Added more pose landmarker tests and updated face landmarker tests to cover all the results
This commit is contained in:
kinaryml
parent
39742b6641
commit
1688d0fa79
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@ -58,17 +58,20 @@ _FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02
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def _get_expected_face_landmarks(file_path: str):
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proto_file_path = test_utils.get_test_data_path(file_path)
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face_landmarks_results = []
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with open(proto_file_path, 'rb') as f:
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proto = landmark_pb2.NormalizedLandmarkList()
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text_format.Parse(f.read(), proto)
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face_landmarks = []
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for landmark in proto.landmark:
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face_landmarks.append(_NormalizedLandmark.create_from_pb2(landmark))
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return face_landmarks
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face_landmarks_results.append(face_landmarks)
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return face_landmarks_results
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def _get_expected_face_blendshapes(file_path: str):
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proto_file_path = test_utils.get_test_data_path(file_path)
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face_blendshapes_results = []
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with open(proto_file_path, 'rb') as f:
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proto = classification_pb2.ClassificationList()
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text_format.Parse(f.read(), proto)
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@ -84,7 +87,8 @@ def _get_expected_face_blendshapes(file_path: str):
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category_name=face_blendshapes.label,
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)
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)
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return face_blendshapes_categories
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face_blendshapes_results.append(face_blendshapes_categories)
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return face_blendshapes_results
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def _get_expected_facial_transformation_matrixes():
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@ -119,13 +123,14 @@ class FaceLandmarkerTest(parameterized.TestCase):
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# Expects to have the same number of faces detected.
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self.assertLen(actual_landmarks, len(expected_landmarks))
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for i, elem in enumerate(actual_landmarks):
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self.assertAlmostEqual(
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elem.x, expected_landmarks[i].x, delta=_LANDMARKS_DIFF_MARGIN
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)
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self.assertAlmostEqual(
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elem.y, expected_landmarks[i].y, delta=_LANDMARKS_DIFF_MARGIN
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)
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for i, _ in enumerate(actual_landmarks):
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for j, elem in enumerate(actual_landmarks[i]):
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self.assertAlmostEqual(
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elem.x, expected_landmarks[i][j].x, delta=_LANDMARKS_DIFF_MARGIN
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)
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self.assertAlmostEqual(
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elem.y, expected_landmarks[i][j].y, delta=_LANDMARKS_DIFF_MARGIN
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)
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def _expect_blendshapes_correct(
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self, actual_blendshapes, expected_blendshapes
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@ -133,13 +138,14 @@ class FaceLandmarkerTest(parameterized.TestCase):
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# Expects to have the same number of blendshapes.
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self.assertLen(actual_blendshapes, len(expected_blendshapes))
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for i, elem in enumerate(actual_blendshapes):
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self.assertEqual(elem.index, expected_blendshapes[i].index)
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self.assertAlmostEqual(
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elem.score,
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expected_blendshapes[i].score,
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delta=_BLENDSHAPES_DIFF_MARGIN,
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)
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for i, _ in enumerate(actual_blendshapes):
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for j, elem in enumerate(actual_blendshapes[i]):
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self.assertEqual(elem.index, expected_blendshapes[i][j].index)
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self.assertAlmostEqual(
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elem.score,
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expected_blendshapes[i][j].score,
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delta=_BLENDSHAPES_DIFF_MARGIN,
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)
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def _expect_facial_transformation_matrixes_correct(
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self, actual_matrix_list, expected_matrix_list
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@ -236,11 +242,11 @@ class FaceLandmarkerTest(parameterized.TestCase):
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# Comparing results.
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if expected_face_landmarks is not None:
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self._expect_landmarks_correct(
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detection_result.face_landmarks[0], expected_face_landmarks
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detection_result.face_landmarks, expected_face_landmarks
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)
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if expected_face_blendshapes is not None:
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self._expect_blendshapes_correct(
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detection_result.face_blendshapes[0], expected_face_blendshapes
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detection_result.face_blendshapes, expected_face_blendshapes
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)
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if expected_facial_transformation_matrixes is not None:
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self._expect_facial_transformation_matrixes_correct(
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@ -302,11 +308,11 @@ class FaceLandmarkerTest(parameterized.TestCase):
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# Comparing results.
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if expected_face_landmarks is not None:
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self._expect_landmarks_correct(
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detection_result.face_landmarks[0], expected_face_landmarks
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detection_result.face_landmarks, expected_face_landmarks
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)
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if expected_face_blendshapes is not None:
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self._expect_blendshapes_correct(
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detection_result.face_blendshapes[0], expected_face_blendshapes
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detection_result.face_blendshapes, expected_face_blendshapes
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)
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if expected_facial_transformation_matrixes is not None:
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self._expect_facial_transformation_matrixes_correct(
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@ -446,11 +452,11 @@ class FaceLandmarkerTest(parameterized.TestCase):
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# Comparing results.
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if expected_face_landmarks is not None:
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self._expect_landmarks_correct(
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detection_result.face_landmarks[0], expected_face_landmarks
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detection_result.face_landmarks, expected_face_landmarks
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)
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if expected_face_blendshapes is not None:
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self._expect_blendshapes_correct(
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detection_result.face_blendshapes[0], expected_face_blendshapes
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detection_result.face_blendshapes, expected_face_blendshapes
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)
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if expected_facial_transformation_matrixes is not None:
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self._expect_facial_transformation_matrixes_correct(
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@ -523,11 +529,11 @@ class FaceLandmarkerTest(parameterized.TestCase):
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# Comparing results.
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if expected_face_landmarks is not None:
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self._expect_landmarks_correct(
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result.face_landmarks[0], expected_face_landmarks
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result.face_landmarks, expected_face_landmarks
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)
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if expected_face_blendshapes is not None:
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self._expect_blendshapes_correct(
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result.face_blendshapes[0], expected_face_blendshapes
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result.face_blendshapes, expected_face_blendshapes
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)
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if expected_facial_transformation_matrixes is not None:
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self._expect_facial_transformation_matrixes_correct(
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@ -14,6 +14,7 @@
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"""Tests for pose landmarker."""
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import enum
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from typing import List
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from unittest import mock
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from absl.testing import absltest
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@ -49,8 +50,8 @@ _POSE_LANDMARKER_BUNDLE_ASSET_FILE = 'pose_landmarker.task'
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_BURGER_IMAGE = 'burger.jpg'
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_POSE_IMAGE = 'pose.jpg'
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_POSE_LANDMARKS = 'pose_landmarks.pbtxt'
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_LANDMARKS_ERROR_TOLERANCE = 0.03
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_LANDMARKS_ON_VIDEO_ERROR_TOLERANCE = 0.03
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_LANDMARKS_DIFF_MARGIN = 0.03
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_LANDMARKS_ON_VIDEO_DIFF_MARGIN = 0.03
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def _get_expected_pose_landmarker_result(
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@ -85,33 +86,34 @@ class PoseLandmarkerTest(parameterized.TestCase):
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self.model_path = test_utils.get_test_data_path(
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_POSE_LANDMARKER_BUNDLE_ASSET_FILE)
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def _expect_pose_landmarker_results_correct(
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self,
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actual_result: PoseLandmarkerResult,
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expected_result: PoseLandmarkerResult,
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error_tolerance: float
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def _expect_pose_landmarks_correct(
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self,
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actual_landmarks: List[List[landmark_module.NormalizedLandmark]],
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expected_landmarks: List[List[landmark_module.NormalizedLandmark]],
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diff_margin: float
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):
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# Expects to have the same number of poses detected.
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self.assertLen(actual_result.pose_landmarks,
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len(expected_result.pose_landmarks))
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self.assertLen(actual_result.pose_world_landmarks,
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len(expected_result.pose_world_landmarks))
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self.assertLen(actual_result.pose_auxiliary_landmarks,
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len(expected_result.pose_auxiliary_landmarks))
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# Actual landmarks match expected landmarks.
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actual_landmarks = actual_result.pose_landmarks[0]
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expected_landmarks = expected_result.pose_landmarks[0]
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for i, pose_landmark in enumerate(actual_landmarks):
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self.assertAlmostEqual(
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pose_landmark.x,
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expected_landmarks[i].x,
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delta=error_tolerance
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)
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self.assertAlmostEqual(
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pose_landmark.y,
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expected_landmarks[i].y,
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delta=error_tolerance
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)
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self.assertLen(actual_landmarks, len(expected_landmarks))
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for i, _ in enumerate(actual_landmarks):
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for j, elem in enumerate(actual_landmarks[i]):
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self.assertAlmostEqual(
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elem.x, expected_landmarks[i][j].x, delta=diff_margin
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)
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self.assertAlmostEqual(
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elem.y, expected_landmarks[i][j].y, delta=diff_margin
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)
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def _expect_pose_landmarker_results_correct(
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self,
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actual_result: PoseLandmarkerResult,
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expected_result: PoseLandmarkerResult,
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diff_margin: float
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):
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self._expect_pose_landmarks_correct(
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actual_result.pose_landmarks, expected_result.pose_landmarks,
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diff_margin
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)
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def test_create_from_file_succeeds_with_valid_model_path(self):
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# Creates with default option and valid model file successfully.
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@ -146,7 +148,8 @@ class PoseLandmarkerTest(parameterized.TestCase):
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(ModelFileType.FILE_NAME,
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_get_expected_pose_landmarker_result(_POSE_LANDMARKS)),
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(ModelFileType.FILE_CONTENT,
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_get_expected_pose_landmarker_result(_POSE_LANDMARKS)))
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_get_expected_pose_landmarker_result(_POSE_LANDMARKS))
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)
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def test_detect(self, model_file_type, expected_detection_result):
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# Creates pose landmarker.
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if model_file_type is ModelFileType.FILE_NAME:
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@ -164,14 +167,229 @@ class PoseLandmarkerTest(parameterized.TestCase):
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# Performs pose landmarks detection on the input.
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detection_result = landmarker.detect(self.test_image)
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# Comparing results.
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self._expect_pose_landmarker_results_correct(
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detection_result, expected_detection_result, _LANDMARKS_ERROR_TOLERANCE
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detection_result, expected_detection_result, _LANDMARKS_DIFF_MARGIN
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)
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# Closes the pose landmarker explicitly when the pose landmarker is not used
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# in a context.
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landmarker.close()
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@parameterized.parameters(
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(ModelFileType.FILE_NAME,
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_get_expected_pose_landmarker_result(_POSE_LANDMARKS)),
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(ModelFileType.FILE_CONTENT,
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_get_expected_pose_landmarker_result(_POSE_LANDMARKS))
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)
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def test_detect_in_context(self, model_file_type, expected_detection_result):
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# Creates pose landmarker.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _PoseLandmarkerOptions(base_options=base_options)
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with _PoseLandmarker.create_from_options(options) as landmarker:
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# Performs pose landmarks detection on the input.
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detection_result = landmarker.detect(self.test_image)
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# Comparing results.
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self._expect_pose_landmarker_results_correct(
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detection_result, expected_detection_result, _LANDMARKS_DIFF_MARGIN
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)
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def test_detect_fails_with_region_of_interest(self):
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# Creates pose landmarker.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _PoseLandmarkerOptions(base_options=base_options)
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with self.assertRaisesRegex(
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ValueError, "This task doesn't support region-of-interest."):
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with _PoseLandmarker.create_from_options(options) as landmarker:
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# Set the `region_of_interest` parameter using `ImageProcessingOptions`.
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image_processing_options = _ImageProcessingOptions(
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region_of_interest=_Rect(0, 0, 1, 1))
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# Attempt to perform pose landmarks detection on the cropped input.
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landmarker.detect(self.test_image, image_processing_options)
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def test_empty_detection_outputs(self):
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# Creates pose landmarker.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _PoseLandmarkerOptions(base_options=base_options)
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with _PoseLandmarker.create_from_options(options) as landmarker:
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# Load an image with no poses.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_BURGER_IMAGE))
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# Performs pose landmarks detection on the input.
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detection_result = landmarker.detect(test_image)
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# Comparing results.
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self.assertEmpty(detection_result.pose_landmarks)
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self.assertEmpty(detection_result.pose_world_landmarks)
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self.assertEmpty(detection_result.pose_auxiliary_landmarks)
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def test_missing_result_callback(self):
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM)
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with self.assertRaisesRegex(ValueError,
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r'result callback must be provided'):
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with _PoseLandmarker.create_from_options(options) as unused_landmarker:
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pass
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@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
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def test_illegal_result_callback(self, running_mode):
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=running_mode,
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result_callback=mock.MagicMock())
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with self.assertRaisesRegex(ValueError,
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r'result callback should not be provided'):
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with _PoseLandmarker.create_from_options(options) as unused_landmarker:
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pass
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def test_calling_detect_for_video_in_image_mode(self):
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE)
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with _PoseLandmarker.create_from_options(options) as landmarker:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the video mode'):
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landmarker.detect_for_video(self.test_image, 0)
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def test_calling_detect_async_in_image_mode(self):
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE)
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with _PoseLandmarker.create_from_options(options) as landmarker:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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landmarker.detect_async(self.test_image, 0)
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def test_calling_detect_in_video_mode(self):
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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with _PoseLandmarker.create_from_options(options) as landmarker:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the image mode'):
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landmarker.detect(self.test_image)
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def test_calling_detect_async_in_video_mode(self):
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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with _PoseLandmarker.create_from_options(options) as landmarker:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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landmarker.detect_async(self.test_image, 0)
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def test_detect_for_video_with_out_of_order_timestamp(self):
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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with _PoseLandmarker.create_from_options(options) as landmarker:
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unused_result = landmarker.detect_for_video(self.test_image, 1)
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with self.assertRaisesRegex(
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ValueError, r'Input timestamp must be monotonically increasing'):
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landmarker.detect_for_video(self.test_image, 0)
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@parameterized.parameters(
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(_POSE_IMAGE, 0,
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_get_expected_pose_landmarker_result(_POSE_LANDMARKS)),
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(_BURGER_IMAGE, 0,
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PoseLandmarkerResult([], [], []))
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)
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def test_detect_for_video(self, image_path, rotation, expected_result):
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(image_path))
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# Set rotation parameters using ImageProcessingOptions.
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image_processing_options = _ImageProcessingOptions(
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rotation_degrees=rotation)
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO)
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with _PoseLandmarker.create_from_options(options) as landmarker:
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for timestamp in range(0, 300, 30):
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result = landmarker.detect_for_video(test_image, timestamp,
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image_processing_options)
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if result.pose_landmarks:
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self._expect_pose_landmarker_results_correct(
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result, expected_result, _LANDMARKS_ON_VIDEO_DIFF_MARGIN
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)
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else:
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self.assertEqual(result, expected_result)
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def test_calling_detect_in_live_stream_mode(self):
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=mock.MagicMock())
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with _PoseLandmarker.create_from_options(options) as landmarker:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the image mode'):
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landmarker.detect(self.test_image)
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def test_calling_detect_for_video_in_live_stream_mode(self):
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options = _PoseLandmarkerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=mock.MagicMock())
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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,
|
||||
_get_expected_pose_landmarker_result(_POSE_LANDMARKS)),
|
||||
(_BURGER_IMAGE, 0,
|
||||
PoseLandmarkerResult([], [], []))
|
||||
)
|
||||
def test_detect_async_calls(self, image_path, rotation, 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, _LANDMARKS_DIFF_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),
|
||||
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()
|
||||
|
|
|
|||
|
|
@ -64,7 +64,7 @@ class PoseLandmarkerResult:
|
|||
pose_world_landmarks: Detected pose landmarks in world coordinates.
|
||||
pose_auxiliary_landmarks: Detected auxiliary landmarks, used for deriving
|
||||
ROI for next frame.
|
||||
segmentation_masks: Segmentation masks for pose.
|
||||
segmentation_masks: Optional segmentation masks for pose.
|
||||
"""
|
||||
|
||||
pose_landmarks: List[List[landmark_module.NormalizedLandmark]]
|
||||
|
|
@ -77,7 +77,7 @@ def _build_landmarker_result(
|
|||
output_packets: Mapping[str, packet_module.Packet]
|
||||
) -> PoseLandmarkerResult:
|
||||
"""Constructs a `PoseLandmarkerResult` from output packets."""
|
||||
pose_landmarker_result = PoseLandmarkerResult([], [], [], [])
|
||||
pose_landmarker_result = PoseLandmarkerResult([], [], [])
|
||||
|
||||
if _SEGMENTATION_MASK_STREAM_NAME in output_packets:
|
||||
pose_landmarker_result.segmentation_masks = packet_getter.get_image_list(
|
||||
|
|
@ -356,7 +356,7 @@ class PoseLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The pose landmarks detection results.
|
||||
The pose landmarker detection results.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
|
|
@ -402,7 +402,7 @@ class PoseLandmarker(base_vision_task_api.BaseVisionTaskApi):
|
|||
per input image.
|
||||
|
||||
The `result_callback` provides:
|
||||
- The pose landmarks detection results.
|
||||
- The pose landmarker detection results.
|
||||
- The input image that the pose landmarker runs on.
|
||||
- The input timestamp in milliseconds.
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user