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Merge pull request #626 from superannotateai/fix_issues
fix issues
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‎docs/source/api_reference/helpers.rst‎

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@@ -22,10 +22,3 @@ ________________________
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.. _ref_aggregate_annotations_as_df:
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.. automethod:: superannotate.SAClient.validate_annotations
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.. automethod:: superannotate.SAClient.aggregate_annotations_as_df
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----------
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Utility functions
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--------------------------------
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.. autofunction:: superannotate.SAClient.consensus

‎docs/source/userguide/quickstart.rst‎

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@@ -25,13 +25,6 @@ It can be installed on Ubuntu with:
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sudo apt-get install ffmpeg
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To use the :py:obj:`consensus` function on Windows and Mac platforms, you might also need to install the shapely package
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beforehand. The package works well only under the Anaconda distribution with:
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.. code-block:: bash
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conda install shapely
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----------
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‎docs/source/userguide/utilities.rst‎

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@@ -126,64 +126,3 @@ Example of created DataFrame:
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Each row represents annotation information. One full annotation with multiple
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attribute groups can be grouped under :code:`instanceId` field.
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Working with DICOM files
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------------------------
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JPEG images with names :file:`<dicom_file_name>_<frame_num>.jpg` will be created
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in :file:`<path_to_output_dir>`. Those JPEG images can be uploaded to
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SuperAnnotate platform using the regular:
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.. code-block:: python
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sa.upload_images_from_folder_to_project(project, "<path_to_output_dir>")
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Some DICOM files can have image frames that are compressed. To load them, `GDCM :
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Grassroots DICOM library <http://gdcm.sourceforge.net/wiki/index.php/Main_Page>`_ needs to be installed:
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.. code-block:: bash
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# using conda
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conda install -c conda-forge gdcm
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# or on Ubuntu with versions above 19.04
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sudo apt install python3-gdcm
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Computing consensus scores for instances between several projects
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-----------------------------------------------------------------
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Consensus is a tool to compare the quallity of the annotations of the same image that is present in several projects.
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To compute the consensus scores:
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.. code-block:: python
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res_df = sa.consensus([project_names], "<path_to_export_folder>", [image_list], "<annotation_type>")
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Here pandas DataFrame with following columns is returned: creatorEmail, imageName, instanceId, className, area, attribute, projectName, score
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.. image:: images/consensus_dataframe.png
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Besides the pandas DataFrame there is an option to get the following plots by setting the show_plots flag to True:
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* Box plot of consensus scores for each annotators
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* Box plot of consensus scores for each project
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* Scatter plots of consensus score vs instance area for each project
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.. code-block:: python
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sa.consensus([project_names], "<path_to_export_folder>", [image_list], "<annotation_type>", show_plots=True)
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To the left of each box plot the original score points of that annotator is depicted, the box plots are colored by annotator.
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.. image:: images/consensus_annotators_box.png
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Analogically the box plots of consensus scores for each project are colored according to project name.
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.. image:: images/consensus_projects_box.png
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Scatter plot of consensus score vs instance area is separated by projects. Hovering on a point reveals its annotator and image name.
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The points are colored according to class name. Each annotator is represented with separate symbol.
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.. image:: images/consensus_scatter.png

‎src/superannotate/lib/app/interface/sdk_interface.py‎

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@@ -1514,10 +1514,6 @@ def download_export(
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to_s3_bucket=None,
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):
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"""Download prepared export.
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WARNING: Starting from version 1.9.0 :ref:`download_export <ref_download_export>` additionally
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requires :py:obj:`project` as first argument.
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:param project: project name
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:type project: str
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:param export: export name

‎src/superannotate/lib/core/__init__.py‎

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@@ -153,6 +153,36 @@ def setup_logging(level=DEFAULT_LOGGING_LEVEL, file_path=LOG_FILE_LOCATION):
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INVALID_JSON_MESSAGE = "Invalid json"
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PROJECT_SETTINGS_VALID_ATTRIBUTES = [
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"Brightness",
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"Fill",
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"Contrast",
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"ShowLabels",
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"ShowComments",
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"Image",
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"Lines",
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"AnnotatorFinish",
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"PointSize",
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"FontSize",
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"WorkflowEnable",
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"ClassChange",
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"ShowEntropy",
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"UploadImages",
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"DeleteImages",
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"Download",
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"RunPredictions",
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"RunSegmentations",
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"ImageQuality",
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"ImageAutoAssignCount",
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"FrameMode",
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"FrameRate",
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"JumpBackward",
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"JumpForward",
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"UploadFileType",
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"Tokenization",
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"ImageAutoAssignEnable",
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]
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__alL__ = (
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FolderStatus,
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ProjectStatus,

‎src/superannotate/lib/core/repositories.py‎

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@@ -36,11 +36,13 @@ def __init__(
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secret_key: str,
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session_token: str,
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bucket: str,
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region: str,
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):
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self._session = boto3.Session(
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aws_access_key_id=access_key,
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aws_secret_access_key=secret_key,
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aws_session_token=session_token,
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region_name=region,
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)
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self._resource = self._session.resource("s3")

‎src/superannotate/lib/core/usecases/annotations.py‎

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@@ -591,13 +591,20 @@ def prepare_annotation(self, annotation: dict, size) -> dict:
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)
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return annotation
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@staticmethod
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def get_mask_path(path: str) -> str:
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if path.endswith(constants.PIXEL_ANNOTATION_POSTFIX):
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replacement = constants.PIXEL_ANNOTATION_POSTFIX
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else:
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replacement = ".json"
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parts = path.rsplit(replacement, 1)
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return constants.ANNOTATION_MASK_POSTFIX.join(parts)
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async def get_annotation(
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self, path: str
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) -> (Optional[Tuple[io.StringIO]], Optional[io.BytesIO]):
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mask = None
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mask_path = path.replace(
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constants.PIXEL_ANNOTATION_POSTFIX, constants.ANNOTATION_MASK_POSTFIX
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)
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mask_path = self.get_mask_path(path)
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if self._client_s3_bucket:
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content = self.get_annotation_from_s3(self._client_s3_bucket, path).read()
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if self._project.type == constants.ProjectType.PIXEL.value:

‎src/superannotate/lib/core/usecases/images.py‎

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@@ -756,6 +756,7 @@ def s3_repo(self):
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self._auth_data.data["secretAccessKey"],
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self._auth_data.data["sessionToken"],
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self._auth_data.data["bucket"],
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self._auth_data.data["region"],
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)
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def validate_project_type(self):
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self.auth_data["secretAccessKey"],
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self.auth_data["sessionToken"],
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self.auth_data["bucket"],
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self.auth_data["region"],
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)
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return self._s3_repo_instance
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‎src/superannotate/lib/core/usecases/projects.py‎

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@@ -177,7 +177,7 @@ def __init__(
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def validate_settings(self):
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for setting in self._project.settings[:]:
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if setting.attribute == "WorkflowType":
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if setting.attribute not in constances.PROJECT_SETTINGS_VALID_ATTRIBUTES:
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self._project.settings.remove(setting)
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if setting.attribute == "ImageQuality" and isinstance(setting.value, str):
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setting.value = constances.ImageQuality.get_value(setting.value)
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self._project = project
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self._service_provider = service_provider
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def validate_settings(self):
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for setting in self._project.settings[:]:
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if setting.attribute not in constances.PROJECT_SETTINGS_VALID_ATTRIBUTES:
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self._project.settings.remove(setting)
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if setting.attribute == "ImageQuality" and isinstance(setting.value, str):
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setting.value = constances.ImageQuality.get_value(setting.value)
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elif setting.attribute == "FrameRate":
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if not self._project.type == constances.ProjectType.VIDEO.value:
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raise AppValidationException(
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"FrameRate is available only for Video projects"
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)
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try:
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setting.value = float(setting.value)
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if (
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not (0.0001 < setting.value < 120)
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or decimal.Decimal(str(setting.value)).as_tuple().exponent < -3
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):
355+
raise AppValidationException(
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"The FrameRate value range is between 0.001 - 120"
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)
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frame_mode = next(
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filter(
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lambda x: x.attribute == "FrameMode", self._project.settings
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),
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None,
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)
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if not frame_mode:
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self._project.settings.append(
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SettingEntity(attribute="FrameMode", value=1)
367+
)
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except ValueError:
369+
raise AppValidationException("The FrameRate value should be float")
370+
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def validate_project_name(self):
339372
if self._project.name:
340373
if (
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489522
new_settings_to_update = []
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for new_setting in self._to_update:
491-
new_settings_to_update.append(
492-
SettingEntity(
493-
id=attr_id_mapping[new_setting["attribute"]],
494-
attribute=new_setting["attribute"],
495-
value=new_setting["value"],
524+
if (
525+
new_setting["attribute"]
526+
in constances.PROJECT_SETTINGS_VALID_ATTRIBUTES
527+
):
528+
new_settings_to_update.append(
529+
SettingEntity(
530+
id=attr_id_mapping[new_setting["attribute"]],
531+
attribute=new_setting["attribute"],
532+
value=new_setting["value"],
533+
)
496534
)
497-
)
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response = self._service_provider.projects.set_settings(
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project=self._project,

‎src/superannotate/lib/infrastructure/controller.py‎

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@@ -214,6 +214,7 @@ def _get_s3_repository(self, project: ProjectEntity, folder: FolderEntity):
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auth_data["secretAccessKey"],
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auth_data["sessionToken"],
216216
auth_data["bucket"],
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auth_data["region"],
217218
)
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219220
def create(self, project: ProjectEntity, annotation_class: AnnotationClassEntity):

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