From 022838a7f378d199876a9ab1c1c6b9ace03c8b29 Mon Sep 17 00:00:00 2001 From: kinaryml Date: Thu, 9 Mar 2023 01:36:39 -0800 Subject: [PATCH 1/3] Added Face Detector implementation and tests --- mediapipe/python/BUILD | 1 + .../tasks/python/components/containers/BUILD | 10 + .../components/containers/detections.py | 40 +- .../python/components/containers/keypoint.py | 78 ++++ mediapipe/tasks/python/test/vision/BUILD | 22 + .../python/test/vision/face_detector_test.py | 407 ++++++++++++++++++ mediapipe/tasks/python/vision/BUILD | 20 + .../tasks/python/vision/face_detector.py | 308 +++++++++++++ 8 files changed, 882 insertions(+), 4 deletions(-) create mode 100644 mediapipe/tasks/python/components/containers/keypoint.py create mode 100644 mediapipe/tasks/python/test/vision/face_detector_test.py create mode 100644 mediapipe/tasks/python/vision/face_detector.py diff --git a/mediapipe/python/BUILD b/mediapipe/python/BUILD index f56e5b3d4..141b59d71 100644 --- a/mediapipe/python/BUILD +++ b/mediapipe/python/BUILD @@ -94,6 +94,7 @@ cc_library( "//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph", "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph", "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph", + "//mediapipe/tasks/cc/vision/face_detector:face_detector_graph", ] + select({ # TODO: Build text_classifier_graph and text_embedder_graph on Windows. "//mediapipe:windows": [], diff --git a/mediapipe/tasks/python/components/containers/BUILD b/mediapipe/tasks/python/components/containers/BUILD index 7108617ff..b84ab744d 100644 --- a/mediapipe/tasks/python/components/containers/BUILD +++ b/mediapipe/tasks/python/components/containers/BUILD @@ -73,12 +73,22 @@ py_library( ], ) +py_library( + name = "keypoint", + srcs = ["keypoint.py"], + deps = [ + "//mediapipe/framework/formats:location_data_py_pb2", + "//mediapipe/tasks/python/core:optional_dependencies", + ], +) + py_library( name = "detections", srcs = ["detections.py"], deps = [ ":bounding_box", ":category", + ":keypoint", "//mediapipe/framework/formats:detection_py_pb2", "//mediapipe/framework/formats:location_data_py_pb2", "//mediapipe/tasks/python/core:optional_dependencies", diff --git a/mediapipe/tasks/python/components/containers/detections.py b/mediapipe/tasks/python/components/containers/detections.py index b4d550633..94fe16096 100644 --- a/mediapipe/tasks/python/components/containers/detections.py +++ b/mediapipe/tasks/python/components/containers/detections.py @@ -20,6 +20,7 @@ from mediapipe.framework.formats import detection_pb2 from mediapipe.framework.formats import location_data_pb2 from mediapipe.tasks.python.components.containers import bounding_box as bounding_box_module from mediapipe.tasks.python.components.containers import category as category_module +from mediapipe.tasks.python.components.containers import keypoint as keypoint_module from mediapipe.tasks.python.core.optional_dependencies import doc_controls _DetectionListProto = detection_pb2.DetectionList @@ -34,10 +35,12 @@ class Detection: Attributes: bounding_box: A BoundingBox object. categories: A list of Category objects. + keypoints: A list of NormalizedKeypoint objects. """ - bounding_box: bounding_box_module.BoundingBox - categories: List[category_module.Category] + bounding_box: bounding_box_module.BoundingBox = None + categories: List[category_module.Category] = None + keypoints: List[keypoint_module.NormalizedKeypoint] = None @doc_controls.do_not_generate_docs def to_pb2(self) -> _DetectionProto: @@ -46,6 +49,8 @@ class Detection: label_ids = [] scores = [] display_names = [] + relative_keypoints = [] + for category in self.categories: scores.append(category.score) if category.index: @@ -54,6 +59,20 @@ class Detection: labels.append(category.category_name) if category.display_name: display_names.append(category.display_name) + + if self.keypoints: + for keypoint in self.keypoints: + relative_keypoint_proto = _LocationDataProto.RelativeKeypoint() + if keypoint.x: + relative_keypoint_proto.x = keypoint.x + if keypoint.y: + relative_keypoint_proto.y = keypoint.y + if keypoint.label: + relative_keypoint_proto.keypoint_label = keypoint.label + if keypoint.score: + relative_keypoint_proto.score = keypoint.score + relative_keypoints.append(relative_keypoint_proto) + return _DetectionProto( label=labels, label_id=label_ids, @@ -61,13 +80,16 @@ class Detection: display_name=display_names, location_data=_LocationDataProto( format=_LocationDataProto.Format.BOUNDING_BOX, - bounding_box=self.bounding_box.to_pb2())) + bounding_box=self.bounding_box.to_pb2(), + relative_keypoints=relative_keypoints)) @classmethod @doc_controls.do_not_generate_docs def create_from_pb2(cls, pb2_obj: _DetectionProto) -> 'Detection': """Creates a `Detection` object from the given protobuf object.""" categories = [] + keypoints = [] + for idx, score in enumerate(pb2_obj.score): categories.append( category_module.Category( @@ -79,10 +101,20 @@ class Detection: display_name=pb2_obj.display_name[idx] if idx < len(pb2_obj.display_name) else None)) + if pb2_obj.location_data.relative_keypoints: + for idx in range(len(pb2_obj.location_data.relative_keypoints)): + keypoints.append( + keypoint_module.NormalizedKeypoint( + x=pb2_obj.location_data.relative_keypoints[idx].x, + y=pb2_obj.location_data.relative_keypoints[idx].y, + label=pb2_obj.location_data.relative_keypoints[idx].keypoint_label, + score=pb2_obj.location_data.relative_keypoints[idx].score)) + return Detection( bounding_box=bounding_box_module.BoundingBox.create_from_pb2( pb2_obj.location_data.bounding_box), - categories=categories) + categories=categories, + keypoints=keypoints) def __eq__(self, other: Any) -> bool: """Checks if this object is equal to the given object. diff --git a/mediapipe/tasks/python/components/containers/keypoint.py b/mediapipe/tasks/python/components/containers/keypoint.py new file mode 100644 index 000000000..ef70c00b9 --- /dev/null +++ b/mediapipe/tasks/python/components/containers/keypoint.py @@ -0,0 +1,78 @@ +# 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. +"""Keypoint data class.""" + +import dataclasses +from typing import Any, Optional + +from mediapipe.framework.formats import location_data_pb2 +from mediapipe.tasks.python.core.optional_dependencies import doc_controls + +_RelativeKeypointProto = location_data_pb2.LocationData.RelativeKeypoint + + +@dataclasses.dataclass +class NormalizedKeypoint: + """A normalized keypoint. + + Normalized keypoint represents a point in 2D space with x, y coordinates. + x and y are normalized to [0.0, 1.0] by the image width and height + respectively. + + Attributes: + x: The x coordinates of the normalized keypoint. + y: The y coordinates of the normalized keypoint. + label: The optional label of the keypoint. + score: The score of the keypoint. + """ + + x: Optional[float] = None + y: Optional[float] = None + label: Optional[str] = None + score: Optional[str] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _RelativeKeypointProto: + """Generates a RelativeKeypoint protobuf object.""" + return _RelativeKeypointProto( + x=self.x, + y=self.y, + keypoint_label=self.label, + score=self.score + ) + + @classmethod + @doc_controls.do_not_generate_docs + def create_from_pb2(cls, + pb2_obj: _RelativeKeypointProto) -> 'NormalizedKeypoint': + """Creates a `NormalizedKeypoint` object from the given protobuf object.""" + return NormalizedKeypoint( + x=pb2_obj.x, + y=pb2_obj.y, + label=pb2_obj.keypoint_label, + score=pb2_obj.score) + + def __eq__(self, other: Any) -> bool: + """Checks if this object is equal to the given object. + + Args: + other: The object to be compared with. + + Returns: + True if the objects are equal. + """ + if not isinstance(other, NormalizedKeypoint): + return False + + return self.to_pb2().__eq__(other.to_pb2()) diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD index 48ecc30b3..813f76bdb 100644 --- a/mediapipe/tasks/python/test/vision/BUILD +++ b/mediapipe/tasks/python/test/vision/BUILD @@ -114,3 +114,25 @@ py_test( "@com_google_protobuf//:protobuf_python", ], ) + +py_test( + name = "face_detector_test", + srcs = ["face_detector_test.py"], + data = [ + "//mediapipe/tasks/testdata/vision:test_images", + "//mediapipe/tasks/testdata/vision:test_models", + "//mediapipe/tasks/testdata/vision:test_protos", + ], + deps = [ + "//mediapipe/python:_framework_bindings", + "//mediapipe/tasks/python/components/containers:bounding_box", + "//mediapipe/tasks/python/components/containers:category", + "//mediapipe/tasks/python/components/containers:detections", + "//mediapipe/tasks/python/core:base_options", + "//mediapipe/tasks/python/test:test_utils", + "//mediapipe/tasks/python/vision:face_detector", + "//mediapipe/tasks/python/vision/core:image_processing_options", + "//mediapipe/tasks/python/vision/core:vision_task_running_mode", + "@com_google_protobuf//:protobuf_python", + ], +) diff --git a/mediapipe/tasks/python/test/vision/face_detector_test.py b/mediapipe/tasks/python/test/vision/face_detector_test.py new file mode 100644 index 000000000..90a52d110 --- /dev/null +++ b/mediapipe/tasks/python/test/vision/face_detector_test.py @@ -0,0 +1,407 @@ +# Copyright 2022 The MediaPipe Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Tests for face detector.""" + +import enum +import os +from unittest import mock + +from absl.testing import absltest +from absl.testing import parameterized +import numpy as np +from google.protobuf import text_format + +from mediapipe.framework.formats import detection_pb2 +from mediapipe.python._framework_bindings import image as image_module +from mediapipe.tasks.python.components.containers import bounding_box as bounding_box_module +from mediapipe.tasks.python.components.containers import category as category_module +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.test import test_utils +from mediapipe.tasks.python.vision import face_detector +from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module +from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module + + +FaceDetectorResult = detections_module.DetectionResult +_BaseOptions = base_options_module.BaseOptions +_Category = category_module.Category +_BoundingBox = bounding_box_module.BoundingBox +_Detection = detections_module.Detection +_Image = image_module.Image +_FaceDetector = face_detector.FaceDetector +_FaceDetectorOptions = face_detector.FaceDetectorOptions +_RUNNING_MODE = running_mode_module.VisionTaskRunningMode +_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions + +_SHORT_RANGE_BLAZE_FACE_MODEL = 'face_detection_short_range.tflite' +_PORTRAIT_IMAGE = 'portrait.jpg' +_PORTRAIT_EXPECTED_DETECTION = 'portrait_expected_detection.pbtxt' +_PORTRAIT_ROTATED_IMAGE = 'portrait_rotated.jpg' +_PORTRAIT_ROTATED_EXPECTED_DETECTION = 'portrait_rotated_expected_detection.pbtxt' +_CAT_IMAGE = 'cat.jpg' +_KEYPOINT_ERROR_THRESHOLD = 1e-2 +_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision' + + +def _get_expected_face_detector_result(file_name: str) -> FaceDetectorResult: + face_detection_result_file_path = test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, file_name)) + with open(face_detection_result_file_path, "rb") as f: + face_detection_proto = detection_pb2.Detection() + text_format.Parse(f.read(), face_detection_proto) + face_detection = detections_module.Detection.create_from_pb2(face_detection_proto) + return FaceDetectorResult(detections=[face_detection]) + + +class ModelFileType(enum.Enum): + FILE_CONTENT = 1 + FILE_NAME = 2 + + +class FaceDetectorTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.test_image = _Image.create_from_file( + test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, _PORTRAIT_IMAGE))) + self.model_path = test_utils.get_test_data_path( + os.path.join(_TEST_DATA_DIR, _SHORT_RANGE_BLAZE_FACE_MODEL)) + + def test_create_from_file_succeeds_with_valid_model_path(self): + # Creates with default option and valid model file successfully. + with _FaceDetector.create_from_model_path(self.model_path) as detector: + self.assertIsInstance(detector, _FaceDetector) + + def test_create_from_options_succeeds_with_valid_model_path(self): + # Creates with options containing model file successfully. + base_options = _BaseOptions(model_asset_path=self.model_path) + options = _FaceDetectorOptions(base_options=base_options) + with _FaceDetector.create_from_options(options) as detector: + self.assertIsInstance(detector, _FaceDetector) + + def test_create_from_options_fails_with_invalid_model_path(self): + with self.assertRaisesRegex( + RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'): + base_options = _BaseOptions( + model_asset_path='/path/to/invalid/model.tflite') + options = _FaceDetectorOptions(base_options=base_options) + _FaceDetector.create_from_options(options) + + def test_create_from_options_succeeds_with_valid_model_content(self): + # Creates with options containing model content successfully. + with open(self.model_path, 'rb') as f: + base_options = _BaseOptions(model_asset_buffer=f.read()) + options = _FaceDetectorOptions(base_options=base_options) + detector = _FaceDetector.create_from_options(options) + self.assertIsInstance(detector, _FaceDetector) + + def _expect_keypoints_correct(self, actual_keypoints, expected_keypoints): + self.assertLen(actual_keypoints, len(expected_keypoints)) + for i in range(len(actual_keypoints)): + self.assertAlmostEqual( + actual_keypoints[i].x, expected_keypoints[i].x, + delta=_KEYPOINT_ERROR_THRESHOLD) + self.assertAlmostEqual( + actual_keypoints[i].y, expected_keypoints[i].y, + delta=_KEYPOINT_ERROR_THRESHOLD) + + def _expect_face_detector_results_correct(self, actual_results, expected_results): + self.assertLen(actual_results.detections, len(expected_results.detections)) + for i in range(len(actual_results.detections)): + actual_bbox = actual_results.detections[i].bounding_box + expected_bbox = expected_results.detections[i].bounding_box + self.assertEqual(actual_bbox, expected_bbox) + self.assertNotEmpty(actual_results.detections[i].keypoints) + self._expect_keypoints_correct(actual_results.detections[i].keypoints, + expected_results.detections[i].keypoints) + + @parameterized.parameters( + (ModelFileType.FILE_NAME, _PORTRAIT_EXPECTED_DETECTION), + (ModelFileType.FILE_CONTENT, _PORTRAIT_EXPECTED_DETECTION)) + def test_detect(self, model_file_type, expected_detection_result_file): + # 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) + detector = _FaceDetector.create_from_options(options) + + # Performs face detection on the input. + detection_result = detector.detect(self.test_image) + # 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) + # Closes the detector explicitly when the detector is not used in + # a context. + detector.close() + + @parameterized.parameters( + (ModelFileType.FILE_NAME, _PORTRAIT_EXPECTED_DETECTION), + (ModelFileType.FILE_CONTENT, _PORTRAIT_EXPECTED_DETECTION)) + def test_detect_in_context(self, model_file_type, expected_detection_result_file): + # 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) + + with _FaceDetector.create_from_options(options) as detector: + # Performs face detection on the input. + detection_result = detector.detect(self.test_image) + # 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() diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD index eda8e290d..891286641 100644 --- a/mediapipe/tasks/python/vision/BUILD +++ b/mediapipe/tasks/python/vision/BUILD @@ -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", + ], +) diff --git a/mediapipe/tasks/python/vision/face_detector.py b/mediapipe/tasks/python/vision/face_detector.py new file mode 100644 index 000000000..91baecff4 --- /dev/null +++ b/mediapipe/tasks/python/vision/face_detector.py @@ -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) + }) From 24114ec2fec7a216903cb3b8fb08b657569f8648 Mon Sep 17 00:00:00 2001 From: kinaryml Date: Thu, 9 Mar 2023 01:41:42 -0800 Subject: [PATCH 2/3] Updated comment in test --- mediapipe/tasks/python/test/vision/face_detector_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mediapipe/tasks/python/test/vision/face_detector_test.py b/mediapipe/tasks/python/test/vision/face_detector_test.py index 90a52d110..f78c9c94e 100644 --- a/mediapipe/tasks/python/test/vision/face_detector_test.py +++ b/mediapipe/tasks/python/test/vision/face_detector_test.py @@ -209,7 +209,7 @@ class FaceDetectorTest(parameterized.TestCase): 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. + # Performs face detection on the input. detection_result = detector.detect(test_image) self.assertEmpty(detection_result.detections) From f48909cab63243a477207e65f0ad08c079613baa Mon Sep 17 00:00:00 2001 From: kinaryml Date: Thu, 9 Mar 2023 02:13:34 -0800 Subject: [PATCH 3/3] Fixed score's data type --- mediapipe/tasks/python/components/containers/keypoint.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mediapipe/tasks/python/components/containers/keypoint.py b/mediapipe/tasks/python/components/containers/keypoint.py index ef70c00b9..ef91d0950 100644 --- a/mediapipe/tasks/python/components/containers/keypoint.py +++ b/mediapipe/tasks/python/components/containers/keypoint.py @@ -40,7 +40,7 @@ class NormalizedKeypoint: x: Optional[float] = None y: Optional[float] = None label: Optional[str] = None - score: Optional[str] = None + score: Optional[float] = None @doc_controls.do_not_generate_docs def to_pb2(self) -> _RelativeKeypointProto: