Fix dataframe to json output

Author: hovnatan

superannotate/analytics/common.py

@@ -24,11 +24,16 @@ def df_to_annotations(df, output_dir):
     project_suffix = "objects.json"
     images = df["imageName"].dropna().unique()
     for image in images:
+        image_status = None
+        image_pinned = None
+        image_height = None
+        image_width = None
         image_df = df[df["imageName"] == image]
         image_annotation = []
         instances = image_df["instanceId"].dropna().unique()
         for instance in instances:
             instance_df = image_df[image_df["instanceId"] == instance]
+            # print(instance_df["instanceId"])
             annotation_type = instance_df.iloc[0]["type"]
             annotation_meta = instance_df.iloc[0]["meta"]
 
@@ -60,6 +65,10 @@ def df_to_annotations(df, output_dir):
                         }
                     )
             image_annotation.append(instance_annotation)
+            image_width = image_width or instance_df.iloc[0]["imageWidth"]
+            image_height = image_height or instance_df.iloc[0]["imageHeight"]
+            image_pinned = image_pinned or instance_df.iloc[0]["imagePinned"]
+            image_status = image_status or instance_df.iloc[0]["imageStatus"]
 
         comments = image_df[image_df["type"] == "comment"]
         for _, comment in comments.iterrows():
@@ -68,6 +77,14 @@ def df_to_annotations(df, output_dir):
             comment_json["resolved"] = comment["commentResolved"]
             image_annotation.append(comment_json)
 
+        meta = {
+            "type": "meta",
+            "width": int(image_width),
+            "height": int(image_height),
+            "status": image_status,
+            "pinned": bool(image_pinned)
+        }
+        image_annotation.append(meta)
         json.dump(
             image_annotation,
             open(output_dir / f"{image}___{project_suffix}", "w"),
@@ -232,7 +249,7 @@ def __get_user_metadata(annotation):
         if annotation_updated_by:
             annotation_updator_email = annotation_updated_by.get("email")
             annotation_updator_role = annotation_updated_by.get("role")
-        user_metadata = {                   
+        user_metadata = {
             "createdAt": annotation_created_at,
             "creatorRole": annotation_creator_role,
             "creatorEmail": annotation_creator_email,
@@ -242,17 +259,19 @@ def __get_user_metadata(annotation):
             "updatorEmail": annotation_updator_email
         }
         return user_metadata
-    
+
     annotations_paths = []
-    
+
     for path in Path(project_root).glob('*.json'):
         annotations_paths.append(path)
 
     if not annotations_paths:
         logger.warning(
             "No annotations found in project export root %s", project_root
         )
-    type_postfix = "___objects.json" if glob.glob("{}/*___objects.json".format(project_root)) else "___pixel.json"
+    type_postfix = "___objects.json" if glob.glob(
+        "{}/*___objects.json".format(project_root)
+    ) else "___pixel.json"
     for annotation_path in annotations_paths:
         annotation_json = json.load(open(annotation_path))
         image_name = annotation_path.name.split(type_postfix)[0]
@@ -290,7 +309,6 @@ def __get_user_metadata(annotation):
                     annotation_dict.update(image_metadata)
                     __append_annotation(annotation_dict)
                 continue
-            annotation_instance_id += 1
             annotation_class_name = annotation.get("className")
             if annotation_class_name is None or annotation_class_name not in class_name_to_color:
                 logger.warning(
@@ -328,6 +346,7 @@ def __get_user_metadata(annotation):
             annotation_point_labels = annotation.get("pointLabels")
             attributes = annotation.get("attributes")
             user_metadata = __get_user_metadata(annotation)
+            num_added = 0
             if not attributes:
                 annotation_dict = {
                     "imageName": image_name,
@@ -347,6 +366,7 @@ def __get_user_metadata(annotation):
                 annotation_dict.update(user_metadata)
                 annotation_dict.update(image_metadata)
                 __append_annotation(annotation_dict)
+                num_added = 1
             else:
                 for attribute in attributes:
                     attribute_group = attribute.get("groupName")
@@ -385,6 +405,10 @@ def __get_user_metadata(annotation):
                     annotation_dict.update(user_metadata)
                     annotation_dict.update(image_metadata)
                     __append_annotation(annotation_dict)
+                    num_added += 1
+
+            if num_added > 0:
+                annotation_instance_id += 1
 
     df = pd.DataFrame(annotation_data)
 

superannotate/version.py

@@ -1 +1 @@
-__version__ = "2.4.0"
+__version__ = "2.4.1"

tests/test_create_from_full_info.py

@@ -1,16 +1,10 @@
-from pathlib import Path
-
 import superannotate as sa
 
-import pytest
-
 PROJECT_NAME1 = "test create from full info1"
 PROJECT_NAME2 = "test create from full info2"
 
 
-def test_create_from_full_info(tmpdir):
-    tmpdir = Path(tmpdir)
-
+def test_create_from_full_info():
     projects = sa.search_projects(PROJECT_NAME1, return_metadata=True)
     for project in projects:
         sa.delete_project(project)
@@ -27,7 +21,7 @@ def test_create_from_full_info(tmpdir):
     for setting in old_settings:
         if "attribute" in setting and setting["attribute"] == "Brightness":
             brightness_value = setting["value"]
-    new_settings = sa.set_project_settings(
+    sa.set_project_settings(
         PROJECT_NAME1,
         [{
             "attribute": "Brightness",

tests/test_filter_instances.py

@@ -173,85 +173,94 @@ def test_filter_instances(tmpdir):
     # print(not_filtered[not_filtered["className"] == "Human
 
 
-# def test_df_to_annotations(tmpdir):
-#     tmpdir = Path(tmpdir)
-
-#     df = sa.aggregate_annotations_as_df(PROJECT_DIR)
-#     sa.df_to_annotations(df, tmpdir)
-#     df_new = sa.aggregate_annotations_as_df(tmpdir)
-
-#     print(df_new["imageName"].value_counts())
-#     print(df["imageName"].value_counts())
-
-#     for _index, row in enumerate(df.iterrows()):
-#         for _, row_2 in enumerate(df_new.iterrows()):
-#             if row_2[1].equals(row[1]):
-#                 break
-#         else:
-#             assert False
-#     for project in sa.search_projects("test df to annotations 2"):
-#         sa.delete_project(project)
-#     project = sa.create_project("test df to annotations 2", "test", "Vector")
-#     sa.upload_images_from_folder_to_project(
-#         project, "./tests/sample_project_vector"
-#     )
-#     sa.create_annotation_classes_from_classes_json(
-#         project, "./tests/sample_project_vector/classes/classes.json"
-#     )
-#     sa.upload_annotations_from_folder_to_project(
-#         project, "./tests/sample_project_vector"
-#     )
-
-# def test_df_to_annotations_full(tmpdir):
-#     tmpdir = Path(tmpdir)
-
-#     df = sa.aggregate_annotations_as_df(
-#         PROJECT_DIR, include_classes_wo_annotations=True, include_comments=True
-#     )
-#     sa.df_to_annotations(df, tmpdir)
-#     df_new = sa.aggregate_annotations_as_df(
-#         tmpdir, include_classes_wo_annotations=True, include_comments=True
-#     )
-#     for project in sa.search_projects("test df to annotations 4"):
-#         sa.delete_project(project)
-#     project = sa.create_project("test df to annotations 4", "test", "Vector")
-#     sa.upload_images_from_folder_to_project(project, PROJECT_DIR)
-#     sa.create_annotation_classes_from_classes_json(
-#         project, tmpdir / "classes" / "classes.json"
-#     )
-#     sa.upload_annotations_from_folder_to_project(project, tmpdir)
-#     # print(df_new["image_name"].value_counts())
-#     # print(df["image_name"].value_counts())
-#     for _index, row in enumerate(df.iterrows()):
-#         for _, row_2 in enumerate(df_new.iterrows()):
-#             if row_2[1].equals(row[1]):
-#                 break
-#         else:
-#             assert False
-
-#     fil1 = sa.filter_annotation_instances(
-#         df_new,
-#         include=[
-#             {
-#                 "className": "Personal vehicle",
-#                 "attributes": [{
-#                     "name": "4",
-#                     "groupName": "Num doors"
-#                 }]
-#             }
-#         ],
-#         exclude=[{
-#             "type": "polygon"
-#         }]
-#     )
-#     filtered_export = (tmpdir / "filtered")
-#     filtered_export.mkdir()
-#     sa.df_to_annotations(fil1, filtered_export)
-#     for project in sa.search_projects("test df to annotations 3"):
-#         sa.delete_project(project)
-#     project = sa.create_project("test df to annotations 3", "test", "Vector")
-#     sa.upload_images_from_folder_to_project(project, PROJECT_DIR)
-#     sa.create_annotation_classes_from_classes_json(
-#         project, filtered_export / "classes" / "classes.json"
-#     )
-#     sa.upload_annotations_from_folder_to_project(project, filtered_export)
+def test_df_to_annotations(tmpdir):
+    tmpdir = Path(tmpdir)
+
+    df = sa.aggregate_annotations_as_df(PROJECT_DIR)
+    sa.df_to_annotations(df, tmpdir)
+    df_new = sa.aggregate_annotations_as_df(tmpdir)
+
+    assert len(df) == len(df_new)
+    # print(df_new["imageName"].value_counts())
+    # print(df["imageName"].value_counts())
+    # print(len(df.columns))
+    # print(len(df_new.columns))
+
+    # print(df[df["imageName"] == "example_image_1.jpg"]["instanceId"])
+    # print(df_new[(df_new["imageName"] == "example_image_1.jpg")]["instanceId"])
+    for _index, row in enumerate(df.iterrows()):
+        for _, row_2 in enumerate(df_new.iterrows()):
+            if row_2[1].equals(row[1]):
+                break
+            # if row_2[1]["imageName"] == "example_image_1.jpg":
+            #     print(row_2[1])
+        else:
+            assert False, print("Error on ", row[1])
+
+    for project in sa.search_projects("test df to annotations 2"):
+        sa.delete_project(project)
+    project = sa.create_project("test df to annotations 2", "test", "Vector")
+    sa.upload_images_from_folder_to_project(
+        project, "./tests/sample_project_vector"
+    )
+    sa.create_annotation_classes_from_classes_json(
+        project, "./tests/sample_project_vector/classes/classes.json"
+    )
+    sa.upload_annotations_from_folder_to_project(
+        project, "./tests/sample_project_vector"
+    )
+
+
+def test_df_to_annotations_full(tmpdir):
+    tmpdir = Path(tmpdir)
+
+    df = sa.aggregate_annotations_as_df(
+        PROJECT_DIR, include_classes_wo_annotations=True, include_comments=True
+    )
+    sa.df_to_annotations(df, tmpdir)
+    df_new = sa.aggregate_annotations_as_df(
+        tmpdir, include_classes_wo_annotations=True, include_comments=True
+    )
+    for project in sa.search_projects("test df to annotations 4"):
+        sa.delete_project(project)
+    project = sa.create_project("test df to annotations 4", "test", "Vector")
+    sa.upload_images_from_folder_to_project(project, PROJECT_DIR)
+    sa.create_annotation_classes_from_classes_json(
+        project, tmpdir / "classes" / "classes.json"
+    )
+    sa.upload_annotations_from_folder_to_project(project, tmpdir)
+    # print(df_new["image_name"].value_counts())
+    # print(df["image_name"].value_counts())
+    for _index, row in enumerate(df.iterrows()):
+        for _, row_2 in enumerate(df_new.iterrows()):
+            if row_2[1].equals(row[1]):
+                break
+        else:
+            assert False
+
+    fil1 = sa.filter_annotation_instances(
+        df_new,
+        include=[
+            {
+                "className": "Personal vehicle",
+                "attributes": [{
+                    "name": "4",
+                    "groupName": "Num doors"
+                }]
+            }
+        ],
+        exclude=[{
+            "type": "polygon"
+        }]
+    )
+    filtered_export = (tmpdir / "filtered")
+    filtered_export.mkdir()
+    sa.df_to_annotations(fil1, filtered_export)
+    for project in sa.search_projects("test df to annotations 3"):
+        sa.delete_project(project)
+    project = sa.create_project("test df to annotations 3", "test", "Vector")
+    sa.upload_images_from_folder_to_project(project, PROJECT_DIR)
+    sa.create_annotation_classes_from_classes_json(
+        project, filtered_export / "classes" / "classes.json"
+    )
+    sa.upload_annotations_from_folder_to_project(project, filtered_export)