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Removed optional for defaults in some tasks and updated various tests to be consistent with that of Pose Landmarker's

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This commit is contained in:
kinaryml 2023-04-18 23:28:10 -07:00
parent 00f966655b
commit f87ffd92a0
7 changed files with 82 additions and 73 deletions
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14
mediapipe/tasks/python/test/vision/face_landmarker_test.py
View File

@ -51,9 +51,9 @@ _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):
@ -126,10 +126,10 @@ class FaceLandmarkerTest(parameterized.TestCase):
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_DIFF_MARGIN
elem.x, expected_landmarks[i][j].x, delta=_LANDMARKS_MARGIN
)
self.assertAlmostEqual(
elem.y, expected_landmarks[i][j].y, delta=_LANDMARKS_DIFF_MARGIN
elem.y, expected_landmarks[i][j].y, delta=_LANDMARKS_MARGIN
)
def _expect_blendshapes_correct(
@ -144,7 +144,7 @@ class FaceLandmarkerTest(parameterized.TestCase):
self.assertAlmostEqual(
elem.score,
expected_blendshapes[i][j].score,
delta=_BLENDSHAPES_DIFF_MARGIN,
delta=_BLENDSHAPES_MARGIN,
)
def _expect_facial_transformation_matrixes_correct(
@ -158,7 +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):

81
mediapipe/tasks/python/test/vision/hand_landmarker_test.py
View File

@ -54,7 +54,7 @@ _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 +89,52 @@ 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.
@ -175,7 +188,7 @@ 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(
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.
@ -203,7 +216,7 @@ 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(
self._expect_hand_landmarker_results_correct(
detection_result, expected_detection_result)
def test_detect_succeeds_with_num_hands(self):
@ -234,7 +247,7 @@ 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(
self._expect_hand_landmarker_results_correct(
detection_result, expected_detection_result)
def test_detect_fails_with_region_of_interest(self):
@ -351,7 +364,7 @@ class HandLandmarkerTest(parameterized.TestCase):
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(
self._expect_hand_landmarker_results_correct(
result, expected_result)
else:
self.assertEqual(result, expected_result)
@ -406,7 +419,7 @@ 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(
self._expect_hand_landmarker_results_correct(
result, expected_result)
else:
self.assertEqual(result, expected_result)

24
mediapipe/tasks/python/test/vision/pose_landmarker_test.py
View File

@ -50,8 +50,7 @@ _POSE_LANDMARKER_BUNDLE_ASSET_FILE = 'pose_landmarker.task'
_BURGER_IMAGE = 'burger.jpg'
_POSE_IMAGE = 'pose.jpg'
_POSE_LANDMARKS = 'pose_landmarks.pbtxt'
_LANDMARKS_DIFF_MARGIN = 0.03
_LANDMARKS_ON_VIDEO_DIFF_MARGIN = 0.03
_LANDMARKS_MARGIN = 0.03
def _get_expected_pose_landmarker_result(
@ -87,10 +86,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
_POSE_LANDMARKER_BUNDLE_ASSET_FILE)
def _expect_pose_landmarks_correct(
self,
actual_landmarks: List[List[landmark_module.NormalizedLandmark]],
expected_landmarks: List[List[landmark_module.NormalizedLandmark]],
diff_margin: float
self, actual_landmarks, expected_landmarks, margin
):
# Expects to have the same number of poses detected.
self.assertLen(actual_landmarks, len(expected_landmarks))
@ -98,21 +94,21 @@ class PoseLandmarkerTest(parameterized.TestCase):
for i, _ in enumerate(actual_landmarks):
for j, elem in enumerate(actual_landmarks[i]):
self.assertAlmostEqual(
elem.x, expected_landmarks[i][j].x, delta=diff_margin
elem.x, expected_landmarks[i][j].x, delta=margin
)
self.assertAlmostEqual(
elem.y, expected_landmarks[i][j].y, delta=diff_margin
elem.y, expected_landmarks[i][j].y, delta=margin
)
def _expect_pose_landmarker_results_correct(
self,
actual_result: PoseLandmarkerResult,
expected_result: PoseLandmarkerResult,
diff_margin: float
margin: float
):
self._expect_pose_landmarks_correct(
actual_result.pose_landmarks, expected_result.pose_landmarks,
diff_margin
margin
)
def test_create_from_file_succeeds_with_valid_model_path(self):
@ -170,7 +166,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
# Comparing results.
self._expect_pose_landmarker_results_correct(
detection_result, expected_detection_result, _LANDMARKS_DIFF_MARGIN
detection_result, expected_detection_result, _LANDMARKS_MARGIN
)
# Closes the pose landmarker explicitly when the pose landmarker is not used
# in a context.
@ -201,7 +197,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
# Comparing results.
self._expect_pose_landmarker_results_correct(
detection_result, expected_detection_result, _LANDMARKS_DIFF_MARGIN
detection_result, expected_detection_result, _LANDMARKS_MARGIN
)
def test_detect_fails_with_region_of_interest(self):
@ -319,7 +315,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
image_processing_options)
if result.pose_landmarks:
self._expect_pose_landmarker_results_correct(
result, expected_result, _LANDMARKS_ON_VIDEO_DIFF_MARGIN
result, expected_result, _LANDMARKS_MARGIN
)
else:
self.assertEqual(result, expected_result)
@ -373,7 +369,7 @@ class PoseLandmarkerTest(parameterized.TestCase):
timestamp_ms: int):
if result.pose_landmarks:
self._expect_pose_landmarker_results_correct(
result, expected_result, _LANDMARKS_DIFF_MARGIN
result, expected_result, _LANDMARKS_MARGIN
)
else:
self.assertEqual(result, expected_result)

4
mediapipe/tasks/python/vision/face_detector.py
View File

@ -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

12
mediapipe/tasks/python/vision/face_landmarker.py
View File

@ -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

12
mediapipe/tasks/python/vision/gesture_recognizer.py
View File

@ -194,14 +194,14 @@ 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] = (
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] = (
custom_gesture_classifier_options: _ClassifierOptions = (
dataclasses.field(default_factory=_ClassifierOptions)
)
result_callback: Optional[

8
mediapipe/tasks/python/vision/hand_landmarker.py
View File

@ -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