def test_identical_img_bboxes_recovered_using_csv_writer(
tmp_path, sample_image, annot_loader, normalized, annot_writer):
img, annotations = sample_image
bboxes = annotations_to_numpy_array(annotations)
with DirectoryWriter(directory=tmp_path,
clean_directory=False) as image_writer, annot_writer(
annotations_file=os.path.join(
tmp_path, "annot.csv"),
normalized=normalized) as annotation_writer:
image_writer.write_image("image.jpg", img)
annotation_writer.write_annotations_for_image("image.jpg", img,
annotations)
with DirectoryLoader(directory=tmp_path) as image_loader, annot_loader(
annotations_file=os.path.join(tmp_path, "annot.csv"),
normalized=normalized) as annotation_loader:
images = annotation_loader.load_annotated_images(image_loader)
_, loaded_annotations = images["image.jpg"]
loaded_bboxes = annotations_to_numpy_array(loaded_annotations)
assert np.allclose(bboxes,
loaded_bboxes), "Annotations not the same after writing"
def test_yolokeras_loader(yolokeras_imageset, yolokeras_equiv_csv_imageset):
for image_name in yolokeras_equiv_csv_imageset:
_, yk_annotations = yolokeras_imageset[image_name]
_, csv_annotations = yolokeras_equiv_csv_imageset[image_name]
yk_bboxes = annotations_to_numpy_array(yk_annotations)
csv_bboxes = annotations_to_numpy_array(csv_annotations)
assert np.allclose(
yk_bboxes, csv_bboxes
), "Same bboxes loaded with YOLOKeras, FourCornersCSV not equal." ""
def test_coco_loader(coco_imageset, coco_equiv_csv_imageset):
for image_name in coco_equiv_csv_imageset:
_, cc_annotations = coco_imageset[image_name]
_, csv_annotations = coco_equiv_csv_imageset[image_name]
cc_bboxes = annotations_to_numpy_array(cc_annotations)
csv_bboxes = annotations_to_numpy_array(csv_annotations)
assert np.allclose(
cc_bboxes, csv_bboxes
), "Same bboxes loaded with COCO, FourCornersCSV not equal." ""
def test_pascalvoc_loader(pascalvoc_imageset, pascalvoc_equiv_csv_imageset):
for image_name in pascalvoc_imageset:
_, pv_annotations = pascalvoc_imageset[image_name]
_, csv_annotations = pascalvoc_equiv_csv_imageset[image_name]
pv_bboxes = annotations_to_numpy_array(pv_annotations)
csv_bboxes = annotations_to_numpy_array(csv_annotations)
assert np.allclose(
pv_bboxes, csv_bboxes
), "Same bboxes loaded with PascalVOC, FourCornersCSV are not equal"
def test_run(snapshot, sample_query, tmp_path):
random.seed(1)
with open(os.path.join(tmp_path, "query.yml"), "w") as query_file:
query_file.write(sample_query)
main(['generate', os.path.join(tmp_path, "query.yml")])
os.remove(os.path.join(tmp_path, "query.yml"))
if snapshot["update_snapshots"]:
if os.path.isdir("./snapshots/run"):
shutil.rmtree("./snapshots/run")
shutil.copytree(tmp_path, "./snapshots/run")
return
with FourCornersCSV(annotations_file="./snapshots/run/aug_annotations.csv",
normalized=True) as annotation_loader, Directory(
directory="./snapshots/run") as image_loader:
snapshot_images = annotation_loader.load_annotated_images(image_loader)
with FourCornersCSV(annotations_file=os.path.join(tmp_path,
"aug_annotations.csv"),
normalized=True) as annotation_loader, Directory(
directory=tmp_path) as image_loader:
run_images = annotation_loader.load_annotated_images(image_loader)
for image_name in snapshot_images:
snapshot_image, snapshot_annot = snapshot_images[image_name]
run_image, run_annot = run_images[image_name]
snapshot_bboxes = annotations_to_numpy_array(snapshot_annot)
run_bboxes = annotations_to_numpy_array(run_annot)
assert np.allclose(
snapshot_image,
run_image), "Image for {} differs".format(image_name)
assert np.allclose(
snapshot_bboxes,
run_bboxes), "Bounding boxes for {} differ".format(image_name)
for image_name in run_images:
if image_name not in snapshot_images:
assert False, "Image {} not in snapshot".format(image_name)
def test_disco_constructed_augmentation_same_as_factory_constructed(
augmentation_name_params, snapshot, tmp_path, sample_image):
random.seed(1)
if snapshot["update_snapshots"]:
raise RuntimeError(
"This test must be run after snapshots are generated")
name, params = augmentation_name_params
aug = getattr(disco, name)(**params)
img, annotations = sample_image
aug_img, aug_annotations = aug(img, annotations)
save_image(os.path.join(tmp_path, "aug_image.jpg"), aug_img)
aug_bboxes = annotations_to_numpy_array(aug_annotations)
snapshot_bboxes = np.load(
os.path.join("./snapshots/augmentations/{}-bboxes.npy".format(name)))
assert np.array_equal(
aug_bboxes,
snapshot_bboxes), "Bounding boxes for {} do not match".format(name)
assert filecmp.cmp(os.path.join(tmp_path, "aug_image.jpg"),
"./snapshots/augmentations/{}-image.jpg".format(
name)), "Images for {} do not match".format(name)
def test_my_augmentation_sepia():
img = np.array(([
[[ 62, 238, 229],
[242, 39, 182]],
[[167, 153, 41],
[130, 63, 162]]]))
annotations = []
# Values calculated by hand using formula from <https://www.techrepublic.com/blog/how-do-i/how-do-i-convert-images-to-grayscale-and-sepia-tone-using-c/>
expected_red = np.array([[250, 159], [191, 130]])
expected_green = np.array([[223, 141], [170, 115]])
expected_blue = np.array([[173, 110], [132, 90]])
expected_aug_img = np.zeros(img.shape, dtype=np.uint8)
expected_aug_img[:, :, 0] = expected_red
expected_aug_img[:, :, 1] = expected_green
expected_aug_img[:, :, 2] = expected_blue
bboxes = annotations_to_numpy_array(annotations)
augmentation = Sepia()
aug_img, aug_bboxes = augmentation.augment(img.copy(), bboxes.copy())
assert np.allclose(
expected_aug_img, aug_img
), "Performing augmentation does not yield expected image"
assert np.array_equal(
bboxes, aug_bboxes
), "Performing augmentation does not yield original augmentation"
def test_four_corners_width_height_equivalent(sample_image,
sample_image_wh_bboxes):
img_fc, annot_fc = sample_image
img_wh, annot_wh = sample_image_wh_bboxes
assert np.array_equal(
img_fc, img_wh
), "Same image loaded with FourCornersCSV and WidthHeightCSV is not equal"
bboxes_fc = annotations_to_numpy_array(annot_fc)
bboxes_wh = annotations_to_numpy_array(annot_wh)
assert np.allclose(
bboxes_fc, bboxes_wh
), "Same bboxes loaded with FourCornersCSV, WidthHeightCSV are not equal"
def test_augmentation(augmentation, snapshot, tmp_path, sample_image):
random.seed(1)
img, annotations = sample_image
bboxes = annotations_to_numpy_array(annotations)
aug_img, aug_bboxes = augmentation.augment(img.copy(), bboxes.copy())
if isinstance(augmentation, ColorAugmentation):
assert np.array_equal(
bboxes, aug_bboxes
), "{} is a ColorAugmentation, but it modified bboxes".format(
augmentation.__name__)
with DirectoryWriter(directory=tmp_path,
clean_directory=False) as image_writer:
np.save(
os.path.join(tmp_path,
"{}-bboxes.npy".format(augmentation.__name__)),
aug_bboxes)
image_writer.write_image("{}-image.jpg".format(augmentation.__name__),
aug_img)
if snapshot["update_snapshots"]:
if not os.path.isdir("./snapshots/augmentations"):
os.mkdir("./snapshots/augmentations")
shutil.copy(
os.path.join(tmp_path,
"{}-bboxes.npy".format(augmentation.__name__)),
"./snapshots/augmentations/{}-bboxes.npy".format(
augmentation.__name__))
shutil.copy(
os.path.join(tmp_path,
"{}-image.jpg".format(augmentation.__name__)),
"./snapshots/augmentations/{}-image.jpg".format(
augmentation.__name__))
return
snapshot_bboxes = np.load(
os.path.join("./snapshots/augmentations/{}-bboxes.npy".format(
augmentation.__name__)))
assert np.array_equal(
aug_bboxes,
snapshot_bboxes), "Bounding boxes for {} do not match".format(
augmentation.__name__)
assert filecmp.cmp(
os.path.join(tmp_path, "{}-image.jpg".format(augmentation.__name__)),
"./snapshots/augmentations/{}-image.jpg".format(
augmentation.__name__)), "Images for {} do not match".format(
augmentation.__name__)
def test_one_of_no_augmentations(sample_image):
img, annotations = sample_image
bboxes = annotations_to_numpy_array(annotations)
augmentation = OneOf(augmentations=[])
aug_img, aug_bboxes = augmentation.augment(img.copy(), bboxes.copy())
assert np.array_equal(img, aug_img)
assert np.array_equal(bboxes, aug_bboxes)
def test_motionblur(sample_image, direction):
img, annotations = sample_image
bboxes = annotations_to_numpy_array(annotations)
augmentation = MotionBlur(kernel_size=10, direction=direction)
aug_img, aug_bboxes = augmentation.augment(img.copy(), bboxes.copy())
assert np.array_equal(bboxes, aug_bboxes)
assert np.array_equal(img.shape, aug_img.shape)
def test_coarsedropout(sample_image):
img, annotations = sample_image
bboxes = annotations_to_numpy_array(annotations)
augmentation = CoarseDropout(0.1, 25)
aug_img, aug_bboxes = augmentation.augment(img.copy(), bboxes.copy())
assert np.allclose(
expected_aug_img,
aug_img), "Performing augmentation does not yield expected image"
assert np.array_equal(
bboxes, aug_bboxes
), "Performing augmentation does not yield original augmentation"
def test_augmentation_is_involution(augmentation, sample_image):
img, annotations = sample_image
bboxes = annotations_to_numpy_array(annotations)
aug_img, aug_bboxes = augmentation.augment(img.copy(), bboxes.copy())
re_aug_img, re_aug_bboxes = augmentation.augment(aug_img.copy(),
aug_bboxes.copy())
assert np.allclose(
img, re_aug_img
), "Performing augmentation twice does not yield origial image"
assert np.allclose(
bboxes, re_aug_bboxes
), "Performing augmentation twice does not yield original annotations"
def test_my_augmentation_warmer(sample_image):
img, annotations = sample_image
bboxes = annotations_to_numpy_array(annotations)
# Kelvin values smaller than 6600K map to RGB multipliers
# acting as warming filters.
kelvin_input = random.randint(1000, 6560)
augmentation = ColorTemperature(kelvin=kelvin_input)
aug_img, aug_bboxes = augmentation.augment(img.copy(), bboxes.copy())
# Warming filters make reds more intensive and blue less.
assert np.less_equal(img[:, :, 0], aug_img[:, :, 0]).all(
), "Performing augmentation does not yield expected red channel"
assert np.greater_equal(img[:, :, 2], aug_img[:, :, 2]).all(
), "Performing augmentation does not yield expected blue channel"
assert np.array_equal(
bboxes, aug_bboxes
), "Performing augmentation does not yield original bounding boxes"
def save_image(path, image, annotations=None):
"""Save an image loaded with load_image or load_image_from_bytes.
Keyword Arguments:
path - The filename to save the image to. This must include an extension
to indicate the format (e.g., .jpg, .png)
image - The OpenCV image to save. This should have been originally
loaded with load_image or load_image_from_bytes and optionally
augmented
annotations - An array of BoundingBox objects that will be drawn on top
of the image in red, or None if no annotations are to be
drawn.
"""
img_copy = image.copy()
if annotations is not None:
bboxes = annotations_to_numpy_array(annotations)
img_copy = draw_rect(img_copy, bboxes, (255, 0, 0))
img_copy = cv2.cvtColor(img_copy, cv2.COLOR_RGB2BGR)
return cv2.imwrite(path, img_copy)
def test_my_augmentation_colder():
img = np.array(([[[62, 238, 229], [242, 39, 182]],
[[167, 153, 41], [130, 63, 162]]]))
annotations = []
bboxes = annotations_to_numpy_array(annotations)
# Kelvin values larger than 6600K map to RGB multipliers
# acting as cooling filters.
kelvin_input = random.randint(6710, 40000)
augmentation = ColorTemperature(kelvin=kelvin_input)
aug_img, aug_bboxes = augmentation.augment(img.copy(), bboxes.copy())
# Cooling filters make blues more intensive and reds less.
assert np.greater_equal(img[:, :, 0], aug_img[:, :, 0]).all(
), "Performing augmentation does not yield expected red channel"
assert np.less_equal(img[:, :, 2], aug_img[:, :, 2]).all(
), "Performing augmentation does not yield expected blue channel"
assert np.array_equal(
bboxes, aug_bboxes
), "Performing augmentation does not yield original bounding boxes"
