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from flask_login import current_user
from mongoengine import *
from config import Config
from .tasks import TaskModel
import os
class DatasetModel(DynamicDocument):
id = SequenceField(primary_key=True)
name = StringField(required=True, unique=True)
directory = StringField()
thumbnails = StringField()
categories = ListField(default=[])
owner = StringField(required=True)
users = ListField(default=[])
annotate_url = StringField(default="")
default_annotation_metadata = DictField(default={})
deleted = BooleanField(default=False)
deleted_date = DateTimeField()
def save(self, *args, **kwargs):
directory = os.path.join(Config.DATASET_DIRECTORY, self.name + '/')
os.makedirs(directory, mode=0o777, exist_ok=True)
self.directory = directory
self.owner = current_user.username if current_user else 'system'
return super(DatasetModel, self).save(*args, **kwargs)
def get_users(self):
from .users import UserModel
members = self.users
members.append(self.owner)
return UserModel.objects(username__in=members)\
.exclude('password', 'id', 'preferences')
def import_coco(self, coco_json):
from workers.tasks import import_annotations
task = TaskModel(
name="Import COCO format into {}".format(self.name),
dataset_id=self.id,
group="Annotation Import"
)
task.save()
cel_task = import_annotations.delay(task.id, self.id, coco_json)
return {
"celery_id": cel_task.id,
"id": task.id,
"name": task.name
}
def predict_coco(self):
from workers.tasks import predict_annotations,unify_predictions
from celery import chord
# Setup
#TODO Get images from the image model
images_path = self.directory
catmus_labels_folder = os.path.join(images_path, 'labels', 'catmus')
emanuskript_labels_folder = os.path.join(images_path, 'labels', 'emanuskript')
zone_detection_labels_folder = os.path.join(images_path, 'labels', 'zone_detection')
dict_labels_folders = {'catmus':catmus_labels_folder,
'emanuskript':emanuskript_labels_folder,
'zone':zone_detection_labels_folder}
for label_path in [dict_labels_folders['catmus'],dict_labels_folders['emanuskript'],dict_labels_folders['zone']]:
os.makedirs(label_path,exist_ok=True)
#Predict
image_files = [f for f in os.listdir(images_path) if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
prediction_tasks = []
for image_path in image_files:
image_id = os.path.splitext(os.path.basename(image_path))[0]
image_full_path = os.path.join(images_path, image_path)
for model in dict_labels_folders.keys():
task = TaskModel(
name=f"Predicting {model} annotations for {image_id}",
dataset_id=self.id,
group="Annotation Prediction"
)
task.save()
prediction_tasks.append(predict_annotations.s(task.id, model, image_full_path,image_id,dict_labels_folders))
# List to hold the task details for each image
unify_task = TaskModel(
name=f"Unifying annotations for dataset {self.name}",
dataset_id=self.id,
group="Annotation Prediction"
)
unify_task.save()
# This task will be triggered after all image predictions are completed
unify_task_signature = unify_predictions.s(unify_task.id, self.id, images_path, dict_labels_folders)
# Use Celery `chord` to handle the parallel predictions and trigger unification
chord(prediction_tasks)(unify_task_signature)
return {
"unify_task_id": unify_task.id,
}
def export_coco(self, categories=None, style="COCO", with_empty_images=False):
from workers.tasks import export_annotations
if categories is None or len(categories) == 0:
categories = self.categories
task = TaskModel(
name=f"Exporting {self.name} into {style} format",
dataset_id=self.id,
group="Annotation Export"
)
task.save()
cel_task = export_annotations.delay(task.id, self.id, categories, with_empty_images)
return {
"celery_id": cel_task.id,
"id": task.id,
"name": task.name
}
def scan(self):
from workers.tasks import scan_dataset
task = TaskModel(
name=f"Scanning {self.name} for new images",
dataset_id=self.id,
group="Directory Image Scan"
)
task.save()
cel_task = scan_dataset.delay(task.id, self.id)
return {
"celery_id": cel_task.id,
"id": task.id,
"name": task.name
}
def is_owner(self, user):
if user.is_admin:
return True
return user.username.lower() == self.owner.lower()
def can_download(self, user):
return self.is_owner(user)
def can_delete(self, user):
return self.is_owner(user)
def can_share(self, user):
return self.is_owner(user)
def can_generate(self, user):
return self.is_owner(user)
def can_edit(self, user):
return user.username in self.users or self.is_owner(user)
def permissions(self, user):
return {
'owner': self.is_owner(user),
'edit': self.can_edit(user),
'share': self.can_share(user),
'generate': self.can_generate(user),
'delete': self.can_delete(user),
'download': self.can_download(user)
}
__all__ = ["DatasetModel"]
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