127 lines
4.6 KiB
Python
127 lines
4.6 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.processors.proto import classifier_options
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from mediapipe.tasks.python.components.containers.proto import category as category_module
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from mediapipe.tasks.python.components.containers.proto 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_utils
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from mediapipe.tasks.python.vision import image_classifier
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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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_ClassifierOptions = classifier_options.ClassifierOptions
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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_classifier.ImageClassifier
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_ImageClassifierOptions = image_classifier.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=0.7939587831497192,
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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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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 = _Image.create_from_file(
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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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@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(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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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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if __name__ == '__main__':
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absltest.main()
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