json corrected

Author: agyurjin

superannotate/input_converters/converters/coco_converters/coco_api.py

@@ -103,7 +103,7 @@ def _area(bitmask):
 
 def _toBbox(bitmask):
     contours, _ = cv2.findContours(
-        bitmask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+        bitmask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
     )
     segments = []
     for contour in contours:
@@ -115,7 +115,7 @@ def _toBbox(bitmask):
     ymin = min(segments[1::2])
     ymax = max(segments[1::2])
 
-    return [xmin, ymin, xmax - xmin, ymax - ymin]
+    return [xmin, ymin, xmax - xmin + 1, ymax - ymin + 1]
 
 
 def _merge(list_of_bitmask):
@@ -130,12 +130,13 @@ def _merge(list_of_bitmask):
 def _polytoMask(polygons, height, width):
     masks = []
     for polygon in polygons:
-        polygon = np.round(polygon)
-        bitmask = np.zeros((height, width)).astype(np.uint8)
+        polygon = np.array(polygon, dtype=np.uint16)
         pts = np.array(
             [polygon[2 * i:2 * (i + 1)] for i in range(len(polygon) // 2)],
             dtype=np.int32
         )
+        bitmask = np.zeros((height, width)).astype(np.uint8)
         cv2.fillPoly(bitmask, [pts], 1)
+
         masks.append(bitmask)
     return masks

superannotate/input_converters/converters/coco_converters/coco_strategies.py

@@ -96,14 +96,19 @@ def _prepare_single_image_commons_pixel(self, id_, json_path):
         rm_len = len('___pixel.json')
 
         sa_json = json.load(open(json_path))
-        # sa_ann_json = json.load(open(json_path))
         sa_ann_json = sa_json['instances']
 
         sa_bluemask_path = str(json_path)[:-rm_len] + '___save.png'
 
-        # image_info = self.__make_image_info(json_path, id_, self.project_type)
         image_info = self.__make_image_info(id_, sa_json['metadata'])
 
+        if image_info['height'] is None or image_info['width'] is None:
+            img_height, img_width = self.get_dimensions(
+                json_path, self.project_type
+            )
+            image_info['height'] = img_height
+            image_info['width'] = img_width
+
         sa_bluemask_rgb = np.asarray(
             Image.open(sa_bluemask_path).convert('RGB'), dtype=np.uint32
         )
@@ -121,15 +126,16 @@ def _prepare_single_image_commons_pixel(self, id_, json_path):
 
         return res
 
-    # def __make_image_info(self, json_path, id_, source_type):
-    # if source_type == 'Pixel':
-    #     rm_len = len('___pixel.json')
-    # elif source_type == 'Vector':
-    #     rm_len = len('___objects.json')
+    def get_dimensions(self, json_path, source_type):
+        if source_type == 'Pixel':
+            rm_len = len('___pixel.json')
+        elif source_type == 'Vector':
+            rm_len = len('___objects.json')
 
-    # image_path = str(json_path)[:-rm_len]
+        image_path = str(json_path)[:-rm_len]
+        img_width, img_height = Image.open(image_path).size
+        return img_height, img_width
 
-    # img_width, img_height = Image.open(image_path).size
     def __make_image_info(self, id_, sa_meta_json):
         image_info = {
             'id': id_,
@@ -144,11 +150,9 @@ def __make_image_info(self, id_, sa_meta_json):
     def _prepare_single_image_commons_vector(self, id_, json_path):
         ImgCommons = namedtuple('ImgCommons', ['image_info', 'sa_ann_json'])
 
-        # sa_ann_json = json.load(open(json_path))
         sa_json = json.load(open(json_path))
         sa_ann_json = sa_json['instances']
 
-        # image_info = self.__make_image_info(json_path, id_, self.project_type)
         image_info = self.__make_image_info(id_, sa_json['metadata'])
 
         res = ImgCommons(image_info, sa_ann_json)

superannotate/input_converters/converters/coco_converters/coco_to_sa_pixel.py

@@ -75,7 +75,7 @@ def coco_panoptic_segmentation_to_sa_pixel(coco_path, output_dir):
             'width': img_id_to_shape[str(annot['image_id'])]['width'],
             'height': img_id_to_shape[str(annot['image_id'])]['height']
         }
-        json_template = _create_sa_json(sa_instances, metadata)
+        json_template = _create_sa_json(sa_instances, sa_metadata)
         write_to_json(output_dir / file_name, json_template)
         (output_dir / ("%s.png" % annot_name)).unlink()
 

superannotate/input_converters/converters/coco_converters/sa_pixel_to_coco.py

@@ -2,7 +2,6 @@
 import numpy as np
 
 from .coco_api import (encode, _toBbox, _area)
-# from ....pycocotools_sa import mask as cocomask
 
 
 def __instance_object_commons_per_instance(
@@ -24,11 +23,7 @@ def __instance_object_commons_per_instance(
         databytes, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE
     )
 
-    # coco_instance_mask = cocomask.encode(np.asfortranarray(instance_bitmask))
-    # coco_instance_mask = encode(instance_bitmask.astype(np.uint8))
-    # bbox = cocomask.toBbox(coco_instance_mask).tolist()
-    bbox = list(_toBbox(instance_bitmask.astype(np.uint8)))
-    # area = int(cocomask.area(coco_instance_mask))
+    bbox = list(_toBbox(instance_bitmask))
     area = int(_area(instance_bitmask.astype(np.uint8)))
     return (bbox, area, contours, category_id, anno_id)
 
@@ -96,14 +91,7 @@ def sa_pixel_to_coco_panoptic_segmentation(
         instance_bitmask = np.isin(flat_mask, parts)
         segment_id = next(id_generator)
         ann_mask[instance_bitmask] = segment_id
-        # coco_instance_mask = cocomask.encode(
-        #     np.asfortranarray(instance_bitmask)
-        # )
-        # bbox = cocomask.toBbox(coco_instance_mask).tolist()
-        # area = int(cocomask.area(coco_instance_mask))
-
-        # coco_instance_mask = encode(instance_bitmask.astype(np.uint8))
-        bbox = list(_toBbox(instance_bitmask.astype(np.uint8)))
+        bbox = list(_toBbox(instance_bitmask))
         area = int(_area(instance_bitmask.astype(np.uint8)))
 
         segment_info = {

superannotate/input_converters/converters/coco_converters/sa_vector_to_coco.py

@@ -1,9 +1,8 @@
 import json
 import logging
-
-from .coco_api import (encode, _toBbox, _merge, _area, _polytoMask)
-
-from ....pycocotools_sa import mask as cocomask
+import numpy as np
+import cv2
+from .coco_api import (_toBbox, _merge, _area, _polytoMask)
 
 logger = logging.getLogger("superannotate-python-sdk")
 
@@ -82,12 +81,6 @@ def sa_vector_to_coco_instance_segmentation(
         for cat_id, polygons in polygon_group.items():
             anno_id = next(id_generator)
             try:
-                # masks = cocomask.frPyObjects(
-                #     polygons, image_info['height'], image_info['width']
-                # )
-                # mask = cocomask.merge(masks)
-                # area = int(cocomask.area(mask))
-                # bbox = cocomask.toBbox(mask).tolist()
                 masks = _polytoMask(
                     polygons, image_info['height'], image_info['width']
                 )
@@ -165,7 +158,7 @@ def __make_annotations(template, id_generator, cat_id, image_id):
     images = []
 
     for path_ in json_paths:
-        json_data = __load_one_json(path_)
+        json_data = __load_one_json(path_)['instances']
 
         for instance in json_data:
             if instance['type'] == 'template' and 'templateId' not in instance:

superannotate/input_converters/converters/dataloop_converters/dataloop_to_sa_vector.py

@@ -25,6 +25,8 @@ def dataloop_to_sa(input_dir, task, output_dir):
     comment_type = 'note'
 
     for json_file in json_data:
+        dl_data = json.load(open(json_file))
+
         sa_metadata = {}
         if 'itemMetadata' in dl_data and 'system' in dl_data['itemMetadata']:
             temp = dl_data['itemMetadata']['system']
@@ -36,8 +38,6 @@ def dataloop_to_sa(input_dir, task, output_dir):
         sa_tags = []
         sa_comments = []
 
-        dl_data = json.load(open(json_file))
-
         for ann in dl_data['annotations']:
             if ann['type'] in instance_types:
                 classes = _update_classes_dict(
@@ -91,9 +91,7 @@ def dataloop_to_sa(input_dir, task, output_dir):
                     sa_comment = _create_comment(points, comments)
                 sa_comments.append(sa_comment)
             elif ann['type'] == tags_type:
-                sa_tags.append(ann['labe'])
-                # sa_tags = {'type': 'tag', 'name': ann['label']}
-                # sa_instances.append(sa_tags)
+                sa_tags.append(ann['label'])
 
         if 'name' in sa_metadata:
             file_name = '%s___objects.json' % sa_metadata['name']

superannotate/input_converters/converters/supervisely_converters/supervisely_to_sa_vector.py

@@ -19,14 +19,14 @@ def supervisely_to_sa(json_files, class_id_map, task, output_dir):
         ]
 
     for json_file in json_files:
+        json_data = json.load(open(json_file))
         file_name = '%s___objects.json' % Path(json_file).stem
         sa_metadata = {
             'name': Path(json_file).stem,
             'width': json_data['size']['width'],
             'height': json_data['size']['height']
         }
 
-        json_data = json.load(open(json_file))
         sa_instances = []
         for obj in json_data['objects']:
             if 'classTitle' in obj and obj['classTitle'] in class_id_map.keys():

superannotate/input_converters/sa_conversion.py

@@ -8,7 +8,7 @@
 import cv2
 import numpy as np
 
-from ..common import blue_color_generator, hex_to_rgb
+from ..common import (blue_color_generator, hex_to_rgb, write_to_json)
 from ..exceptions import SABaseException
 
 logger = logging.getLogger("superannotate-python-sdk")
@@ -18,8 +18,8 @@ def copy_file(src_path, dst_path):
     shutil.copy(src_path, dst_path)
 
 
-def from_pixel_to_vector(json_paths):
-    sa_jsons = {}
+def from_pixel_to_vector(json_paths, output_dir):
+    img_names = []
     for json_path in json_paths:
         file_name = str(json_path.name
                        ).replace('___pixel.json', '___objects.json')
@@ -28,13 +28,14 @@ def from_pixel_to_vector(json_paths):
         img = cv2.imread(mask_name)
         H, W, _ = img.shape
 
-        sa_loader = []
-        instances = json.load(open(json_path))
+        sa_json = json.load(open(json_path))
+        instances = sa_json['instances']
         idx = 0
+        sa_instances = []
         for instance in instances:
             if 'parts' not in instance.keys():
                 if 'type' in instance.keys() and instance['type'] == 'meta':
-                    sa_loader.append(instance)
+                    sa_instances.append(instance)
                 continue
 
             parts = instance['parts']
@@ -68,22 +69,24 @@ def from_pixel_to_vector(json_paths):
                     temp['groupId'] = group_id
                     temp['type'] = 'polygon'
                     temp['points'] = polygon
-                    sa_loader.append(temp.copy())
+                    sa_instances.append(temp.copy())
                     temp['type'] = 'bbox'
                     temp['points'] = {
                         'x1': min(polygon[::2]),
                         'x2': max(polygon[::2]),
                         'y1': min(polygon[1::2]),
                         'y2': max(polygon[1::2])
                     }
-                    sa_loader.append(temp.copy())
+                    sa_instances.append(temp.copy())
 
-        sa_jsons[file_name] = {'json': sa_loader, 'mask': None}
-    return sa_jsons
+        sa_json['instances'] = sa_instances
+        write_to_json(output_dir / file_name, sa_json)
+        img_names.append(file_name.replace('___objects.json', ''))
+    return img_names
 
 
-def from_vector_to_pixel(json_paths):
-    sa_jsons = {}
+def from_vector_to_pixel(json_paths, output_dir):
+    img_names = []
     for json_path in json_paths:
         file_name = str(json_path.name
                        ).replace('___objects.json', '___pixel.json')
@@ -92,10 +95,11 @@ def from_vector_to_pixel(json_paths):
         img = cv2.imread(img_name)
         H, W, _ = img.shape
 
+        sa_json = json.load(open(json_path))
+        instances = sa_json['instances']
         mask = np.zeros((H, W, 4))
-        sa_loader = []
 
-        instances = json.load(open(json_path))
+        sa_instances = []
         blue_colors = blue_color_generator(len(instances))
         instances_group = {}
         for idx, instance in enumerate(instances):
@@ -105,7 +109,7 @@ def from_vector_to_pixel(json_paths):
                 else:
                     instances_group[instance['groupId']] = [instance]
             elif instance['type'] == 'meta':
-                sa_loader.append(instance)
+                sa_instances.append(instance)
 
         idx = 0
         for key, instances in instances_group.items():
@@ -127,7 +131,7 @@ def from_vector_to_pixel(json_paths):
                     del instance['pointLabels']
                     del instance['groupId']
                     instance['parts'] = [{'color': blue_colors[idx]}]
-                    sa_loader.append(instance.copy())
+                    sa_instances.append(instance.copy())
                     idx += 1
             else:
                 parts = []
@@ -150,45 +154,38 @@ def from_vector_to_pixel(json_paths):
                 del instance['pointLabels']
                 del instance['groupId']
                 instance['parts'] = parts
-                sa_loader.append(instance.copy())
+                sa_instances.append(instance.copy())
 
-        sa_jsons[file_name] = {'json': sa_loader, 'mask': mask}
+        mask_name = file_name.replace('___pixel.json', '___save.png')
+        cv2.imwrite(str(output_dir.joinpath(mask_name)), mask)
 
-    return sa_jsons
+        sa_json['instances'] = sa_instances
+        write_to_json(output_dir / file_name, sa_json)
+        img_names.append(file_name.replace('___pixel.json', ''))
+    return img_names
 
 
 def sa_convert_project_type(input_dir, output_dir):
-    json_generator = input_dir.glob('*.json')
-    json_paths = list(json_generator)
+    json_paths = list(input_dir.glob('*.json'))
+
+    output_dir.joinpath('classes').mkdir(parents=True)
+    copy_file(
+        input_dir.joinpath('classes', 'classes.json'),
+        output_dir.joinpath('classes', 'classes.json')
+    )
 
-    extension = ''
     if '___pixel.json' in json_paths[0].name:
-        sa_jsons = from_pixel_to_vector(json_paths)
-        extension = '___objects.json'
+        img_names = from_pixel_to_vector(json_paths, output_dir)
     elif '__objects.json' in json_paths[0].name:
-        sa_jsons = from_vector_to_pixel(json_paths)
-        extension = '___pixel.json'
+        img_names = from_vector_to_pixel(json_paths, output_dir)
     else:
         raise SABaseException(
             0,
             "'input_dir' should contain JSON files with '[IMAGE_NAME]___objects.json'  or '[IMAGE_NAME]___pixel.json' names structure."
         )
 
-    output_dir.joinpath('classes').mkdir(parents=True)
-    copy_file(
-        input_dir.joinpath('classes', 'classes.json'),
-        output_dir.joinpath('classes', 'classes.json')
-    )
-
-    for key, value in sa_jsons.items():
-        with open(output_dir.joinpath(key), 'w') as fw:
-            json.dump(value['json'], fw, indent=2)
-        file_name = key.replace(extension, '')
-        copy_file(input_dir.joinpath(file_name), output_dir.joinpath(file_name))
-
-        if value['mask'] is not None:
-            mask_name = key.replace(extension, '___save.png')
-            cv2.imwrite(str(output_dir.joinpath(mask_name)), value['mask'])
+    for img_name in img_names:
+        copy_file(input_dir.joinpath(img_name), output_dir.joinpath(img_name))
 
 
 def split_coco(

tests/converter_test/COCO/input/fromSuperAnnotate/cats_dogs_panoptic_segm/cat1.jpg___pixel.json

@@ -1,8 +1,8 @@
 {
   "metadata": {
     "name": "cat1.jpg",
-    "width": null,
-    "height": null,
+    "width": 2000,
+    "height": 2000,
     "status": null,
     "pinned": null,
     "isPredicted": null,