Added remaining tests for the GestureRecognizer Python MediaPipe Tasks API
This commit is contained in:
kinaryml
parent
18eb089d39
commit
8762d15c81
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@ -11,7 +11,7 @@
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Landmark Detection Result data class."""
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"""Landmarks Detection Result data class."""
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import dataclasses
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from typing import Any, Optional
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@ -56,6 +56,7 @@ py_test(
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"//mediapipe/tasks/python/test:test_utils",
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"//mediapipe/tasks/python/vision:gesture_recognizer",
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"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
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"//mediapipe/tasks/python/vision/core:image_processing_options",
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"@com_google_protobuf//:protobuf_python"
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],
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)
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@ -14,7 +14,9 @@
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"""Tests for gesture recognizer."""
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import enum
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from unittest import mock
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import numpy as np
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from google.protobuf import text_format
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from absl.testing import absltest
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from absl.testing import parameterized
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@ -29,10 +31,11 @@ from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.test import test_utils
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from mediapipe.tasks.python.vision import gesture_recognizer
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
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_LandmarksDetectionResultProto = landmarks_detection_result_pb2.LandmarksDetectionResult
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_BaseOptions = base_options_module.BaseOptions
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_NormalizedRect = rect_module.NormalizedRect
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_Rect = rect_module.Rect
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_Classification = classification_module.Classification
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_ClassificationList = classification_module.ClassificationList
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_Landmark = landmark_module.Landmark
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@ -45,12 +48,19 @@ _GestureRecognizer = gesture_recognizer.GestureRecognizer
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_GestureRecognizerOptions = gesture_recognizer.GestureRecognizerOptions
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_GestureRecognitionResult = gesture_recognizer.GestureRecognitionResult
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_GESTURE_RECOGNIZER_MODEL_FILE = 'gesture_recognizer.task'
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_NO_HANDS_IMAGE = 'cats_and_dogs.jpg'
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_TWO_HANDS_IMAGE = 'right_hands.jpg'
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_THUMB_UP_IMAGE = 'thumb_up.jpg'
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_THUMB_UP_LANDMARKS = "thumb_up_landmarks.pbtxt"
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_THUMB_UP_LABEL = "Thumb_Up"
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_THUMB_UP_LANDMARKS = 'thumb_up_landmarks.pbtxt'
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_THUMB_UP_LABEL = 'Thumb_Up'
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_THUMB_UP_INDEX = 5
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_POINTING_UP_ROTATED_IMAGE = 'pointing_up_rotated.jpg'
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_POINTING_UP_LANDMARKS = 'pointing_up_rotated_landmarks.pbtxt'
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_POINTING_UP_LABEL = 'Pointing_Up'
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_POINTING_UP_INDEX = 3
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_LANDMARKS_ERROR_TOLERANCE = 0.03
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@ -89,7 +99,7 @@ class GestureRecognizerTest(parameterized.TestCase):
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super().setUp()
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self.test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_THUMB_UP_IMAGE))
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self.gesture_recognizer_model_path = test_utils.get_test_data_path(
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self.model_path = test_utils.get_test_data_path(
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_GESTURE_RECOGNIZER_MODEL_FILE)
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def _assert_actual_result_approximately_matches_expected_result(
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@ -105,8 +115,15 @@ class GestureRecognizerTest(parameterized.TestCase):
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self.assertLen(actual_result.handedness, len(expected_result.handedness))
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self.assertLen(actual_result.gestures, len(expected_result.gestures))
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# Actual landmarks match expected landmarks.
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self.assertEqual(actual_result.hand_landmarks,
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expected_result.hand_landmarks)
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self.assertLen(actual_result.hand_landmarks[0].landmarks,
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len(expected_result.hand_landmarks[0].landmarks))
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actual_landmarks = actual_result.hand_landmarks[0].landmarks
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expected_landmarks = expected_result.hand_landmarks[0].landmarks
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for i in range(len(actual_landmarks)):
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self.assertAlmostEqual(actual_landmarks[i].x, expected_landmarks[i].x,
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delta=_LANDMARKS_ERROR_TOLERANCE)
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self.assertAlmostEqual(actual_landmarks[i].y, 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].classifications[0]
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expected_top_handedness = expected_result.handedness[0].classifications[0]
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@ -118,32 +135,56 @@ class GestureRecognizerTest(parameterized.TestCase):
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self.assertEqual(actual_top_gesture.index, expected_top_gesture.index)
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self.assertEqual(actual_top_gesture.label, expected_top_gesture.label)
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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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with _GestureRecognizer.create_from_model_path(self.model_path) as recognizer:
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self.assertIsInstance(recognizer, _GestureRecognizer)
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def test_create_from_options_succeeds_with_valid_model_path(self):
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# Creates with options containing model file successfully.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _GestureRecognizerOptions(base_options=base_options)
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with _GestureRecognizer.create_from_options(options) as recognizer:
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self.assertIsInstance(recognizer, _GestureRecognizer)
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def test_create_from_options_fails_with_invalid_model_path(self):
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# Invalid empty model path.
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with self.assertRaisesRegex(
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ValueError,
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r"ExternalFile must specify at least one of 'file_content', "
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r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."):
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base_options = _BaseOptions(model_asset_path='')
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options = _GestureRecognizerOptions(base_options=base_options)
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_GestureRecognizer.create_from_options(options)
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def test_create_from_options_succeeds_with_valid_model_content(self):
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# Creates with options containing model content successfully.
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with open(self.model_path, 'rb') as f:
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base_options = _BaseOptions(model_asset_buffer=f.read())
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options = _GestureRecognizerOptions(base_options=base_options)
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recognizer = _GestureRecognizer.create_from_options(options)
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self.assertIsInstance(recognizer, _GestureRecognizer)
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, 0.3, _get_expected_gesture_recognition_result(
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(ModelFileType.FILE_NAME, _get_expected_gesture_recognition_result(
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_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
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)),
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(ModelFileType.FILE_CONTENT, 0.3, _get_expected_gesture_recognition_result(
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(ModelFileType.FILE_CONTENT, _get_expected_gesture_recognition_result(
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_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
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)))
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def test_recognize(self, model_file_type, min_gesture_confidence,
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expected_recognition_result):
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def test_recognize(self, model_file_type, expected_recognition_result):
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# Creates gesture recognizer.
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if model_file_type is ModelFileType.FILE_NAME:
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gesture_recognizer_base_options = _BaseOptions(
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model_asset_path=self.gesture_recognizer_model_path)
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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.gesture_recognizer_model_path, 'rb') as f:
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with open(self.model_path, 'rb') as f:
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model_content = f.read()
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gesture_recognizer_base_options = _BaseOptions(
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model_asset_buffer=model_content)
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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 = _GestureRecognizerOptions(
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base_options=gesture_recognizer_base_options,
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min_gesture_confidence=min_gesture_confidence
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)
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options = _GestureRecognizerOptions(base_options=base_options)
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recognizer = _GestureRecognizer.create_from_options(options)
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# Performs hand gesture recognition on the input.
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@ -151,10 +192,238 @@ class GestureRecognizerTest(parameterized.TestCase):
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# Comparing results.
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self._assert_actual_result_approximately_matches_expected_result(
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recognition_result, expected_recognition_result)
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# Closes the gesture recognizer explicitly when the detector is not used in
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# a context.
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# Closes the gesture recognizer explicitly when the gesture recognizer is
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# not used in a context.
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recognizer.close()
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, _get_expected_gesture_recognition_result(
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_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
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)),
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(ModelFileType.FILE_CONTENT, _get_expected_gesture_recognition_result(
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_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX
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)))
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def test_recognize_in_context(self, model_file_type,
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expected_recognition_result):
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# Creates gesture recognizer.
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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 = _GestureRecognizerOptions(base_options=base_options)
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with _GestureRecognizer.create_from_options(options) as recognizer:
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# Performs hand gesture recognition on the input.
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recognition_result = recognizer.recognize(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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recognition_result, expected_recognition_result)
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def test_recognize_succeeds_with_num_hands(self):
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# Creates gesture recognizer.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _GestureRecognizerOptions(base_options=base_options, num_hands=2)
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with _GestureRecognizer.create_from_options(options) as recognizer:
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# Load the pointing up rotated image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_TWO_HANDS_IMAGE))
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# Performs hand gesture recognition on the input.
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recognition_result = recognizer.recognize(test_image)
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# Comparing results.
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self.assertLen(recognition_result.handedness, 2)
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def test_recognize_succeeds_with_rotation(self):
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# Creates gesture recognizer.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _GestureRecognizerOptions(base_options=base_options, num_hands=1)
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with _GestureRecognizer.create_from_options(options) as recognizer:
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# Load the pointing up rotated image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_POINTING_UP_ROTATED_IMAGE))
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# Set rotation parameters using ImageProcessingOptions.
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image_processing_options = _ImageProcessingOptions(rotation_degrees=-90)
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# Performs hand gesture recognition on the input.
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recognition_result = recognizer.recognize(test_image,
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image_processing_options)
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expected_recognition_result = _get_expected_gesture_recognition_result(
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_POINTING_UP_LANDMARKS, _POINTING_UP_LABEL, _POINTING_UP_INDEX)
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# Comparing results.
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self._assert_actual_result_approximately_matches_expected_result(
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recognition_result, expected_recognition_result)
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def test_recognize_fails_with_region_of_interest(self):
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# Creates gesture recognizer.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _GestureRecognizerOptions(base_options=base_options, num_hands=1)
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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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 hand gesture recognition on the cropped input.
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recognizer.recognize(self.test_image, image_processing_options)
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def test_empty_recognition_outputs(self):
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options = _GestureRecognizerOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path))
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with _GestureRecognizer.create_from_options(options) as recognizer:
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# Load the image with no hands.
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no_hands_test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_NO_HANDS_IMAGE))
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# Performs gesture recognition on the input.
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recognition_result = recognizer.recognize(no_hands_test_image)
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self.assertEmpty(recognition_result.hand_landmarks)
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self.assertEmpty(recognition_result.hand_world_landmarks)
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self.assertEmpty(recognition_result.handedness)
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self.assertEmpty(recognition_result.gestures)
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def test_missing_result_callback(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as unused_recognizer:
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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 = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as unused_recognizer:
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pass
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def test_calling_recognize_for_video_in_image_mode(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the video mode'):
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recognizer.recognize_for_video(self.test_image, 0)
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def test_calling_recognize_async_in_image_mode(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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recognizer.recognize_async(self.test_image, 0)
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def test_calling_recognize_in_video_mode(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the image mode'):
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recognizer.recognize(self.test_image)
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def test_calling_recognize_async_in_video_mode(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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recognizer.recognize_async(self.test_image, 0)
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def test_recognize_for_video_with_out_of_order_timestamp(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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unused_result = recognizer.recognize_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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recognizer.recognize_for_video(self.test_image, 0)
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def test_recognize_for_video(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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for timestamp in range(0, 300, 30):
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recognition_result = recognizer.recognize_for_video(self.test_image,
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timestamp)
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expected_recognition_result = _get_expected_gesture_recognition_result(
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_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX)
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self._assert_actual_result_approximately_matches_expected_result(
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recognition_result, expected_recognition_result)
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def test_calling_recognize_in_live_stream_mode(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the image mode'):
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recognizer.recognize(self.test_image)
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def test_calling_recognize_for_video_in_live_stream_mode(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the video mode'):
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recognizer.recognize_for_video(self.test_image, 0)
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def test_recognize_async_calls_with_illegal_timestamp(self):
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options = _GestureRecognizerOptions(
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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 _GestureRecognizer.create_from_options(options) as recognizer:
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recognizer.recognize_async(self.test_image, 100)
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with self.assertRaisesRegex(
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ValueError, r'Input timestamp must be monotonically increasing'):
|
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recognizer.recognize_async(self.test_image, 0)
|
||||
|
||||
@parameterized.parameters(
|
||||
(_THUMB_UP_IMAGE, _get_expected_gesture_recognition_result(
|
||||
_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL, _THUMB_UP_INDEX)),
|
||||
(_NO_HANDS_IMAGE, _GestureRecognitionResult([], [], [], [])))
|
||||
def test_recognize_async_calls(self, image_path, expected_result):
|
||||
test_image = _Image.create_from_file(
|
||||
test_utils.get_test_data_path(image_path))
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(result: _GestureRecognitionResult, output_image: _Image,
|
||||
timestamp_ms: int):
|
||||
if result.hand_landmarks and result.hand_world_landmarks and \
|
||||
result.handedness and result.gestures:
|
||||
self._assert_actual_result_approximately_matches_expected_result(
|
||||
result, expected_result)
|
||||
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 = _GestureRecognizerOptions(
|
||||
base_options=_BaseOptions(model_asset_path=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
result_callback=check_result)
|
||||
with _GestureRecognizer.create_from_options(options) as recognizer:
|
||||
for timestamp in range(0, 300, 30):
|
||||
recognizer.recognize_async(test_image, timestamp)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
absltest.main()
|
||||
|
|
|
|||
|
|
@ -52,7 +52,6 @@ py_library(
|
|||
"//mediapipe/tasks/cc/vision/hand_detector/proto:hand_detector_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarker_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarks_detector_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/python/components/containers:rect",
|
||||
"//mediapipe/tasks/python/components/containers:classification",
|
||||
"//mediapipe/tasks/python/components/containers:landmark",
|
||||
"//mediapipe/tasks/python/components/processors:classifier_options",
|
||||
|
|
|
|||
|
|
@ -23,6 +23,14 @@ py_library(
|
|||
srcs = ["vision_task_running_mode.py"],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "image_processing_options",
|
||||
srcs = ["image_processing_options.py"],
|
||||
deps = [
|
||||
"//mediapipe/tasks/python/components/containers:rect",
|
||||
],
|
||||
)
|
||||
|
||||
py_library(
|
||||
name = "base_vision_task_api",
|
||||
srcs = [
|
||||
|
|
@ -30,6 +38,7 @@ py_library(
|
|||
],
|
||||
deps = [
|
||||
":vision_task_running_mode",
|
||||
":image_processing_options",
|
||||
"//mediapipe/framework:calculator_py_pb2",
|
||||
"//mediapipe/python:_framework_bindings",
|
||||
"//mediapipe/tasks/python/core:optional_dependencies",
|
||||
|
|
|
|||
|
|
@ -13,17 +13,22 @@
|
|||
# limitations under the License.
|
||||
"""MediaPipe vision task base api."""
|
||||
|
||||
import math
|
||||
from typing import Callable, Mapping, Optional
|
||||
|
||||
from mediapipe.framework import calculator_pb2
|
||||
from mediapipe.python._framework_bindings import packet as packet_module
|
||||
from mediapipe.python._framework_bindings import task_runner as task_runner_module
|
||||
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
|
||||
from mediapipe.tasks.python.components.containers import rect as rect_module
|
||||
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
|
||||
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
|
||||
|
||||
_TaskRunner = task_runner_module.TaskRunner
|
||||
_Packet = packet_module.Packet
|
||||
_NormalizedRect = rect_module.NormalizedRect
|
||||
_RunningMode = running_mode_module.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
|
||||
|
||||
class BaseVisionTaskApi(object):
|
||||
|
|
@ -122,6 +127,50 @@ class BaseVisionTaskApi(object):
|
|||
+ self._running_mode.name)
|
||||
self._runner.send(inputs)
|
||||
|
||||
@staticmethod
|
||||
def convert_to_normalized_rect(
|
||||
options: _ImageProcessingOptions,
|
||||
roi_allowed: bool = True
|
||||
) -> _NormalizedRect:
|
||||
"""
|
||||
Convert from ImageProcessingOptions to NormalizedRect, performing sanity
|
||||
checks on-the-fly. If the input ImageProcessingOptions is not present,
|
||||
returns a default NormalizedRect covering the whole image with rotation set
|
||||
to 0. If 'roi_allowed' is false, an error will be returned if the input
|
||||
ImageProcessingOptions has its 'region_of_interest' field set.
|
||||
|
||||
Args:
|
||||
options: Options for image processing.
|
||||
roi_allowed: Indicates if the `region_of_interest` field is allowed to be
|
||||
set. By default, it's set to True.
|
||||
|
||||
"""
|
||||
normalized_rect = _NormalizedRect(rotation=0, x_center=0.5, y_center=0.5,
|
||||
width=1, height=1)
|
||||
if options is None:
|
||||
return normalized_rect
|
||||
|
||||
if options.rotation_degrees % 90 != 0:
|
||||
raise ValueError("Expected rotation to be a multiple of 90°.")
|
||||
|
||||
# Convert to radians counter-clockwise.
|
||||
normalized_rect.rotation = -options.rotation_degrees * math.pi / 180.0
|
||||
|
||||
if options.region_of_interest:
|
||||
if not roi_allowed:
|
||||
raise ValueError("This task doesn't support region-of-interest.")
|
||||
roi = options.region_of_interest
|
||||
if roi.x_center >= roi.width or roi.y_center >= roi.height:
|
||||
raise ValueError(
|
||||
"Expected Rect with x_center < width and y_center < height.")
|
||||
if roi.x_center < 0 or roi.y_center < 0 or roi.width > 1 or roi.height > 1:
|
||||
raise ValueError("Expected Rect values to be in [0,1].")
|
||||
normalized_rect.x_center = roi.x_center + roi.width / 2.0
|
||||
normalized_rect.y_center = roi.y_center + roi.height / 2.0
|
||||
normalized_rect.width = roi.width - roi.x_center
|
||||
normalized_rect.height = roi.height - roi.y_center
|
||||
return normalized_rect
|
||||
|
||||
def close(self) -> None:
|
||||
"""Shuts down the mediapipe vision task instance.
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,39 @@
|
|||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""MediaPipe vision options for image processing."""
|
||||
|
||||
import dataclasses
|
||||
from typing import Optional
|
||||
|
||||
from mediapipe.tasks.python.components.containers import rect as rect_module
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class ImageProcessingOptions:
|
||||
"""Options for image processing.
|
||||
|
||||
If both region-of-interest and rotation are specified, the crop around the
|
||||
region-of-interest is extracted first, then the specified rotation is applied
|
||||
to the crop.
|
||||
|
||||
Attributes:
|
||||
region_of_interest: The optional region-of-interest to crop from the image.
|
||||
If not specified, the full image is used. Coordinates must be in [0,1]
|
||||
with 'left' < 'right' and 'top' < bottom.
|
||||
rotation_degress: The rotation to apply to the image (or cropped
|
||||
region-of-interest), in degrees clockwise. The rotation must be a
|
||||
multiple (positive or negative) of 90°.
|
||||
"""
|
||||
region_of_interest: Optional[rect_module.Rect] = None
|
||||
rotation_degrees: int = 0
|
||||
|
|
@ -27,7 +27,6 @@ from mediapipe.tasks.cc.vision.gesture_recognizer.proto import hand_gesture_reco
|
|||
from mediapipe.tasks.cc.vision.hand_detector.proto import hand_detector_graph_options_pb2
|
||||
from mediapipe.tasks.cc.vision.hand_landmarker.proto import hand_landmarker_graph_options_pb2
|
||||
from mediapipe.tasks.cc.vision.hand_landmarker.proto import hand_landmarks_detector_graph_options_pb2
|
||||
from mediapipe.tasks.python.components.containers import rect as rect_module
|
||||
from mediapipe.tasks.python.components.containers import classification as classification_module
|
||||
from mediapipe.tasks.python.components.containers import landmark as landmark_module
|
||||
from mediapipe.tasks.python.components.processors import classifier_options
|
||||
|
|
@ -36,8 +35,8 @@ 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 vision_task_running_mode as running_mode_module
|
||||
from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
|
||||
|
||||
_NormalizedRect = rect_module.NormalizedRect
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_GestureClassifierGraphOptionsProto = gesture_classifier_graph_options_pb2.GestureClassifierGraphOptions
|
||||
_GestureRecognizerGraphOptionsProto = gesture_recognizer_graph_options_pb2.GestureRecognizerGraphOptions
|
||||
|
|
@ -47,6 +46,7 @@ _HandLandmarkerGraphOptionsProto = hand_landmarker_graph_options_pb2.HandLandmar
|
|||
_HandLandmarksDetectorGraphOptionsProto = hand_landmarks_detector_graph_options_pb2.HandLandmarksDetectorGraphOptions
|
||||
_ClassifierOptions = classifier_options.ClassifierOptions
|
||||
_RunningMode = running_mode_module.VisionTaskRunningMode
|
||||
_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
|
||||
_TaskInfo = task_info_module.TaskInfo
|
||||
_TaskRunner = task_runner_module.TaskRunner
|
||||
|
||||
|
|
@ -67,11 +67,6 @@ _TASK_GRAPH_NAME = 'mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerG
|
|||
_MICRO_SECONDS_PER_MILLISECOND = 1000
|
||||
|
||||
|
||||
def _build_full_image_norm_rect() -> _NormalizedRect:
|
||||
# Builds a NormalizedRect covering the entire image.
|
||||
return _NormalizedRect(x_center=0.5, y_center=0.5, width=1, height=1)
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class GestureRecognitionResult:
|
||||
"""The gesture recognition result from GestureRecognizer, where each vector
|
||||
|
|
@ -278,7 +273,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
|
|||
def recognize(
|
||||
self,
|
||||
image: image_module.Image,
|
||||
roi: Optional[_NormalizedRect] = None
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> GestureRecognitionResult:
|
||||
"""Performs hand gesture recognition on the given image. Only use this
|
||||
method when the GestureRecognizer is created with the image running mode.
|
||||
|
|
@ -289,7 +284,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
|
|||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
roi: The region of interest.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The hand gesture recognition results.
|
||||
|
|
@ -298,11 +293,16 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
|
|||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If gesture recognition failed to run.
|
||||
"""
|
||||
norm_rect = roi if roi is not None else _build_full_image_norm_rect()
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options,
|
||||
roi_allowed=False)
|
||||
output_packets = self._process_image_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
norm_rect.to_pb2())})
|
||||
normalized_rect.to_pb2())})
|
||||
|
||||
if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
|
||||
return GestureRecognitionResult([], [], [], [])
|
||||
|
||||
gestures_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_HAND_GESTURE_STREAM_NAME])
|
||||
handedness_proto_list = packet_getter.get_proto_list(
|
||||
|
|
@ -331,7 +331,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
|
|||
def recognize_for_video(
|
||||
self, image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
roi: Optional[_NormalizedRect] = None
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> GestureRecognitionResult:
|
||||
"""Performs gesture recognition on the provided video frame. Only use this
|
||||
method when the GestureRecognizer is created with the video running mode.
|
||||
|
|
@ -344,7 +344,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
|
|||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input video frame in milliseconds.
|
||||
roi: The region of interest.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Returns:
|
||||
The hand gesture recognition results.
|
||||
|
|
@ -353,14 +353,19 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
|
|||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If gesture recognition failed to run.
|
||||
"""
|
||||
norm_rect = roi if roi is not None else _build_full_image_norm_rect()
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options,
|
||||
roi_allowed=False)
|
||||
output_packets = self._process_video_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
norm_rect.to_pb2()).at(
|
||||
normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
|
||||
if output_packets[_HAND_GESTURE_STREAM_NAME].is_empty():
|
||||
return GestureRecognitionResult([], [], [], [])
|
||||
|
||||
gestures_proto_list = packet_getter.get_proto_list(
|
||||
output_packets[_HAND_GESTURE_STREAM_NAME])
|
||||
handedness_proto_list = packet_getter.get_proto_list(
|
||||
|
|
@ -390,7 +395,7 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
|
|||
self,
|
||||
image: image_module.Image,
|
||||
timestamp_ms: int,
|
||||
roi: Optional[_NormalizedRect] = None
|
||||
image_processing_options: Optional[_ImageProcessingOptions] = None
|
||||
) -> None:
|
||||
"""Sends live image data to perform gesture recognition, and the results
|
||||
will be available via the "result_callback" provided in the
|
||||
|
|
@ -415,17 +420,18 @@ class GestureRecognizer(base_vision_task_api.BaseVisionTaskApi):
|
|||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input image in milliseconds.
|
||||
roi: The region of interest.
|
||||
image_processing_options: Options for image processing.
|
||||
|
||||
Raises:
|
||||
ValueError: If the current input timestamp is smaller than what the
|
||||
gesture recognizer has already processed.
|
||||
"""
|
||||
norm_rect = roi if roi is not None else _build_full_image_norm_rect()
|
||||
normalized_rect = self.convert_to_normalized_rect(image_processing_options,
|
||||
roi_allowed=False)
|
||||
self._send_live_stream_data({
|
||||
_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
|
||||
_NORM_RECT_STREAM_NAME: packet_creator.create_proto(
|
||||
norm_rect.to_pb2()).at(
|
||||
normalized_rect.to_pb2()).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user