Added more tests and updated the APIs to use a new constant
This commit is contained in:
kinaryml
parent
e250c903f5
commit
cb806071ba
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@ -14,6 +14,7 @@
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"""Tests for image classifier."""
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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 absl.testing import absltest
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@ -41,33 +42,46 @@ _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_CATEGORIES = [
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_Category(
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index=934,
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score=0.7939587831497192,
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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=0.02739289402961731,
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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=0.01934075355529785,
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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=0.006327860057353973,
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display_name='',
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category_name='meat loaf')
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]
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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=0.7939587831497192,
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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=0.02739289402961731,
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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=0.01934075355529785,
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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=0.006327860057353973,
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display_name='',
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category_name='meat loaf')
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],
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categories=_EXPECTED_CATEGORIES,
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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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_EMPTY_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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timestamp_ms=0
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)
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],
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@ -93,6 +107,36 @@ class ImageClassifierTest(parameterized.TestCase):
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test_utils.get_test_data_path(_IMAGE_FILE))
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self.model_path = test_utils.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(model_asset_path=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(model_asset_path='')
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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(model_asset_buffer=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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@ -122,6 +166,183 @@ class ImageClassifierTest(parameterized.TestCase):
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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(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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classifier_options = _ClassifierOptions(max_results=max_results)
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options = _ImageClassifierOptions(
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base_options=base_options, classifier_options=classifier_options)
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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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# 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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classifier_options = _ClassifierOptions(score_threshold=_SCORE_THRESHOLD)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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classifier_options=classifier_options)
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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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classifier_options = _ClassifierOptions(score_threshold=_SCORE_THRESHOLD)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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classifier_options=classifier_options)
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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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classifier_options = _ClassifierOptions(category_allowlist=_ALLOW_LIST)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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classifier_options=classifier_options)
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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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classifier_options = _ClassifierOptions(category_denylist=_DENY_LIST)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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classifier_options=classifier_options)
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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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classifier_options = _ClassifierOptions(category_allowlist=['foo'],
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category_denylist=['bar'])
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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classifier_options=classifier_options)
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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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classifier_options = _ClassifierOptions(score_threshold=1)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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classifier_options=classifier_options)
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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(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 _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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options = _ImageClassifierOptions(
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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 _ImageClassifier.create_from_options(options) as unused_classifier:
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pass
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def test_calling_classify_for_video_in_image_mode(self):
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options = _ImageClassifierOptions(
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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 _ImageClassifier.create_from_options(options) as classifier:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the video mode'):
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classifier.classify_for_video(self.test_image, 0)
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def test_calling_classify_async_in_image_mode(self):
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options = _ImageClassifierOptions(
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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 _ImageClassifier.create_from_options(options) as classifier:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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classifier.classify_async(self.test_image, 0)
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def test_calling_classify_in_video_mode(self):
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options = _ImageClassifierOptions(
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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 _ImageClassifier.create_from_options(options) as classifier:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the image mode'):
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classifier.classify(self.test_image)
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def test_calling_classify_async_in_video_mode(self):
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options = _ImageClassifierOptions(
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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 _ImageClassifier.create_from_options(options) as classifier:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the live stream mode'):
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classifier.classify_async(self.test_image, 0)
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def test_classify_for_video_with_out_of_order_timestamp(self):
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options = _ImageClassifierOptions(
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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 _ImageClassifier.create_from_options(options) as classifier:
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unused_result = classifier.classify_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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classifier.classify_for_video(self.test_image, 0)
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def test_classify_for_video(self):
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classifier_options = _ClassifierOptions(max_results=4)
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options = _ImageClassifierOptions(
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@ -132,7 +353,78 @@ class ImageClassifierTest(parameterized.TestCase):
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for timestamp in range(0, 300, 30):
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classification_result = classifier.classify_for_video(
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self.test_image, timestamp)
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self.assertEqual(classification_result, _EXPECTED_CLASSIFICATION_RESULT)
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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=_EXPECTED_CATEGORIES, timestamp_ms=timestamp)
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],
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head_index=0, head_name='probability')
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])
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self.assertEqual(classification_result, expected_classification_result)
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def test_calling_classify_in_live_stream_mode(self):
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options = _ImageClassifierOptions(
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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 _ImageClassifier.create_from_options(options) as classifier:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the image mode'):
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classifier.classify(self.test_image)
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def test_calling_classify_for_video_in_live_stream_mode(self):
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options = _ImageClassifierOptions(
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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 _ImageClassifier.create_from_options(options) as classifier:
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with self.assertRaisesRegex(ValueError,
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r'not initialized with the video mode'):
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classifier.classify_for_video(self.test_image, 0)
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def test_classify_async_calls_with_illegal_timestamp(self):
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classifier_options = _ClassifierOptions(max_results=4)
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options = _ImageClassifierOptions(
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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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classifier_options=classifier_options,
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result_callback=mock.MagicMock())
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with _ImageClassifier.create_from_options(options) as classifier:
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classifier.classify_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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classifier.classify_async(self.test_image, 0)
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# TODO: Fix the packet is empty issue.
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"""
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@parameterized.parameters((0, _EXPECTED_CLASSIFICATION_RESULT),
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(1, _EMPTY_CLASSIFICATION_RESULT))
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def test_classify_async_calls(self, threshold, expected_result):
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observed_timestamp_ms = -1
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def check_result(result: _ClassificationResult, output_image: _Image,
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timestamp_ms: int):
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self.assertEqual(result, expected_result)
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self.assertTrue(
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np.array_equal(output_image.numpy_view(),
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self.test_image.numpy_view()))
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self.assertLess(observed_timestamp_ms, timestamp_ms)
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self.observed_timestamp_ms = timestamp_ms
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classifier_options = _ClassifierOptions(
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max_results=4, score_threshold=threshold)
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options = _ImageClassifierOptions(
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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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classifier_options=classifier_options,
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result_callback=check_result)
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classifier = _ImageClassifier.create_from_options(options)
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for timestamp in range(0, 300, 30):
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classifier.classify_async(self.test_image, timestamp)
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classifier.close()
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"""
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if __name__ == '__main__':
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@ -43,6 +43,7 @@ _IMAGE_IN_STREAM_NAME = 'image_in'
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_IMAGE_OUT_STREAM_NAME = 'image_out'
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_IMAGE_TAG = 'IMAGE'
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_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph'
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_MICRO_SECONDS_PER_MILLISECOND = 1000
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@dataclasses.dataclass
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@ -91,7 +92,7 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
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"""Creates an `ImageClassifier` object from a TensorFlow Lite model and the default `ImageClassifierOptions`.
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Note that the created `ImageClassifier` instance is in image mode, for
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detecting objects on single image inputs.
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classifying objects on single image inputs.
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Args:
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model_path: Path to the model.
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@ -137,7 +138,8 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
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])
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image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
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timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
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options.result_callback(classification_result, image, timestamp)
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options.result_callback(classification_result, image,
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timestamp.value / _MICRO_SECONDS_PER_MILLISECOND)
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task_info = _TaskInfo(
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task_graph=_TASK_GRAPH_NAME,
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@ -156,7 +158,6 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
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_RunningMode.LIVE_STREAM), options.running_mode,
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packets_callback if options.result_callback else None)
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# TODO: Create an Image class for MediaPipe Tasks.
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def classify(
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self,
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image: image_module.Image,
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@ -206,8 +207,9 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
RuntimeError: If image classification failed to run.
|
||||
"""
|
||||
output_packets = self._process_video_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(timestamp_ms)
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
classification_result_proto = packet_getter.get_proto(
|
||||
output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
|
||||
|
|
@ -216,3 +218,36 @@ class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
|
|||
classifications_module.Classifications.create_from_pb2(classification)
|
||||
for classification in classification_result_proto.classifications
|
||||
])
|
||||
|
||||
def classify_async(self, image: image_module.Image, timestamp_ms: int) -> None:
|
||||
"""Sends live image data (an Image with a unique timestamp) to perform
|
||||
image classification.
|
||||
|
||||
Only use this method when the ImageClassifier is created with the live
|
||||
stream running mode. The input timestamps should be monotonically increasing
|
||||
for adjacent calls of this method. This method will return immediately after
|
||||
the input image is accepted. The results will be available via the
|
||||
`result_callback` provided in the `ImageClassifierOptions`. The
|
||||
`classify_async` method is designed to process live stream data such as
|
||||
camera input. To lower the overall latency, image classifier may drop the
|
||||
input images if needed. In other words, it's not guaranteed to have output
|
||||
per input image.
|
||||
|
||||
The `result_callback` provides:
|
||||
- A classification result object that contains a list of classifications.
|
||||
- The input image that the image classifier runs on.
|
||||
- The input timestamp in milliseconds.
|
||||
|
||||
Args:
|
||||
image: MediaPipe Image.
|
||||
timestamp_ms: The timestamp of the input image in milliseconds.
|
||||
|
||||
Raises:
|
||||
ValueError: If the current input timestamp is smaller than what the image
|
||||
classifier has already processed.
|
||||
"""
|
||||
self._send_live_stream_data({
|
||||
_IMAGE_IN_STREAM_NAME:
|
||||
packet_creator.create_image(image).at(
|
||||
timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
|
||||
})
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user