Module brevettiai.tests.test_data_image

Classes

class TestImageAugmentation (methodName='runTest')

A class whose instances are single test cases.

By default, the test code itself should be placed in a method named 'runTest'.

If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.

Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test's environment ('fixture') can be implemented by overriding the 'setUp' and 'tearDown' methods respectively.

If it is necessary to override the init method, the base class init method must always be called. It is important that subclasses should not change the signature of their init method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.

When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when the instance's assertion methods fail; test methods raising this exception will be deemed to have 'failed' rather than 'errored'. * longMessage: determines whether long messages (including repr of objects used in assert methods) will be printed on failure in addition to any explicit message passed. * maxDiff: sets the maximum length of a diff in failure messages by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

Expand source code

class TestImageAugmentation(unittest.TestCase):
    def test_augmentation_config(self):
        test_img = dict(img=tf.random.uniform((2, 160, 120, 3), dtype=tf.float32))
        init_settings = {
            "random_transformer": {"translate_horizontal": 0.2},
            "image_noise": {"hue": 0.5}
        }

        img_aug = ImageAugmenter.from_settings(deepcopy(init_settings))

        sh = test_img["img"].shape
        img = img_aug(test_img, seed=0)
        assert img["img"].shape == sh

        config = img_aug.get_config()
        for kk, ss in init_settings.items():
            for kk2 in ss:
                assert config[kk][kk2] == ss[kk2]

    def test_image_noise(self):
        test_img = tf.image.decode_bmp(tf.io.read_file(get_resource("0_1543413266626.bmp"), -1))
        test_img = tf.cast(test_img, tf.float32)[None] * [0.5, .1, .9] / 255

        aug_noise_config = {'brightness': 0.25,
                            'contrast': [0.25, 0.5],
                            'hue': 0.5,
                            'saturation': [1.0, 2.0],
                            'stddev': 0.01,
                            'chance': 0.99}
        aug_noise = ImageNoise.from_config(aug_noise_config)
        image_noise = aug_noise(test_img, seed=42)
        assert image_noise is not None
        # TODO: With tensorflow 2.4.0 stateless_uniform and stateless_random_brightness etc
        # Test that image_noise does not change

    def test_image_filtering(self):
        test_img = tf.image.decode_bmp(tf.io.read_file(get_resource("0_1543413266626.bmp"), -1))
        test_img = tf.cast(test_img, tf.float32)[None] * [0.5, .1, .9] / 255
        aug_filter_config = {'emboss_strength': (1.0, 1.25),
                             'avg_blur': (3, 3),
                             'gaussian_blur_sigma': .31,
                             'chance': 0.99}
        aug_filter = ImageFiltering.from_config(aug_filter_config)
        image_filter = aug_filter(test_img, seed=42)
        assert image_filter is not None
        # TODO: With tensorflow 2.4.0 stateless_uniform and stateless_random_brightness etc
        # Test that image_noise does not change

    def test_image_deformation(self):
        test_img = tf.image.decode_bmp(tf.io.read_file(get_resource("0_1543413266626.bmp"), -1))
        test_img = tf.cast(test_img, tf.float32)[None] * [0.5, .1, .9] / 255
        aug_deformation_config = {'alpha': 35.0, 'sigma': 5.0, 'chance': 0.99}
        aug_deformation = ImageDeformation.from_config(aug_deformation_config)
        deformations, probabilities = aug_deformation(tf.shape(test_img), 42)
        image_deformation = aug_deformation.apply(test_img, deformations, probabilities)
        assert image_deformation is not None
        # TODO: With tensorflow 2.4.0 stateless_uniform and stateless_random_brightness etc
        # Test that image_noise does not change

    def test_image_transformation(self):
        test_img = tf.image.decode_bmp(tf.io.read_file(get_resource("0_1543413266626.bmp"), -1))
        test_img = tf.cast(test_img, tf.float32)[None] * [0.5, .7, .9] / 255
        aug_transformer_config = {
            "chance": 0.5,
            "flip_up_down": False,
            "flip_left_right": True,
            "scale": 0.1,
            "rotate": 0,
            "translate_horizontal": 0.1,
            "translate_vertical": 0.1,
            "shear": 0.04,
            "interpolation": "bilinear"
        }
        aug_transformation = RandomTransformer.from_config(aug_transformer_config)
        A = aug_transformation(test_img.shape, 42)
        image_transformation = aug_transformation.transform_images(test_img, A)

        assert image_transformation is not None

Ancestors

• unittest.case.TestCase

Methods

def test_augmentation_config(self)

Expand source code

def test_augmentation_config(self):
    test_img = dict(img=tf.random.uniform((2, 160, 120, 3), dtype=tf.float32))
    init_settings = {
        "random_transformer": {"translate_horizontal": 0.2},
        "image_noise": {"hue": 0.5}
    }

    img_aug = ImageAugmenter.from_settings(deepcopy(init_settings))

    sh = test_img["img"].shape
    img = img_aug(test_img, seed=0)
    assert img["img"].shape == sh

    config = img_aug.get_config()
    for kk, ss in init_settings.items():
        for kk2 in ss:
            assert config[kk][kk2] == ss[kk2]

def test_image_deformation(self)

Expand source code

def test_image_deformation(self):
    test_img = tf.image.decode_bmp(tf.io.read_file(get_resource("0_1543413266626.bmp"), -1))
    test_img = tf.cast(test_img, tf.float32)[None] * [0.5, .1, .9] / 255
    aug_deformation_config = {'alpha': 35.0, 'sigma': 5.0, 'chance': 0.99}
    aug_deformation = ImageDeformation.from_config(aug_deformation_config)
    deformations, probabilities = aug_deformation(tf.shape(test_img), 42)
    image_deformation = aug_deformation.apply(test_img, deformations, probabilities)
    assert image_deformation is not None

def test_image_filtering(self)

Expand source code

def test_image_filtering(self):
    test_img = tf.image.decode_bmp(tf.io.read_file(get_resource("0_1543413266626.bmp"), -1))
    test_img = tf.cast(test_img, tf.float32)[None] * [0.5, .1, .9] / 255
    aug_filter_config = {'emboss_strength': (1.0, 1.25),
                         'avg_blur': (3, 3),
                         'gaussian_blur_sigma': .31,
                         'chance': 0.99}
    aug_filter = ImageFiltering.from_config(aug_filter_config)
    image_filter = aug_filter(test_img, seed=42)
    assert image_filter is not None

def test_image_noise(self)

Expand source code

def test_image_noise(self):
    test_img = tf.image.decode_bmp(tf.io.read_file(get_resource("0_1543413266626.bmp"), -1))
    test_img = tf.cast(test_img, tf.float32)[None] * [0.5, .1, .9] / 255

    aug_noise_config = {'brightness': 0.25,
                        'contrast': [0.25, 0.5],
                        'hue': 0.5,
                        'saturation': [1.0, 2.0],
                        'stddev': 0.01,
                        'chance': 0.99}
    aug_noise = ImageNoise.from_config(aug_noise_config)
    image_noise = aug_noise(test_img, seed=42)
    assert image_noise is not None

def test_image_transformation(self)

Expand source code

def test_image_transformation(self):
    test_img = tf.image.decode_bmp(tf.io.read_file(get_resource("0_1543413266626.bmp"), -1))
    test_img = tf.cast(test_img, tf.float32)[None] * [0.5, .7, .9] / 255
    aug_transformer_config = {
        "chance": 0.5,
        "flip_up_down": False,
        "flip_left_right": True,
        "scale": 0.1,
        "rotate": 0,
        "translate_horizontal": 0.1,
        "translate_vertical": 0.1,
        "shear": 0.04,
        "interpolation": "bilinear"
    }
    aug_transformation = RandomTransformer.from_config(aug_transformer_config)
    A = aug_transformation(test_img.shape, 42)
    image_transformation = aug_transformation.transform_images(test_img, A)

    assert image_transformation is not None

class TestImagePipeline (methodName='runTest')

A class whose instances are single test cases.

By default, the test code itself should be placed in a method named 'runTest'.

If the fixture may be used for many test cases, create as many test methods as are needed. When instantiating such a TestCase subclass, specify in the constructor arguments the name of the test method that the instance is to execute.

Test authors should subclass TestCase for their own tests. Construction and deconstruction of the test's environment ('fixture') can be implemented by overriding the 'setUp' and 'tearDown' methods respectively.

If it is necessary to override the init method, the base class init method must always be called. It is important that subclasses should not change the signature of their init method, since instances of the classes are instantiated automatically by parts of the framework in order to be run.

When subclassing TestCase, you can set these attributes: * failureException: determines which exception will be raised when the instance's assertion methods fail; test methods raising this exception will be deemed to have 'failed' rather than 'errored'. * longMessage: determines whether long messages (including repr of objects used in assert methods) will be printed on failure in addition to any explicit message passed. * maxDiff: sets the maximum length of a diff in failure messages by assert methods using difflib. It is looked up as an instance attribute so can be configured by individual tests if required.

Create an instance of the class that will use the named test method when executed. Raises a ValueError if the instance does not have a method with the specified name.

Expand source code

class TestImagePipeline(unittest.TestCase):
    def test_image_pipeline_from_config(self):
        ip = ImagePipeline.from_config({"segmentation": {"classes": ["test"]}})

        assert id(ip.segmentation._ip) == id(ip)

    def test_image_pipeline_get_schema(self):
        schema = ImagePipeline.get_schema()

Ancestors

• unittest.case.TestCase

Methods

def test_image_pipeline_from_config(self)

Expand source code

def test_image_pipeline_from_config(self):
    ip = ImagePipeline.from_config({"segmentation": {"classes": ["test"]}})

    assert id(ip.segmentation._ip) == id(ip)

def test_image_pipeline_get_schema(self)

Expand source code

def test_image_pipeline_get_schema(self):
    schema = ImagePipeline.get_schema()