Added Face Detector implementation and tests
This commit is contained in:
kinaryml
parent
5398b8881d
commit
022838a7f3
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@ -94,6 +94,7 @@ cc_library(
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"//mediapipe/tasks/cc/vision/image_embedder:image_embedder_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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"//mediapipe/tasks/cc/vision/face_detector:face_detector_graph",
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] + select({
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# TODO: Build text_classifier_graph and text_embedder_graph on Windows.
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"//mediapipe:windows": [],
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@ -73,12 +73,22 @@ py_library(
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],
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)
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py_library(
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name = "keypoint",
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srcs = ["keypoint.py"],
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deps = [
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"//mediapipe/framework/formats:location_data_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 = "detections",
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srcs = ["detections.py"],
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deps = [
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":bounding_box",
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":category",
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":keypoint",
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"//mediapipe/framework/formats:detection_py_pb2",
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"//mediapipe/framework/formats:location_data_py_pb2",
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"//mediapipe/tasks/python/core:optional_dependencies",
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@ -20,6 +20,7 @@ from mediapipe.framework.formats import detection_pb2
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from mediapipe.framework.formats import location_data_pb2
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from mediapipe.tasks.python.components.containers import bounding_box as bounding_box_module
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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 keypoint as keypoint_module
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_DetectionListProto = detection_pb2.DetectionList
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@ -34,10 +35,12 @@ class Detection:
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Attributes:
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bounding_box: A BoundingBox object.
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categories: A list of Category objects.
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keypoints: A list of NormalizedKeypoint objects.
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"""
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bounding_box: bounding_box_module.BoundingBox
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categories: List[category_module.Category]
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bounding_box: bounding_box_module.BoundingBox = None
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categories: List[category_module.Category] = None
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keypoints: List[keypoint_module.NormalizedKeypoint] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _DetectionProto:
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@ -46,6 +49,8 @@ class Detection:
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label_ids = []
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scores = []
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display_names = []
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relative_keypoints = []
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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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@ -54,6 +59,20 @@ class Detection:
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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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if self.keypoints:
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for keypoint in self.keypoints:
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relative_keypoint_proto = _LocationDataProto.RelativeKeypoint()
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if keypoint.x:
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relative_keypoint_proto.x = keypoint.x
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if keypoint.y:
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relative_keypoint_proto.y = keypoint.y
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if keypoint.label:
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relative_keypoint_proto.keypoint_label = keypoint.label
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if keypoint.score:
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relative_keypoint_proto.score = keypoint.score
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relative_keypoints.append(relative_keypoint_proto)
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return _DetectionProto(
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label=labels,
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label_id=label_ids,
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@ -61,13 +80,16 @@ class Detection:
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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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bounding_box=self.bounding_box.to_pb2(),
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relative_keypoints=relative_keypoints))
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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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keypoints = []
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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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@ -79,10 +101,20 @@ class Detection:
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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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if pb2_obj.location_data.relative_keypoints:
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for idx in range(len(pb2_obj.location_data.relative_keypoints)):
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keypoints.append(
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keypoint_module.NormalizedKeypoint(
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x=pb2_obj.location_data.relative_keypoints[idx].x,
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y=pb2_obj.location_data.relative_keypoints[idx].y,
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label=pb2_obj.location_data.relative_keypoints[idx].keypoint_label,
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score=pb2_obj.location_data.relative_keypoints[idx].score))
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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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categories=categories,
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keypoints=keypoints)
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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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78
mediapipe/tasks/python/components/containers/keypoint.py
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78
mediapipe/tasks/python/components/containers/keypoint.py
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@ -0,0 +1,78 @@
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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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"""Keypoint data class."""
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import dataclasses
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from typing import Any, Optional
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from mediapipe.framework.formats import location_data_pb2
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from mediapipe.tasks.python.core.optional_dependencies import doc_controls
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_RelativeKeypointProto = location_data_pb2.LocationData.RelativeKeypoint
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@dataclasses.dataclass
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class NormalizedKeypoint:
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"""A normalized keypoint.
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Normalized keypoint represents a point in 2D space with x, y coordinates.
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x and y are normalized to [0.0, 1.0] by the image width and height
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respectively.
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Attributes:
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x: The x coordinates of the normalized keypoint.
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y: The y coordinates of the normalized keypoint.
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label: The optional label of the keypoint.
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score: The score of the keypoint.
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"""
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x: Optional[float] = None
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y: Optional[float] = None
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label: Optional[str] = None
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score: Optional[str] = None
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@doc_controls.do_not_generate_docs
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def to_pb2(self) -> _RelativeKeypointProto:
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"""Generates a RelativeKeypoint protobuf object."""
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return _RelativeKeypointProto(
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x=self.x,
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y=self.y,
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keypoint_label=self.label,
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score=self.score
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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,
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pb2_obj: _RelativeKeypointProto) -> 'NormalizedKeypoint':
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"""Creates a `NormalizedKeypoint` object from the given protobuf object."""
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return NormalizedKeypoint(
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x=pb2_obj.x,
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y=pb2_obj.y,
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label=pb2_obj.keypoint_label,
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score=pb2_obj.score)
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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, NormalizedKeypoint):
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return False
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return self.to_pb2().__eq__(other.to_pb2())
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@ -114,3 +114,25 @@ py_test(
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"@com_google_protobuf//:protobuf_python",
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],
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)
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py_test(
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name = "face_detector_test",
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srcs = ["face_detector_test.py"],
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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/python/components/containers:bounding_box",
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/components/containers:detections",
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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:face_detector",
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"//mediapipe/tasks/python/vision/core:image_processing_options",
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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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407
mediapipe/tasks/python/test/vision/face_detector_test.py
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407
mediapipe/tasks/python/test/vision/face_detector_test.py
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@ -0,0 +1,407 @@
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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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"""Tests for face detector."""
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import enum
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import os
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from unittest import mock
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from absl.testing import absltest
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from absl.testing import parameterized
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import numpy as np
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from google.protobuf import text_format
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from mediapipe.framework.formats import detection_pb2
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.tasks.python.components.containers import bounding_box as bounding_box_module
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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 detections as detections_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 face_detector
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
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FaceDetectorResult = detections_module.DetectionResult
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_BaseOptions = base_options_module.BaseOptions
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_Category = category_module.Category
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_BoundingBox = bounding_box_module.BoundingBox
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_Detection = detections_module.Detection
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_Image = image_module.Image
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_FaceDetector = face_detector.FaceDetector
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_FaceDetectorOptions = face_detector.FaceDetectorOptions
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_SHORT_RANGE_BLAZE_FACE_MODEL = 'face_detection_short_range.tflite'
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_PORTRAIT_IMAGE = 'portrait.jpg'
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_PORTRAIT_EXPECTED_DETECTION = 'portrait_expected_detection.pbtxt'
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_PORTRAIT_ROTATED_IMAGE = 'portrait_rotated.jpg'
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_PORTRAIT_ROTATED_EXPECTED_DETECTION = 'portrait_rotated_expected_detection.pbtxt'
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_CAT_IMAGE = 'cat.jpg'
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_KEYPOINT_ERROR_THRESHOLD = 1e-2
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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
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def _get_expected_face_detector_result(file_name: str) -> FaceDetectorResult:
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face_detection_result_file_path = test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, file_name))
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with open(face_detection_result_file_path, "rb") as f:
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face_detection_proto = detection_pb2.Detection()
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text_format.Parse(f.read(), face_detection_proto)
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face_detection = detections_module.Detection.create_from_pb2(face_detection_proto)
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return FaceDetectorResult(detections=[face_detection])
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class ModelFileType(enum.Enum):
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FILE_CONTENT = 1
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FILE_NAME = 2
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class FaceDetectorTest(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(
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os.path.join(_TEST_DATA_DIR, _PORTRAIT_IMAGE)))
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self.model_path = test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, _SHORT_RANGE_BLAZE_FACE_MODEL))
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def test_create_from_file_succeeds_with_valid_model_path(self):
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# Creates with default option and valid model file successfully.
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with _FaceDetector.create_from_model_path(self.model_path) as detector:
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self.assertIsInstance(detector, _FaceDetector)
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def test_create_from_options_succeeds_with_valid_model_path(self):
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# Creates with options containing model file successfully.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _FaceDetectorOptions(base_options=base_options)
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with _FaceDetector.create_from_options(options) as detector:
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self.assertIsInstance(detector, _FaceDetector)
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def test_create_from_options_fails_with_invalid_model_path(self):
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with self.assertRaisesRegex(
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RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
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base_options = _BaseOptions(
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model_asset_path='/path/to/invalid/model.tflite')
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options = _FaceDetectorOptions(base_options=base_options)
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_FaceDetector.create_from_options(options)
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def test_create_from_options_succeeds_with_valid_model_content(self):
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# Creates with options containing model content successfully.
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with open(self.model_path, 'rb') as f:
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base_options = _BaseOptions(model_asset_buffer=f.read())
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options = _FaceDetectorOptions(base_options=base_options)
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detector = _FaceDetector.create_from_options(options)
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self.assertIsInstance(detector, _FaceDetector)
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def _expect_keypoints_correct(self, actual_keypoints, expected_keypoints):
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self.assertLen(actual_keypoints, len(expected_keypoints))
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for i in range(len(actual_keypoints)):
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self.assertAlmostEqual(
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actual_keypoints[i].x, expected_keypoints[i].x,
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delta=_KEYPOINT_ERROR_THRESHOLD)
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self.assertAlmostEqual(
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actual_keypoints[i].y, expected_keypoints[i].y,
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delta=_KEYPOINT_ERROR_THRESHOLD)
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def _expect_face_detector_results_correct(self, actual_results, expected_results):
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self.assertLen(actual_results.detections, len(expected_results.detections))
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for i in range(len(actual_results.detections)):
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actual_bbox = actual_results.detections[i].bounding_box
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expected_bbox = expected_results.detections[i].bounding_box
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self.assertEqual(actual_bbox, expected_bbox)
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self.assertNotEmpty(actual_results.detections[i].keypoints)
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self._expect_keypoints_correct(actual_results.detections[i].keypoints,
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expected_results.detections[i].keypoints)
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, _PORTRAIT_EXPECTED_DETECTION),
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(ModelFileType.FILE_CONTENT, _PORTRAIT_EXPECTED_DETECTION))
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def test_detect(self, model_file_type, expected_detection_result_file):
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# Creates detector.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _FaceDetectorOptions(base_options=base_options)
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detector = _FaceDetector.create_from_options(options)
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# Performs face detection on the input.
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detection_result = detector.detect(self.test_image)
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# Comparing results.
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expected_detection_result = _get_expected_face_detector_result(
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expected_detection_result_file)
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self._expect_face_detector_results_correct(detection_result,
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expected_detection_result)
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# Closes the detector explicitly when the detector is not used in
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# a context.
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detector.close()
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, _PORTRAIT_EXPECTED_DETECTION),
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(ModelFileType.FILE_CONTENT, _PORTRAIT_EXPECTED_DETECTION))
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def test_detect_in_context(self, model_file_type, expected_detection_result_file):
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# Creates detector.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _FaceDetectorOptions(base_options=base_options)
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with _FaceDetector.create_from_options(options) as detector:
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# Performs face detection on the input.
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detection_result = detector.detect(self.test_image)
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# Comparing results.
|
||||
expected_detection_result = _get_expected_face_detector_result(
|
||||
expected_detection_result_file)
|
||||
self._expect_face_detector_results_correct(detection_result,
|
||||
expected_detection_result)
|
||||
|
||||
def test_detect_succeeds_with_rotated_image(self):
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
options = _FaceDetectorOptions(base_options=base_options)
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
# Load the test image.
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, _PORTRAIT_ROTATED_IMAGE)))
|
||||
# Rotated input image.
|
||||
image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
|
||||
# Performs face detection on the input.
|
||||
detection_result = detector.detect(test_image, image_processing_options)
|
||||
# Comparing results.
|
||||
expected_detection_result = _get_expected_face_detector_result(
|
||||
_PORTRAIT_ROTATED_EXPECTED_DETECTION)
|
||||
self._expect_face_detector_results_correct(detection_result,
|
||||
expected_detection_result)
|
||||
|
||||
def test_empty_detection_outputs(self):
|
||||
# Load a test image with no faces.
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, _CAT_IMAGE)))
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path))
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
# Performs object detection on the input.
|
||||
detection_result = detector.detect(test_image)
|
||||
self.assertEmpty(detection_result.detections)
|
||||
|
||||
def test_missing_result_callback(self):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM)
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'result callback must be provided'):
|
||||
with _FaceDetector.create_from_options(options) as unused_detector:
|
||||
pass
|
||||
|
||||
@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
|
||||
def test_illegal_result_callback(self, running_mode):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=running_mode,
|
||||
result_callback=mock.MagicMock())
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'result callback should not be provided'):
|
||||
with _FaceDetector.create_from_options(options) as unused_detector:
|
||||
pass
|
||||
|
||||
def test_calling_detect_for_video_in_image_mode(self):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE)
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the video mode'):
|
||||
detector.detect_for_video(self.test_image, 0)
|
||||
|
||||
def test_calling_detect_async_in_image_mode(self):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.IMAGE)
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the live stream mode'):
|
||||
detector.detect_async(self.test_image, 0)
|
||||
|
||||
def test_calling_detect_in_video_mode(self):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the image mode'):
|
||||
detector.detect(self.test_image)
|
||||
|
||||
def test_calling_detect_async_in_video_mode(self):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the live stream mode'):
|
||||
detector.detect_async(self.test_image, 0)
|
||||
|
||||
def test_detect_for_video_with_out_of_order_timestamp(self):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
unused_result = detector.detect_for_video(self.test_image, 1)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'Input timestamp must be monotonically increasing'):
|
||||
detector.detect_for_video(self.test_image, 0)
|
||||
|
||||
@parameterized.parameters(
|
||||
(ModelFileType.FILE_NAME, _PORTRAIT_IMAGE, 0,
|
||||
_get_expected_face_detector_result(_PORTRAIT_EXPECTED_DETECTION)),
|
||||
(ModelFileType.FILE_CONTENT, _PORTRAIT_IMAGE, 0,
|
||||
_get_expected_face_detector_result(_PORTRAIT_EXPECTED_DETECTION)),
|
||||
(ModelFileType.FILE_NAME, _PORTRAIT_ROTATED_IMAGE, -90,
|
||||
_get_expected_face_detector_result(_PORTRAIT_ROTATED_EXPECTED_DETECTION)),
|
||||
(ModelFileType.FILE_CONTENT, _PORTRAIT_ROTATED_IMAGE, -90,
|
||||
_get_expected_face_detector_result(_PORTRAIT_ROTATED_EXPECTED_DETECTION)),
|
||||
(ModelFileType.FILE_NAME, _CAT_IMAGE, 0, FaceDetectorResult([])),
|
||||
(ModelFileType.FILE_CONTENT, _CAT_IMAGE, 0, FaceDetectorResult([])))
|
||||
def test_detect_for_video(self, model_file_type, test_image_file_name,
|
||||
rotation_degrees, expected_detection_result):
|
||||
# Creates detector.
|
||||
if model_file_type is ModelFileType.FILE_NAME:
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||||
with open(self.model_path, 'rb') as f:
|
||||
model_content = f.read()
|
||||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||||
else:
|
||||
# Should never happen
|
||||
raise ValueError('model_file_type is invalid.')
|
||||
|
||||
options = _FaceDetectorOptions(base_options=base_options,
|
||||
running_mode=_RUNNING_MODE.VIDEO)
|
||||
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
for timestamp in range(0, 300, 30):
|
||||
# Load the test image.
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, test_image_file_name)))
|
||||
# Set the image processing options.
|
||||
image_processing_options = _ImageProcessingOptions(
|
||||
rotation_degrees=rotation_degrees)
|
||||
# Performs face detection on the input.
|
||||
detection_result = detector.detect_for_video(test_image, timestamp,
|
||||
image_processing_options)
|
||||
# Comparing results.
|
||||
self._expect_face_detector_results_correct(detection_result,
|
||||
expected_detection_result)
|
||||
|
||||
def test_calling_detect_in_live_stream_mode(self):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the image mode'):
|
||||
detector.detect(self.test_image)
|
||||
|
||||
def test_calling_detect_for_video_in_live_stream_mode(self):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
with self.assertRaisesRegex(ValueError,
|
||||
r'not initialized with the video mode'):
|
||||
detector.detect_for_video(self.test_image, 0)
|
||||
|
||||
def test_detect_async_calls_with_illegal_timestamp(self):
|
||||
options = _FaceDetectorOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=mock.MagicMock())
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
detector.detect_async(self.test_image, 100)
|
||||
with self.assertRaisesRegex(
|
||||
ValueError, r'Input timestamp must be monotonically increasing'):
|
||||
detector.detect_async(self.test_image, 0)
|
||||
|
||||
@parameterized.parameters(
|
||||
(ModelFileType.FILE_NAME, _PORTRAIT_IMAGE, 0,
|
||||
_get_expected_face_detector_result(_PORTRAIT_EXPECTED_DETECTION)),
|
||||
(ModelFileType.FILE_CONTENT, _PORTRAIT_IMAGE, 0,
|
||||
_get_expected_face_detector_result(_PORTRAIT_EXPECTED_DETECTION)),
|
||||
(ModelFileType.FILE_NAME, _PORTRAIT_ROTATED_IMAGE, -90,
|
||||
_get_expected_face_detector_result(_PORTRAIT_ROTATED_EXPECTED_DETECTION)),
|
||||
(ModelFileType.FILE_CONTENT, _PORTRAIT_ROTATED_IMAGE, -90,
|
||||
_get_expected_face_detector_result(_PORTRAIT_ROTATED_EXPECTED_DETECTION)),
|
||||
(ModelFileType.FILE_NAME, _CAT_IMAGE, 0, FaceDetectorResult([])),
|
||||
(ModelFileType.FILE_CONTENT, _CAT_IMAGE, 0, FaceDetectorResult([])))
|
||||
def test_detect_async_calls(self, model_file_type, test_image_file_name,
|
||||
rotation_degrees, expected_detection_result):
|
||||
# Creates detector.
|
||||
if model_file_type is ModelFileType.FILE_NAME:
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||||
with open(self.model_path, 'rb') as f:
|
||||
model_content = f.read()
|
||||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||||
else:
|
||||
# Should never happen
|
||||
raise ValueError('model_file_type is invalid.')
|
||||
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(result: FaceDetectorResult, output_image: _Image,
|
||||
timestamp_ms: int):
|
||||
self._expect_face_detector_results_correct(result,
|
||||
expected_detection_result)
|
||||
self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||
self.observed_timestamp_ms = timestamp_ms
|
||||
|
||||
options = _FaceDetectorOptions(base_options=base_options,
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=check_result)
|
||||
|
||||
# Load the test image.
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, test_image_file_name)))
|
||||
|
||||
with _FaceDetector.create_from_options(options) as detector:
|
||||
for timestamp in range(0, 300, 30):
|
||||
# Set the image processing options.
|
||||
image_processing_options = _ImageProcessingOptions(
|
||||
rotation_degrees=rotation_degrees)
|
||||
detector.detect_async(test_image, timestamp, image_processing_options)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
absltest.main()
|
||||
|
|
@ -152,3 +152,23 @@ py_library(
|
|||
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
|
||||
],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "face_detector",
|
||||
srcs = [
|
||||
"face_detector.py",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/python:_framework_bindings",
|
||||
"//mediapipe/python:packet_creator",
|
||||
"//mediapipe/python:packet_getter",
|
||||
"//mediapipe/tasks/cc/vision/face_detector/proto:face_detector_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/python/components/containers:detections",
|
||||
"//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",
|
||||
],
|
||||
)
|
||||
|
|
|
|||
308
mediapipe/tasks/python/vision/face_detector.py
Normal file
308
mediapipe/tasks/python/vision/face_detector.py
Normal file
|
|
@ -0,0 +1,308 @@
|
|||
# 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 face detector task."""
|
||||
|
||||
import dataclasses
|
||||
from typing import Callable, Mapping, Optional
|
||||
|
||||
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.face_detector.proto import face_detector_graph_options_pb2
|
||||
from mediapipe.tasks.python.components.containers import detections as detections_module
|
||||
from mediapipe.tasks.python.core import base_options as base_options_module
|
||||
from mediapipe.tasks.python.core import task_info as task_info_module
|
||||
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
|
||||
from mediapipe.tasks.python.vision.core import base_vision_task_api
|
||||
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
|
||||
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
|
||||
|
||||
FaceDetectorResult = detections_module.DetectionResult
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_FaceDetectorGraphOptionsProto = face_detector_graph_options_pb2.FaceDetectorGraphOptions
|
||||
_RunningMode = running_mode_module.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
_TaskInfo = task_info_module.TaskInfo
|
||||
|
||||
_DETECTIONS_OUT_STREAM_NAME = 'detections'
|
||||
_DETECTIONS_TAG = 'DETECTIONS'
|
||||
_NORM_RECT_STREAM_NAME = 'norm_rect_in'
|
||||
_NORM_RECT_TAG = 'NORM_RECT'
|
||||
_IMAGE_IN_STREAM_NAME = 'image_in'
|
||||
_IMAGE_OUT_STREAM_NAME = 'image_out'
|
||||
_IMAGE_TAG = 'IMAGE'
|
||||
_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_detector.FaceDetectorGraph'
|
||||
_MICRO_SECONDS_PER_MILLISECOND = 1000
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class FaceDetectorOptions:
|
||||
"""Options for the face detector task.
|
||||
|
||||
Attributes:
|
||||
base_options: Base options for the face detector task.
|
||||
running_mode: The running mode of the task. Default to the image mode.
|
||||
Face detector task has three running modes:
|
||||
1) The image mode for detecting faces on single image inputs.
|
||||
2) The video mode for detecting faces on the decoded frames of a video.
|
||||
3) The live stream mode for detecting faces on a live stream of input
|
||||
data, such as from camera.
|
||||
min_detection_confidence: The minimum confidence score for the face
|
||||
detection to be considered successful.
|
||||
min_suppression_threshold: The minimum non-maximum-suppression threshold
|
||||
for face detection to be considered overlapped.
|
||||
num_faces: Maximum number of faces to detect in the image.
|
||||
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
|
||||
min_detection_confidence: Optional[float] = None
|
||||
min_suppression_threshold: Optional[float] = None
|
||||
num_faces: Optional[int] = None
|
||||
result_callback: Optional[
|
||||
Callable[[detections_module.DetectionResult, image_module.Image, int],
|
||||
None]] = None
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _FaceDetectorGraphOptionsProto:
|
||||
"""Generates an FaceDetectorOptions 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
|
||||
return _FaceDetectorGraphOptionsProto(
|
||||
base_options=base_options_proto,
|
||||
min_detection_confidence=self.min_detection_confidence,
|
||||
min_suppression_threshold=self.min_suppression_threshold,
|
||||
num_faces=self.num_faces
|
||||
)
|
||||
|
||||
|
||||
class FaceDetector(base_vision_task_api.BaseVisionTaskApi):
|
||||
"""Class that performs face detection on images."""
|
||||
|
||||
@classmethod
|
||||
def create_from_model_path(cls, model_path: str) -> 'FaceDetector':
|
||||
"""Creates an `FaceDetector` object from a TensorFlow Lite model and the default `FaceDetectorOptions`.
|
||||
|
||||
Note that the created `FaceDetector` instance is in image mode, for
|
||||
detecting faces on single image inputs.
|
||||
|
||||
Args:
|
||||
model_path: Path to the model.
|
||||
|
||||
Returns:
|
||||
`FaceDetector` object that's created from the model file and the default
|
||||
`FaceDetectorOptions`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `FaceDetector` 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 = FaceDetectorOptions(
|
||||
base_options=base_options, running_mode=_RunningMode.IMAGE)
|
||||
return cls.create_from_options(options)
|
||||
|
||||
@classmethod
|
||||
def create_from_options(cls,
|
||||
options: FaceDetectorOptions) -> 'FaceDetector':
|
||||
"""Creates the `FaceDetector` object from face detector options.
|
||||
|
||||
Args:
|
||||
options: Options for the face detector task.
|
||||
|
||||
Returns:
|
||||
`FaceDetector` object that's created from `options`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `FaceDetector` object from
|
||||
`FaceDetectorOptions` 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[_DETECTIONS_OUT_STREAM_NAME].is_empty():
|
||||
empty_packet = output_packets[_DETECTIONS_OUT_STREAM_NAME]
|
||||
options.result_callback(
|
||||
FaceDetectorResult([]), image,
|
||||
empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
|
||||
return
|
||||
detection_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_DETECTIONS_OUT_STREAM_NAME])
|
||||
detection_result = detections_module.DetectionResult([
|
||||
detections_module.Detection.create_from_pb2(result)
|
||||
for result in detection_proto_list
|
||||
])
|
||||
|
||||
timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
|
||||
options.result_callback(detection_result, image,
|
||||
timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
|
||||
|
||||
task_info = _TaskInfo(
|
||||
task_graph=_TASK_GRAPH_NAME,
|
||||
input_streams=[
|
||||
':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
|
||||
':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
|
||||
],
|
||||
output_streams=[
|
||||
':'.join([_DETECTIONS_TAG, _DETECTIONS_OUT_STREAM_NAME]),
|
||||
':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
|
||||
],
|
||||
task_options=options)
|
||||
return cls(
|
||||
task_info.generate_graph_config(
|
||||
enable_flow_limiting=options.running_mode ==
|
||||
_RunningMode.LIVE_STREAM), options.running_mode,
|
||||
packets_callback if options.result_callback else None)
|
||||
|
||||
def detect(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> FaceDetectorResult:
|
||||
"""Performs face detection on the provided MediaPipe Image.
|
||||
|
||||
Only use this method when the FaceDetector is created with the image
|
||||
running mode.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
A face detection result object that contains a list of face detections,
|
||||
each detection has a bounding box that is expressed in the unrotated input
|
||||
frame of reference coordinates system, i.e. in `[0,image_width) x [0,
|
||||
image_height)`, which are the dimensions of the underlying image data.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If face detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options,
|
||||
roi_allowed=False)
|
||||
output_packets = self._process_image_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image),
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2())
|
||||
})
|
||||
if output_packets[_DETECTIONS_OUT_STREAM_NAME].is_empty():
|
||||
return FaceDetectorResult([])
|
||||
detection_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_DETECTIONS_OUT_STREAM_NAME])
|
||||
return detections_module.DetectionResult([
|
||||
detections_module.Detection.create_from_pb2(result)
|
||||
for result in detection_proto_list
|
||||
])
|
||||
|
||||
def detect_for_video(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> detections_module.DetectionResult:
|
||||
"""Performs face detection on the provided video frames.
|
||||
|
||||
Only use this method when the FaceDetector 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:
|
||||
A face detection result object that contains a list of face detections,
|
||||
each detection has a bounding box that is expressed in the unrotated input
|
||||
frame of reference coordinates system, i.e. in `[0,image_width) x [0,
|
||||
image_height)`, which are the dimensions of the underlying image data.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If face detection failed to run.
|
||||
"""
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options,
|
||||
roi_allowed=False)
|
||||
output_packets = self._process_video_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
_NORM_RECT_STREAM_NAME:
|
||||
packet_creator.create_proto(normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
if output_packets[_DETECTIONS_OUT_STREAM_NAME].is_empty():
|
||||
return FaceDetectorResult([])
|
||||
detection_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_DETECTIONS_OUT_STREAM_NAME])
|
||||
return detections_module.DetectionResult([
|
||||
detections_module.Detection.create_from_pb2(result)
|
||||
for result in detection_proto_list
|
||||
])
|
||||
|
||||
def detect_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 face detection.
|
||||
|
||||
Only use this method when the FaceDetector 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 `FaceDetectorOptions`. The
|
||||
`detect_async` method is designed to process live stream data such as camera
|
||||
input. To lower the overall latency, face detector may drop the input
|
||||
images if needed. In other words, it's not guaranteed to have output per
|
||||
input image.
|
||||
|
||||
The `result_callback` provides:
|
||||
- A face detection result object that contains a list of face detections,
|
||||
each detection has a bounding box that is expressed in the unrotated
|
||||
input frame of reference coordinates system,
|
||||
i.e. in `[0,image_width) x [0,image_height)`, which are the dimensions
|
||||
of the underlying image data.
|
||||
- The input image that the face detector 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 face
|
||||
detector 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)
|
||||
})
|
||||
Loading…
Reference in New Issue
Block a user