def test_png_img_writer():
base_dir = get_temp_path(prefix="test_png_img_writer")
manager = CacheManager(base_dir)
tagged_data = ImageMockTaggedData(relative_path="img.png")
assert manager.get(tagged_data.tag) is None
ds_object = DataSetObject(tagged_data)
ds_object.add_cache_manager(manager)
object_bytes = ds_object.get() # This load the object into the cache
assert manager.get(ds_object.tag) == object_bytes
def test_classification_output():
img = get_img()
image_data = MockTaggedData("", img)
image_ds_object = DataSetObject(image_data,
output_function=classification_output)
image_ds_object.label = 5
img_output, label = image_ds_object.output()
assert np.all(img_output == img)
assert label == 5
def test_image():
rgb_img = get_img(width=300, height=200)
rgb_tagged_data = MockTaggedData("", rgb_img)
image_ds_object_01 = DataSetObject(rgb_tagged_data)
assert image_ds_object_01.width == 300
assert image_ds_object_01.height == 200
bw_img = get_img(width=300, height=200, bw=True)
bw_tagged_data = MockTaggedData("", bw_img)
image_ds_object_03 = DataSetObject(bw_tagged_data)
assert image_ds_object_03.width == 300
assert image_ds_object_03.height == 200
def detection_collection(
image_count=15,
max_annotations_per_image=5,
img_width=1280,
img_height=720,
amount_of_classes=3,
):
images = [
DataSetObject(tagged_data=MockTaggedData(f"{i}.jpg", get_img()))
for i in range(image_count)
]
for image in images:
img_annotation_iterations = random.randint(
1, max_annotations_per_image - 1)
for _ in range(img_annotation_iterations):
image.add_annotation(
get_random_bounding_box(
img_width=img_width,
img_height=img_height,
amount_of_classes=amount_of_classes,
))
label_mapping = {
label_index: f"label_{label_index}"
for label_index in range(amount_of_classes)
}
return images, label_mapping
def mask_objects() -> List[DataSetObject]:
inputs = []
for subset_name in ["train", "test", "val"]:
for img_type in ["mask", "image"]:
for img_index in range(10):
tagged_data = MockTaggedData(
relative_path=f"{subset_name}/{img_type}/{img_index}.jpg",
data="{subset_name}{img_index}",
)
inputs.append(DataSetObject(tagged_data))
return inputs
def transform_single_object(
self, ds_input: DataSetObject) -> List[DataSetObject]:
values = extract_values(template=self.template,
path=ds_input.relative_path)
if self.input_item in values:
label = values[self.input_item]
ds_input.label = label
return [ds_input]
elif self.drop_if_no_match:
return []
else:
return [ds_input]
def test_mask():
img = get_img()
mask = get_img()
image_data = MockTaggedData("", img)
mask_data = MockTaggedData("", mask)
image_ds_object = DataSetObject(image_data, output_function=single_mask_output)
image_ds_object.annotations.append(Mask(mask_data))
ds = Dataset()
ds.form_from_ds_objects([image_ds_object])
img_output, mask_output = ds[0]
assert np.all(img_output == img)
assert np.all(mask_output == mask)
def transform_single_object(
self, dataset_object: DataSetObject) -> List[DataSetObject]:
dataset_object.add_cache_manager(self.cache_manager)
return [dataset_object]
def triple_output(ds_object: DataSetObject) -> int:
parsed_output = ds_object.get_parsed()
assert isinstance(parsed_output, int)
return parsed_output * 3
def form(self, data: List[TaggedData]):
inputs = [DataSetObject(tagged_data, output_function=self.output_function) for tagged_data in data]
self.form_from_ds_objects(inputs)