658 lines
25 KiB
Python
658 lines
25 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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import os
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from unittest import mock
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from absl.testing import absltest
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from absl.testing import parameterized
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import numpy as np
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from mediapipe.python._framework_bindings import image
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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 classification_result as classification_result_module
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from mediapipe.tasks.python.components.containers import rect
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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 image_processing_options as image_processing_options_module
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from mediapipe.tasks.python.vision.core import vision_task_running_mode
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ImageClassifierResult = classification_result_module.ClassificationResult
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_Rect = rect.Rect
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_BaseOptions = base_options_module.BaseOptions
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_Category = category_module.Category
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_Classifications = classification_result_module.Classifications
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_Image = image.Image
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_ImageClassifier = image_classifier.ImageClassifier
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_ImageClassifierOptions = image_classifier.ImageClassifierOptions
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_RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
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_IMAGE_FILE = 'burger.jpg'
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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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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
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def _generate_empty_results() -> ImageClassifierResult:
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return ImageClassifierResult(
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classifications=[
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_Classifications(categories=[], head_index=0, head_name='probability')
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],
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timestamp_ms=0,
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)
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def _generate_burger_results(timestamp_ms=0) -> ImageClassifierResult:
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return ImageClassifierResult(
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classifications=[
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_Classifications(
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categories=[
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_Category(
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index=934,
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score=0.793959,
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display_name='',
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category_name='cheeseburger',
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),
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_Category(
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index=932,
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score=0.0273929,
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display_name='',
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category_name='bagel',
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),
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_Category(
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index=925,
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score=0.0193408,
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display_name='',
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category_name='guacamole',
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),
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_Category(
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index=963,
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score=0.00632786,
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display_name='',
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category_name='meat loaf',
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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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],
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timestamp_ms=timestamp_ms,
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)
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def _generate_soccer_ball_results(timestamp_ms=0) -> ImageClassifierResult:
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return ImageClassifierResult(
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classifications=[
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_Classifications(
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categories=[
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_Category(
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index=806,
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score=0.996527,
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display_name='',
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category_name='soccer ball',
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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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],
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timestamp_ms=timestamp_ms,
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)
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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(os.path.join(_TEST_DATA_DIR, _IMAGE_FILE))
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)
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self.model_path = test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, _MODEL_FILE)
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)
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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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with self.assertRaisesRegex(
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RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'
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):
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base_options = _BaseOptions(
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model_asset_path='/path/to/invalid/model.tflite'
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)
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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, _generate_burger_results()),
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(ModelFileType.FILE_CONTENT, 4, _generate_burger_results()),
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)
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def test_classify(
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self, model_file_type, max_results, expected_classification_result
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):
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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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options = _ImageClassifierOptions(
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base_options=base_options, max_results=max_results
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)
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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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test_utils.assert_proto_equals(
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self, image_result.to_pb2(), expected_classification_result.to_pb2()
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)
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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, _generate_burger_results()),
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(ModelFileType.FILE_CONTENT, 4, _generate_burger_results()),
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)
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def test_classify_in_context(
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self, model_file_type, max_results, expected_classification_result
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):
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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 = _ImageClassifierOptions(
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base_options=base_options, max_results=max_results
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)
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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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test_utils.assert_proto_equals(
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self, image_result.to_pb2(), expected_classification_result.to_pb2()
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)
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def test_classify_succeeds_with_region_of_interest(self):
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _ImageClassifierOptions(base_options=base_options, max_results=1)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Load the test image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')
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)
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)
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# Region-of-interest around the soccer ball.
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roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
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image_processing_options = _ImageProcessingOptions(roi)
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# Performs image classification on the input.
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image_result = classifier.classify(test_image, image_processing_options)
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# Comparing results.
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test_utils.assert_proto_equals(
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self, image_result.to_pb2(), _generate_soccer_ball_results().to_pb2()
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)
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def test_classify_succeeds_with_rotation(self):
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _ImageClassifierOptions(base_options=base_options, max_results=3)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Load the test image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, 'burger_rotated.jpg')
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)
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)
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# Specify a 90° anti-clockwise rotation.
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image_processing_options = _ImageProcessingOptions(None, -90)
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# Performs image classification on the input.
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image_result = classifier.classify(test_image, image_processing_options)
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# Comparing results.
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expected = ImageClassifierResult(
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classifications=[
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_Classifications(
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categories=[
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_Category(
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index=934,
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score=0.754467,
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display_name='',
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category_name='cheeseburger',
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),
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_Category(
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index=925,
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score=0.0288028,
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display_name='',
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category_name='guacamole',
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),
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_Category(
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index=932,
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score=0.0286119,
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display_name='',
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category_name='bagel',
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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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],
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timestamp_ms=0,
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)
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test_utils.assert_proto_equals(
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self, image_result.to_pb2(), expected.to_pb2()
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)
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def test_classify_succeeds_with_region_of_interest_and_rotation(self):
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _ImageClassifierOptions(base_options=base_options, max_results=1)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Load the test image.
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test_image = _Image.create_from_file(
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test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, 'multi_objects_rotated.jpg')
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)
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)
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# Region-of-interest around the soccer ball, with 90° anti-clockwise
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# rotation.
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roi = _Rect(left=0.2655, top=0.45, right=0.6925, bottom=0.614)
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image_processing_options = _ImageProcessingOptions(roi, -90)
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# Performs image classification on the input.
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image_result = classifier.classify(test_image, image_processing_options)
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# Comparing results.
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expected = ImageClassifierResult(
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classifications=[
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_Classifications(
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categories=[
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_Category(
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index=806,
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score=0.997684,
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display_name='',
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category_name='soccer ball',
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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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],
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timestamp_ms=0,
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)
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test_utils.assert_proto_equals(
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self, image_result.to_pb2(), expected.to_pb2()
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)
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def test_score_threshold_option(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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score_threshold=_SCORE_THRESHOLD,
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)
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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 category in classification.categories:
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score = category.score
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self.assertGreaterEqual(
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score,
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_SCORE_THRESHOLD,
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(
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'Classification with score lower than threshold found. '
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f'{classification}'
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),
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)
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def test_max_results_option(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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score_threshold=_SCORE_THRESHOLD,
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)
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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].categories
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self.assertLessEqual(
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len(categories), _MAX_RESULTS, 'Too many results returned.'
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)
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def test_allow_list_option(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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category_allowlist=_ALLOW_LIST,
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)
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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 category in classification.categories:
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label = category.category_name
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self.assertIn(
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label,
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_ALLOW_LIST,
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f'Label {label} found but not in label allow list',
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)
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def test_deny_list_option(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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category_denylist=_DENY_LIST,
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)
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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 category in classification.categories:
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label = category.category_name
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self.assertNotIn(
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label, _DENY_LIST, f'Label {label} found but in deny list.'
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)
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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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):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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category_allowlist=['foo'],
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category_denylist=['bar'],
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)
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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(model_asset_path=self.model_path),
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score_threshold=1,
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)
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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].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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)
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with self.assertRaisesRegex(
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ValueError, r'result callback must be provided'
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):
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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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)
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with self.assertRaisesRegex(
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ValueError, r'result callback should not be provided'
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):
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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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)
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with _ImageClassifier.create_from_options(options) as classifier:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the video mode'
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):
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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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)
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with _ImageClassifier.create_from_options(options) as classifier:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the live stream mode'
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):
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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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)
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with _ImageClassifier.create_from_options(options) as classifier:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the image mode'
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):
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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),
|
|
running_mode=_RUNNING_MODE.VIDEO,
|
|
)
|
|
with _ImageClassifier.create_from_options(options) as classifier:
|
|
with self.assertRaisesRegex(
|
|
ValueError, r'not initialized with the live stream mode'
|
|
):
|
|
classifier.classify_async(self.test_image, 0)
|
|
|
|
def test_classify_for_video_with_out_of_order_timestamp(self):
|
|
options = _ImageClassifierOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.VIDEO,
|
|
)
|
|
with _ImageClassifier.create_from_options(options) as classifier:
|
|
unused_result = classifier.classify_for_video(self.test_image, 1)
|
|
with self.assertRaisesRegex(
|
|
ValueError, r'Input timestamp must be monotonically increasing'
|
|
):
|
|
classifier.classify_for_video(self.test_image, 0)
|
|
|
|
def test_classify_for_video(self):
|
|
options = _ImageClassifierOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.VIDEO,
|
|
max_results=4,
|
|
)
|
|
with _ImageClassifier.create_from_options(options) as classifier:
|
|
for timestamp in range(0, 300, 30):
|
|
classification_result = classifier.classify_for_video(
|
|
self.test_image, timestamp
|
|
)
|
|
test_utils.assert_proto_equals(
|
|
self,
|
|
classification_result.to_pb2(),
|
|
_generate_burger_results(timestamp).to_pb2(),
|
|
)
|
|
|
|
def test_classify_for_video_succeeds_with_region_of_interest(self):
|
|
options = _ImageClassifierOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.VIDEO,
|
|
max_results=1,
|
|
)
|
|
with _ImageClassifier.create_from_options(options) as classifier:
|
|
# Load the test image.
|
|
test_image = _Image.create_from_file(
|
|
test_utils.get_test_data_path(
|
|
os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')
|
|
)
|
|
)
|
|
# Region-of-interest around the soccer ball.
|
|
roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
|
|
image_processing_options = _ImageProcessingOptions(roi)
|
|
for timestamp in range(0, 300, 30):
|
|
classification_result = classifier.classify_for_video(
|
|
test_image, timestamp, image_processing_options
|
|
)
|
|
test_utils.assert_proto_equals(
|
|
self,
|
|
classification_result.to_pb2(),
|
|
_generate_soccer_ball_results(timestamp).to_pb2(),
|
|
)
|
|
|
|
def test_calling_classify_in_live_stream_mode(self):
|
|
options = _ImageClassifierOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
|
result_callback=mock.MagicMock(),
|
|
)
|
|
with _ImageClassifier.create_from_options(options) as classifier:
|
|
with self.assertRaisesRegex(
|
|
ValueError, r'not initialized with the image mode'
|
|
):
|
|
classifier.classify(self.test_image)
|
|
|
|
def test_calling_classify_for_video_in_live_stream_mode(self):
|
|
options = _ImageClassifierOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
|
result_callback=mock.MagicMock(),
|
|
)
|
|
with _ImageClassifier.create_from_options(options) as classifier:
|
|
with self.assertRaisesRegex(
|
|
ValueError, r'not initialized with the video mode'
|
|
):
|
|
classifier.classify_for_video(self.test_image, 0)
|
|
|
|
def test_classify_async_calls_with_illegal_timestamp(self):
|
|
options = _ImageClassifierOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
|
max_results=4,
|
|
result_callback=mock.MagicMock(),
|
|
)
|
|
with _ImageClassifier.create_from_options(options) as classifier:
|
|
classifier.classify_async(self.test_image, 100)
|
|
with self.assertRaisesRegex(
|
|
ValueError, r'Input timestamp must be monotonically increasing'
|
|
):
|
|
classifier.classify_async(self.test_image, 0)
|
|
|
|
@parameterized.parameters(
|
|
(0, _generate_burger_results()), (1, _generate_empty_results())
|
|
)
|
|
def test_classify_async_calls(self, threshold, expected_result):
|
|
observed_timestamp_ms = -1
|
|
|
|
def check_result(
|
|
result: ImageClassifierResult, output_image: _Image, timestamp_ms: int
|
|
):
|
|
test_utils.assert_proto_equals(
|
|
self, result.to_pb2(), expected_result.to_pb2()
|
|
)
|
|
self.assertTrue(
|
|
np.array_equal(
|
|
output_image.numpy_view(), self.test_image.numpy_view()
|
|
)
|
|
)
|
|
self.assertLess(observed_timestamp_ms, timestamp_ms)
|
|
self.observed_timestamp_ms = timestamp_ms
|
|
|
|
options = _ImageClassifierOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
|
max_results=4,
|
|
score_threshold=threshold,
|
|
result_callback=check_result,
|
|
)
|
|
with _ImageClassifier.create_from_options(options) as classifier:
|
|
classifier.classify_async(self.test_image, 0)
|
|
|
|
def test_classify_async_succeeds_with_region_of_interest(self):
|
|
# Load the test image.
|
|
test_image = _Image.create_from_file(
|
|
test_utils.get_test_data_path(
|
|
os.path.join(_TEST_DATA_DIR, 'multi_objects.jpg')
|
|
)
|
|
)
|
|
# Region-of-interest around the soccer ball.
|
|
roi = _Rect(left=0.45, top=0.3075, right=0.614, bottom=0.7345)
|
|
image_processing_options = _ImageProcessingOptions(roi)
|
|
observed_timestamp_ms = -1
|
|
|
|
def check_result(
|
|
result: ImageClassifierResult, output_image: _Image, timestamp_ms: int
|
|
):
|
|
test_utils.assert_proto_equals(
|
|
self, result.to_pb2(), _generate_soccer_ball_results(100).to_pb2()
|
|
)
|
|
self.assertEqual(output_image.width, test_image.width)
|
|
self.assertEqual(output_image.height, test_image.height)
|
|
self.assertLess(observed_timestamp_ms, timestamp_ms)
|
|
self.observed_timestamp_ms = timestamp_ms
|
|
|
|
options = _ImageClassifierOptions(
|
|
base_options=_BaseOptions(model_asset_path=self.model_path),
|
|
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
|
max_results=1,
|
|
result_callback=check_result,
|
|
)
|
|
with _ImageClassifier.create_from_options(options) as classifier:
|
|
classifier.classify_async(test_image, 100, image_processing_options)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
absltest.main()
|