def load_kernel(self) -> None:
""" Load the metadate before training. """
self.classes = set()
for sample_id in tqdm(self.sample_ids):
try:
image_path = get_image_local_path(sample_id, self.DIR_PATH)
image = read_image(image_path)
# print(f"image shape {image.shape}")
annotation_path = get_xml_local_path(sample_id, self.DIR_PATH)
annotations, image_shape = load_xml(annotation_path)
_, class_labels = convert_annotations(annotations, image_shape)
if (len(class_labels) == 0):
print(f'Skip sample {sample_id}, no gt_objects')
continue
for class_label in class_labels:
self.classes.update({class_label})
self.add_image(
source=self.source,
image_id=sample_id,
sub_class_label="BloodCell",
path=image_path,
image_size=image.shape,
)
except Exception as e:
print(f'EXCP: {e} during handling {sample_id}')
continue
for i, c in enumerate(list(self.class_names_preset[1:])):
self.add_class(self.source, i + 1, c)
self.prepare()
return
def load_image(self, image_id, zero_image=False):
""" Load the images during training. """
info = self.image_info[image_id]
image_id = info['id']
image_path = get_image_local_path(image_id, self.DIR_PATH)
image = read_image(image_path)
return image
def load_kernel(self) -> None:
""" Load the metadate before training. """
self.classes = set()
for sample_id in tqdm(self.sample_ids):
try:
image_path = get_image_local_path(sample_id, self.DIR_PATH)
image = read_image(image_path)
annotation_path = get_json_local_path(sample_id, self.DIR_PATH)
with open(annotation_path) as f:
annotations = json.load(f)
if (len(annotations["objects"]) == 0):
print(f'Skip sample {sample_id}, no gt_objects')
continue
for obj in annotations["objects"]:
self.classes.update({obj["classTitle"]})
self.add_image(
source=self.source,
image_id=sample_id,
sub_class_label="ConstructionSite",
path=image_path,
image_size=image.shape,
)
except Exception as e:
print(f'EXCP: {e} during handling {sample_id}')
continue
for i, c in enumerate(list(self.class_names_preset[1:])):
self.add_class(self.source, i + 1, c)
self.prepare()
return
def load_image(self, image_id, zero_image=False):
""" Load the images during training. """
info = self.image_info[image_id]
image_id = info['id']
origin = info['origin']
if origin == "A":
image_path = get_image_local_path(image_id, self.DIR_PATH_A)
elif origin == "B":
image_path = get_image_local_path_B(image_id, self.DIR_PATH_B)
image = read_image(image_path)
return image
def load_kernel(self) -> None:
""" Load the metadate before training. """
self.classes = set()
for i, row in self.df.iterrows():
try:
sample_id = row["sample_id"]
WBC_type = row["WBC_types"]
origin = row["origin"]
if origin == "A":
image_path = get_image_local_path(sample_id,
self.DIR_PATH_A)
image = read_image(image_path)
annotation_path = get_xml_local_path(
sample_id, self.DIR_PATH_A)
annotations, image_shape = load_xml(annotation_path)
masks, class_labels = convert_annotations(
annotations, image_shape)
class_labels = [
correct_class_label(l, WBC_type) for l in class_labels
]
elif origin == "B":
image_path = get_image_local_path_B(
sample_id, self.DIR_PATH_B)
image = read_image(image_path)
mask = read_image(
get_mask_local_path_B(sample_id, self.DIR_PATH_B))
masks = mask[..., 0:1]
class_labels = WBC_type
# print(class_labels)
if (len(class_labels) == 0):
print(f'Skip sample {sample_id}, no gt_objects')
continue
for class_label in class_labels:
self.classes.update({class_label})
self.add_image(
source=self.source,
origin=origin,
image_id=sample_id,
sub_class_label=WBC_type,
path=image_path,
image_size=image.shape,
)
except Exception as e:
print(f'EXCP: {e} during handling {sample_id}')
continue
for i, c in enumerate(list(self.class_names_preset[1:])):
self.add_class(self.source, i + 1, c)
self.prepare()
return
def load_kernel(self) -> None:
""" Load the dataset before inference. """
self.classes = set()
for sample_id in tqdm(self.sample_ids):
try:
image_path = get_image_local_path(sample_id, self.DIR_PATH)
image = read_image(image_path)
self.add_image(
source=self.source,
image_id=sample_id,
sub_class_label="ConstructionSite",
path=image_path,
image_size=image.shape,
)
except Exception as e:
print(f'EXCP: {e} during handling {sample_id}')
continue
for i, c in enumerate(list(self.class_names_preset[1:])):
self.add_class(self.source, i + 1, c)
self.prepare()
return