Removed code from other branch

Author: LeoEATLinden

src/superannotate_databricks_connector/vector.py

@@ -160,116 +160,3 @@ def get_vector_dataframe(annotations, spark, custom_id_map=None):
 
     spark_df = spark.createDataFrame(rows, schema=schema)
     return spark_df
-
-
-def process_attribute(attribute):
-    return {"groupName": attribute["groupName"],
-            "name": attribute["name"]}
-
-
-def create_instance_dict(item):
-    """Funciton that unpacks a dataframe row converted into a dict
-    and transformes it to a superannotate annotaiton dictionary
-
-    Arg:
-        item (dict): dictionary with a instance in the superannotate-databricks
-                     format
-    Returns:
-        dict processed into superannotate json format
-    """
-    item_dict = {
-        "type": item["instance_type"],
-        "classId": int(item["classId"]),
-        "probability": item["probability"],
-        "groupId": int(item["groupId"]),
-        # "pointLabels": item.get("pointLabels", None), # uncomment if needed
-        "locked": item["locked"],
-        "trackingId": item["trackingId"],
-        "attributes": [process_attribute(att) for att in item["attributes"]],
-        "error": item["error"],
-        "createdAt": item["createdAt"],
-        "createdBy": item["createdBy"],
-        "creationType": item["creationType"],
-        "updatedAt": item["updatedAt"],
-        "updatedBy": item["updatedBy"],
-        "className": item["className"]
-    }
-
-    instance_type = item["instance_type"]
-    if instance_type in ["bbox", "rbbox"]:
-        item_dict["points"] = item[instance_type].asDict()
-    elif instance_type == "cuboid":
-        item_dict["points"] = {k: v.asDict()
-                               for k, v in item["cuboid"].asDict().items()}
-    elif instance_type == "polygon":
-        item_dict["points"] = item["polygon"].asDict()["points"]
-        item_dict["exclude"] = item["polygon"].asDict()["exclude"]
-    elif instance_type == "point":
-        item_dict["x"] = item["point"].asDict()["x"]
-        item_dict["y"] = item["point"].asDict()["y"]
-    elif instance_type == "ellipse":
-        ellipse = item["ellipse"].asDict()
-        item_dict["cx"] = ellipse["cx"]
-        item_dict["cy"] = ellipse["cy"]
-        item_dict["rx"] = ellipse["rx"]
-        item_dict["ry"] = ellipse["ry"]
-        item_dict["angle"] = ellipse["angle"]
-    elif instance_type == "polyline":
-        item_dict["points"] = item["polyline"]
-    return item_dict
-
-
-def create_tag_dict(item):
-    """Funciton that unpacks a dataframe row converted into a dict
-    and transformes it to a superannotate annotaiton dictionary
-
-    Arg:
-        item (dict): dictionary with a tag in the superannotate-databricks
-                     format
-    Returns:
-        dict processed into superannotate json format
-    """
-    item_dict = {
-        "type": item["instance_type"],
-        "classId": item["classId"],
-        "probability": item["probability"],
-        "attributes": [process_attribute(att) for att in item["attributes"]],
-        "createdAt": item["createdAt"],
-        "createdBy": item["createdBy"],
-        "creationType": item["creationType"],
-        "updatedAt": item["updatedAt"],
-        "updatedBy": item["updatedBy"],
-        "className": item["className"]
-    }
-    return item_dict
-
-
-def unpack_vector_row(row):
-    """Funciton that unpacks a dataframe row into a
-       superannotate json ready to be uploaded
-
-    Arg:
-        row (spark.sql.Row): row from superannotate formated dataframe
-    Returns:
-        dict processed into superannotate json format
-    """
-    metadata = {"height": row["image_height"],
-                "width": row["image_width"],
-                "name": row["image_name"],
-                "projectId": row["projectId"],
-                "isPredicted": row["isPredicted"],
-                "status": row["status"],
-                "pinned": row["pinned"],
-                "annotatorEmail": row["annotatorEmail"],
-                "qaEmail": row["qaEmail"]}
-    instances = []
-    for instance in row["instances"]:
-        instances.append(create_instance_dict(instance.asDict()))
-    for tag in row["tags"]:
-        instances.append(create_tag_dict(tag.asDict()))
-    comments = []
-    for comment in row["comments"]:
-        comments.append(comment.asDict())
-    return {"metadata": metadata,
-            "instances": instances,
-            "comments": comments}