def test_random_horizontal_flip_with_bbox_op_coco_c(plot_vis=False):
"""
Prints images and bboxes side by side with and without RandomHorizontalFlipWithBBox Op applied,
Testing with COCO dataset
"""
logger.info("test_random_horizontal_flip_with_bbox_op_coco_c")
dataCoco1 = ds.CocoDataset(DATA_DIR_2[0],
annotation_file=DATA_DIR_2[1],
task="Detection",
decode=True,
shuffle=False)
dataCoco2 = ds.CocoDataset(DATA_DIR_2[0],
annotation_file=DATA_DIR_2[1],
task="Detection",
decode=True,
shuffle=False)
test_op = c_vision.RandomHorizontalFlipWithBBox(1)
dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
columns_order=["image", "bbox"],
operations=[test_op])
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataCoco1.create_dict_iterator(),
dataCoco2.create_dict_iterator()):
unaugSamp.append(unAug)
augSamp.append(Aug)
if plot_vis:
visualize_with_bounding_boxes(unaugSamp, augSamp, "bbox")
def test_random_horizontal_flip_with_bbox_invalid_bounds_c():
"""
Test RandomHorizontalFlipWithBBox op with invalid bounding boxes
"""
logger.info("test_random_horizontal_bbox_invalid_bounds_c")
test_op = c_vision.RandomHorizontalFlipWithBBox(1)
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.WidthOverflow,
"bounding boxes is out of bounds of the image")
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.HeightOverflow,
"bounding boxes is out of bounds of the image")
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.WrongShape, "4 features")
def test_random_horizontal_flip_with_bbox_op_c(plot_vis=False):
"""
Prints images and bboxes side by side with and without RandomHorizontalFlipWithBBox Op applied
"""
logger.info("test_random_horizontal_flip_with_bbox_op_c")
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
test_op = c_vision.RandomHorizontalFlipWithBBox(1)
dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
output_columns=["image", "bbox"],
columns_order=["image", "bbox"],
operations=[test_op])
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataVoc1.create_dict_iterator(),
dataVoc2.create_dict_iterator()):
unaugSamp.append(unAug)
augSamp.append(Aug)
if plot_vis:
visualize_with_bounding_boxes(unaugSamp, augSamp)
def test_random_horizontal_flip_with_bbox_invalid_prob_c():
"""
Test RandomHorizontalFlipWithBBox op with invalid input probability
"""
logger.info("test_random_horizontal_bbox_invalid_prob_c")
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
try:
# Note: Valid range of prob should be [0.0, 1.0]
test_op = c_vision.RandomHorizontalFlipWithBBox(1.5)
# map to apply ops
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op
]) # Add column for "annotation"
except ValueError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert "Input prob is not within the required interval of (0.0 to 1.0)." in str(
error)
def check_bad_box(data, box_type, expected_error):
"""
:param data: de object detection pipeline
:param box_type: type of bad box
:param expected_error: error expected to get due to bad box
:return: None
"""
# DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM)
try:
test_op = c_vision.RandomHorizontalFlipWithBBox(1)
data = data.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
# map to use width overflow
data = data.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=lambda img, bboxes: add_bad_annotation(img, bboxes, box_type))
# map to apply ops
data = data.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op]) # Add column for "annotation"
for _, _ in enumerate(data.create_dict_iterator()):
break
except RuntimeError as error:
logger.info("Got an exception in DE: {}".format(str(error)))
assert expected_error in str(error)
def test_random_horizontal_bbox_valid_prob_c(plot=False):
"""
Test RandomHorizontalFlipWithBBox op
Prints images side by side with and without Aug applied + bboxes to compare and test
"""
logger.info("test_random_horizontal_bbox_valid_prob_c")
data_voc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
# DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM)
test_op = c_vision.RandomHorizontalFlipWithBBox(0.3)
# maps to fix annotations to minddata standard
data_voc1 = data_voc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
data_voc2 = data_voc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
# map to apply ops
data_voc2 = data_voc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op]) # Add column for "annotation"
if plot:
visualize(data_voc1, data_voc2)
def test_random_horizontal_flip_with_bbox_valid_edge_c(plot_vis=False):
"""
Test RandomHorizontalFlipWithBBox op (testing with valid edge case, box covering full image).
Prints images side by side with and without Aug applied + bboxes to compare and test
"""
logger.info("test_horizontal_flip_with_bbox_valid_edge_c")
dataVoc1 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
test_op = c_vision.RandomHorizontalFlipWithBBox(1)
# maps to fix annotations to minddata standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
# map to apply ops
# Add column for "annotation"
dataVoc1 = dataVoc1.map(
input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=lambda img, bbox:
(img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype(
np.uint32)))
dataVoc2 = dataVoc2.map(
input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=lambda img, bbox:
(img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype(
np.uint32)))
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op])
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataVoc1.create_dict_iterator(),
dataVoc2.create_dict_iterator()):
unaugSamp.append(unAug)
augSamp.append(Aug)
if plot_vis:
visualize_with_bounding_boxes(unaugSamp, augSamp)
def test_random_horizontal_bbox_with_bbox_valid_rand_c(plot_vis=False):
"""
Uses a valid non-default input, expect to pass
Prints images side by side with and without Aug applied + bboxes to
compare and test
"""
logger.info("test_random_horizontal_bbox_valid_rand_c")
original_seed = config_get_set_seed(1)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# Load dataset
dataVoc1 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
dataVoc2 = ds.VOCDataset(DATA_DIR,
task="Detection",
mode="train",
decode=True,
shuffle=False)
test_op = c_vision.RandomHorizontalFlipWithBBox(0.6)
# maps to fix annotations to minddata standard
dataVoc1 = dataVoc1.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
dataVoc2 = dataVoc2.map(input_columns=["annotation"],
output_columns=["annotation"],
operations=fix_annotate)
# map to apply ops
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
output_columns=["image", "annotation"],
columns_order=["image", "annotation"],
operations=[test_op])
filename = "random_horizontal_flip_with_bbox_01_c_result.npz"
save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
unaugSamp, augSamp = [], []
for unAug, Aug in zip(dataVoc1.create_dict_iterator(),
dataVoc2.create_dict_iterator()):
unaugSamp.append(unAug)
augSamp.append(Aug)
if plot_vis:
visualize_with_bounding_boxes(unaugSamp, augSamp)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)