# Copyright 2022 The TensorFlow 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 parameterized
import tensorflow as tf
from tensorflow_lite_support.python.task.core import base_options as base_options_module
from tensorflow_lite_support.python.task.processor.proto import bounding_box_pb2
from tensorflow_lite_support.python.task.processor.proto import class_pb2
from tensorflow_lite_support.python.task.processor.proto import classification_options_pb2
from tensorflow_lite_support.python.task.processor.proto import classifications_pb2
from tensorflow_lite_support.python.task.vision import image_classifier
from tensorflow_lite_support.python.task.vision.core import tensor_image
from tensorflow_lite_support.python.test import test_util
_BaseOptions = base_options_module.BaseOptions
_Category = class_pb2.Category
_Classifications = classifications_pb2.Classifications
_ClassificationResult = classifications_pb2.ClassificationResult
_ImageClassifier = image_classifier.ImageClassifier
_ImageClassifierOptions = image_classifier.ImageClassifierOptions
_MODEL_FILE = 'mobilenet_v2_1.0_224.tflite'
_IMAGE_FILE = 'burger.jpg'
_EXPECTED_CLASSIFICATION_RESULT = _ClassificationResult(classifications=[
_Classifications(
categories=[
_Category(
index=934,
score=0.739974,
display_name='',
category_name='cheeseburger'),
_Category(
index=925,
score=0.026929,
display_name='',
category_name='guacamole'),
_Category(
index=932,
score=0.025737,
display_name='',
category_name='bagel')
],
head_index=0,
head_name='')
])
_ALLOW_LIST = ['cheeseburger', 'guacamole']
_DENY_LIST = ['cheeseburger']
_SCORE_THRESHOLD = 0.5
_MAX_RESULTS = 3
def _create_classifier_from_options(base_options, **classification_options):
classification_options = classification_options_pb2.ClassificationOptions(
**classification_options)
options = _ImageClassifierOptions(
base_options=base_options, classification_options=classification_options)
classifier = _ImageClassifier.create_from_options(options)
return classifier
class ModelFileType(enum.Enum):
FILE_CONTENT = 1
FILE_NAME = 2
class ImageClassifierTest(parameterized.TestCase, tf.test.TestCase):
def setUp(self):
super().setUp()
self.test_image_path = 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.
classifier = _ImageClassifier.create_from_file(self.model_path)
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)
classifier = _ImageClassifier.create_from_options(options)
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, 3, _EXPECTED_CLASSIFICATION_RESULT),
(ModelFileType.FILE_CONTENT, 3, _EXPECTED_CLASSIFICATION_RESULT))
def test_classify_model(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 = _create_classifier_from_options(
base_options, max_results=max_results)
# Loads image.
image = tensor_image.TensorImage.create_from_file(self.test_image_path)
# Classifies the input.
image_result = classifier.classify(image, bounding_box=None)
# Comparing results (classification w/o bounding box).
self.assertProtoEquals(image_result.to_pb2(),
expected_classification_result.to_pb2())
def test_classify_model_with_bounding_box(self):
# Creates classifier.
base_options = _BaseOptions(file_name=self.model_path)
classifier = _create_classifier_from_options(base_options, max_results=3)
# Loads image.
image = tensor_image.TensorImage.create_from_file(self.test_image_path)
# Bounding box in "burger.jpg" corresponding to "burger_crop.jpg".
bounding_box = bounding_box_pb2.BoundingBox(
origin_x=0, origin_y=0, width=400, height=325)
# Classifies the input.
image_result = classifier.classify(image, bounding_box)
# Expected results.
expected_classification_result = _ClassificationResult(classifications=[
_Classifications(
categories=[
_Category(
index=934,
score=0.881507,
display_name='',
category_name='cheeseburger'),
_Category(
index=925,
score=0.019457,
display_name='',
category_name='guacamole'),
_Category(
index=932,
score=0.012489,
display_name='',
category_name='bagel')
],
head_index=0,
head_name='')
])
# Comparing results (classification w/ bounding box).
self.assertProtoEquals(image_result.to_pb2(),
expected_classification_result.to_pb2())
def test_max_results_option(self):
# Creates classifier.
base_options = _BaseOptions(file_name=self.model_path)
classifier = _create_classifier_from_options(
base_options, max_results=_MAX_RESULTS)
# Loads image.
image = tensor_image.TensorImage.create_from_file(self.test_image_path)
# Classifies the input.
image_result = classifier.classify(image, bounding_box=None)
categories = image_result.classifications[0].categories
self.assertLessEqual(
len(categories), _MAX_RESULTS, 'Too many results returned.')
def test_score_threshold_option(self):
# Creates classifier.
base_options = _BaseOptions(file_name=self.model_path)
classifier = _create_classifier_from_options(
base_options, score_threshold=_SCORE_THRESHOLD)
# Loads image.
image = tensor_image.TensorImage.create_from_file(self.test_image_path)
# Classifies the input.
image_result = classifier.classify(image, bounding_box=None)
categories = image_result.classifications[0].categories
for category in categories:
self.assertGreaterEqual(
category.score, _SCORE_THRESHOLD,
f'Classification with score lower than threshold found. {category}')
def test_allowlist_option(self):
# Creates classifier.
base_options = _BaseOptions(file_name=self.model_path)
classifier = _create_classifier_from_options(
base_options, category_name_allowlist=_ALLOW_LIST)
# Loads image.
image = tensor_image.TensorImage.create_from_file(self.test_image_path)
# Classifies the input.
image_result = classifier.classify(image, bounding_box=None)
categories = image_result.classifications[0].categories
for category in categories:
label = category.category_name
self.assertIn(label, _ALLOW_LIST,
f'Label {label} found but not in label allow list')
def test_denylist_option(self):
# Creates classifier.
base_options = _BaseOptions(file_name=self.model_path)
classifier = _create_classifier_from_options(
base_options, score_threshold=0.01, category_name_denylist=_DENY_LIST)
# Loads image
image = tensor_image.TensorImage.create_from_file(self.test_image_path)
# Classifies the input.
image_result = classifier.classify(image, bounding_box=None)
categories = image_result.classifications[0].categories
for category in categories:
label = category.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'`class_name_whitelist` and `class_name_blacklist` are mutually '
r'exclusive options.'):
base_options = _BaseOptions(file_name=self.model_path)
classification_options = classification_options_pb2.ClassificationOptions(
category_name_allowlist=['foo'], category_name_denylist=['bar'])
options = _ImageClassifierOptions(
base_options=base_options,
classification_options=classification_options)
_ImageClassifier.create_from_options(options)
if __name__ == '__main__':
tf.test.main()
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