test corrected

Author: agyurjin

tests/converter_test/test_coco.py

@@ -5,121 +5,102 @@
 import superannotate as sa
 
 
-def test_coco_vector_instance(tmpdir):
-    input_dir = Path(
-        "tests"
-    ) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
-    out_dir = Path(tmpdir) / "instance_vector"
-    sa.import_annotation(
-        input_dir, out_dir, "COCO", "instances_test", "Vector",
-        "instance_segmentation"
-    )
-
-    project_name = "coco2sa_vector_instance"
-
+def upload_project(project_path, project_name, description, ptype):
     projects = sa.search_projects(project_name, True)
     if projects:
         sa.delete_project(projects[0])
-    project = sa.create_project(project_name, "converter vector", "Vector")
+    project = sa.create_project(project_name, description, ptype)
 
     sa.create_annotation_classes_from_classes_json(
-        project, out_dir / "classes" / "classes.json"
+        project, project_path / "classes" / "classes.json"
     )
-    sa.upload_images_from_folder_to_project(project, out_dir)
-    sa.upload_annotations_from_folder_to_project(project, out_dir)
+    sa.upload_images_from_folder_to_project(project, project_path)
+    sa.upload_annotations_from_folder_to_project(project, project_path)
 
 
-def test_coco_vector_object(tmpdir):
+def test_coco_vector_instance(tmpdir):
+    project_name = "coco2sa_vector_instance"
+
     input_dir = Path(
         "tests"
     ) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
-    out_dir = Path(tmpdir) / "object_vector_desktop"
+    out_dir = Path(tmpdir) / project_name
     sa.import_annotation(
         input_dir, out_dir, "COCO", "instances_test", "Vector",
-        "object_detection"
+        "instance_segmentation"
     )
 
+    description = 'coco vector instance segmentation'
+    ptype = 'Vector'
+    upload_project(out_dir, project_name, description, ptype)
+
+
+def test_coco_vector_object(tmpdir):
+    project_name = "coco2sa_vector_object"
 
-def test_coco_vector_object2(tmpdir):
     input_dir = Path(
         "tests"
     ) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
-    out_dir = Path(tmpdir) / "object_vector_object_new"
+    out_dir = Path(tmpdir) / project_name
     sa.import_annotation(
         input_dir, out_dir, "COCO", "instances_test", "Vector",
         "object_detection"
     )
-    project_name = "coco2sa_vector_object"
 
-    projects = sa.search_projects(project_name, True)
-    if projects:
-        sa.delete_project(projects[0])
-    project = sa.create_project(project_name, "converter vector", "Vector")
+    description = 'coco vector object detection'
+    ptype = 'Vector'
+    upload_project(out_dir, project_name, description, ptype)
 
-    sa.create_annotation_classes_from_classes_json(
-        project, out_dir / "classes" / "classes.json"
-    )
-    sa.upload_images_from_folder_to_project(project, out_dir)
-    sa.upload_annotations_from_folder_to_project(project, out_dir)
 
+def test_coco_vector_keypoint(tmpdir):
+    project_name = "coco2sa_keypoint"
 
-def test_coco_vector_object_instance(tmpdir):
     input_dir = Path(
         "tests"
-    ) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
-    out_dir = Path(tmpdir) / "object_vector_instance_desktop"
+    ) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "keypoint_detection/"
+    out_dir = Path(tmpdir) / project_name
     sa.import_annotation(
-        input_dir, out_dir, "COCO", "instances_test", "Vector",
-        "instance_segmentation"
+        input_dir, out_dir, "COCO", "person_keypoints_test", "Vector",
+        "keypoint_detection"
     )
 
+    description = 'coco vector keypoint detection'
+    ptype = 'Vector'
+    upload_project(out_dir, project_name, description, ptype)
+
+
+def test_coco_panoptic(tmpdir):
+    project_name = "coco2sa_panoptic"
 
-def test_coco_pixel_instance(tmpdir):
     input_dir = Path(
         "tests"
-    ) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
-    out_dir = Path(tmpdir) / "instance_pixel"
+    ) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "panoptic_segmentation"
+    out_dir = Path(tmpdir) / project_name
     sa.import_annotation(
-        input_dir, out_dir, "COCO", "instances_test", "Pixel",
-        "instance_segmentation"
+        input_dir, out_dir, "COCO", "panoptic_test", "Pixel",
+        "panoptic_segmentation"
     )
 
-    project_name = "coco2sa_pixel_instance"
+    description = 'coco pixel panoptic segmentation'
+    ptype = 'Pixel'
+    upload_project(out_dir, project_name, description, ptype)
 
-    projects = sa.search_projects(project_name, True)
-    if projects:
-        sa.delete_project(projects[0])
-    project = sa.create_project(project_name, "converter vector", "Pixel")
-
-    sa.create_annotation_classes_from_classes_json(
-        project, out_dir / "classes" / "classes.json"
-    )
-    sa.upload_images_from_folder_to_project(project, out_dir)
-    sa.upload_annotations_from_folder_to_project(project, out_dir)
 
+def test_coco_pixel_instance(tmpdir):
+    project_name = "coco2sa_pixel_instance"
 
-def test_coco_vector_keypoint(tmpdir):
     input_dir = Path(
         "tests"
-    ) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "keypoint_detection/"
-    out_dir = Path(tmpdir) / "vector_keypoint"
+    ) / "converter_test" / "COCO" / "input" / "toSuperAnnotate" / "instance_segmentation"
+    out_dir = Path(tmpdir) / project_name
     sa.import_annotation(
-        input_dir, out_dir, "COCO", "person_keypoints_test", "Vector",
-        "keypoint_detection"
+        input_dir, out_dir, "COCO", "instances_test", "Pixel",
+        "instance_segmentation"
     )
 
-    project_name = "coco2sa_keypoint"
-
-    projects = sa.search_projects(project_name, True)
-    if projects:
-        sa.delete_project(projects[0])
-    project = sa.create_project(project_name, "converter vector", "Vector")
-
-    sa.create_annotation_classes_from_classes_json(
-        project, out_dir / "classes" / "classes.json"
-    )
-    sa.upload_images_from_folder_to_project(project, out_dir)
-    sa.upload_annotations_from_folder_to_project(project, out_dir)
+    description = 'coco pixel instance segmentation'
+    ptype = 'Pixel'
+    upload_project(out_dir, project_name, description, ptype)
 
 
 def test_sa_to_coco_to_sa(tmpdir):
@@ -138,13 +119,6 @@ def test_sa_to_coco_to_sa(tmpdir):
     )
 
     project_name = 'coco_pipeline_new'
-    project = sa.search_projects(project_name, return_metadata=True)
-    for pr in project:
-        sa.delete_project(pr)
-
-    project = sa.create_project(project_name, 'test_instane', 'Pixel')
-    sa.upload_images_from_folder_to_project(project, output2)
-    sa.create_annotation_classes_from_classes_json(
-        project, output2 / "classes" / "classes.json"
-    )
-    sa.upload_annotations_from_folder_to_project(project, output2)
+    description = 'test_instane'
+    ptype = 'Pixel'
+    upload_project(output2, project_name, description, ptype)

tests/converter_test/test_dataloop.py

@@ -3,55 +3,61 @@
 import superannotate as sa
 
 
+def upload_project(project_path, project_name, description, ptype):
+    projects = sa.search_projects(project_name, True)
+    if projects:
+        sa.delete_project(projects[0])
+    project = sa.create_project(project_name, description, ptype)
+
+    sa.create_annotation_classes_from_classes_json(
+        project, project_path / "classes" / "classes.json"
+    )
+    sa.upload_images_from_folder_to_project(project, project_path)
+    sa.upload_annotations_from_folder_to_project(project, project_path)
+
+
 def test_dataloop_convert_vector(tmpdir):
+    project_name = "dataloop2sa_vector_annotation"
+
     input_dir = Path(
         'tests'
     ) / 'converter_test' / 'DataLoop' / 'input' / 'toSuperAnnotate'
-    out_dir = Path(tmpdir) / 'output_vector'
+    out_dir = Path(tmpdir) / project_name
     sa.import_annotation(
         input_dir, out_dir, 'DataLoop', '', 'Vector', 'vector_annotation'
     )
-    project_name = "dataloop_test_vector"
 
-    projects = sa.search_projects(project_name, True)
-    if projects:
-        sa.delete_project(projects[0])
-    project = sa.create_project(project_name, "converter vector", "Vector")
-
-    sa.create_annotation_classes_from_classes_json(
-        project, out_dir / "classes" / "classes.json"
-    )
-    sa.upload_images_from_folder_to_project(project, out_dir)
-    sa.upload_annotations_from_folder_to_project(project, out_dir)
+    description = 'dataloop vector annotation'
+    ptype = 'Vector'
+    upload_project(out_dir, project_name, description, ptype)
 
 
 def test_dataloop_convert_object(tmpdir):
+    project_name = "dataloop2sa_vector_object"
+
     input_dir = Path(
         'tests'
     ) / 'converter_test' / 'DataLoop' / 'input' / 'toSuperAnnotate'
-    out_dir = Path(tmpdir) / 'output_object'
+    out_dir = Path(tmpdir) / project_name
     sa.import_annotation(
         input_dir, out_dir, 'DataLoop', '', 'Vector', 'object_detection'
     )
 
+    description = 'dataloop object detection'
+    ptype = 'Vector'
+    upload_project(out_dir, project_name, description, ptype)
+
 
 def test_dataloop_convert_instance(tmpdir):
+    project_name = "dataloop2sa_vector_instance"
+
     input_dir = Path(
         'tests'
     ) / 'converter_test' / 'DataLoop' / 'input' / 'toSuperAnnotate'
-    out_dir = Path(tmpdir) / 'output_instance'
+    out_dir = Path(tmpdir) / project_name
     sa.import_annotation(
         input_dir, out_dir, 'DataLoop', '', 'Vector', 'instance_segmentation'
     )
-    project_name = "dataloop_test_instance"
-
-    projects = sa.search_projects(project_name, True)
-    if projects:
-        sa.delete_project(projects[0])
-    project = sa.create_project(project_name, "converter vector", "Vector")
-
-    sa.create_annotation_classes_from_classes_json(
-        project, out_dir / "classes" / "classes.json"
-    )
-    sa.upload_images_from_folder_to_project(project, out_dir)
-    sa.upload_annotations_from_folder_to_project(project, out_dir)
+    description = 'dataloop instance segmentation'
+    ptype = 'Vector'
+    upload_project(out_dir, project_name, description, ptype)

tests/converter_test/test_googlecloud.py

@@ -2,36 +2,31 @@
 import superannotate as sa
 
 
-def test_googlecloud_convert_web(tmpdir):
-    input_dir = Path(
-        "tests"
-    ) / "converter_test" / "GoogleCloud" / "input" / "toSuperAnnotate"
-    out_dir = Path(tmpdir) / "output_web"
-    sa.import_annotation(
-        input_dir, out_dir, "GoogleCloud", "image_object_detection", "Vector",
-        "object_detection"
-    )
-
-    project_name = "googlcloud_vector"
-
+def upload_project(project_path, project_name, description, ptype):
     projects = sa.search_projects(project_name, True)
     if projects:
         sa.delete_project(projects[0])
-    project = sa.create_project(project_name, "converter vector", "Vector")
+    project = sa.create_project(project_name, description, ptype)
 
     sa.create_annotation_classes_from_classes_json(
-        project, out_dir / "classes" / "classes.json"
+        project, project_path / "classes" / "classes.json"
     )
-    sa.upload_images_from_folder_to_project(project, out_dir)
-    sa.upload_annotations_from_folder_to_project(project, out_dir)
+    sa.upload_images_from_folder_to_project(project, project_path)
+    sa.upload_annotations_from_folder_to_project(project, project_path)
 
 
-def test_googlecloud_convert_desktop(tmpdir):
+def test_googlecloud_convert_web(tmpdir):
+    project_name = "googlcloud_object"
+
     input_dir = Path(
         "tests"
     ) / "converter_test" / "GoogleCloud" / "input" / "toSuperAnnotate"
-    out_dir = Path(tmpdir) / "output_desktop"
+    out_dir = Path(tmpdir) / project_name
     sa.import_annotation(
         input_dir, out_dir, "GoogleCloud", "image_object_detection", "Vector",
         "object_detection"
     )
+
+    description = 'googlecloud object detection'
+    ptype = 'Vector'
+    upload_project(out_dir, project_name, description, ptype)