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Sebastian Schmidt 2022-09-26 13:56:54 -06:00
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18
mediapipe/tasks/python/test/vision/BUILD
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@ -19,3 +19,21 @@ package(default_visibility = ["//mediapipe/tasks:internal"])
licenses(["notice"]) licenses(["notice"])
# TODO: This test fails in OSS # TODO: This test fails in OSS
py_test(
name = "image_classification_test",
srcs = ["image_classification_test.py"],
data = [
"//mediapipe/tasks/testdata/vision:test_images",
"//mediapipe/tasks/testdata/vision:test_models",
],
deps = [
"//mediapipe/tasks/python/components/containers:category",
"//mediapipe/tasks/python/components/containers:classifications",
"//mediapipe/tasks/python/core:base_options",
"//mediapipe/tasks/python/test:test_util",
"//mediapipe/tasks/python/vision:image_classification",
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
"@absl_py//absl/testing:parameterized",
],
)

291
mediapipe/tasks/python/test/vision/image_classification_test.py Normal file
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@ -0,0 +1,291 @@
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tests for image classifier."""
import enum
from absl.testing import absltest
from absl.testing import parameterized
from mediapipe.python._framework_bindings import image as image_module
from mediapipe.tasks.python.components import classifier_options
from mediapipe.tasks.python.components.containers import category as category_module
from mediapipe.tasks.python.components.containers import classifications as classifications_module
from mediapipe.tasks.python.core import base_options as base_options_module
from mediapipe.tasks.python.test import test_util
from mediapipe.tasks.python.vision import image_classification
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
_BaseOptions = base_options_module.BaseOptions
_ClassifierOptions = classifier_options.ClassifierOptions
_Category = category_module.Category
_ClassificationEntry = classifications_module.ClassificationEntry
_Classifications = classifications_module.Classifications
_ClassificationResult = classifications_module.ClassificationResult
_Image = image_module.Image
_ImageClassifier = image_classification.ImageClassifier
_ImageClassifierOptions = image_classification.ImageClassifierOptions
_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
_IMAGE_FILE = 'burger.jpg'
_EXPECTED_CLASSIFICATION_RESULT = _ClassificationResult(
classifications=[
_Classifications(
entries=[
_ClassificationEntry(
categories=[
_Category(
index=934,
score=0.7952049970626831,
display_name='',
category_name='cheeseburger'),
_Category(
index=932,
score=0.02732999622821808,
display_name='',
category_name='bagel'),
_Category(
index=925,
score=0.01933487318456173,
display_name='',
category_name='guacamole'),
_Category(
index=963,
score=0.006279350258409977,
display_name='',
category_name='meat loaf')
],
timestamp_ms=0
)
],
head_index=0,
head_name='probability')
])
_ALLOW_LIST = ['cheeseburger', 'guacamole']
_DENY_LIST = ['cheeseburger']
_SCORE_THRESHOLD = 0.5
_MAX_RESULTS = 3
class ModelFileType(enum.Enum):
FILE_CONTENT = 1
FILE_NAME = 2
class ImageClassifierTest(parameterized.TestCase):
def setUp(self):
super().setUp()
self.test_image = test_util.read_test_image(
test_util.get_test_data_path(_IMAGE_FILE))
self.model_path = test_util.get_test_data_path(_MODEL_FILE)
def test_create_from_file_succeeds_with_valid_model_path(self):
# Creates with default option and valid model file successfully.
with _ImageClassifier.create_from_model_path(self.model_path) as classifier:
self.assertIsInstance(classifier, _ImageClassifier)
def test_create_from_options_succeeds_with_valid_model_path(self):
# Creates with options containing model file successfully.
base_options = _BaseOptions(file_name=self.model_path)
options = _ImageClassifierOptions(base_options=base_options)
with _ImageClassifier.create_from_options(options) as classifier:
self.assertIsInstance(classifier, _ImageClassifier)
def test_create_from_options_fails_with_invalid_model_path(self):
# Invalid empty model path.
with self.assertRaisesRegex(
ValueError,
r"ExternalFile must specify at least one of 'file_content', "
r"'file_name' or 'file_descriptor_meta'."):
base_options = _BaseOptions(file_name='')
options = _ImageClassifierOptions(base_options=base_options)
_ImageClassifier.create_from_options(options)
def test_create_from_options_succeeds_with_valid_model_content(self):
# Creates with options containing model content successfully.
with open(self.model_path, 'rb') as f:
base_options = _BaseOptions(file_content=f.read())
options = _ImageClassifierOptions(base_options=base_options)
classifier = _ImageClassifier.create_from_options(options)
self.assertIsInstance(classifier, _ImageClassifier)
@parameterized.parameters(
(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
def test_classify(self, model_file_type, max_results,
expected_classification_result):
# Creates classifier.
if model_file_type is ModelFileType.FILE_NAME:
base_options = _BaseOptions(file_name=self.model_path)
elif model_file_type is ModelFileType.FILE_CONTENT:
with open(self.model_path, 'rb') as f:
model_content = f.read()
base_options = _BaseOptions(file_content=model_content)
else:
# Should never happen
raise ValueError('model_file_type is invalid.')
classifier_options = _ClassifierOptions(max_results=max_results)
options = _ImageClassifierOptions(
base_options=base_options, classifier_options=classifier_options)
classifier = _ImageClassifier.create_from_options(options)
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
# Comparing results.
self.assertEqual(image_result, expected_classification_result)
# Closes the classifier explicitly when the classifier is not used in
# a context.
classifier.close()
@parameterized.parameters(
(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
def test_classify_in_context(self, model_file_type, max_results,
expected_classification_result):
if model_file_type is ModelFileType.FILE_NAME:
base_options = _BaseOptions(file_name=self.model_path)
elif model_file_type is ModelFileType.FILE_CONTENT:
with open(self.model_path, 'rb') as f:
model_content = f.read()
base_options = _BaseOptions(file_content=model_content)
else:
# Should never happen
raise ValueError('model_file_type is invalid.')
classifier_options = _ClassifierOptions(max_results=max_results)
options = _ImageClassifierOptions(
base_options=base_options, classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs object detection on the input.
image_result = classifier.classify(self.test_image)
# Comparing results.
self.assertEqual(image_result, expected_classification_result)
def test_score_threshold_option(self):
classifier_options = _ClassifierOptions(score_threshold=_SCORE_THRESHOLD)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
classifications = image_result.classifications
for classification in classifications:
for entry in classification.entries:
score = entry.categories[0].score
self.assertGreaterEqual(
score, _SCORE_THRESHOLD,
f'Classification with score lower than threshold found. '
f'{classification}')
def test_max_results_option(self):
classifier_options = _ClassifierOptions(score_threshold=_SCORE_THRESHOLD)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
categories = image_result.classifications[0].entries[0].categories
self.assertLessEqual(
len(categories), _MAX_RESULTS, 'Too many results returned.')
def test_allow_list_option(self):
classifier_options = _ClassifierOptions(category_allowlist=_ALLOW_LIST)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
classifications = image_result.classifications
for classification in classifications:
for entry in classification.entries:
label = entry.categories[0].category_name
self.assertIn(label, _ALLOW_LIST,
f'Label {label} found but not in label allow list')
def test_deny_list_option(self):
classifier_options = _ClassifierOptions(category_denylist=_DENY_LIST)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
classifications = image_result.classifications
for classification in classifications:
for entry in classification.entries:
label = entry.categories[0].category_name
self.assertNotIn(label, _DENY_LIST,
f'Label {label} found but in deny list.')
def test_combined_allowlist_and_denylist(self):
# Fails with combined allowlist and denylist
with self.assertRaisesRegex(
ValueError,
r'`category_allowlist` and `category_denylist` are mutually '
r'exclusive options.'):
classifier_options = _ClassifierOptions(category_allowlist=['foo'],
category_denylist=['bar'])
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as unused_classifier:
pass
def test_empty_classification_outputs(self):
classifier_options = _ClassifierOptions(score_threshold=1)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
self.assertEmpty(image_result.classifications[0].entries[0].categories)
def test_missing_result_callback(self):
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
running_mode=_RUNNING_MODE.LIVE_STREAM)
with self.assertRaisesRegex(ValueError,
r'result callback must be provided'):
with _ImageClassifier.create_from_options(options) as unused_classifier:
pass
@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
def test_illegal_result_callback(self, running_mode):
def pass_through(unused_result: _ClassificationResult):
pass
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
running_mode=running_mode,
result_callback=pass_through)
with self.assertRaisesRegex(ValueError,
r'result callback should not be provided'):
with _ImageClassifier.create_from_options(options) as unused_classifier:
pass
if __name__ == '__main__':
absltest.main()

20
mediapipe/tasks/python/vision/core/BUILD
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@ -35,3 +35,23 @@ py_library(
"//mediapipe/tasks/python/core:optional_dependencies", "//mediapipe/tasks/python/core:optional_dependencies",
], ],
) )
py_library(
name = "image_classification",
srcs = [
"image_classification.py",
],
deps = [
"//mediapipe/python:_framework_bindings",
"//mediapipe/python:packet_creator",
"//mediapipe/python:packet_getter",
"//mediapipe/tasks/cc/vision/image_classification:image_classifier_options_py_pb2",
"//mediapipe/tasks/python/components:classifier_options",
"//mediapipe/tasks/python/components/containers:classifications",
"//mediapipe/tasks/python/core:base_options",
"//mediapipe/tasks/python/core:optional_dependencies",
"//mediapipe/tasks/python/core:task_info",
"//mediapipe/tasks/python/vision/core:base_vision_task_api",
"//mediapipe/tasks/python/vision/core:vision_task_running_mode",
],
)

180
mediapipe/tasks/python/vision/core/image_classification.py Normal file
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@ -0,0 +1,180 @@
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""MediaPipe image classifier task."""
import dataclasses
from typing import Callable, List, Mapping, Optional
from mediapipe.python import packet_creator
from mediapipe.python import packet_getter
from mediapipe.python._framework_bindings import image as image_module
from mediapipe.python._framework_bindings import packet as packet_module
from mediapipe.python._framework_bindings import task_runner as task_runner_module
from mediapipe.tasks.cc.vision.image_classification import image_classifier_options_pb2
from mediapipe.tasks.python.components import classifier_options
from mediapipe.tasks.python.components.containers import classifications as classifications_module
from mediapipe.tasks.python.core import base_options as base_options_module
from mediapipe.tasks.python.core import task_info as task_info_module
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
from mediapipe.tasks.python.vision.core import base_vision_task_api
from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
_BaseOptions = base_options_module.BaseOptions
_ImageClassifierOptionsProto = image_classifier_options_pb2.ImageClassifierOptions
_ClassifierOptions = classifier_options.ClassifierOptions
_RunningMode = running_mode_module.VisionTaskRunningMode
_TaskInfo = task_info_module.TaskInfo
_TaskRunner = task_runner_module.TaskRunner
_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
_CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT'
_IMAGE_IN_STREAM_NAME = 'image_in'
_IMAGE_TAG = 'IMAGE'
_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.ImageClassifierGraph'
@dataclasses.dataclass
class ImageClassifierOptions:
"""Options for the image classifier task.
Attributes:
base_options: Base options for the image classifier task.
running_mode: The running mode of the task. Default to the image mode.
Image classifier task has three running modes:
1) The image mode for classifying objects on single image inputs.
2) The video mode for classifying objects on the decoded frames of a
video.
3) The live stream mode for classifying objects on a live stream of input
data, such as from camera.
display_names_locale: The locale to use for display names specified through
the TFLite Model Metadata.
max_results: The maximum number of top-scored classification results to
return.
score_threshold: Overrides the ones provided in the model metadata. Results
below this value are rejected.
category_allowlist: Allowlist of category names. If non-empty, detection
results whose category name is not in this set will be filtered out.
Duplicate or unknown category names are ignored. Mutually exclusive with
`category_denylist`.
category_denylist: Denylist of category names. If non-empty, detection
results whose category name is in this set will be filtered out. Duplicate
or unknown category names are ignored. Mutually exclusive with
`category_allowlist`.
result_callback: The user-defined result callback for processing live stream
data. The result callback should only be specified when the running mode
is set to the live stream mode.
"""
base_options: _BaseOptions
running_mode: _RunningMode = _RunningMode.IMAGE
classifier_options: _ClassifierOptions = _ClassifierOptions()
result_callback: Optional[
Callable[[classifications_module.ClassificationResult],
None]] = None
@doc_controls.do_not_generate_docs
def to_pb2(self) -> _ImageClassifierOptionsProto:
"""Generates an ImageClassifierOptions protobuf object."""
base_options_proto = self.base_options.to_pb2()
base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
classifier_options_proto = self.classifier_options.to_pb2()
return _ImageClassifierOptionsProto(
base_options=base_options_proto,
classifier_options=classifier_options_proto
)
class ImageClassifier(base_vision_task_api.BaseVisionTaskApi):
"""Class that performs image classification on images."""
@classmethod
def create_from_model_path(cls, model_path: str) -> 'ImageClassifier':
"""Creates an `ImageClassifier` object from a TensorFlow Lite model and the default `ImageClassifierOptions`.
Note that the created `ImageClassifier` instance is in image mode, for
detecting objects on single image inputs.
Args:
model_path: Path to the model.
Returns:
`ImageClassifier` object that's created from the model file and the default
`ImageClassifierOptions`.
Raises:
ValueError: If failed to create `ImageClassifier` object from the provided
file such as invalid file path.
RuntimeError: If other types of error occurred.
"""
base_options = _BaseOptions(file_name=model_path)
options = ImageClassifierOptions(
base_options=base_options, running_mode=_RunningMode.IMAGE)
return cls.create_from_options(options)
@classmethod
def create_from_options(cls,
options: ImageClassifierOptions) -> 'ImageClassifier':
"""Creates the `ImageClassifier` object from image classifier options.
Args:
options: Options for the image classifier task.
Returns:
`ImageClassifier` object that's created from `options`.
Raises:
ValueError: If failed to create `ImageClassifier` object from
`ImageClassifierOptions` such as missing the model.
RuntimeError: If other types of error occurred.
"""
def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
classification_result_proto = packet_getter.get_proto(
output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
classification_result = classifications_module.ClassificationResult([
classifications_module.Classifications.create_from_pb2(classification)
for classification in classification_result_proto.classifications
])
options.result_callback(classification_result)
task_info = _TaskInfo(
task_graph=_TASK_GRAPH_NAME,
input_streams=[':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME])],
output_streams=[
':'.join([_CLASSIFICATION_RESULT_TAG,
_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
],
task_options=options)
return cls(
task_info.generate_graph_config(
enable_flow_limiting=options.running_mode ==
_RunningMode.LIVE_STREAM), options.running_mode,
packets_callback if options.result_callback else None)
# TODO: Create an Image class for MediaPipe Tasks.
def classify(
self,
image: image_module.Image
) -> classifications_module.ClassificationResult:
"""Performs image classification on the provided MediaPipe Image.
Args:
image: MediaPipe Image.
Returns:
A classification result object that contains a list of classifications.
Raises:
ValueError: If any of the input arguments is invalid.
RuntimeError: If image classification failed to run.
"""
output_packets = self._process_image_data(
{_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
classification_result_proto = packet_getter.get_proto(
output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME])
return classifications_module.ClassificationResult([
classifications_module.Classifications.create_from_pb2(classification)
for classification in classification_result_proto.classifications
])