281 lines
11 KiB
Python
281 lines
11 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 image classifier."""
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import enum
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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.python.components.containers import category as category_module
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from mediapipe.tasks.python.components.containers import classifications as classifications_module
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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_util
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from mediapipe.tasks.python.vision import image_classification
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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_BaseOptions = base_options_module.BaseOptions
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_Category = category_module.Category
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_ClassificationEntry = classifications_module.ClassificationEntry
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_Classifications = classifications_module.Classifications
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_ClassificationResult = classifications_module.ClassificationResult
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_Image = image_module.Image
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_ImageClassifier = image_classification.ImageClassifier
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_ImageClassifierOptions = image_classification.ImageClassifierOptions
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
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_IMAGE_FILE = 'burger.jpg'
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_EXPECTED_CLASSIFICATION_RESULT = _ClassificationResult(
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classifications=[
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_Classifications(
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entries=[
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_ClassificationEntry(
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categories=[
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_Category(
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index=934,
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score=0.7952049970626831,
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display_name='',
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category_name='cheeseburger'),
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_Category(
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index=932,
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score=0.02732999622821808,
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display_name='',
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category_name='bagel'),
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_Category(
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index=925,
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score=0.01933487318456173,
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display_name='',
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category_name='guacamole'),
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_Category(
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index=963,
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score=0.006279350258409977,
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display_name='',
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category_name='meat loaf')
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],
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timestamp_ms=0
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)
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],
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head_index=0,
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head_name='probability')
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])
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_ALLOW_LIST = ['cheeseburger', 'guacamole']
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_DENY_LIST = ['cheeseburger']
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_SCORE_THRESHOLD = 0.5
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_MAX_RESULTS = 3
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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 ImageClassifierTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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self.test_image = test_util.read_test_image(
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test_util.get_test_data_path(_IMAGE_FILE))
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self.model_path = test_util.get_test_data_path(_MODEL_FILE)
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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 _ImageClassifier.create_from_model_path(self.model_path) as classifier:
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self.assertIsInstance(classifier, _ImageClassifier)
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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(file_name=self.model_path)
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options = _ImageClassifierOptions(base_options=base_options)
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with _ImageClassifier.create_from_options(options) as classifier:
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self.assertIsInstance(classifier, _ImageClassifier)
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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' or 'file_descriptor_meta'."):
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base_options = _BaseOptions(file_name='')
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options = _ImageClassifierOptions(base_options=base_options)
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_ImageClassifier.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(file_content=f.read())
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options = _ImageClassifierOptions(base_options=base_options)
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classifier = _ImageClassifier.create_from_options(options)
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self.assertIsInstance(classifier, _ImageClassifier)
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
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(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
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def test_classify(self, model_file_type, max_results,
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expected_classification_result):
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# Creates classifier.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(file_name=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(file_content=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 = _ImageClassifierOptions(
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base_options=base_options, max_results=max_results)
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classifier = _ImageClassifier.create_from_options(options)
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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# Comparing results.
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self.assertEqual(image_result, expected_classification_result)
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# Closes the classifier explicitly when the classifier is not used in
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# a context.
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classifier.close()
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
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(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
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def test_classify_in_context(self, model_file_type, max_results,
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expected_classification_result):
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(file_name=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(file_content=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 = _ImageClassifierOptions(
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base_options=base_options, max_results=max_results)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs object detection on the input.
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image_result = classifier.classify(self.test_image)
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# Comparing results.
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self.assertEqual(image_result, expected_classification_result)
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def test_score_threshold_option(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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score_threshold=_SCORE_THRESHOLD)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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classifications = image_result.classifications
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for classification in classifications:
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for entry in classification.entries:
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score = entry.categories[0].score
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self.assertGreaterEqual(
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score, _SCORE_THRESHOLD,
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f'Classification with score lower than threshold found. '
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f'{classification}')
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def test_max_results_option(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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max_results=_MAX_RESULTS)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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categories = image_result.classifications[0].entries[0].categories
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self.assertLessEqual(
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len(categories), _MAX_RESULTS, 'Too many results returned.')
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def test_allow_list_option(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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category_allowlist=_ALLOW_LIST)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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classifications = image_result.classifications
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for classification in classifications:
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for entry in classification.entries:
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label = entry.categories[0].category_name
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self.assertIn(label, _ALLOW_LIST,
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f'Label {label} found but not in label allow list')
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def test_deny_list_option(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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category_denylist=_DENY_LIST)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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classifications = image_result.classifications
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for classification in classifications:
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for entry in classification.entries:
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label = entry.categories[0].category_name
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self.assertNotIn(label, _DENY_LIST,
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f'Label {label} found but in deny list.')
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def test_combined_allowlist_and_denylist(self):
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# Fails with combined allowlist and denylist
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with self.assertRaisesRegex(
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ValueError,
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r'`category_allowlist` and `category_denylist` are mutually '
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r'exclusive options.'):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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category_allowlist=['foo'],
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category_denylist=['bar'])
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with _ImageClassifier.create_from_options(options) as unused_classifier:
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pass
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def test_empty_classification_outputs(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path), score_threshold=1)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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self.assertEmpty(image_result.classifications[0].entries[0].categories)
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def test_missing_result_callback(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=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 _ImageClassifier.create_from_options(options) as unused_classifier:
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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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def pass_through(unused_result: _ClassificationResult):
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pass
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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running_mode=running_mode,
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result_callback=pass_through)
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with self.assertRaisesRegex(ValueError,
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r'result callback should not be provided'):
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with _ImageClassifier.create_from_options(options) as unused_classifier:
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pass
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if __name__ == '__main__':
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absltest.main()
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