Removed optional for defaults in some tasks and updated various tests to be consistent with that of Pose Landmarker's
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@ -51,9 +51,9 @@ _PORTRAIT_IMAGE = 'portrait.jpg'
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_CAT_IMAGE = 'cat.jpg'
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_PORTRAIT_EXPECTED_FACE_LANDMARKS = 'portrait_expected_face_landmarks.pbtxt'
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_PORTRAIT_EXPECTED_BLENDSHAPES = 'portrait_expected_blendshapes.pbtxt'
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_LANDMARKS_DIFF_MARGIN = 0.03
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_BLENDSHAPES_DIFF_MARGIN = 0.13
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_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN = 0.02
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_LANDMARKS_MARGIN = 0.03
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_BLENDSHAPES_MARGIN = 0.13
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_FACIAL_TRANSFORMATION_MATRIX_MARGIN = 0.02
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def _get_expected_face_landmarks(file_path: str):
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@ -126,10 +126,10 @@ class FaceLandmarkerTest(parameterized.TestCase):
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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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elem.x, expected_landmarks[i][j].x, delta=_LANDMARKS_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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elem.y, expected_landmarks[i][j].y, delta=_LANDMARKS_MARGIN
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)
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def _expect_blendshapes_correct(
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@ -144,7 +144,7 @@ class FaceLandmarkerTest(parameterized.TestCase):
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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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delta=_BLENDSHAPES_MARGIN,
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)
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def _expect_facial_transformation_matrixes_correct(
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@ -158,7 +158,7 @@ class FaceLandmarkerTest(parameterized.TestCase):
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self.assertSequenceAlmostEqual(
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elem.flatten(),
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expected_matrix_list[i].flatten(),
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delta=_FACIAL_TRANSFORMATION_MATRIX_DIFF_MARGIN,
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delta=_FACIAL_TRANSFORMATION_MATRIX_MARGIN,
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)
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def test_create_from_file_succeeds_with_valid_model_path(self):
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@ -54,7 +54,7 @@ _POINTING_UP_IMAGE = 'pointing_up.jpg'
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_POINTING_UP_LANDMARKS = 'pointing_up_landmarks.pbtxt'
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_POINTING_UP_ROTATED_IMAGE = 'pointing_up_rotated.jpg'
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_POINTING_UP_ROTATED_LANDMARKS = 'pointing_up_rotated_landmarks.pbtxt'
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_LANDMARKS_ERROR_TOLERANCE = 0.03
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_LANDMARKS_MARGIN = 0.03
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_HANDEDNESS_MARGIN = 0.05
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@ -89,39 +89,52 @@ class HandLandmarkerTest(parameterized.TestCase):
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self.model_path = test_utils.get_test_data_path(
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_HAND_LANDMARKER_BUNDLE_ASSET_FILE)
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def _assert_actual_result_approximately_matches_expected_result(
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self, actual_result: _HandLandmarkerResult,
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expected_result: _HandLandmarkerResult):
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def _expect_hand_landmarks_correct(
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self, actual_landmarks, expected_landmarks, margin
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):
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# Expects to have the same number of hands detected.
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self.assertLen(actual_result.hand_landmarks,
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len(expected_result.hand_landmarks))
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self.assertLen(actual_result.hand_world_landmarks,
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len(expected_result.hand_world_landmarks))
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self.assertLen(actual_result.handedness, len(expected_result.handedness))
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# Actual landmarks match expected landmarks.
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self.assertLen(actual_result.hand_landmarks[0],
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len(expected_result.hand_landmarks[0]))
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actual_landmarks = actual_result.hand_landmarks[0]
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expected_landmarks = expected_result.hand_landmarks[0]
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for i, rename_me in enumerate(actual_landmarks):
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self.assertAlmostEqual(
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rename_me.x,
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expected_landmarks[i].x,
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delta=_LANDMARKS_ERROR_TOLERANCE)
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self.assertAlmostEqual(
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rename_me.y,
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expected_landmarks[i].y,
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delta=_LANDMARKS_ERROR_TOLERANCE)
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# Actual handedness matches expected handedness.
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actual_top_handedness = actual_result.handedness[0][0]
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expected_top_handedness = expected_result.handedness[0][0]
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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,
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expected_landmarks[i][j].x,
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delta=margin
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)
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self.assertAlmostEqual(
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elem.y,
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expected_landmarks[i][j].y,
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delta=margin
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)
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def _expect_handedness_correct(
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self, actual_handedness, expected_handedness, margin
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):
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# Actual top handedness matches expected top handedness.
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actual_top_handedness = actual_handedness[0][0]
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expected_top_handedness = expected_handedness[0][0]
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self.assertEqual(actual_top_handedness.index, expected_top_handedness.index)
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self.assertEqual(actual_top_handedness.category_name,
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expected_top_handedness.category_name)
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self.assertAlmostEqual(
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actual_top_handedness.score,
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expected_top_handedness.score,
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delta=_HANDEDNESS_MARGIN)
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actual_top_handedness.score,
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expected_top_handedness.score,
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delta=margin)
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def _expect_hand_landmarker_results_correct(
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self,
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actual_result: _HandLandmarkerResult,
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expected_result: _HandLandmarkerResult
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):
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self._expect_hand_landmarks_correct(
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actual_result.hand_landmarks, expected_result.hand_landmarks,
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_LANDMARKS_MARGIN
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)
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self._expect_handedness_correct(
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actual_result.handedness, expected_result.handedness,
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_HANDEDNESS_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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@ -175,7 +188,7 @@ class HandLandmarkerTest(parameterized.TestCase):
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# Performs hand 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._assert_actual_result_approximately_matches_expected_result(
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self._expect_hand_landmarker_results_correct(
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detection_result, expected_detection_result)
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# Closes the hand landmarker explicitly when the hand landmarker is not used
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# in a context.
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@ -203,7 +216,7 @@ class HandLandmarkerTest(parameterized.TestCase):
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# Performs hand 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._assert_actual_result_approximately_matches_expected_result(
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self._expect_hand_landmarker_results_correct(
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detection_result, expected_detection_result)
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def test_detect_succeeds_with_num_hands(self):
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@ -234,7 +247,7 @@ class HandLandmarkerTest(parameterized.TestCase):
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expected_detection_result = _get_expected_hand_landmarker_result(
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_POINTING_UP_ROTATED_LANDMARKS)
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# Comparing results.
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self._assert_actual_result_approximately_matches_expected_result(
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self._expect_hand_landmarker_results_correct(
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detection_result, expected_detection_result)
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def test_detect_fails_with_region_of_interest(self):
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@ -351,7 +364,7 @@ class HandLandmarkerTest(parameterized.TestCase):
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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.hand_landmarks and result.hand_world_landmarks and result.handedness:
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self._assert_actual_result_approximately_matches_expected_result(
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self._expect_hand_landmarker_results_correct(
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result, expected_result)
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else:
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self.assertEqual(result, expected_result)
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@ -406,7 +419,7 @@ class HandLandmarkerTest(parameterized.TestCase):
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def check_result(result: _HandLandmarkerResult, output_image: _Image,
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timestamp_ms: int):
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if result.hand_landmarks and result.hand_world_landmarks and result.handedness:
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self._assert_actual_result_approximately_matches_expected_result(
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self._expect_hand_landmarker_results_correct(
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result, expected_result)
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else:
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self.assertEqual(result, expected_result)
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@ -50,8 +50,7 @@ _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_DIFF_MARGIN = 0.03
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_LANDMARKS_ON_VIDEO_DIFF_MARGIN = 0.03
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_LANDMARKS_MARGIN = 0.03
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def _get_expected_pose_landmarker_result(
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@ -87,10 +86,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
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_POSE_LANDMARKER_BUNDLE_ASSET_FILE)
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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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self, actual_landmarks, expected_landmarks, margin
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):
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# Expects to have the same number of poses detected.
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self.assertLen(actual_landmarks, len(expected_landmarks))
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@ -98,21 +94,21 @@ class PoseLandmarkerTest(parameterized.TestCase):
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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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elem.x, expected_landmarks[i][j].x, delta=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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elem.y, expected_landmarks[i][j].y, delta=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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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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margin
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)
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def test_create_from_file_succeeds_with_valid_model_path(self):
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@ -170,7 +166,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
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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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detection_result, expected_detection_result, _LANDMARKS_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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@ -201,7 +197,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
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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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detection_result, expected_detection_result, _LANDMARKS_MARGIN
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)
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def test_detect_fails_with_region_of_interest(self):
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@ -319,7 +315,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
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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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result, expected_result, _LANDMARKS_MARGIN
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)
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else:
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self.assertEqual(result, expected_result)
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@ -373,7 +369,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
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timestamp_ms: int):
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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_DIFF_MARGIN
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result, expected_result, _LANDMARKS_MARGIN
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)
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else:
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self.assertEqual(result, expected_result)
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@ -71,8 +71,8 @@ class FaceDetectorOptions:
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base_options: _BaseOptions
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running_mode: _RunningMode = _RunningMode.IMAGE
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min_detection_confidence: Optional[float] = None
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min_suppression_threshold: Optional[float] = None
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min_detection_confidence: float = 0.5
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min_suppression_threshold: float = 0.3
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result_callback: Optional[
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Callable[
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[detections_module.DetectionResult, image_module.Image, int], None
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@ -2966,12 +2966,12 @@ class FaceLandmarkerOptions:
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base_options: _BaseOptions
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running_mode: _RunningMode = _RunningMode.IMAGE
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num_faces: Optional[int] = 1
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min_face_detection_confidence: Optional[float] = 0.5
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min_face_presence_confidence: Optional[float] = 0.5
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min_tracking_confidence: Optional[float] = 0.5
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output_face_blendshapes: Optional[bool] = False
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output_facial_transformation_matrixes: Optional[bool] = False
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num_faces: int = 1
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min_face_detection_confidence: float = 0.5
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min_face_presence_confidence: float = 0.5
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min_tracking_confidence: float = 0.5
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output_face_blendshapes: bool = False
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output_facial_transformation_matrixes: bool = False
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result_callback: Optional[
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Callable[[FaceLandmarkerResult, image_module.Image, int], None]
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] = None
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@ -194,14 +194,14 @@ class GestureRecognizerOptions:
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base_options: _BaseOptions
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running_mode: _RunningMode = _RunningMode.IMAGE
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num_hands: Optional[int] = 1
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min_hand_detection_confidence: Optional[float] = 0.5
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min_hand_presence_confidence: Optional[float] = 0.5
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min_tracking_confidence: Optional[float] = 0.5
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canned_gesture_classifier_options: Optional[_ClassifierOptions] = (
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num_hands: int = 1
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min_hand_detection_confidence: float = 0.5
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min_hand_presence_confidence: float = 0.5
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min_tracking_confidence: float = 0.5
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canned_gesture_classifier_options: _ClassifierOptions = (
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dataclasses.field(default_factory=_ClassifierOptions)
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)
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custom_gesture_classifier_options: Optional[_ClassifierOptions] = (
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custom_gesture_classifier_options: _ClassifierOptions = (
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dataclasses.field(default_factory=_ClassifierOptions)
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)
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result_callback: Optional[
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@ -182,10 +182,10 @@ class HandLandmarkerOptions:
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base_options: _BaseOptions
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running_mode: _RunningMode = _RunningMode.IMAGE
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num_hands: Optional[int] = 1
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min_hand_detection_confidence: Optional[float] = 0.5
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min_hand_presence_confidence: Optional[float] = 0.5
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min_tracking_confidence: Optional[float] = 0.5
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num_hands: int = 1
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min_hand_detection_confidence: float = 0.5
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min_hand_presence_confidence: float = 0.5
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min_tracking_confidence: float = 0.5
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result_callback: Optional[
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Callable[[HandLandmarkerResult, image_module.Image, int], None]
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] = None
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