diff --git a/mediapipe/python/BUILD b/mediapipe/python/BUILD index 2fdc08149..085fbc96b 100644 --- a/mediapipe/python/BUILD +++ b/mediapipe/python/BUILD @@ -99,6 +99,7 @@ cc_library( "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph", "//mediapipe/tasks/cc/vision/interactive_segmenter:interactive_segmenter_graph", "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph", + "//mediapipe/tasks/cc/vision/pose_landmarker:pose_landmarker_graph", ] + select({ # TODO: Build text_classifier_graph and text_embedder_graph on Windows. "//mediapipe:windows": [], diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD index d5d7f4671..e55e1b572 100644 --- a/mediapipe/tasks/python/test/vision/BUILD +++ b/mediapipe/tasks/python/test/vision/BUILD @@ -162,3 +162,26 @@ py_test( "@com_google_protobuf//:protobuf_python", ], ) + +py_test( + name = "pose_landmarker_test", + srcs = ["pose_landmarker_test.py"], + data = [ + "//mediapipe/tasks/testdata/vision:test_images", + "//mediapipe/tasks/testdata/vision:test_models", + "//mediapipe/tasks/testdata/vision:test_protos", + ], + deps = [ + "//mediapipe/python:_framework_bindings", + "//mediapipe/tasks/cc/components/containers/proto:landmarks_detection_result_py_pb2", + "//mediapipe/tasks/python/components/containers:landmark", + "//mediapipe/tasks/python/components/containers:landmark_detection_result", + "//mediapipe/tasks/python/components/containers:rect", + "//mediapipe/tasks/python/core:base_options", + "//mediapipe/tasks/python/test:test_utils", + "//mediapipe/tasks/python/vision:pose_landmarker", + "//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_landmarker_test.py b/mediapipe/tasks/python/test/vision/face_landmarker_test.py index b91999191..fb02c5007 100644 --- a/mediapipe/tasks/python/test/vision/face_landmarker_test.py +++ b/mediapipe/tasks/python/test/vision/face_landmarker_test.py @@ -51,24 +51,27 @@ _PORTRAIT_IMAGE = 'portrait.jpg' _CAT_IMAGE = 'cat.jpg' _PORTRAIT_EXPECTED_FACE_LANDMARKS = 'portrait_expected_face_landmarks.pbtxt' _PORTRAIT_EXPECTED_BLENDSHAPES = 'portrait_expected_blendshapes.pbtxt' -_LANDMARKS_DIFF_MARGIN = 0.03 -_BLENDSHAPES_DIFF_MARGIN = 0.13 -_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02 +_LANDMARKS_MARGIN = 0.03 +_BLENDSHAPES_MARGIN = 0.13 +_FACIAL_TRANSFORMATION_MATRIX_MARGIN = 0.02 def _get_expected_face_landmarks(file_path: str): proto_file_path = test_utils.get_test_data_path(file_path) + face_landmarks_results = [] with open(proto_file_path, 'rb') as f: proto = landmark_pb2.NormalizedLandmarkList() text_format.Parse(f.read(), proto) face_landmarks = [] for landmark in proto.landmark: face_landmarks.append(_NormalizedLandmark.create_from_pb2(landmark)) - return face_landmarks + face_landmarks_results.append(face_landmarks) + return face_landmarks_results def _get_expected_face_blendshapes(file_path: str): proto_file_path = test_utils.get_test_data_path(file_path) + face_blendshapes_results = [] with open(proto_file_path, 'rb') as f: proto = classification_pb2.ClassificationList() text_format.Parse(f.read(), proto) @@ -84,7 +87,8 @@ def _get_expected_face_blendshapes(file_path: str): category_name=face_blendshapes.label, ) ) - return face_blendshapes_categories + face_blendshapes_results.append(face_blendshapes_categories) + return face_blendshapes_results def _get_expected_facial_transformation_matrixes(): @@ -119,13 +123,14 @@ class FaceLandmarkerTest(parameterized.TestCase): # Expects to have the same number of faces detected. self.assertLen(actual_landmarks, len(expected_landmarks)) - for i, elem in enumerate(actual_landmarks): - self.assertAlmostEqual( - elem.x, expected_landmarks[i].x, delta=_LANDMARKS_DIFF_MARGIN - ) - self.assertAlmostEqual( - elem.y, expected_landmarks[i].y, delta=_LANDMARKS_DIFF_MARGIN - ) + for i, _ in enumerate(actual_landmarks): + for j, elem in enumerate(actual_landmarks[i]): + self.assertAlmostEqual( + elem.x, expected_landmarks[i][j].x, delta=_LANDMARKS_MARGIN + ) + self.assertAlmostEqual( + elem.y, expected_landmarks[i][j].y, delta=_LANDMARKS_MARGIN + ) def _expect_blendshapes_correct( self, actual_blendshapes, expected_blendshapes @@ -133,13 +138,14 @@ class FaceLandmarkerTest(parameterized.TestCase): # Expects to have the same number of blendshapes. self.assertLen(actual_blendshapes, len(expected_blendshapes)) - for i, elem in enumerate(actual_blendshapes): - self.assertEqual(elem.index, expected_blendshapes[i].index) - self.assertAlmostEqual( - elem.score, - expected_blendshapes[i].score, - delta=_BLENDSHAPES_DIFF_MARGIN, - ) + for i, _ in enumerate(actual_blendshapes): + for j, elem in enumerate(actual_blendshapes[i]): + self.assertEqual(elem.index, expected_blendshapes[i][j].index) + self.assertAlmostEqual( + elem.score, + expected_blendshapes[i][j].score, + delta=_BLENDSHAPES_MARGIN, + ) def _expect_facial_transformation_matrixes_correct( self, actual_matrix_list, expected_matrix_list @@ -152,7 +158,7 @@ class FaceLandmarkerTest(parameterized.TestCase): self.assertSequenceAlmostEqual( elem.flatten(), expected_matrix_list[i].flatten(), - delta=_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN, + delta=_FACIAL_TRANSFORMATION_MATRIX_MARGIN, ) def test_create_from_file_succeeds_with_valid_model_path(self): @@ -236,11 +242,11 @@ class FaceLandmarkerTest(parameterized.TestCase): # Comparing results. if expected_face_landmarks is not None: self._expect_landmarks_correct( - detection_result.face_landmarks[0], expected_face_landmarks + detection_result.face_landmarks, expected_face_landmarks ) if expected_face_blendshapes is not None: self._expect_blendshapes_correct( - detection_result.face_blendshapes[0], expected_face_blendshapes + detection_result.face_blendshapes, expected_face_blendshapes ) if expected_facial_transformation_matrixes is not None: self._expect_facial_transformation_matrixes_correct( @@ -302,11 +308,11 @@ class FaceLandmarkerTest(parameterized.TestCase): # Comparing results. if expected_face_landmarks is not None: self._expect_landmarks_correct( - detection_result.face_landmarks[0], expected_face_landmarks + detection_result.face_landmarks, expected_face_landmarks ) if expected_face_blendshapes is not None: self._expect_blendshapes_correct( - detection_result.face_blendshapes[0], expected_face_blendshapes + detection_result.face_blendshapes, expected_face_blendshapes ) if expected_facial_transformation_matrixes is not None: self._expect_facial_transformation_matrixes_correct( @@ -446,11 +452,11 @@ class FaceLandmarkerTest(parameterized.TestCase): # Comparing results. if expected_face_landmarks is not None: self._expect_landmarks_correct( - detection_result.face_landmarks[0], expected_face_landmarks + detection_result.face_landmarks, expected_face_landmarks ) if expected_face_blendshapes is not None: self._expect_blendshapes_correct( - detection_result.face_blendshapes[0], expected_face_blendshapes + detection_result.face_blendshapes, expected_face_blendshapes ) if expected_facial_transformation_matrixes is not None: self._expect_facial_transformation_matrixes_correct( @@ -523,11 +529,11 @@ class FaceLandmarkerTest(parameterized.TestCase): # Comparing results. if expected_face_landmarks is not None: self._expect_landmarks_correct( - result.face_landmarks[0], expected_face_landmarks + result.face_landmarks, expected_face_landmarks ) if expected_face_blendshapes is not None: self._expect_blendshapes_correct( - result.face_blendshapes[0], expected_face_blendshapes + result.face_blendshapes, expected_face_blendshapes ) if expected_facial_transformation_matrixes is not None: self._expect_facial_transformation_matrixes_correct( diff --git a/mediapipe/tasks/python/test/vision/hand_landmarker_test.py b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py index 1e8ebe7f2..8f991705e 100644 --- a/mediapipe/tasks/python/test/vision/hand_landmarker_test.py +++ b/mediapipe/tasks/python/test/vision/hand_landmarker_test.py @@ -32,12 +32,14 @@ from mediapipe.tasks.python.vision import hand_landmarker 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 -_LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult +_LandmarksDetectionResultProto = ( + landmarks_detection_result_pb2.LandmarksDetectionResult) _BaseOptions = base_options_module.BaseOptions _Rect = rect_module.Rect _Landmark = landmark_module.Landmark _NormalizedLandmark = landmark_module.NormalizedLandmark -_LandmarksDetectionResult = landmark_detection_result_module.LandmarksDetectionResult +_LandmarksDetectionResult = ( + landmark_detection_result_module.LandmarksDetectionResult) _Image = image_module.Image _HandLandmarker = hand_landmarker.HandLandmarker _HandLandmarkerOptions = hand_landmarker.HandLandmarkerOptions @@ -54,7 +56,7 @@ _POINTING_UP_IMAGE = 'pointing_up.jpg' _POINTING_UP_LANDMARKS = 'pointing_up_landmarks.pbtxt' _POINTING_UP_ROTATED_IMAGE = 'pointing_up_rotated.jpg' _POINTING_UP_ROTATED_LANDMARKS = 'pointing_up_rotated_landmarks.pbtxt' -_LANDMARKS_ERROR_TOLERANCE = 0.03 +_LANDMARKS_MARGIN = 0.03 _HANDEDNESS_MARGIN = 0.05 @@ -89,39 +91,43 @@ class HandLandmarkerTest(parameterized.TestCase): self.model_path = test_utils.get_test_data_path( _HAND_LANDMARKER_BUNDLE_ASSET_FILE) - def _assert_actual_result_approximately_matches_expected_result( - self, actual_result: _HandLandmarkerResult, - expected_result: _HandLandmarkerResult): + def _expect_hand_landmarks_correct( + self, actual_landmarks, expected_landmarks, margin + ): # Expects to have the same number of hands detected. - self.assertLen(actual_result.hand_landmarks, - len(expected_result.hand_landmarks)) - self.assertLen(actual_result.hand_world_landmarks, - len(expected_result.hand_world_landmarks)) - self.assertLen(actual_result.handedness, len(expected_result.handedness)) - # Actual landmarks match expected landmarks. - self.assertLen(actual_result.hand_landmarks[0], - len(expected_result.hand_landmarks[0])) - actual_landmarks = actual_result.hand_landmarks[0] - expected_landmarks = expected_result.hand_landmarks[0] - for i, rename_me in enumerate(actual_landmarks): - self.assertAlmostEqual( - rename_me.x, - expected_landmarks[i].x, - delta=_LANDMARKS_ERROR_TOLERANCE) - self.assertAlmostEqual( - rename_me.y, - expected_landmarks[i].y, - delta=_LANDMARKS_ERROR_TOLERANCE) - # Actual handedness matches expected handedness. - actual_top_handedness = actual_result.handedness[0][0] - expected_top_handedness = expected_result.handedness[0][0] + self.assertLen(actual_landmarks, len(expected_landmarks)) + + for i, _ in enumerate(actual_landmarks): + for j, elem in enumerate(actual_landmarks[i]): + self.assertAlmostEqual(elem.x, expected_landmarks[i][j].x, delta=margin) + self.assertAlmostEqual(elem.y, expected_landmarks[i][j].y, delta=margin) + + def _expect_handedness_correct( + self, actual_handedness, expected_handedness, margin + ): + # Actual top handedness matches expected top handedness. + actual_top_handedness = actual_handedness[0][0] + expected_top_handedness = expected_handedness[0][0] self.assertEqual(actual_top_handedness.index, expected_top_handedness.index) self.assertEqual(actual_top_handedness.category_name, expected_top_handedness.category_name) self.assertAlmostEqual( - actual_top_handedness.score, - expected_top_handedness.score, - delta=_HANDEDNESS_MARGIN) + actual_top_handedness.score, expected_top_handedness.score, delta=margin + ) + + def _expect_hand_landmarker_results_correct( + self, + actual_result: _HandLandmarkerResult, + expected_result: _HandLandmarkerResult, + ): + self._expect_hand_landmarks_correct( + actual_result.hand_landmarks, + expected_result.hand_landmarks, + _LANDMARKS_MARGIN, + ) + self._expect_handedness_correct( + actual_result.handedness, expected_result.handedness, _HANDEDNESS_MARGIN + ) def test_create_from_file_succeeds_with_valid_model_path(self): # Creates with default option and valid model file successfully. @@ -175,8 +181,9 @@ class HandLandmarkerTest(parameterized.TestCase): # Performs hand landmarks detection on the input. detection_result = landmarker.detect(self.test_image) # Comparing results. - self._assert_actual_result_approximately_matches_expected_result( - detection_result, expected_detection_result) + self._expect_hand_landmarker_results_correct( + detection_result, expected_detection_result + ) # Closes the hand landmarker explicitly when the hand landmarker is not used # in a context. landmarker.close() @@ -203,8 +210,9 @@ class HandLandmarkerTest(parameterized.TestCase): # Performs hand landmarks detection on the input. detection_result = landmarker.detect(self.test_image) # Comparing results. - self._assert_actual_result_approximately_matches_expected_result( - detection_result, expected_detection_result) + self._expect_hand_landmarker_results_correct( + detection_result, expected_detection_result + ) def test_detect_succeeds_with_num_hands(self): # Creates hand landmarker. @@ -234,8 +242,9 @@ class HandLandmarkerTest(parameterized.TestCase): expected_detection_result = _get_expected_hand_landmarker_result( _POINTING_UP_ROTATED_LANDMARKS) # Comparing results. - self._assert_actual_result_approximately_matches_expected_result( - detection_result, expected_detection_result) + self._expect_hand_landmarker_results_correct( + detection_result, expected_detection_result + ) def test_detect_fails_with_region_of_interest(self): # Creates hand landmarker. @@ -350,9 +359,9 @@ class HandLandmarkerTest(parameterized.TestCase): for timestamp in range(0, 300, 30): result = landmarker.detect_for_video(test_image, timestamp, image_processing_options) - if result.hand_landmarks and result.hand_world_landmarks and result.handedness: - self._assert_actual_result_approximately_matches_expected_result( - result, expected_result) + if (result.hand_landmarks and result.hand_world_landmarks and + result.handedness): + self._expect_hand_landmarker_results_correct(result, expected_result) else: self.assertEqual(result, expected_result) @@ -405,9 +414,9 @@ class HandLandmarkerTest(parameterized.TestCase): def check_result(result: _HandLandmarkerResult, output_image: _Image, timestamp_ms: int): - if result.hand_landmarks and result.hand_world_landmarks and result.handedness: - self._assert_actual_result_approximately_matches_expected_result( - result, expected_result) + if (result.hand_landmarks and result.hand_world_landmarks and + result.handedness): + self._expect_hand_landmarker_results_correct(result, expected_result) else: self.assertEqual(result, expected_result) self.assertTrue( diff --git a/mediapipe/tasks/python/test/vision/pose_landmarker_test.py b/mediapipe/tasks/python/test/vision/pose_landmarker_test.py new file mode 100644 index 000000000..1b73ecdfb --- /dev/null +++ b/mediapipe/tasks/python/test/vision/pose_landmarker_test.py @@ -0,0 +1,520 @@ +# Copyright 2023 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 pose landmarker.""" + +import enum +from typing import List +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.python._framework_bindings import image as image_module +from mediapipe.tasks.cc.components.containers.proto import landmarks_detection_result_pb2 +from mediapipe.tasks.python.components.containers import landmark as landmark_module +from mediapipe.tasks.python.components.containers import landmark_detection_result as landmark_detection_result_module +from mediapipe.tasks.python.components.containers import rect as rect_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 pose_landmarker +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 + +PoseLandmarkerResult = pose_landmarker.PoseLandmarkerResult +_LandmarksDetectionResultProto = ( + landmarks_detection_result_pb2.LandmarksDetectionResult +) +_BaseOptions = base_options_module.BaseOptions +_Rect = rect_module.Rect +_Landmark = landmark_module.Landmark +_NormalizedLandmark = landmark_module.NormalizedLandmark +_LandmarksDetectionResult = ( + landmark_detection_result_module.LandmarksDetectionResult +) +_Image = image_module.Image +_PoseLandmarker = pose_landmarker.PoseLandmarker +_PoseLandmarkerOptions = pose_landmarker.PoseLandmarkerOptions +_RUNNING_MODE = running_mode_module.VisionTaskRunningMode +_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions + +_POSE_LANDMARKER_BUNDLE_ASSET_FILE = 'pose_landmarker.task' +_BURGER_IMAGE = 'burger.jpg' +_POSE_IMAGE = 'pose.jpg' +_POSE_LANDMARKS = 'pose_landmarks.pbtxt' +_LANDMARKS_MARGIN = 0.03 + + +def _get_expected_pose_landmarker_result( + file_path: str, +) -> PoseLandmarkerResult: + landmarks_detection_result_file_path = test_utils.get_test_data_path( + file_path + ) + with open(landmarks_detection_result_file_path, 'rb') as f: + landmarks_detection_result_proto = _LandmarksDetectionResultProto() + # Use this if a .pb file is available. + # landmarks_detection_result_proto.ParseFromString(f.read()) + text_format.Parse(f.read(), landmarks_detection_result_proto) + landmarks_detection_result = _LandmarksDetectionResult.create_from_pb2( + landmarks_detection_result_proto + ) + return PoseLandmarkerResult( + pose_landmarks=[landmarks_detection_result.landmarks], + pose_world_landmarks=[], + pose_auxiliary_landmarks=[], + ) + + +class ModelFileType(enum.Enum): + FILE_CONTENT = 1 + FILE_NAME = 2 + + +class PoseLandmarkerTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.test_image = _Image.create_from_file( + test_utils.get_test_data_path(_POSE_IMAGE) + ) + self.model_path = test_utils.get_test_data_path( + _POSE_LANDMARKER_BUNDLE_ASSET_FILE + ) + + def _expect_pose_landmarks_correct( + self, actual_landmarks, expected_landmarks, margin + ): + # Expects to have the same number of poses detected. + self.assertLen(actual_landmarks, len(expected_landmarks)) + + for i, _ in enumerate(actual_landmarks): + for j, elem in enumerate(actual_landmarks[i]): + self.assertAlmostEqual(elem.x, expected_landmarks[i][j].x, delta=margin) + self.assertAlmostEqual(elem.y, expected_landmarks[i][j].y, delta=margin) + + def _expect_pose_landmarker_results_correct( + self, + actual_result: PoseLandmarkerResult, + expected_result: PoseLandmarkerResult, + output_segmentation_masks: bool, + margin: float, + ): + self._expect_pose_landmarks_correct( + actual_result.pose_landmarks, expected_result.pose_landmarks, margin + ) + if output_segmentation_masks: + self.assertIsInstance(actual_result.segmentation_masks, List) + for _, mask in enumerate(actual_result.segmentation_masks): + self.assertIsInstance(mask, _Image) + else: + self.assertIsNone(actual_result.segmentation_masks) + + def test_create_from_file_succeeds_with_valid_model_path(self): + # Creates with default option and valid model file successfully. + with _PoseLandmarker.create_from_model_path(self.model_path) as landmarker: + self.assertIsInstance(landmarker, _PoseLandmarker) + + 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 = _PoseLandmarkerOptions(base_options=base_options) + with _PoseLandmarker.create_from_options(options) as landmarker: + self.assertIsInstance(landmarker, _PoseLandmarker) + + def test_create_from_options_fails_with_invalid_model_path(self): + # Invalid empty model path. + 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 = _PoseLandmarkerOptions(base_options=base_options) + _PoseLandmarker.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 = _PoseLandmarkerOptions(base_options=base_options) + landmarker = _PoseLandmarker.create_from_options(options) + self.assertIsInstance(landmarker, _PoseLandmarker) + + @parameterized.parameters( + ( + ModelFileType.FILE_NAME, + False, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ( + ModelFileType.FILE_CONTENT, + False, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ( + ModelFileType.FILE_NAME, + True, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ( + ModelFileType.FILE_CONTENT, + True, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ) + def test_detect( + self, + model_file_type, + output_segmentation_masks, + expected_detection_result, + ): + # Creates pose landmarker. + if model_file_type is ModelFileType.FILE_NAME: + base_options = _BaseOptions(model_asset_path=self.model_path) + elif model_file_type is ModelFileType.FILE_CONTENT: + with open(self.model_path, 'rb') as f: + model_content = f.read() + base_options = _BaseOptions(model_asset_buffer=model_content) + else: + # Should never happen + raise ValueError('model_file_type is invalid.') + + options = _PoseLandmarkerOptions( + base_options=base_options, + output_segmentation_masks=output_segmentation_masks, + ) + landmarker = _PoseLandmarker.create_from_options(options) + + # Performs pose landmarks detection on the input. + detection_result = landmarker.detect(self.test_image) + + # Comparing results. + self._expect_pose_landmarker_results_correct( + detection_result, + expected_detection_result, + output_segmentation_masks, + _LANDMARKS_MARGIN, + ) + # Closes the pose landmarker explicitly when the pose landmarker is not used + # in a context. + landmarker.close() + + @parameterized.parameters( + ( + ModelFileType.FILE_NAME, + False, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ( + ModelFileType.FILE_CONTENT, + False, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ( + ModelFileType.FILE_NAME, + True, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ( + ModelFileType.FILE_CONTENT, + True, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ) + def test_detect_in_context( + self, + model_file_type, + output_segmentation_masks, + expected_detection_result, + ): + # Creates pose landmarker. + if model_file_type is ModelFileType.FILE_NAME: + base_options = _BaseOptions(model_asset_path=self.model_path) + elif model_file_type is ModelFileType.FILE_CONTENT: + with open(self.model_path, 'rb') as f: + model_content = f.read() + base_options = _BaseOptions(model_asset_buffer=model_content) + else: + # Should never happen + raise ValueError('model_file_type is invalid.') + + options = _PoseLandmarkerOptions( + base_options=base_options, + output_segmentation_masks=output_segmentation_masks, + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + # Performs pose landmarks detection on the input. + detection_result = landmarker.detect(self.test_image) + + # Comparing results. + self._expect_pose_landmarker_results_correct( + detection_result, + expected_detection_result, + output_segmentation_masks, + _LANDMARKS_MARGIN, + ) + + def test_detect_fails_with_region_of_interest(self): + # Creates pose landmarker. + base_options = _BaseOptions(model_asset_path=self.model_path) + options = _PoseLandmarkerOptions(base_options=base_options) + with self.assertRaisesRegex( + ValueError, "This task doesn't support region-of-interest." + ): + with _PoseLandmarker.create_from_options(options) as landmarker: + # Set the `region_of_interest` parameter using `ImageProcessingOptions`. + image_processing_options = _ImageProcessingOptions( + region_of_interest=_Rect(0, 0, 1, 1) + ) + # Attempt to perform pose landmarks detection on the cropped input. + landmarker.detect(self.test_image, image_processing_options) + + def test_empty_detection_outputs(self): + # Creates pose landmarker. + base_options = _BaseOptions(model_asset_path=self.model_path) + options = _PoseLandmarkerOptions(base_options=base_options) + with _PoseLandmarker.create_from_options(options) as landmarker: + # Load an image with no poses. + test_image = _Image.create_from_file( + test_utils.get_test_data_path(_BURGER_IMAGE) + ) + # Performs pose landmarks detection on the input. + detection_result = landmarker.detect(test_image) + # Comparing results. + self.assertEmpty(detection_result.pose_landmarks) + self.assertEmpty(detection_result.pose_world_landmarks) + self.assertEmpty(detection_result.pose_auxiliary_landmarks) + + def test_missing_result_callback(self): + options = _PoseLandmarkerOptions( + 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 _PoseLandmarker.create_from_options(options) as unused_landmarker: + pass + + @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO)) + def test_illegal_result_callback(self, running_mode): + options = _PoseLandmarkerOptions( + 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 _PoseLandmarker.create_from_options(options) as unused_landmarker: + pass + + def test_calling_detect_for_video_in_image_mode(self): + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE, + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex( + ValueError, r'not initialized with the video mode' + ): + landmarker.detect_for_video(self.test_image, 0) + + def test_calling_detect_async_in_image_mode(self): + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.IMAGE, + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex( + ValueError, r'not initialized with the live stream mode' + ): + landmarker.detect_async(self.test_image, 0) + + def test_calling_detect_in_video_mode(self): + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO, + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex( + ValueError, r'not initialized with the image mode' + ): + landmarker.detect(self.test_image) + + def test_calling_detect_async_in_video_mode(self): + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO, + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex( + ValueError, r'not initialized with the live stream mode' + ): + landmarker.detect_async(self.test_image, 0) + + def test_detect_for_video_with_out_of_order_timestamp(self): + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.VIDEO, + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + unused_result = landmarker.detect_for_video(self.test_image, 1) + with self.assertRaisesRegex( + ValueError, r'Input timestamp must be monotonically increasing' + ): + landmarker.detect_for_video(self.test_image, 0) + + @parameterized.parameters( + ( + _POSE_IMAGE, + 0, + False, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ( + _POSE_IMAGE, + 0, + True, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + (_BURGER_IMAGE, 0, False, PoseLandmarkerResult([], [], [])), + ) + def test_detect_for_video( + self, image_path, rotation, output_segmentation_masks, expected_result + ): + test_image = _Image.create_from_file( + test_utils.get_test_data_path(image_path) + ) + # Set rotation parameters using ImageProcessingOptions. + image_processing_options = _ImageProcessingOptions( + rotation_degrees=rotation + ) + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + output_segmentation_masks=output_segmentation_masks, + running_mode=_RUNNING_MODE.VIDEO, + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + for timestamp in range(0, 300, 30): + result = landmarker.detect_for_video( + test_image, timestamp, image_processing_options + ) + if result.pose_landmarks: + self._expect_pose_landmarker_results_correct( + result, + expected_result, + output_segmentation_masks, + _LANDMARKS_MARGIN, + ) + else: + self.assertEqual(result, expected_result) + + def test_calling_detect_in_live_stream_mode(self): + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock(), + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex( + ValueError, r'not initialized with the image mode' + ): + landmarker.detect(self.test_image) + + def test_calling_detect_for_video_in_live_stream_mode(self): + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock(), + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + with self.assertRaisesRegex( + ValueError, r'not initialized with the video mode' + ): + landmarker.detect_for_video(self.test_image, 0) + + def test_detect_async_calls_with_illegal_timestamp(self): + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=mock.MagicMock(), + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + landmarker.detect_async(self.test_image, 100) + with self.assertRaisesRegex( + ValueError, r'Input timestamp must be monotonically increasing' + ): + landmarker.detect_async(self.test_image, 0) + + @parameterized.parameters( + ( + _POSE_IMAGE, + 0, + False, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + ( + _POSE_IMAGE, + 0, + True, + _get_expected_pose_landmarker_result(_POSE_LANDMARKS), + ), + (_BURGER_IMAGE, 0, False, PoseLandmarkerResult([], [], [])), + ) + def test_detect_async_calls( + self, image_path, rotation, output_segmentation_masks, expected_result + ): + test_image = _Image.create_from_file( + test_utils.get_test_data_path(image_path) + ) + # Set rotation parameters using ImageProcessingOptions. + image_processing_options = _ImageProcessingOptions( + rotation_degrees=rotation + ) + observed_timestamp_ms = -1 + + def check_result( + result: PoseLandmarkerResult, output_image: _Image, timestamp_ms: int + ): + if result.pose_landmarks: + self._expect_pose_landmarker_results_correct( + result, + expected_result, + output_segmentation_masks, + _LANDMARKS_MARGIN, + ) + else: + self.assertEqual(result, expected_result) + self.assertTrue( + np.array_equal(output_image.numpy_view(), test_image.numpy_view()) + ) + self.assertLess(observed_timestamp_ms, timestamp_ms) + self.observed_timestamp_ms = timestamp_ms + + options = _PoseLandmarkerOptions( + base_options=_BaseOptions(model_asset_path=self.model_path), + output_segmentation_masks=output_segmentation_masks, + running_mode=_RUNNING_MODE.LIVE_STREAM, + result_callback=check_result, + ) + with _PoseLandmarker.create_from_options(options) as landmarker: + for timestamp in range(0, 300, 30): + landmarker.detect_async(test_image, timestamp, image_processing_options) + + +if __name__ == '__main__': + absltest.main() diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD index 733631a22..d0c97434f 100644 --- a/mediapipe/tasks/python/vision/BUILD +++ b/mediapipe/tasks/python/vision/BUILD @@ -180,6 +180,27 @@ py_library( ], ) +py_library( + name = "pose_landmarker", + srcs = [ + "pose_landmarker.py", + ], + deps = [ + "//mediapipe/framework/formats:landmark_py_pb2", + "//mediapipe/python:_framework_bindings", + "//mediapipe/python:packet_creator", + "//mediapipe/python:packet_getter", + "//mediapipe/tasks/cc/vision/pose_landmarker/proto:pose_landmarker_graph_options_py_pb2", + "//mediapipe/tasks/python/components/containers:landmark", + "//mediapipe/tasks/python/core:base_options", + "//mediapipe/tasks/python/core:optional_dependencies", + "//mediapipe/tasks/python/core:task_info", + "//mediapipe/tasks/python/vision/core:base_vision_task_api", + "//mediapipe/tasks/python/vision/core:image_processing_options", + "//mediapipe/tasks/python/vision/core:vision_task_running_mode", + ], +) + py_library( name = "face_detector", srcs = [ diff --git a/mediapipe/tasks/python/vision/face_detector.py b/mediapipe/tasks/python/vision/face_detector.py index 9d28123e8..ff4a883c1 100644 --- a/mediapipe/tasks/python/vision/face_detector.py +++ b/mediapipe/tasks/python/vision/face_detector.py @@ -71,8 +71,8 @@ class FaceDetectorOptions: base_options: _BaseOptions running_mode: _RunningMode = _RunningMode.IMAGE - min_detection_confidence: Optional[float] = None - min_suppression_threshold: Optional[float] = None + min_detection_confidence: float = 0.5 + min_suppression_threshold: float = 0.3 result_callback: Optional[ Callable[ [detections_module.DetectionResult, image_module.Image, int], None diff --git a/mediapipe/tasks/python/vision/face_landmarker.py b/mediapipe/tasks/python/vision/face_landmarker.py index a6750c71c..870e7e43e 100644 --- a/mediapipe/tasks/python/vision/face_landmarker.py +++ b/mediapipe/tasks/python/vision/face_landmarker.py @@ -2966,12 +2966,12 @@ class FaceLandmarkerOptions: base_options: _BaseOptions running_mode: _RunningMode = _RunningMode.IMAGE - num_faces: Optional[int] = 1 - min_face_detection_confidence: Optional[float] = 0.5 - min_face_presence_confidence: Optional[float] = 0.5 - min_tracking_confidence: Optional[float] = 0.5 - output_face_blendshapes: Optional[bool] = False - output_facial_transformation_matrixes: Optional[bool] = False + num_faces: int = 1 + min_face_detection_confidence: float = 0.5 + min_face_presence_confidence: float = 0.5 + min_tracking_confidence: float = 0.5 + output_face_blendshapes: bool = False + output_facial_transformation_matrixes: bool = False result_callback: Optional[ Callable[[FaceLandmarkerResult, image_module.Image, int], None] ] = None diff --git a/mediapipe/tasks/python/vision/gesture_recognizer.py b/mediapipe/tasks/python/vision/gesture_recognizer.py index ab43408a1..044dd17bd 100644 --- a/mediapipe/tasks/python/vision/gesture_recognizer.py +++ b/mediapipe/tasks/python/vision/gesture_recognizer.py @@ -194,15 +194,15 @@ class GestureRecognizerOptions: base_options: _BaseOptions running_mode: _RunningMode = _RunningMode.IMAGE - num_hands: Optional[int] = 1 - min_hand_detection_confidence: Optional[float] = 0.5 - min_hand_presence_confidence: Optional[float] = 0.5 - min_tracking_confidence: Optional[float] = 0.5 - canned_gesture_classifier_options: Optional[_ClassifierOptions] = ( - dataclasses.field(default_factory=_ClassifierOptions) + num_hands: int = 1 + min_hand_detection_confidence: float = 0.5 + min_hand_presence_confidence: float = 0.5 + min_tracking_confidence: float = 0.5 + canned_gesture_classifier_options: _ClassifierOptions = dataclasses.field( + default_factory=_ClassifierOptions ) - custom_gesture_classifier_options: Optional[_ClassifierOptions] = ( - dataclasses.field(default_factory=_ClassifierOptions) + custom_gesture_classifier_options: _ClassifierOptions = dataclasses.field( + default_factory=_ClassifierOptions ) result_callback: Optional[ Callable[[GestureRecognizerResult, image_module.Image, int], None] diff --git a/mediapipe/tasks/python/vision/hand_landmarker.py b/mediapipe/tasks/python/vision/hand_landmarker.py index 33f72ba1f..1f2c629d2 100644 --- a/mediapipe/tasks/python/vision/hand_landmarker.py +++ b/mediapipe/tasks/python/vision/hand_landmarker.py @@ -182,10 +182,10 @@ class HandLandmarkerOptions: base_options: _BaseOptions running_mode: _RunningMode = _RunningMode.IMAGE - num_hands: Optional[int] = 1 - min_hand_detection_confidence: Optional[float] = 0.5 - min_hand_presence_confidence: Optional[float] = 0.5 - min_tracking_confidence: Optional[float] = 0.5 + num_hands: int = 1 + min_hand_detection_confidence: float = 0.5 + min_hand_presence_confidence: float = 0.5 + min_tracking_confidence: float = 0.5 result_callback: Optional[ Callable[[HandLandmarkerResult, image_module.Image, int], None] ] = None diff --git a/mediapipe/tasks/python/vision/pose_landmarker.py b/mediapipe/tasks/python/vision/pose_landmarker.py new file mode 100644 index 000000000..b91eb0326 --- /dev/null +++ b/mediapipe/tasks/python/vision/pose_landmarker.py @@ -0,0 +1,431 @@ +# Copyright 2023 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 pose landmarker task.""" + +import dataclasses +from typing import Callable, Mapping, Optional, List + +from mediapipe.framework.formats import landmark_pb2 +from mediapipe.python import packet_creator +from mediapipe.python import packet_getter +from mediapipe.python._framework_bindings import image as image_module +from mediapipe.python._framework_bindings import packet as packet_module +from mediapipe.tasks.cc.vision.pose_landmarker.proto import pose_landmarker_graph_options_pb2 +from mediapipe.tasks.python.components.containers import landmark as landmark_module +from mediapipe.tasks.python.core import base_options as base_options_module +from mediapipe.tasks.python.core import task_info as task_info_module +from mediapipe.tasks.python.core.optional_dependencies import doc_controls +from mediapipe.tasks.python.vision.core import base_vision_task_api +from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module +from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module + +_BaseOptions = base_options_module.BaseOptions +_PoseLandmarkerGraphOptionsProto = ( + pose_landmarker_graph_options_pb2.PoseLandmarkerGraphOptions +) +_RunningMode = running_mode_module.VisionTaskRunningMode +_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions +_TaskInfo = task_info_module.TaskInfo + +_IMAGE_IN_STREAM_NAME = 'image_in' +_IMAGE_OUT_STREAM_NAME = 'image_out' +_IMAGE_TAG = 'IMAGE' +_NORM_RECT_STREAM_NAME = 'norm_rect_in' +_NORM_RECT_TAG = 'NORM_RECT' +_SEGMENTATION_MASK_STREAM_NAME = 'segmentation_mask' +_SEGMENTATION_MASK_TAG = 'SEGMENTATION_MASK' +_NORM_LANDMARKS_STREAM_NAME = 'norm_landmarks' +_NORM_LANDMARKS_TAG = 'NORM_LANDMARKS' +_POSE_WORLD_LANDMARKS_STREAM_NAME = 'world_landmarks' +_POSE_WORLD_LANDMARKS_TAG = 'WORLD_LANDMARKS' +_POSE_AUXILIARY_LANDMARKS_STREAM_NAME = 'auxiliary_landmarks' +_POSE_AUXILIARY_LANDMARKS_TAG = 'AUXILIARY_LANDMARKS' +_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.pose_landmarker.PoseLandmarkerGraph' +_MICRO_SECONDS_PER_MILLISECOND = 1000 + + +@dataclasses.dataclass +class PoseLandmarkerResult: + """The pose landmarks detection result from PoseLandmarker, where each vector element represents a single pose detected in the image. + + Attributes: + pose_landmarks: Detected pose landmarks in normalized image coordinates. + pose_world_landmarks: Detected pose landmarks in world coordinates. + pose_auxiliary_landmarks: Detected auxiliary landmarks, used for deriving + ROI for next frame. + segmentation_masks: Optional segmentation masks for pose. + """ + + pose_landmarks: List[List[landmark_module.NormalizedLandmark]] + pose_world_landmarks: List[List[landmark_module.Landmark]] + pose_auxiliary_landmarks: List[List[landmark_module.NormalizedLandmark]] + segmentation_masks: Optional[List[image_module.Image]] = None + + +def _build_landmarker_result( + output_packets: Mapping[str, packet_module.Packet] +) -> PoseLandmarkerResult: + """Constructs a `PoseLandmarkerResult` from output packets.""" + pose_landmarker_result = PoseLandmarkerResult([], [], []) + + if _SEGMENTATION_MASK_STREAM_NAME in output_packets: + pose_landmarker_result.segmentation_masks = packet_getter.get_image_list( + output_packets[_SEGMENTATION_MASK_STREAM_NAME] + ) + + pose_landmarks_proto_list = packet_getter.get_proto_list( + output_packets[_NORM_LANDMARKS_STREAM_NAME] + ) + pose_world_landmarks_proto_list = packet_getter.get_proto_list( + output_packets[_POSE_WORLD_LANDMARKS_STREAM_NAME] + ) + pose_auxiliary_landmarks_proto_list = packet_getter.get_proto_list( + output_packets[_POSE_AUXILIARY_LANDMARKS_STREAM_NAME] + ) + + for proto in pose_landmarks_proto_list: + pose_landmarks = landmark_pb2.NormalizedLandmarkList() + pose_landmarks.MergeFrom(proto) + pose_landmarks_list = [] + for pose_landmark in pose_landmarks.landmark: + pose_landmarks_list.append( + landmark_module.NormalizedLandmark.create_from_pb2(pose_landmark) + ) + pose_landmarker_result.pose_landmarks.append(pose_landmarks_list) + + for proto in pose_world_landmarks_proto_list: + pose_world_landmarks = landmark_pb2.LandmarkList() + pose_world_landmarks.MergeFrom(proto) + pose_world_landmarks_list = [] + for pose_world_landmark in pose_world_landmarks.landmark: + pose_world_landmarks_list.append( + landmark_module.Landmark.create_from_pb2(pose_world_landmark) + ) + pose_landmarker_result.pose_world_landmarks.append( + pose_world_landmarks_list + ) + + for proto in pose_auxiliary_landmarks_proto_list: + pose_auxiliary_landmarks = landmark_pb2.NormalizedLandmarkList() + pose_auxiliary_landmarks.MergeFrom(proto) + pose_auxiliary_landmarks_list = [] + for pose_auxiliary_landmark in pose_auxiliary_landmarks.landmark: + pose_auxiliary_landmarks_list.append( + landmark_module.NormalizedLandmark.create_from_pb2( + pose_auxiliary_landmark + ) + ) + pose_landmarker_result.pose_auxiliary_landmarks.append( + pose_auxiliary_landmarks_list + ) + return pose_landmarker_result + + +@dataclasses.dataclass +class PoseLandmarkerOptions: + """Options for the pose landmarker task. + + Attributes: + base_options: Base options for the pose landmarker task. + running_mode: The running mode of the task. Default to the image mode. + PoseLandmarker has three running modes: 1) The image mode for detecting + pose landmarks on single image inputs. 2) The video mode for detecting + pose landmarks on the decoded frames of a video. 3) The live stream mode + for detecting pose landmarks on the live stream of input data, such as + from camera. In this mode, the "result_callback" below must be specified + to receive the detection results asynchronously. + num_poses: The maximum number of poses can be detected by the + PoseLandmarker. + min_pose_detection_confidence: The minimum confidence score for the pose + detection to be considered successful. + min_pose_presence_confidence: The minimum confidence score of pose presence + score in the pose landmark detection. + min_tracking_confidence: The minimum confidence score for the pose tracking + to be considered successful. + output_segmentation_masks: whether to output segmentation masks. + result_callback: The user-defined result callback for processing live stream + data. The result callback should only be specified when the running mode + is set to the live stream mode. + """ + + base_options: _BaseOptions + running_mode: _RunningMode = _RunningMode.IMAGE + num_poses: int = 1 + min_pose_detection_confidence: float = 0.5 + min_pose_presence_confidence: float = 0.5 + min_tracking_confidence: float = 0.5 + output_segmentation_masks: bool = False + result_callback: Optional[ + Callable[[PoseLandmarkerResult, image_module.Image, int], None] + ] = None + + @doc_controls.do_not_generate_docs + def to_pb2(self) -> _PoseLandmarkerGraphOptionsProto: + """Generates an PoseLandmarkerGraphOptions protobuf object.""" + base_options_proto = self.base_options.to_pb2() + base_options_proto.use_stream_mode = ( + False if self.running_mode == _RunningMode.IMAGE else True + ) + + # Initialize the pose landmarker options from base options. + pose_landmarker_options_proto = _PoseLandmarkerGraphOptionsProto( + base_options=base_options_proto + ) + pose_landmarker_options_proto.min_tracking_confidence = ( + self.min_tracking_confidence + ) + pose_landmarker_options_proto.pose_detector_graph_options.num_poses = ( + self.num_poses + ) + pose_landmarker_options_proto.pose_detector_graph_options.min_detection_confidence = ( + self.min_pose_detection_confidence + ) + pose_landmarker_options_proto.pose_landmarks_detector_graph_options.min_detection_confidence = ( + self.min_pose_presence_confidence + ) + return pose_landmarker_options_proto + + +class PoseLandmarker(base_vision_task_api.BaseVisionTaskApi): + """Class that performs pose landmarks detection on images.""" + + @classmethod + def create_from_model_path(cls, model_path: str) -> 'PoseLandmarker': + """Creates a `PoseLandmarker` object from a model bundle file and the default `PoseLandmarkerOptions`. + + Note that the created `PoseLandmarker` instance is in image mode, for + detecting pose landmarks on single image inputs. + + Args: + model_path: Path to the model. + + Returns: + `PoseLandmarker` object that's created from the model file and the + default `PoseLandmarkerOptions`. + + Raises: + ValueError: If failed to create `PoseLandmarker` 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 = PoseLandmarkerOptions( + base_options=base_options, running_mode=_RunningMode.IMAGE + ) + return cls.create_from_options(options) + + @classmethod + def create_from_options( + cls, options: PoseLandmarkerOptions + ) -> 'PoseLandmarker': + """Creates the `PoseLandmarker` object from pose landmarker options. + + Args: + options: Options for the pose landmarker task. + + Returns: + `PoseLandmarker` object that's created from `options`. + + Raises: + ValueError: If failed to create `PoseLandmarker` object from + `PoseLandmarkerOptions` 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[_NORM_LANDMARKS_STREAM_NAME].is_empty(): + empty_packet = output_packets[_NORM_LANDMARKS_STREAM_NAME] + options.result_callback( + PoseLandmarkerResult([], [], []), + image, + empty_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND, + ) + return + + pose_landmarker_result = _build_landmarker_result(output_packets) + timestamp = output_packets[_NORM_LANDMARKS_STREAM_NAME].timestamp + options.result_callback( + pose_landmarker_result, + image, + timestamp.value // _MICRO_SECONDS_PER_MILLISECOND, + ) + + output_streams = [ + ':'.join([_NORM_LANDMARKS_TAG, _NORM_LANDMARKS_STREAM_NAME]), + ':'.join( + [_POSE_WORLD_LANDMARKS_TAG, _POSE_WORLD_LANDMARKS_STREAM_NAME] + ), + ':'.join([ + _POSE_AUXILIARY_LANDMARKS_TAG, + _POSE_AUXILIARY_LANDMARKS_STREAM_NAME, + ]), + ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME]), + ] + + if options.output_segmentation_masks: + output_streams.append( + ':'.join([_SEGMENTATION_MASK_TAG, _SEGMENTATION_MASK_STREAM_NAME]) + ) + + 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=output_streams, + 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, + ) -> PoseLandmarkerResult: + """Performs pose landmarks detection on the given image. + + Only use this method when the PoseLandmarker is created with the image + running mode. + + Args: + image: MediaPipe Image. + image_processing_options: Options for image processing. + + Returns: + The pose landmarker detection results. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If pose landmarker detection failed to run. + """ + normalized_rect = self.convert_to_normalized_rect( + image_processing_options, image, 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[_NORM_LANDMARKS_STREAM_NAME].is_empty(): + return PoseLandmarkerResult([], [], []) + + return _build_landmarker_result(output_packets) + + def detect_for_video( + self, + image: image_module.Image, + timestamp_ms: int, + image_processing_options: Optional[_ImageProcessingOptions] = None, + ) -> PoseLandmarkerResult: + """Performs pose landmarks detection on the provided video frame. + + Only use this method when the PoseLandmarker is created with the video + running mode. + + Only use this method when the PoseLandmarker is created with the video + running mode. It's required to provide the video frame's timestamp (in + milliseconds) along with the video frame. The input timestamps should be + monotonically increasing for adjacent calls of this method. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input video frame in milliseconds. + image_processing_options: Options for image processing. + + Returns: + The pose landmarker detection results. + + Raises: + ValueError: If any of the input arguments is invalid. + RuntimeError: If pose landmarker detection failed to run. + """ + normalized_rect = self.convert_to_normalized_rect( + image_processing_options, image, 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[_NORM_LANDMARKS_STREAM_NAME].is_empty(): + return PoseLandmarkerResult([], [], []) + + return _build_landmarker_result(output_packets) + + def detect_async( + self, + image: image_module.Image, + timestamp_ms: int, + image_processing_options: Optional[_ImageProcessingOptions] = None, + ) -> None: + """Sends live image data to perform pose landmarks detection. + + The results will be available via the "result_callback" provided in the + PoseLandmarkerOptions. Only use this method when the PoseLandmarker is + created with the live stream running mode. + + Only use this method when the PoseLandmarker 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 `PoseLandmarkerOptions`. The + `detect_async` method is designed to process live stream data such as + camera input. To lower the overall latency, pose landmarker may drop the + input images if needed. In other words, it's not guaranteed to have output + per input image. + + The `result_callback` provides: + - The pose landmarker detection results. + - The input image that the pose landmarker runs on. + - The input timestamp in milliseconds. + + Args: + image: MediaPipe Image. + timestamp_ms: The timestamp of the input image in milliseconds. + image_processing_options: Options for image processing. + + Raises: + ValueError: If the current input timestamp is smaller than what the + pose landmarker has already processed. + """ + normalized_rect = self.convert_to_normalized_rect( + image_processing_options, image, 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), + })