def test_get_target_indexes_when_int():
test = ImageFileGroup("tests/data/image", recursive=True)
pc = PipelineComponent()
pc.applies_to_available = True
pc.applies_to([0])
targets = pc.get_target_indexes(test)
assert len(targets) == 1 and targets[0] == 0
def test_to_grayscale():
test = ImageFileGroup("tests/data/image", recursive=True)
test.configure_pipeline(["TransformToGrayscale"])
test.load_all()
test_ind = 0
while repr(test.files[test_ind]) != "sample.jpg":
test_ind += 1
assert test.files[test_ind].image.mode != "L"
test.run_pipeline()
assert test.files[test_ind].image.mode == "L"
def test_limit_dimensions_no_setting():
test = ImageFileGroup("tests/data/image", recursive=True)
test.configure_pipeline(["TransformLimitDimensions"])
test.load_all()
heights_before = [fobj.image.size[0] for fobj in test.files if fobj.image]
widths_before = [fobj.image.size[0] for fobj in test.files if fobj.image]
test.run_pipeline()
heights_after = [fobj.image.size[0] for fobj in test.files if fobj.image]
widths_after = [fobj.image.size[0] for fobj in test.files if fobj.image]
assert len(test.files) > 0
assert sum(heights_before) == sum(heights_after)
assert sum(widths_before) == sum(widths_after)
def test_limit_dimensions_height_only():
test = ImageFileGroup("tests/data/image", recursive=True)
test.configure_pipeline(["TransformLimitDimensions"])
test.pipeline.components[0].configure(max_height=100)
test.run_pipeline()
heights = [fobj.image.size[1] for fobj in test.files if fobj.image]
assert len(test.files) > 0
assert max(heights) == 100
def __new__(self, source: str = None, recursive: bool = False):
"""Initialize a kaishi image dataset (currently a directory of files is the only option).
:param source: string pointing to the data source
:type source: str
:param recusive: flag indicating recursion
:type recursive: bool
"""
if torch.cuda.is_available() is False:
warnings.warn(
"No GPU detected, ConvNet prediction tasks will be very slow")
if os.path.exists(source):
return ImageFileGroup(source=source, recursive=recursive)
else:
raise NotImplementedError(
"Currently only supports a valid path as input")
def test_fix_rotation():
test = ImageFileGroup("tests/data/image", recursive=True)
test.configure_pipeline(["TransformFixRotation"])
test.run_pipeline()
found_rectified = False
found_rotated = False
for fobj in test.files:
if fobj.has_label("RECTIFIED"):
found_rectified = True
if fobj.has_label("ROTATED_LEFT"):
found_rotated = True
if fobj.has_label("ROTATED_RIGHT"):
found_rotated = True
if fobj.has_label("UPSIDE_DOWN"):
found_rotated = True
assert found_rectified is True and found_rotated is False
def test_invalid_file_extensions():
test = ImageFileGroup("tests/data/image", recursive=True)
test.configure_pipeline(["FilterInvalidFileExtensions"])
test.run_pipeline()
assert len(test.filtered["unsupported_extension"]) > 0
def test_similar():
test = ImageFileGroup("tests/data/image", recursive=True)
test.configure_pipeline(["FilterSimilar"])
test.run_pipeline()
assert len(test.filtered["similar"]) > 0
def test_invalid_image_headers():
test = ImageFileGroup("tests/data/image", recursive=True)
test.configure_pipeline(["FilterInvalidImageHeaders"])
test.run_pipeline()
assert len(test.filtered["invalid_header"]) > 0
def test_get_target_indexes_when_regex():
test = ImageFileGroup("tests/data/image", recursive=True)
pc = PipelineComponent()
pc.applies_to_available = True
pc.applies_to(["real_near.*"])
assert len(pc.get_target_indexes(test)) == 2
def test_init_and_load_dir():
test = ImageFileGroup("tests/data/image", recursive=True)
assert len(test.files) > 0
def test_save():
test = ImageFileGroup("tests/data/image", recursive=True)
test.load_all()
tempdir = tempfile.TemporaryDirectory()
test.save(tempdir.name)
assert len(os.listdir(tempdir.name)) > 0
def test_load_all():
test = ImageFileGroup("tests/data/image", recursive=True)
test.load_all()
assert sum([fobj.image is not None for fobj in test.files]) > 0
def test_generic_convnet():
test = ImageFileGroup("tests/data/image", recursive=True)
test.configure_pipeline(["LabelerGenericConvnet"])
test.run_pipeline()
label_count = sum([len(fobj.labels) for fobj in test.files])
assert label_count > 0