def create_segmentation_dataset(images_folder,
target_labels,
color_palette,
n_images,
batch_size=10,
split="train"):
try:
# creating dataset
dataset = Coco(
version=2017, # versions 5 and 6 supported
split=split,
task="segmentation",
labels=list(target_labels.keys()), # target labels
n_images=n_images, # number of images by class
batch_size=batch_size # batch images size
)
print(len(dataset)) # size of dataset
data_folder = Path(images_folder)
data_folder.mkdir(exist_ok=True)
FileUtil.clear_folder(data_folder)
# Download images
for i, batch_images in enumerate(dataset):
print(f"download done for batch {i+1} of {dataset.batches_count}")
for image in batch_images:
image.export(data_folder, labels_map,
color_palette) # copy images to disk
except Exception as ex:
print(f"[ERROR] Error creating the dataset {ex}")
def train(self,
epochs=100,
val_split=0.3,
clear_folder=False,
override_pipeline=False,
eval=False):
try:
if clear_folder:
FileUtil.clear_folder(self._out_folder)
self.num_steps = epochs
self._mk_labels_map()
self._mk_records(val_split)
# update pipeline
self._out_folder.joinpath(os.path.sep.join(
["export", "Servo"])).mkdir(exist_ok=True, parents=True)
# merge pipelines
save_pipeline_config(self.pipeline, str(self._out_folder))
# start training
tf.logging.set_verbosity(tf.logging.INFO)
if eval:
self._train_and_eval()
else:
self._train()
except Exception as ex:
raise Exception("Error training the model : {}".format(ex)) from ex
return super(TfTrainableModel, self).train()
def create_dataset(images_folder, labels_map, color_palette, n):
try:
# creating dataset
dataset = Coco(v=2017)
dataset.setup(split="train", task="segmentation")
#labels = dataset.labels_map.values() # get valid labels
os.makedirs(images_folder, exist_ok=True)
FileUtil.clear_folder(images_folder)
for batch_images in dataset.fetch(n=n,
labels=list(labels_map.keys()),
batch_size=500):
for img in batch_images:
img.export(images_folder, labels_map, color_palette)
for region in img.regions:
pass
# print(region.shape_attributes["x"],
# region.shape_attributes["y"])
except Exception as ex:
print("error creating the dataset {} ".format(ex))
def create_detection_dataset(images_folder, target_labels, n_images,
batch_size, split):
try:
# creating dataset
dataset = OpenImages(
version=6, # versions 5 and 6 supported
split=split,
task="detection",
labels=target_labels, # target labels
n_images=n_images, # number of images by class
batch_size=batch_size # batch images size
)
print(len(dataset)) # size of dataset
data_folder = Path(images_folder)
data_folder.mkdir(exist_ok=True)
FileUtil.clear_folder(data_folder)
# Download images
for i, batch_images in enumerate(dataset):
print(
f"download done for batch {i + 1} of {dataset.batches_count}")
for image in batch_images:
image.export(data_folder) # copy images to disk
except Exception as ex:
print(f"[ERROR] Error creating the dataset {ex}")