581 lines
28 KiB
Python
581 lines
28 KiB
Python
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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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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"""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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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.tasks.cc.components.containers.proto import landmarks_detection_result_pb2
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from mediapipe.tasks.python.components.containers import rect as rect_module
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from mediapipe.tasks.python.components.containers import category as category_module
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from mediapipe.tasks.python.components.containers import landmark as landmark_module
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from mediapipe.tasks.python.components.containers import landmark_detection_result as landmark_detection_result_module
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from mediapipe.tasks.python.components.processors import classifier_options
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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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_Rect = rect_module.Rect
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_Category = category_module.Category
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_Landmark = landmark_module.Landmark
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_NormalizedLandmark = landmark_module.NormalizedLandmark
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_LandmarksDetectionResult = landmark_detection_result_module.LandmarksDetectionResult
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_ClassifierOptions = classifier_options.ClassifierOptions
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_Image = image_module.Image
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_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_BUNDLE_ASSET_FILE = 'gesture_recognizer.task'
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_GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE = 'gesture_recognizer_with_custom_classifier.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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_FIST_IMAGE = 'fist.jpg'
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_FIST_LANDMARKS = 'fist_landmarks.pbtxt'
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_VICTORY_IMAGE = 'victory.jpg'
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_VICTORY_LANDMARKS = 'victory_landmarks.pbtxt'
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_VICTORY_LABEL = 'Victory'
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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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_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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_POINTING_UP_LABEL = 'Pointing_Up'
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_ROCK_LABEL = 'Rock'
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_LANDMARKS_ERROR_TOLERANCE = 0.03
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_GESTURE_EXPECTED_INDEX = -1
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def _get_expected_gesture_recognition_result(
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file_path: str, gesture_label: str
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) -> _GestureRecognitionResult:
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landmarks_detection_result_file_path = test_utils.get_test_data_path(
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file_path)
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with open(landmarks_detection_result_file_path, "rb") as f:
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landmarks_detection_result_proto = _LandmarksDetectionResultProto()
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# # Use this if a .pb file is available.
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# landmarks_detection_result_proto.ParseFromString(f.read())
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text_format.Parse(f.read(), landmarks_detection_result_proto)
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landmarks_detection_result = _LandmarksDetectionResult.create_from_pb2(
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landmarks_detection_result_proto)
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gesture = _Category(category_name=gesture_label,
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index=_GESTURE_EXPECTED_INDEX,
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display_name='')
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return _GestureRecognitionResult(
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gestures=[[gesture]],
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handedness=[landmarks_detection_result.categories],
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hand_landmarks=[landmarks_detection_result.landmarks],
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hand_world_landmarks=[landmarks_detection_result.world_landmarks])
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class ModelFileType(enum.Enum):
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FILE_CONTENT = 1
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FILE_NAME = 2
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class GestureRecognizerTest(parameterized.TestCase):
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def setUp(self):
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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.model_path = test_utils.get_test_data_path(
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_GESTURE_RECOGNIZER_BUNDLE_ASSET_FILE)
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def _assert_actual_result_approximately_matches_expected_result(
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self,
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actual_result: _GestureRecognitionResult,
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expected_result: _GestureRecognitionResult
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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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self.assertLen(actual_result.gestures, len(expected_result.gestures))
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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 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][0]
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expected_top_handedness = expected_result.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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# Actual gesture with top score matches expected gesture.
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actual_top_gesture = actual_result.gestures[0][0]
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expected_top_gesture = expected_result.gestures[0][0]
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self.assertEqual(actual_top_gesture.index, _GESTURE_EXPECTED_INDEX)
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self.assertEqual(actual_top_gesture.category_name,
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expected_top_gesture.category_name)
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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, _get_expected_gesture_recognition_result(
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_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL
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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
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)))
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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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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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recognizer = _GestureRecognizer.create_from_options(options)
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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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# 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
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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
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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_score_threshold(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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canned_gesture_classifier_options = _ClassifierOptions(score_threshold=.5)
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options = _GestureRecognizerOptions(
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base_options=base_options,
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canned_gesture_classifier_options=canned_gesture_classifier_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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expected_result = _get_expected_gesture_recognition_result(
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_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL)
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# Only contains one top scoring gesture.
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self.assertLen(recognition_result.gestures[0], 1)
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# Actual gesture with top score matches expected gesture.
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actual_top_gesture = recognition_result.gestures[0][0]
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expected_top_gesture = expected_result.gestures[0][0]
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self.assertEqual(actual_top_gesture.index, expected_top_gesture.index)
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self.assertEqual(actual_top_gesture.category_name,
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expected_top_gesture.category_name)
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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_ROTATED_LANDMARKS, _POINTING_UP_LABEL)
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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_canned_gesture_victory(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 victory image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_VICTORY_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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expected_recognition_result = _get_expected_gesture_recognition_result(
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_VICTORY_LANDMARKS, _VICTORY_LABEL)
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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_custom_gesture_rock(self):
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# Creates gesture recognizer.
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model_path = test_utils.get_test_data_path(
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_GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE)
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base_options = _BaseOptions(model_asset_path=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 rock image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_FIST_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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expected_recognition_result = _get_expected_gesture_recognition_result(
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_FIST_LANDMARKS, _ROCK_LABEL)
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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_allow_gesture_pointing_up(self):
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# Creates gesture recognizer.
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model_path = test_utils.get_test_data_path(
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_GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE)
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base_options = _BaseOptions(model_asset_path=model_path)
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canned_gesture_classifier_options = _ClassifierOptions(
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category_allowlist=['Pointing_Up'])
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options = _GestureRecognizerOptions(
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base_options=base_options,
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num_hands=1,
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canned_gesture_classifier_options=canned_gesture_classifier_options)
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with _GestureRecognizer.create_from_options(options) as recognizer:
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# Load the pointing up image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_POINTING_UP_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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expected_recognition_result = _get_expected_gesture_recognition_result(
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_POINTING_UP_LANDMARKS, _POINTING_UP_LABEL)
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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_deny_gesture_pointing_up(self):
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# Creates gesture recognizer.
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model_path = test_utils.get_test_data_path(
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_GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE)
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base_options = _BaseOptions(model_asset_path=model_path)
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canned_gesture_classifier_options = _ClassifierOptions(
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category_denylist=['Pointing_Up'])
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options = _GestureRecognizerOptions(
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base_options=base_options,
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num_hands=1,
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canned_gesture_classifier_options=canned_gesture_classifier_options)
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with _GestureRecognizer.create_from_options(options) as recognizer:
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# Load the pointing up image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_POINTING_UP_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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actual_top_gesture = recognition_result.gestures[0][0]
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self.assertEqual(actual_top_gesture.category_name, 'None')
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def test_recognize_succeeds_with_allow_all_gestures_except_pointing_up(self):
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# Creates gesture recognizer.
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model_path = test_utils.get_test_data_path(
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_GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE)
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base_options = _BaseOptions(model_asset_path=model_path)
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canned_gesture_classifier_options = _ClassifierOptions(
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score_threshold=.5, category_allowlist=[
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'None', 'Open_Palm', 'Victory', 'Thumb_Down', 'Thumb_Up',
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'ILoveYou', 'Closed_Fist'])
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options = _GestureRecognizerOptions(
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base_options=base_options,
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num_hands=1,
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canned_gesture_classifier_options=canned_gesture_classifier_options)
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with _GestureRecognizer.create_from_options(options) as recognizer:
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# Load the pointing up image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(_POINTING_UP_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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actual_top_gesture = recognition_result.gestures[0][0]
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self.assertEqual(actual_top_gesture.category_name, 'None')
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def test_recognize_succeeds_with_prefer_allow_list_than_deny_list(self):
|
|
# Creates gesture recognizer.
|
|
model_path = test_utils.get_test_data_path(
|
|
_GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE)
|
|
base_options = _BaseOptions(model_asset_path=model_path)
|
|
canned_gesture_classifier_options = _ClassifierOptions(
|
|
score_threshold=.5, category_allowlist=['Pointing_Up'],
|
|
category_denylist=['Pointing_Up'])
|
|
options = _GestureRecognizerOptions(
|
|
base_options=base_options,
|
|
num_hands=1,
|
|
canned_gesture_classifier_options=canned_gesture_classifier_options)
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
# Load the pointing up image.
|
|
test_image = _Image.create_from_file(
|
|
test_utils.get_test_data_path(_POINTING_UP_IMAGE))
|
|
# Performs hand gesture recognition on the input.
|
|
recognition_result = recognizer.recognize(test_image)
|
|
expected_recognition_result = _get_expected_gesture_recognition_result(
|
|
_POINTING_UP_LANDMARKS, _POINTING_UP_LABEL)
|
|
# Comparing results.
|
|
self._assert_actual_result_approximately_matches_expected_result(
|
|
recognition_result, expected_recognition_result)
|
|
|
|
def test_recognize_fails_with_region_of_interest(self):
|
|
# Creates gesture recognizer.
|
|
base_options = _BaseOptions(model_asset_path=self.model_path)
|
|
options = _GestureRecognizerOptions(base_options=base_options, num_hands=1)
|
|
with self.assertRaisesRegex(
|
|
ValueError, "This task doesn't support region-of-interest."):
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
# Set the `region_of_interest` parameter using `ImageProcessingOptions`.
|
|
image_processing_options = _ImageProcessingOptions(
|
|
region_of_interest=_Rect(0, 0, 1, 1))
|
|
# Attempt to perform hand gesture recognition on the cropped input.
|
|
recognizer.recognize(self.test_image, image_processing_options)
|
|
|
|
def test_empty_recognition_outputs(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path))
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
# Load the image with no hands.
|
|
no_hands_test_image = _Image.create_from_file(
|
|
test_utils.get_test_data_path(_NO_HANDS_IMAGE))
|
|
# Performs gesture recognition on the input.
|
|
recognition_result = recognizer.recognize(no_hands_test_image)
|
|
self.assertEmpty(recognition_result.hand_landmarks)
|
|
self.assertEmpty(recognition_result.hand_world_landmarks)
|
|
self.assertEmpty(recognition_result.handedness)
|
|
self.assertEmpty(recognition_result.gestures)
|
|
|
|
def test_missing_result_callback(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.LIVE_STREAM)
|
|
with self.assertRaisesRegex(ValueError,
|
|
r'result callback must be provided'):
|
|
with _GestureRecognizer.create_from_options(options) as unused_recognizer:
|
|
pass
|
|
|
|
@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
|
|
def test_illegal_result_callback(self, running_mode):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=running_mode,
|
|
result_callback=mock.MagicMock())
|
|
with self.assertRaisesRegex(ValueError,
|
|
r'result callback should not be provided'):
|
|
with _GestureRecognizer.create_from_options(options) as unused_recognizer:
|
|
pass
|
|
|
|
def test_calling_recognize_for_video_in_image_mode(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.IMAGE)
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
with self.assertRaisesRegex(ValueError,
|
|
r'not initialized with the video mode'):
|
|
recognizer.recognize_for_video(self.test_image, 0)
|
|
|
|
def test_calling_recognize_async_in_image_mode(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.IMAGE)
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
with self.assertRaisesRegex(ValueError,
|
|
r'not initialized with the live stream mode'):
|
|
recognizer.recognize_async(self.test_image, 0)
|
|
|
|
def test_calling_recognize_in_video_mode(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.VIDEO)
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
with self.assertRaisesRegex(ValueError,
|
|
r'not initialized with the image mode'):
|
|
recognizer.recognize(self.test_image)
|
|
|
|
def test_calling_recognize_async_in_video_mode(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.VIDEO)
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
with self.assertRaisesRegex(ValueError,
|
|
r'not initialized with the live stream mode'):
|
|
recognizer.recognize_async(self.test_image, 0)
|
|
|
|
def test_recognize_for_video_with_out_of_order_timestamp(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.VIDEO)
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
unused_result = recognizer.recognize_for_video(self.test_image, 1)
|
|
with self.assertRaisesRegex(
|
|
ValueError, r'Input timestamp must be monotonically increasing'):
|
|
recognizer.recognize_for_video(self.test_image, 0)
|
|
|
|
def test_recognize_for_video(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.VIDEO)
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
for timestamp in range(0, 300, 30):
|
|
recognition_result = recognizer.recognize_for_video(self.test_image,
|
|
timestamp)
|
|
expected_recognition_result = _get_expected_gesture_recognition_result(
|
|
_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL)
|
|
self._assert_actual_result_approximately_matches_expected_result(
|
|
recognition_result, expected_recognition_result)
|
|
|
|
def test_calling_recognize_in_live_stream_mode(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
|
result_callback=mock.MagicMock())
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
with self.assertRaisesRegex(ValueError,
|
|
r'not initialized with the image mode'):
|
|
recognizer.recognize(self.test_image)
|
|
|
|
def test_calling_recognize_for_video_in_live_stream_mode(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
|
result_callback=mock.MagicMock())
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
with self.assertRaisesRegex(ValueError,
|
|
r'not initialized with the video mode'):
|
|
recognizer.recognize_for_video(self.test_image, 0)
|
|
|
|
def test_recognize_async_calls_with_illegal_timestamp(self):
|
|
options = _GestureRecognizerOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
|
result_callback=mock.MagicMock())
|
|
with _GestureRecognizer.create_from_options(options) as recognizer:
|
|
recognizer.recognize_async(self.test_image, 100)
|
|
with self.assertRaisesRegex(
|
|
ValueError, r'Input timestamp must be monotonically increasing'):
|
|
recognizer.recognize_async(self.test_image, 0)
|
|
|
|
@parameterized.parameters(
|
|
(_THUMB_UP_IMAGE, _get_expected_gesture_recognition_result(
|
|
_THUMB_UP_LANDMARKS, _THUMB_UP_LABEL)),
|
|
(_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()
|