Module brevettiai.tests.test_annotation_loader

Classes

class TestAnnotationLoaderBbox (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 TestAnnotationLoaderBbox(unittest.TestCase):
    test_image_path = get_resource("0_1543413266626.bmp")
    test_annotation_path = get_resource("1651574629796.json")
    bbox = BBOX(x1=10, x2=210, y1=30, y2=130)

    def test_loader_bbox_selection(self):
        classes = set([anno["label"] for anno in
                       json.loads(io_tools.read_file(self.test_annotation_path))["annotations"]])
        annotation_bbox, _ = AnnotationLoader(classes=classes).load(path=self.test_annotation_path, bbox=self.bbox)
        image_bbox, _ = ImageLoader(interpolation_method="nearest").load(self.test_image_path, bbox=self.bbox)
        image_raw, _ = ImageLoader(interpolation_method="nearest").load(self.test_image_path)
        annotation_raw, _ = AnnotationLoader(classes=classes).load(path=self.test_annotation_path,
                                                 metadata={"_image_file_shape": image_raw.shape})

        # Test that annotation is not empty
        tf.debugging.assert_greater(tf.reduce_mean(tf.abs(annotation_bbox)), 1e-4)

        # Test that image_bbox is correct region
        tf.debugging.assert_less_equal(tf.reduce_mean(tf.abs(
            image_bbox -
            image_raw[self.bbox.y1:self.bbox.y2 + 1, self.bbox.x1:self.bbox.x2 + 1])), 1e-4)

        # Test that area outputs shape of output image
        tf.debugging.assert_less_equal(tf.cast(tf.abs(
            self.bbox.area - annotation_bbox.shape[0] * annotation_bbox.shape[1]), dtype=tf.int64),
            tf.constant(0, dtype=tf.int64))

        # Test that annotation_bbox is correct region
        tf.debugging.assert_less_equal(tf.reduce_sum(tf.abs(
            annotation_bbox -
            annotation_raw[self.bbox.y1:self.bbox.y2 + 1, self.bbox.x1:self.bbox.x2 + 1])), 25.0)

        tf.debugging.assert_less_equal(tf.reduce_sum(
            tf.abs(tf.convert_to_tensor(annotation_bbox.shape)[:2] - tf.convert_to_tensor(image_bbox.shape)[:2])), 0)

Ancestors

• unittest.case.TestCase

Class variables

var bbox

var test_annotation_path

var test_image_path

Methods

def test_loader_bbox_selection(self)

Expand source code

def test_loader_bbox_selection(self):
    classes = set([anno["label"] for anno in
                   json.loads(io_tools.read_file(self.test_annotation_path))["annotations"]])
    annotation_bbox, _ = AnnotationLoader(classes=classes).load(path=self.test_annotation_path, bbox=self.bbox)
    image_bbox, _ = ImageLoader(interpolation_method="nearest").load(self.test_image_path, bbox=self.bbox)
    image_raw, _ = ImageLoader(interpolation_method="nearest").load(self.test_image_path)
    annotation_raw, _ = AnnotationLoader(classes=classes).load(path=self.test_annotation_path,
                                             metadata={"_image_file_shape": image_raw.shape})

    # Test that annotation is not empty
    tf.debugging.assert_greater(tf.reduce_mean(tf.abs(annotation_bbox)), 1e-4)

    # Test that image_bbox is correct region
    tf.debugging.assert_less_equal(tf.reduce_mean(tf.abs(
        image_bbox -
        image_raw[self.bbox.y1:self.bbox.y2 + 1, self.bbox.x1:self.bbox.x2 + 1])), 1e-4)

    # Test that area outputs shape of output image
    tf.debugging.assert_less_equal(tf.cast(tf.abs(
        self.bbox.area - annotation_bbox.shape[0] * annotation_bbox.shape[1]), dtype=tf.int64),
        tf.constant(0, dtype=tf.int64))

    # Test that annotation_bbox is correct region
    tf.debugging.assert_less_equal(tf.reduce_sum(tf.abs(
        annotation_bbox -
        annotation_raw[self.bbox.y1:self.bbox.y2 + 1, self.bbox.x1:self.bbox.x2 + 1])), 25.0)

    tf.debugging.assert_less_equal(tf.reduce_sum(
        tf.abs(tf.convert_to_tensor(annotation_bbox.shape)[:2] - tf.convert_to_tensor(image_bbox.shape)[:2])), 0)

class TestAnnotationLoaderCropResize (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 TestAnnotationLoaderCropResize(unittest.TestCase):
    test_image_path = get_resource("0_1543413266626.bmp")
    test_annotation_path = get_resource("1651574629796.json")
    image_crop = CropResizeProcessor(roi_horizontal_offset=32,
                                     roi_vertical_offset=64,
                                     roi_width=160,
                                     roi_height=128)

    def test_loader_crop_resize(self):
        classes = sorted(set([anno["label"] for anno in
                         json.loads(io_tools.read_file(self.test_annotation_path))["annotations"]]))
        annotation_cropped, _ = AnnotationLoader(classes=classes, postprocessor=self.image_crop).load(
                                                 path=self.test_annotation_path)
        image_cropped, _ = ImageLoader(interpolation_method="nearest", postprocessor=self.image_crop).load(
                                       self.test_image_path)
        image_raw, _ = ImageLoader(interpolation_method="nearest").load(self.test_image_path)
        annotation_raw, _ = AnnotationLoader(classes=classes).load(path=self.test_annotation_path,
                                             metadata={"_image_file_shape": image_raw.shape})

        image_crop_copy = self.image_crop.copy().set_bbox(self.image_crop.bbox(100, 100))

        assert image_crop_copy.roi_width == self.image_crop.roi_width

        # Test that annotation is not empty
        tf.debugging.assert_greater(tf.reduce_mean(tf.abs(annotation_cropped)), 1e-4)

        # Test that annotation_bbox is correct region
        tf.debugging.assert_less_equal(tf.reduce_sum(tf.abs(
            annotation_cropped -
            annotation_raw[self.image_crop.roi_vertical_offset:self.image_crop.roi_vertical_offset +
                                                               self.image_crop.roi_height,
                           self.image_crop.roi_horizontal_offset:self.image_crop.roi_horizontal_offset +
                                                               self.image_crop.roi_width])), 25.0)

        # Test that annotation_bbox is correct region
        tf.debugging.assert_less_equal(tf.reduce_sum(tf.abs(
            annotation_cropped[30::70, 6::100] - tf.constant([[[0., 0., 0.],
                                                               [1., 0., 1.]],
                                                              [[1., 0., 0.],
                                                               [1., 0., 0.]]], dtype=tf.float32))), 1e-4)

        # test that bboxes match
        tf.debugging.assert_less_equal(tf.reduce_sum(
            tf.abs(tf.convert_to_tensor(annotation_cropped.shape)[:2] - tf.convert_to_tensor(image_cropped.shape)[:2])), 0)

Ancestors

• unittest.case.TestCase

Class variables

var image_crop

var test_annotation_path

var test_image_path

Methods

def test_loader_crop_resize(self)

Expand source code

def test_loader_crop_resize(self):
    classes = sorted(set([anno["label"] for anno in
                     json.loads(io_tools.read_file(self.test_annotation_path))["annotations"]]))
    annotation_cropped, _ = AnnotationLoader(classes=classes, postprocessor=self.image_crop).load(
                                             path=self.test_annotation_path)
    image_cropped, _ = ImageLoader(interpolation_method="nearest", postprocessor=self.image_crop).load(
                                   self.test_image_path)
    image_raw, _ = ImageLoader(interpolation_method="nearest").load(self.test_image_path)
    annotation_raw, _ = AnnotationLoader(classes=classes).load(path=self.test_annotation_path,
                                         metadata={"_image_file_shape": image_raw.shape})

    image_crop_copy = self.image_crop.copy().set_bbox(self.image_crop.bbox(100, 100))

    assert image_crop_copy.roi_width == self.image_crop.roi_width

    # Test that annotation is not empty
    tf.debugging.assert_greater(tf.reduce_mean(tf.abs(annotation_cropped)), 1e-4)

    # Test that annotation_bbox is correct region
    tf.debugging.assert_less_equal(tf.reduce_sum(tf.abs(
        annotation_cropped -
        annotation_raw[self.image_crop.roi_vertical_offset:self.image_crop.roi_vertical_offset +
                                                           self.image_crop.roi_height,
                       self.image_crop.roi_horizontal_offset:self.image_crop.roi_horizontal_offset +
                                                           self.image_crop.roi_width])), 25.0)

    # Test that annotation_bbox is correct region
    tf.debugging.assert_less_equal(tf.reduce_sum(tf.abs(
        annotation_cropped[30::70, 6::100] - tf.constant([[[0., 0., 0.],
                                                           [1., 0., 1.]],
                                                          [[1., 0., 0.],
                                                           [1., 0., 0.]]], dtype=tf.float32))), 1e-4)

    # test that bboxes match
    tf.debugging.assert_less_equal(tf.reduce_sum(
        tf.abs(tf.convert_to_tensor(annotation_cropped.shape)[:2] - tf.convert_to_tensor(image_cropped.shape)[:2])), 0)