coco optimized

Author: agyurjin

superannotate/__init__.py

@@ -74,8 +74,8 @@
     SANonExistingProjectNameException
 )
 from .input_converters.conversion import (
-    coco_split_dataset, convert_platform, convert_project_type,
-    export_annotation, import_annotation
+    coco_split_dataset, convert_project_type, export_annotation,
+    import_annotation
 )
 from .version import __version__
 

superannotate/input_converters/conversion.py

@@ -7,11 +7,7 @@
 from ..exceptions import SABaseException
 from .export_from_sa_conversions import export_from_sa
 from .import_to_sa_conversions import import_to_sa
-from .sa_conversion import (
-    sa_convert_platform, sa_convert_project_type, split_coco
-)
-
-AVAILABLE_PLATFORMS = ["Desktop", "Web"]
+from .sa_conversion import (sa_convert_project_type, split_coco)
 
 ALLOWED_TASK_TYPES = [
     'panoptic_segmentation', 'instance_segmentation', 'keypoint_detection',
@@ -114,18 +110,6 @@ def _passes_sanity_checks(args):
             0, "Please enter valid task '%s'" % (ALLOWED_TASK_TYPES)
         )
 
-    if 'platform' in args:
-        if args.platform not in AVAILABLE_PLATFORMS:
-            raise SABaseException(
-                0, "Please enter valid platform: 'Desktop' or 'Web'"
-            )
-
-    if args.project_type == "Pixel" and args.platform == "Desktop":
-        raise SABaseException(
-            0,
-            "Sorry, but Desktop Application doesn't support 'Pixel' projects."
-        )
-
 
 def _passes_converter_sanity(args, direction):
     converter_values = (args.project_type, args.task)
@@ -153,7 +137,6 @@ def export_annotation(
     dataset_name,
     project_type="Vector",
     task="object_detection",
-    platform="Web",
 ):
     """Converts SuperAnnotate annotation formate to the other annotation formats. Currently available (project_type, task) combinations for converter
     presented below:
@@ -189,8 +172,6 @@ def export_annotation(
                  'instance_segmentation' 'Pixel' project_type converts instance masks and 'Vector' project_type generates bounding boxes and polygons from instance masks. Masks should be in the input folder if it is 'Pixel' project_type.
                  'object_detection' converts objects from/to available annotation format
     :type task: str
-    :param platform: SuperAnnotate has both 'Web' and 'Desktop' platforms. Choose from which one you are converting. (Default: "Web")
-    :type platform: str
 
     """
 
@@ -206,7 +187,6 @@ def export_annotation(
         dataset_name=dataset_name,
         project_type=project_type,
         task=task,
-        platform=platform,
     )
 
     _passes_sanity_checks(args)
@@ -222,7 +202,6 @@ def import_annotation(
     dataset_name='',
     project_type="Vector",
     task="object_detection",
-    platform="Web",
     images_root='',
     images_extensions=['jpg']
 ):
@@ -348,14 +327,10 @@ def import_annotation(
                  'instance_segmentation' 'Pixel' project_type converts instance masks and 'Vector' project_type generates bounding boxes and polygons from instance masks. Masks should be in the input folder if it is 'Pixel' project_type.
                  'object_detection' converts objects from/to available annotation format
                  'vector_annotation' can be used to convert all annotations (point, ellipse, circule, cuboid and etc) to SuperAnnotate vector project.
-    :param platform: SuperAnnotate has both 'Web' and 'Desktop' platforms. Choose to which platform you want convert. (Default: "Web")
-    :type platform: str
-    :param images_root: Additonal path to images directory in input_dir
-    :type platform: str
     :param images_root: Additonal path to images directory in input_dir
-    :type platform: str
+    :type images_root: str
     :param image_extensions: List of image files xtensions in the images_root folder
-    :type platform: list
+    :type image_extensions: list
 
     """
 
@@ -371,7 +346,6 @@ def import_annotation(
         dataset_name=dataset_name,
         project_type=project_type,
         task=task,
-        platform=platform,
         images_root=images_root,
         images_extensions=images_extensions
     )
@@ -382,38 +356,6 @@ def import_annotation(
     import_to_sa(args)
 
 
-def convert_platform(input_dir, output_dir, input_platform):
-    """ Converts SuperAnnotate input file structure from one platform too another.
-
-    :param input_dir: Path to the dataset folder that you want to convert.
-    :type input_dir: str or PathLike
-    :param output_dir: Path to the folder where you want to have converted files.
-    :type output_dir: str or PathLike
-    :param input_platform: Original platform format type
-    :type input_platform: str
-
-    """
-    param_info = [
-        (input_dir, 'input_dir', (str, Path)),
-        (output_dir, 'output_dir', (str, Path)),
-        (input_platform, 'input_platform', str),
-    ]
-    for param in param_info:
-        _type_sanity(param[0], param[1], param[2])
-
-    if input_platform not in AVAILABLE_PLATFORMS:
-        raise SABaseException(
-            0, "Please enter valid platform: 'Desktop' or 'Web'"
-        )
-
-    if isinstance(input_dir, str):
-        input_dir = Path(input_dir)
-    if isinstance(output_dir, str):
-        output_dir = Path(output_dir)
-
-    sa_convert_platform(input_dir, output_dir, input_platform)
-
-
 def convert_project_type(input_dir, output_dir):
     """ Converts SuperAnnotate 'Vector' project type to 'Pixel' or reverse.
 

superannotate/input_converters/converters/baseStrategy.py

@@ -218,7 +218,6 @@ def __init__(self, args):
         self.output_dir = args.output_dir
         self.task = args.task
         self.direction = args.direction
-        self.platform = args.platform
         self.conversion_algorithm = CONVERSION_ALGORITHMS[self.direction][
             args.dataset_format][self.project_type][self.task]
 

superannotate/input_converters/converters/coco_converters/coco_api.py

@@ -0,0 +1,96 @@
+import numpy as np
+
+
+def encode(bitmask):
+    rle = _masktoRLE(bitmask)
+    rle['counts'] = _toString(rle['counts'])
+    return rle
+
+
+def decode(coco_dict):
+    coco_dict['counts'] = _frString(coco_dict['counts'])
+    return _maskfrRLE(coco_dict)
+
+
+def _masktoRLE(bitmask):
+    shape = bitmask.shape
+    bitmask = bitmask.T.flatten()
+    N = len(bitmask)
+    diff_index = np.where(np.array(bitmask[:N - 1]) - np.array(bitmask[1:]))[0]
+    diff = np.array(diff_index[1:]) - np.array(diff_index[:-1])
+
+    counts = np.zeros(len(diff) + 3, dtype=np.int32)
+    counts[1] = diff_index[0] + 1
+    counts[2:-1] = diff
+    counts[-1] = len(bitmask) - diff_index[-1] - 1
+
+    if bitmask[0] == 0:
+        counts = counts[1:]
+
+    return {'counts': counts, 'size': list(shape)}
+
+
+def _maskfrRLE(rle):
+    x = np.arange(len(rle['counts']), dtype=np.uint8) % 2
+    bitmask = np.repeat(x, rle['counts'], axis=0)
+
+    return bitmask.reshape((rle['size'][1], rle['size'][0])).T
+
+
+def _toString(rle_counts):
+    rle_string = ''
+    for i, count in enumerate(rle_counts):
+        if i > 2:
+            count -= rle_counts[i - 2]
+
+        more = True
+        while more:
+            if count > 0:
+                count_binary = bin(count)
+                if len(count_binary[2:]) % 5 != 0:
+                    count_binary = '0b' + '0' * (5 - len(count_binary[2:]) %
+                                                 5) + count_binary[2:]
+            else:
+                count_binary = bin(((1 << 35) - 1) & count)
+
+            count = count >> 5
+            last_bits = count_binary[-5:]
+
+            value = int(last_bits, 2)
+            if last_bits[0] == '1':
+                more = count != -1
+            else:
+                more = count != 0
+
+            if more:
+                char = (value | 0x20) + 48
+            else:
+                char = value + 48
+
+            rle_string += chr(char)
+
+    return rle_string
+
+
+def _frString(rle_string):
+    counts = []
+    i = 0
+    while i < len(rle_string):
+        more = True
+        k = 0
+        count = 0
+        while more:
+            value = ord(rle_string[i]) - 48
+            count |= (value & 0x1f) << 5 * k
+            more = value & 0x20
+            i += 1
+            k += 1
+            if not more and (value & 0x10):
+                count |= -1 << 5 * k
+
+        if len(counts) > 2:
+            count += counts[len(counts) - 2]
+
+        counts.append(count)
+
+    return counts

superannotate/input_converters/converters/coco_converters/coco_strategies.py

@@ -10,7 +10,7 @@
 
 from ..baseStrategy import baseStrategy
 
-from ....common import id2rgb, dump_output
+from ....common import id2rgb, write_to_json
 
 
 class CocoBaseStrategy(baseStrategy):
@@ -164,14 +164,21 @@ def _create_sa_classes(self, json_path):
             hexcolor = "#%02x%02x%02x" % tuple(color)
             classes_dict = {
                 'name': data["name"],
-                'id': data["id"],
+                # 'id': data["id"],
                 'color': hexcolor,
                 'attribute_groups': []
             }
             classes.append(classes_dict)
 
         return classes
 
+    def to_sa_format(self):
+        json_data = self.export_root / (self.dataset_name + ".json")
+        sa_classes = self._create_sa_classes(json_data)
+        (self.output_dir / 'classes').mkdir(parents=True, exist_ok=True)
+        write_to_json(self.output_dir / 'classes' / 'classes.json', sa_classes)
+        self.conversion_algorithm(json_data, self.output_dir)
+
 
 class CocoPanopticConverterStrategy(CocoBaseStrategy):
     def __init__(self, args):
@@ -226,12 +233,6 @@ def sa_to_output_format(self):
 
         self.set_num_converted(len(jsons))
 
-    def to_sa_format(self):
-        json_data = self.export_root / (self.dataset_name + ".json")
-        sa_classes = self._create_sa_classes(json_data)
-        sa_jsons = self.conversion_algorithm(json_data, self.output_dir)
-        dump_output(self.output_dir, self.platform, sa_classes, sa_jsons)
-
 
 class CocoObjectDetectionStrategy(CocoBaseStrategy):
     def __init__(self, args):
@@ -305,12 +306,6 @@ def sa_to_output_format(self):
 
         self.set_num_converted(len(jsons))
 
-    def to_sa_format(self):
-        json_data = self.export_root / (self.dataset_name + ".json")
-        sa_classes = self._create_sa_classes(json_data)
-        sa_jsons = self.conversion_algorithm(json_data, self.output_dir)
-        dump_output(self.output_dir, self.platform, sa_classes, sa_jsons)
-
 
 class CocoKeypointDetectionStrategy(CocoBaseStrategy):
     def __init__(self, args):
@@ -367,9 +362,3 @@ def sa_to_output_format(self):
             coco_json.write(json_data)
 
         self.set_num_converted(len(out_json['images']))
-
-    def to_sa_format(self):
-        json_data = self.export_root / (self.dataset_name + ".json")
-        sa_classes = self._create_sa_classes(json_data)
-        sa_jsons = self.conversion_algorithm(json_data, self.output_dir)
-        dump_output(self.output_dir, self.platform, sa_classes, sa_jsons)