python.components.containers import landmark
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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@dataclasses.dataclass
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class GestureRecognitionResult:
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""" The gesture recognition result from GestureRecognizer, where each vector
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element represents a single hand detected in the image.
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Attributes:
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gestures: Recognized hand gestures with sorted order such that the
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winning label is the first item in the list.
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handedness: Classification of handedness.
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hand_landmarks: Detected hand landmarks in normalized image coordinates.
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hand_world_landmarks: Detected hand landmarks in world coordinates.
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"""
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gestures: List[classification.ClassificationList]
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handedness: List[classification.ClassificationList]
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hand_landmarks: List[landmark.NormalizedLandmarkList]
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hand_world_landmarks: List[landmark.LandmarkList]
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _DetectionProto:
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"""Generates a Detection protobuf object."""
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labels = []
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label_ids = []
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scores = []
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display_names = []
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for category in self.categories:
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scores.append(category.score)
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if category.index:
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label_ids.append(category.index)
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if category.category_name:
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labels.append(category.category_name)
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if category.display_name:
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display_names.append(category.display_name)
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return _DetectionProto(
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label=labels,
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label_id=label_ids,
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score=scores,
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display_name=display_names,
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location_data=_LocationDataProto(
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format=_LocationDataProto.Format.BOUNDING_BOX,
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bounding_box=self.bounding_box.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(cls, pb2_obj: _DetectionProto) -> 'Detection':
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"""Creates a `Detection` object from the given protobuf object."""
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categories = []
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for idx, score in enumerate(pb2_obj.score):
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categories.append(
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category_module.Category(
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score=score,
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index=pb2_obj.label_id[idx]
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if idx < len(pb2_obj.label_id) else None,
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category_name=pb2_obj.label[idx]
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if idx < len(pb2_obj.label) else None,
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display_name=pb2_obj.display_name[idx]
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if idx < len(pb2_obj.display_name) else None))
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return Detection(
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bounding_box=bounding_box_module.BoundingBox.create_from_pb2(
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pb2_obj.location_data.bounding_box),
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categories=categories)
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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, Detection):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@dataclasses.dataclass
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class DetectionResult:
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"""Represents the list of detected objects.
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Attributes:
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detections: A list of `Detection` objects.
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"""
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detections: List[Detection]
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _DetectionListProto:
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"""Generates a DetectionList protobuf object."""
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return _DetectionListProto(
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detection=[detection.to_pb2() for detection in self.detections])
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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: _DetectionListProto) -> 'DetectionResult':
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"""Creates a `DetectionResult` object from the given protobuf object."""
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return DetectionResult(detections=[
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Detection.create_from_pb2(detection) for detection in pb2_obj.detection
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])
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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, DetectionResult):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@ -0,0 +1,82 @@
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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 Detection Result data class."""
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import dataclasses
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from typing import Any, Optional
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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 rect as rect_module
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from mediapipe.tasks.python.components.containers import classification as classification_module
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from mediapipe.tasks.python.components.containers import landmark as landmark_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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_NormalizedRect = rect_module.NormalizedRect
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_ClassificationList = classification_module.ClassificationList
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_NormalizedLandmarkList = landmark_module.NormalizedLandmarkList
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_LandmarkList = landmark_module.LandmarkList
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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:
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landmarks : A `NormalizedLandmarkList` object.
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classifications : A `ClassificationList` object.
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world_landmarks : A `LandmarkList` object.
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rect : A `NormalizedRect` object.
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"""
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landmarks: Optional[_NormalizedLandmarkList]
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classifications: Optional[_ClassificationList]
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world_landmarks: Optional[_LandmarkList]
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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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return _LandmarksDetectionResultProto(
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landmarks=self.landmarks.to_pb2(),
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classifications=self.classifications.to_pb2(),
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world_landmarks=self.world_landmarks.to_pb2(),
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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
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) -> 'LandmarksDetectionResult':
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"""Creates a `LandmarksDetectionResult` object from the given protobuf
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object."""
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return LandmarksDetectionResult(
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landmarks=_NormalizedLandmarkList.create_from_pb2(pb2_obj.landmarks),
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classifications=_ClassificationList.create_from_pb2(
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pb2_obj.classifications),
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world_landmarks=_LandmarkList.create_from_pb2(pb2_obj.world_landmarks),
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rect=_NormalizedRect.create_from_pb2(pb2_obj.rect))
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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, LandmarksDetectionResult):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@ -43,15 +43,19 @@ py_test(
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data = [
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"//mediapipe/tasks/testdata/vision:test_images",
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"//mediapipe/tasks/testdata/vision:test_models",
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"//mediapipe/tasks/testdata/vision:test_protos",
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],
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deps = [
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"//mediapipe/python:_framework_bindings",
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"//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_py_pb2",
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"//mediapipe/tasks/python/components/containers:rect",
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"//mediapipe/tasks/python/components/containers:classification",
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"//mediapipe/tasks/python/components/containers:landmark",
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"//mediapipe/tasks/python/components/containers:rect",
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"//mediapipe/tasks/python/components/containers:landmark_detection_result",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/test:test_utils",
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"//mediapipe/tasks/python/vision:gesture_recognizer",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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"@com_google_protobuf//:protobuf_python"
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],
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)
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@ -15,23 +15,31 @@
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import enum
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from google.protobuf import text_format
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from absl.testing import absltest
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from absl.testing import parameterized
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from mediapipe.python._framework_bindings import image as image_module
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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 rect as rect_module
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from mediapipe.tasks.python.components.containers import classification as classification_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 landmark_detection_result as landmark_detection_result_module
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.test import test_utils
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from mediapipe.tasks.python.vision import gesture_recognizer
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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_LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult
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_BaseOptions = base_options_module.BaseOptions
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_NormalizedRect = rect_module.NormalizedRect
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_Classification = classification_module.Classification
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_ClassificationList = classification_module.ClassificationList
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_Landmark = landmark_module.Landmark
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_LandmarkList = landmark_module.LandmarkList
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_NormalizedLandmark = landmark_module.NormalizedLandmark
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_NormalizedLandmarkList = landmark_module.NormalizedLandmarkList
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_LandmarksDetectionResult = landmark_detection_result_module.LandmarksDetectionResult
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_Image = image_module.Image
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_GestureRecognizer = gesture_recognizer.GestureRecognizer
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_GestureRecognizerOptions = gesture_recognizer.GestureRecognizerOptions
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@ -39,8 +47,35 @@ _GestureRecognitionResult = gesture_recognizer.GestureRecognitionResult
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_GESTURE_RECOGNIZER_MODEL_FILE = 'gesture_recognizer.task'
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_IMAGE_FILE = 'right_hands.jpg'
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_EXPECTED_DETECTION_RESULT = _GestureRecognitionResult([], [], [], [])
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_THUMB_UP_IMAGE = 'thumb_up.jpg'
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_THUMB_UP_LANDMARKS = "thumb_up_landmarks.pbtxt"
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_THUMB_UP_LABEL = "Thumb_Up"
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_THUMB_UP_INDEX = 5
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_LANDMARKS_ERROR_TOLERANCE = 0.03
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def _get_expected_gesture_recognition_result(
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file_path: str, gesture_label: str, gesture_index: int
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) -> _GestureRecognitionResult:
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landmarks_detection_result_file_path = test_utils.get_test_data_path(
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file_path)
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with open(landmarks_detection_result_file_path, "rb") as f:
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landmarks_detection_result_proto = _LandmarksDetectionResultProto()
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# # Use this if a .pb file is available.
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# landmarks_detection_result_proto.ParseFromString(f.read())
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text_format.Parse(f.read(), landmarks_detection_result_proto)
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landmarks_detection_result = _LandmarksDetectionResult.create_from_pb2(
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landmarks_detection_result_proto)
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gesture = _ClassificationList(
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classifications=[
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_Classification(label=gesture_label, index=gesture_index,
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display_name='')
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], tensor_index=0, tensor_name='')
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return _GestureRecognitionResult(
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gestures=[gesture],
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handedness=[landmarks_detection_result.classifications],
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hand_landmarks=[landmarks_detection_result.landmarks],
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hand_world_landmarks=[landmarks_detection_result.world_landmarks])
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class ModelFileType(enum.Enum):
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@ -53,14 +88,45 @@ class GestureRecognizerTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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self.test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_IMAGE_FILE))
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test_utils.get_test_data_path(_THUMB_UP_IMAGE))
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self.gesture_recognizer_model_path = test_utils.get_test_data_path(
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_GESTURE_RECOGNIZER_MODEL_FILE)
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def _assert_actual_result_approximately_matches_expected_result(
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self,
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actual_result: _GestureRecognitionResult,
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expected_result: _GestureRecognitionResult
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):
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# Expects to have the same number of hands detected.
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self.assertLen(actual_result.hand_landmarks,
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len(expected_result.hand_landmarks))
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self.assertLen(actual_result.hand_world_landmarks,
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len(expected_result.hand_world_landmarks))
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self.assertLen(actual_result.handedness, len(expected_result.handedness))
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self.assertLen(actual_result.gestures, len(expected_result.gestures))
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# Actual landmarks match expected landmarks.
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self.assertEqual(actual_result.hand_landmarks,
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expected_result.hand_landmarks)
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# Actual handedness matches expected handedness.
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actual_top_handedness = actual_result.handedness[0].classifications[0]
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expected_top_handedness = expected_result.handedness[0].classifications[0]
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self.assertEqual(actual_top_handedness.index, expected_top_handedness.index)
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self.assertEqual(actual_top_handedness.label, expected_top_handedness.label)
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# Actual gesture with top score matches expected gesture.
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actual_top_gesture = actual_result.gestures[0].classifications[0]
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expected_top_gesture = expected_result.gestures[0].classifications[0]
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self.assertEqual(actual_top_gesture.index, expected_top_gesture.index)
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self.assertEqual(actual_top_gesture.label, expected_top_gesture.label)
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, _EXPECTED_DETECTION_RESULT),
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(ModelFileType.FILE_CONTENT, _EXPECTED_DETECTION_RESULT))
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def test_recognize(self, model_file_type, expected_recognition_result):
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(ModelFileType.FILE_NAME, 0.3, _get_expected_gesture_recognition_result(
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_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
|
||||
)),
|
||||
(ModelFileType.FILE_CONTENT, 0.3, _get_expected_gesture_recognition_result(
|
||||
_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
|
||||
)))
|
||||
def test_recognize(self, model_file_type, min_gesture_confidence,
|
||||
expected_recognition_result):
|
||||
# Creates gesture recognizer.
|
||||
if model_file_type is ModelFileType.FILE_NAME:
|
||||
gesture_recognizer_base_options = _BaseOptions(
|
||||
|
|
@ -75,13 +141,16 @@ class GestureRecognizerTest(parameterized.TestCase):
|
|||
raise ValueError('model_file_type is invalid.')
|
||||
|
||||
options = _GestureRecognizerOptions(
|
||||
base_options=gesture_recognizer_base_options)
|
||||
base_options=gesture_recognizer_base_options,
|
||||
min_gesture_confidence=min_gesture_confidence
|
||||
)
|
||||
recognizer = _GestureRecognizer.create_from_options(options)
|
||||
|
||||
# Performs hand gesture recognition on the input.
|
||||
recognition_result = recognizer.recognize(self.test_image)
|
||||
# Comparing results.
|
||||
self.assertEqual(recognition_result, expected_recognition_result)
|
||||
self._assert_actual_result_approximately_matches_expected_result(
|
||||
recognition_result, expected_recognition_result)
|
||||
# Closes the gesture recognizer explicitly when the detector is not used in
|
||||
# a context.
|
||||
recognizer.close()
|
||||
|
|
|
|||
|
|
@ -136,8 +136,6 @@ class GestureRecognizerOptions:
|
|||
"""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
|
||||
# hand_landmark_detector_base_options_proto = self.hand_landmark_detector_base_options.to_pb2()
|
||||
# hand_landmark_detector_base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
|
||||
|
||||
# Configure hand detector options.
|
||||
hand_detector_options_proto = _HandDetectorGraphOptionsProto(
|
||||
|
|
@ -153,13 +151,12 @@ class GestureRecognizerOptions:
|
|||
min_tracking_confidence=self.min_tracking_confidence)
|
||||
|
||||
# Configure hand gesture recognizer options.
|
||||
hand_gesture_recognizer_options_proto = _HandGestureRecognizerGraphOptionsProto()
|
||||
if self.min_gesture_confidence >= 0:
|
||||
classifier_options = _ClassifierOptions(
|
||||
score_threshold=self.min_gesture_confidence)
|
||||
hand_gesture_recognizer_options_proto.canned_gesture_classifier_graph_options = \
|
||||
_GestureClassifierGraphOptionsProto(
|
||||
classifier_options=classifier_options.to_pb2())
|
||||
classifier_options = _ClassifierOptions(
|
||||
score_threshold=self.min_gesture_confidence)
|
||||
gesture_classifier_options = _GestureClassifierGraphOptionsProto(
|
||||
classifier_options=classifier_options.to_pb2())
|
||||
hand_gesture_recognizer_options_proto = _HandGestureRecognizerGraphOptionsProto(
|
||||
canned_gesture_classifier_graph_options=gesture_classifier_options)
|
||||
|
||||
return _GestureRecognizerGraphOptionsProto(
|
||||
base_options=base_options_proto,
|
||||
|
|
|
|||
1
mediapipe/tasks/testdata/vision/BUILD
vendored
1
mediapipe/tasks/testdata/vision/BUILD
vendored
|
|
@ -121,6 +121,7 @@ filegroup(
|
|||
"hand_landmark_full.tflite",
|
||||
"hand_landmark_lite.tflite",
|
||||
"hand_landmarker.task",
|
||||
"gesture_recognizer.task",
|
||||
"mobilenet_v1_0.25_192_quantized_1_default_1.tflite",
|
||||
"mobilenet_v1_0.25_224_1_default_1.tflite",
|
||||
"mobilenet_v1_0.25_224_1_metadata_1.tflite",
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user