def test_accuracy_u2Squared(capsys):
"""
Test U2Squared FScore on DUST-TE Dataset
"""
dataset = fo.load_dataset("duts_te_validation")
DatasetPathPrediction = "/home/Develop/Dataset/SemanticSegmentation/DUTS-TE/DUTS-TE-Image-Pred-Mask/"
with capsys.disabled():
with fo.ProgressBar() as pb:
for sample in dataset:
head, tail = os.path.split(sample.filepath)
maskPredPath = DatasetPathPrediction + tail
print(maskPredPath)
maskPred = cv2.imread(maskPredPath, cv2.IMREAD_UNCHANGED)
ret, maskPred = cv2.threshold(maskPred, 127, 255,
cv2.THRESH_BINARY)
sample["u2squared"] = fo.Segmentation(mask=maskPred)
sample.save()
results = dataset.evaluate_segmentations(
"u2squared",
gt_field="ground_truth",
eval_key="eval_segmentation",
)
print(results.metrics())
# 0.80 is the value reported in the paper as weighted F-measure for DUTS-TE Dataset
assert (results.metrics()["fscore"] > 0.80)
def test_from_fiftyone(tmpdir):
tmp_dir = Path(tmpdir)
# create random dummy data
images = [
str(tmp_dir / "img1.png"),
str(tmp_dir / "img2.png"),
str(tmp_dir / "img3.png"),
]
num_classes: int = 2
img_size: Tuple[int, int] = (128, 128)
for img_file in images:
_rand_image(img_size).save(img_file)
targets = [np.array(_rand_labels(img_size, num_classes)) for _ in range(3)]
dataset = fo.Dataset.from_dir(
str(tmp_dir),
dataset_type=fo.types.ImageDirectory,
)
for idx, sample in enumerate(dataset):
sample["ground_truth"] = fo.Segmentation(mask=targets[idx][:, :, 0])
sample.save()
# instantiate the data module
dm = SemanticSegmentationData.from_fiftyone(
train_dataset=dataset,
val_dataset=dataset,
test_dataset=dataset,
predict_dataset=dataset,
batch_size=2,
num_workers=0,
num_classes=num_classes,
)
assert dm is not None
assert dm.train_dataloader() is not None
assert dm.val_dataloader() is not None
assert dm.test_dataloader() is not None
# check training data
data = next(iter(dm.train_dataloader()))
imgs, labels = data[DefaultDataKeys.INPUT], data[DefaultDataKeys.TARGET]
assert imgs.shape == (2, 3, 128, 128)
assert labels.shape == (2, 128, 128)
# check val data
data = next(iter(dm.val_dataloader()))
imgs, labels = data[DefaultDataKeys.INPUT], data[DefaultDataKeys.TARGET]
assert imgs.shape == (2, 3, 128, 128)
assert labels.shape == (2, 128, 128)
# check test data
data = next(iter(dm.test_dataloader()))
imgs, labels = data[DefaultDataKeys.INPUT], data[DefaultDataKeys.TARGET]
assert imgs.shape == (2, 3, 128, 128)
assert labels.shape == (2, 128, 128)
# check predict data
data = next(iter(dm.predict_dataloader()))
imgs = data[DefaultDataKeys.INPUT]
assert imgs.shape == (2, 3, 128, 128)
maskList= os.listdir(DatasetPath)
samples=[]
dataset = fo.Dataset("duts_te_validation")
dataset.persistent=True
for maskName in maskList:
maskPath= DatasetPath + maskName
# load mask with opencv
mask = cv2.imread(maskPath, cv2.IMREAD_UNCHANGED)
ret,mask= cv2.threshold(mask, 127,255,cv2.THRESH_BINARY)
sample = fo.Sample(filepath=maskPath,
ground_truth=fo.Segmentation(mask=mask))
samples.append(sample)
dataset.add_samples(samples)
# ADD PREDICTION
#To add prediction you need
# 1) Cycle over all dataset sample GT
# 2) For each sample add a custom field u2squared
dataset = fo.load_dataset("duts_te_validation")
DatasetPathPrediction= "/home/Develop/Dataset/SemanticSegmentation/DUTS-TE/DUTS-TE-Image-Pred-Mask/"