minor changes

Author: agyurjin

superannotate/input_converters/converters/coco_converters/coco_api.py

@@ -102,18 +102,11 @@ def _area(bitmask):
 
 
 def _toBbox(bitmask):
-    contours, _ = cv2.findContours(
-        bitmask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
-    )
-    segments = []
-    for contour in contours:
-        contour = contour.flatten().tolist()
-        segments += contour
-
-    xmin = min(segments[::2])
-    xmax = max(segments[::2])
-    ymin = min(segments[1::2])
-    ymax = max(segments[1::2])
+    y, x = np.where(bitmask)
+    xmin = int(min(x))
+    xmax = int(max(x))
+    ymin = int(min(y))
+    ymax = int(max(y))
 
     return [xmin, ymin, xmax - xmin + 1, ymax - ymin + 1]
 

superannotate/input_converters/converters/coco_converters/coco_strategies.py

@@ -236,12 +236,15 @@ def sa_to_output_format(self):
 
         out_json['annotations'] = annotations
         out_json['images'] = images
-        json_data = json.dumps(out_json, indent=4)
-        with open(
-            self.output_dir / '{}.json'.format(self.dataset_name), 'w+'
-        ) as coco_json:
+        write_to_json(
+            self.output_dir / '{}.json'.format(self.dataset_name), out_json
+        )
+        # json_data = json.dumps(out_json, indent=4)
+        # with open(
+        #     self.output_dir / '{}.json'.format(self.dataset_name), 'w+'
+        # ) as coco_json:
 
-            coco_json.write(json_data)
+        #     coco_json.write(json_data)
 
         self.set_num_converted(len(jsons))
 
@@ -309,11 +312,9 @@ def sa_to_output_format(self):
         out_json['annotations'] = annotations
         out_json['images'] = images
 
-        json_data = json.dumps(out_json, indent=4)
-        with open(
-            self.output_dir / '{}.json'.format(self.dataset_name), 'w+'
-        ) as coco_json:
-            coco_json.write(json_data)
+        write_to_json(
+            self.output_dir / '{}.json'.format(self.dataset_name), out_json
+        )
         print("NUMBER OF IMAGES FAILED TO CONVERT", self.failed_conversion_cnt)
 
         self.set_num_converted(len(jsons))
@@ -366,11 +367,14 @@ def sa_to_output_format(self):
         out_json['categories'] = res[0]
         out_json['annotations'] = res[1]
         out_json['images'] = res[2]
-        json_data = json.dumps(out_json, indent=4)
+        write_to_json(
+            self.output_dir / '{}.json'.format(self.dataset_name), out_json
+        )
+        # json_data = json.dumps(out_json, indent=4)
 
-        with open(
-            self.output_dir / '{}.json'.format(self.dataset_name), 'w+'
-        ) as coco_json:
-            coco_json.write(json_data)
+        # with open(
+        #     self.output_dir / '{}.json'.format(self.dataset_name), 'w+'
+        # ) as coco_json:
+        #     coco_json.write(json_data)
 
         self.set_num_converted(len(out_json['images']))

superannotate/input_converters/converters/labelbox_converters/labelbox_helper.py

@@ -1,14 +1,16 @@
-import requests
 import logging
+import requests
 
 logger = logging.getLogger("superannotate-python-sdk")
 
 
 def image_downloader(url, file_name):
-    logger.info("Downloading mask for {}".format(file_name))
     r = requests.get(url, stream=True)
-    with open(file_name, 'wb') as f:
-        f.write(r.content)
+    if r.status_code == 200:
+        with open(file_name, 'wb') as f:
+            f.write(r.content)
+        return True
+    return False
 
 
 def _create_classes_id_map(json_data):

superannotate/input_converters/converters/labelbox_converters/labelbox_strategies.py

@@ -1,5 +1,4 @@
 import json
-import cv2
 
 from ..baseStrategy import baseStrategy
 
@@ -14,9 +13,14 @@ def to_sa_format(self):
         json_data = json.load(
             open(self.export_root / (self.dataset_name + '.json'))
         )
-        classes = self.conversion_algorithm(
-            json_data, self.output_dir, self.task
-        )
+        if self.project_type == 'Vector':
+            classes = self.conversion_algorithm(
+                json_data, self.output_dir, self.task
+            )
+        elif self.project_type == 'Pixel':
+            classes = self.conversion_algorithm(
+                json_data, self.output_dir, self.export_root
+            )
         sa_classes = self._create_classes(classes)
         (self.output_dir / 'classes').mkdir(exist_ok=True)
         write_to_json(self.output_dir / 'classes' / 'classes.json', sa_classes)

superannotate/input_converters/converters/labelbox_converters/labelbox_to_sa_pixel.py

@@ -11,7 +11,9 @@
 from ....common import hex_to_rgb, blue_color_generator, write_to_json
 
 
-def labelbox_instance_segmentation_to_sa_pixel(json_data, output_dir, task=''):
+def labelbox_instance_segmentation_to_sa_pixel(
+    json_data, output_dir, input_dir
+):
     classes = _create_classes_id_map(json_data)
     for data in json_data:
         file_name = data['External ID'] + '___pixel.json'
@@ -38,8 +40,17 @@ def labelbox_instance_segmentation_to_sa_pixel(json_data, output_dir, task=''):
             ) or 'line' in instance.keys() or 'point' in instance.keys():
                 continue
 
-            image_downloader(instance['instanceURI'], mask_name)
-            mask = cv2.imread(mask_name)
+            bitmask_name = '%s.png' % instance['featureId']
+            downloaded = image_downloader(instance['instanceURI'], bitmask_name)
+            if downloaded:
+                mask = cv2.imread(bitmask_name)
+            else:
+                mask = cv2.imread(str(input_dir / 'bitmasks' / bitmask_name))
+                bitmask_name = output_dir / bitmask_name
+
+            if isinstance(mask, type(None)):
+                continue
+
             if i == 0:
                 H, W, _ = mask.shape
                 sa_metadata['width'] = W
@@ -52,7 +63,7 @@ def labelbox_instance_segmentation_to_sa_pixel(json_data, output_dir, task=''):
             sa_obj = _create_pixel_instance(parts, attributes, class_name)
 
             sa_instances.append(sa_obj.copy())
-            Path(mask_name).unlink()
+            Path(bitmask_name).unlink()
 
         sa_json = _create_sa_json(sa_instances, sa_metadata)
         write_to_json(output_dir / file_name, sa_json)

superannotate/input_converters/converters/voc_converters/voc_helper.py

@@ -10,11 +10,22 @@ def _get_voc_instances_from_xml(file_path):
     voc_instances = []
     for instance in instances:
         class_name = instance.find("name").text
+        class_attributes = []
+        for attr in ['pose', 'occluded', 'difficult', 'truncated']:
+            attr_value = instance.find(attr)
+            if attr_value is not None:
+                class_attributes.append(
+                    {
+                        'name': attr_value.text,
+                        'groupName': attr
+                    }
+                )
+
         bbox = instance.find("bndbox")
         bbox = [
             float(bbox.find(x).text) for x in ["xmin", "ymin", "xmax", "ymax"]
         ]
-        voc_instances.append((class_name, bbox))
+        voc_instances.append(({class_name: class_attributes}, bbox))
     return voc_instances
 
 

superannotate/input_converters/converters/voc_converters/voc_strategies.py

@@ -21,15 +21,40 @@ def to_sa_format(self):
                 if '___save.png' not in str(file.name):
                     (self.output_dir / file.name).unlink()
 
-    def _create_classes(self, classes):
+    def _create_classes(self, instances):
+        classes = {}
+        for instance in instances:
+            for class_, value in instance.items():
+                if class_ not in classes:
+                    classes[class_] = {}
+
+                for attr in value:
+                    if attr['groupName'] in classes[class_]:
+                        classes[class_][attr['groupName']].append(attr['name'])
+                    else:
+                        classes[class_][attr['groupName']] = [attr['name']]
+
         sa_classes = []
-        for class_ in set(classes):
+        for class_ in classes:
             color = np.random.choice(range(256), size=3)
             hexcolor = "#%02x%02x%02x" % tuple(color)
+            attribute_groups = []
+            for attr_group, value in classes[class_].items():
+                attributes = []
+                for attr in set(value):
+                    attributes.append({'name': attr})
+
+                attribute_groups.append(
+                    {
+                        'name': attr_group,
+                        'is_multiselect': 0,
+                        'attributes': attributes
+                    }
+                )
             sa_class = {
                 "name": class_,
                 "color": hexcolor,
-                "attribute_groups": []
+                "attribute_groups": attribute_groups
             }
             sa_classes.append(sa_class)
         return sa_classes

superannotate/input_converters/converters/voc_converters/voc_to_sa_pixel.py

@@ -59,12 +59,15 @@ def _generate_instances(polygon_instances, voc_instances, bluemask_colors):
         for _, bbox in voc_instances:
             ious.append(_iou(bbox_poly, bbox))
         ind = np.argmax(ious)
+        class_name = list(voc_instances[ind][0].keys())[0]
+        attributes = voc_instances[ind][0][class_name]
         instances.append(
             {
                 "className": voc_instances[ind][0],
                 "polygon": polygon,
                 "bbox": voc_instances[ind][1],
-                "blue_color": bluemask_colors[i]
+                "blue_color": bluemask_colors[i],
+                'classAttributes': attributes
             }
         )
         i += 1
@@ -83,16 +86,20 @@ def voc_instance_segmentation_to_sa_pixel(voc_root, output_dir):
         voc_instances = _get_voc_instances_from_xml(
             annotation_dir / filename.name
         )
+        for class_, _ in voc_instances:
+            classes.append(class_)
+
         maped_instances = _generate_instances(
             polygon_instances, voc_instances, bluemask_colors
         )
 
         sa_instances = []
         for instance in maped_instances:
             parts = [{"color": instance["blue_color"]}]
-            sa_obj = _create_pixel_instance(parts, [], instance["className"])
+            sa_obj = _create_pixel_instance(
+                parts, instance['classAttributes'], instance["className"]
+            )
             sa_instances.append(sa_obj)
-            classes.append(instance['className'])
 
         file_name = "%s.jpg___pixel.json" % (filename.stem)
         sa_metadata = {'name': filename.stem}

superannotate/input_converters/converters/voc_converters/voc_to_sa_vector.py

@@ -62,12 +62,15 @@ def _generate_instances(polygon_instances, voc_instances):
             ious.append(_iou(bbox_poly, bbox))
         ind = np.argmax(ious)
         for poly in polygon:
+            class_name = list(voc_instances[ind][0].keys())[0]
+            attributes = voc_instances[ind][0][class_name]
             instances.append(
                 {
-                    'className': voc_instances[ind][0],
+                    'className': class_name,
                     'polygon': poly,
                     'bbox': voc_instances[ind][1],
-                    'groupId': group_id
+                    'groupId': group_id,
+                    'classAttributes': attributes
                 }
             )
     return instances
@@ -84,16 +87,18 @@ def voc_instance_segmentation_to_sa_vector(voc_root, output_dir):
         voc_instances = _get_voc_instances_from_xml(
             annotation_dir / filename.name
         )
+        for class_, _ in voc_instances:
+            classes.append(class_)
+
         maped_instances = _generate_instances(polygon_instances, voc_instances)
         sa_instances = []
         for instance in maped_instances:
             sa_obj = _create_vector_instance(
-                'polygon', instance["polygon"], {}, [], instance["className"]
+                'polygon', instance["polygon"], {}, instance['classAttributes'],
+                instance["className"]
             )
             sa_instances.append(sa_obj)
 
-            classes.append(instance["className"])
-
         file_name = "%s.jpg___objects.json" % filename.stem
         sa_metadata = {'name': filename.stem}
         sa_json = _create_sa_json(sa_instances, sa_metadata)
@@ -110,11 +115,14 @@ def voc_object_detection_to_sa_vector(voc_root, output_dir):
             annotation_dir / filename.name
         )
         sa_instances = []
-        for class_name, bbox in voc_instances:
-            classes.append(class_name)
+        for class_, bbox in voc_instances:
+            class_name = list(class_.keys())[0]
+            classes.append(class_)
 
             points = (bbox[0], bbox[1], bbox[2], bbox[3])
-            sa_obj = _create_vector_instance('bbox', points, {}, [], class_name)
+            sa_obj = _create_vector_instance(
+                'bbox', points, {}, class_[class_name], class_name
+            )
             sa_instances.append(sa_obj)
 
         file_name = "%s.jpg___objects.json" % filename.stem

tests/test_preannotation_upload_cli.py

@@ -171,7 +171,7 @@ def test_preannotation_folder_upload_download_cli_pixel_object_COCO(tmpdir):
         shell=True
     )
     time.sleep(5)
-    count_in = 6
+    count_in = 3
 
     images = sa.search_images(project)
     for image_name in images: