Merge pull request #616 from superannotateai/1969_import

1969 import

Author: nareksa

README.rst

@@ -18,7 +18,7 @@ Welcome to the SuperAnnotate Python Software Development Kit (SDK), which enable
 Resources
 ---------------
 
-- API Reference and User Guide available on `Read the Docs <https://superannotate.readthedocs.io/en/stable/superannotate.sdk.html>`__
+- API Reference and User Guide available on `Read the Docs <https://superannotate.readthedocs.io/en/stable/index.html>`__
 - `Platform documentation <https://doc.superannotate.com/>`__
 
 

requirements_extra.txt

@@ -11,4 +11,5 @@ pytest==7.3.1
 pytest-xdist==3.2.1
 pytest-parallel==0.1.1
 pytest-cov==4.0.0
-pytest-rerunfailures==11.1.2
+pytest-rerunfailures==11.1.2
+jsoncomparison==1.1.0

src/superannotate/lib/app/input_converters/converters/coco_converters/coco_to_sa_pixel.py

@@ -90,7 +90,7 @@ def coco_panoptic_segmentation_to_sa_pixel(coco_path, output_dir):
         cv2.imwrite(str(output_dir / ("%s___save.png" % annot["file_name"])), img)
 
         images_converted.append(annot["file_name"])
-        file_name = "%s___pixel.json" % annot["file_name"]
+        file_name = f"{annot['file_name']}.json"
         sa_metadata = {
             "name": annot_name,
             "width": img_id_to_shape[str(annot["image_id"])]["width"],
@@ -138,7 +138,7 @@ def coco_instance_segmentation_to_sa_pixel(coco_path, output_dir):
     logger.info("Converting to SuperAnnotate JSON format")
     tqdm_thread.start()
     for id_, annotations in images_dict.items():
-        file_name = "%s___pixel.json" % annotations["file_name"]
+        file_name = f"{annotations['file_name']}.json"
         hexcolors = blue_color_generator(len(annotations["annotations"]))
         mask = np.zeros(annotations["shape"])
         H, W, _ = mask.shape

src/superannotate/lib/app/input_converters/converters/coco_converters/coco_to_sa_vector.py

@@ -57,7 +57,7 @@ def save_sa_jsons(coco_json, img_id_to_annot, output_dir):
             sa_instances = []
         else:
             sa_instances = img_id_to_annot[str(img["id"])]
-        file_name = "%s___objects.json" % image_path
+        file_name = f"{image_path}.json"
 
         sa_metadata = {
             "name": image_path,

src/superannotate/lib/app/input_converters/converters/dataloop_converters/dataloop_to_sa_vector.py

@@ -105,11 +105,11 @@ def dataloop_to_sa(input_dir, task, output_dir):
                 sa_tags.append(ann["label"])
 
         if "name" in sa_metadata:
-            file_name = "%s___objects.json" % sa_metadata["name"]
+            file_name = f"{sa_metadata['name']}.json"
         else:
-            file_name = "%s___objects.json" % dl_data["filename"][1:]
+            file_name = f"{dl_data['filename'][1:]}.json"
 
-        images_converted.append(file_name.replace("___objects.json ", ""))
+        images_converted.append(file_name.replace(".json", ""))
         json_template = _create_sa_json(sa_instances, sa_metadata, sa_tags, sa_comments)
         write_to_json(output_dir / file_name, json_template)
     finish_event.set()

src/superannotate/lib/app/input_converters/converters/googlecloud_converters/googlecloud_to_sa_vector.py

@@ -45,7 +45,7 @@ def googlecloud_to_sa_vector(path, output_dir):
             images_not_converted.append(file_name)
             continue
 
-        sa_file_name = "%s___objects.json" % Path(file_name).name
+        sa_file_name = f"{Path(file_name).name}.json"
 
         points = (row[3] * W, row[4] * H, row[5] * W, row[8] * H)
         sa_instances = _create_vector_instance("bbox", points, {}, [], row[2])

src/superannotate/lib/app/input_converters/converters/labelbox_converters/labelbox_to_sa_pixel.py

@@ -35,7 +35,7 @@ def labelbox_instance_segmentation_to_sa_pixel(json_data, output_dir, input_dir)
     logger.info("Converting to SuperAnnotate JSON format")
     tqdm_thread.start()
     for data in json_data:
-        file_name = data["External ID"] + "___pixel.json"
+        file_name = data["External ID"] + ".json"
         mask_name = data["External ID"] + "___save.png"
         sa_metadata = {"name": data["External ID"]}
         if "objects" not in data["Label"].keys():

src/superannotate/lib/app/input_converters/converters/labelbox_converters/labelbox_to_sa_vector.py

@@ -37,7 +37,7 @@ def labelbox_to_sa(json_data, output_dir, task):
     tqdm_thread.start()
     for data in json_data:
         if "objects" not in data["Label"].keys():
-            file_name = data["External ID"] + "___objects.json"
+            file_name = data["External ID"] + ".json"
             write_to_json(
                 output_dir / file_name,
                 {"metadata": {}, "instances": [], "tags": [], "comments": []},
@@ -87,7 +87,7 @@ def labelbox_to_sa(json_data, output_dir, task):
             sa_instances.append(sa_obj)
 
         images_converted.append(data["External ID"])
-        file_name = "%s___objects.json" % data["External ID"]
+        file_name = f"{data['External ID']}.json"
         try:
             img = cv2.imread(str(output_dir / data["External ID"]))
             H, W, _ = img.shape

src/superannotate/lib/app/input_converters/converters/sagemaker_converters/sagemaker_to_sa_pixel.py

@@ -91,7 +91,7 @@ def sagemaker_instance_segmentation_to_sa_pixel(data_path, output_dir):
 
             blue_colors = blue_color_generator(num_of_contours)
             idx = 0
-            file_name = "%s___pixel.json" % (img_mapping[mask_name])
+            file_name = f"{img_mapping[mask_name]}.json"
             sa_metadata = {"name": img_mapping[mask_name], "width": W, "height": H}
             sa_instances = []
             sa_mask = np.zeros((H, W, 4))

src/superannotate/lib/app/input_converters/converters/sagemaker_converters/sagemaker_to_sa_vector.py

@@ -44,7 +44,7 @@ def sagemaker_object_detection_to_sa_vector(data_path, main_key, output_dir):
                 raise Exception
 
             manifest = dataset_manifest[int(img["datasetObjectId"])]
-            file_name = "%s___objects.json" % Path(manifest["source-ref"]).name
+            file_name = f"{Path(manifest['source-ref']).name}.json"
 
             classes = img["consolidatedAnnotation"]["content"][main_key + "-metadata"][
                 "class-map"