Calculates area of coco annotations

examples.py

@@ -199,10 +199,10 @@ def show_detection_example():
 
     # Bboxes creation
     gt_bbox_1 = BBox(10, 10, 100, 100, 0)
-    pred_bbox_1 = BBox(10, 10, 100, 100, 0, .8)
+    pred_bbox_1 = BBox(10, 10, 100, 100, 0, .8212158464)
 
     gt_bbox_2 = BBox(110, 110, 320, 280, 1)
-    pred_bbox_2 = BBox(70, 50, 240, 220, 1, .3)
+    pred_bbox_2 = BBox(70, 50, 240, 220, 1, .34844545)
 
     gt_bbox_3 = BBox(300, 300, 100, 100, 2)
 

lapixdl/convert/labelbox.py

@@ -33,13 +33,20 @@ def __generate_coco_annotations(img_labels: List[dict], img_id: int, annotation_
             'category_id': classes.index(class_name),
             'id': annotation_first_id,
             'bbox': bbox,
+            'area': __calculate_area(segmentation)
         })
 
         annotation_first_id += 1
 
     return annotations
 
 
+def __calculate_area(segmentation: List[float]) -> float:
+    x = segmentation[0:-1:2]
+    y = segmentation[1::2]
+    return 0.5*np.abs(np.dot(x, np.roll(y, 1))-np.dot(y, np.roll(x, 1))) #Shoelace
+
+
 def __generate_coco_file(lblbox_annotations: dict, img_names_to_include: Optional[List[str]] = None) -> dict:
     annotation_id = 1
     image_id = 1

setup.py

@@ -6,7 +6,7 @@
 setup(
     name='lapixdl',
     packages=find_packages(exclude=['tests']),
-    version='0.7.8',
+    version='0.7.9',
     description='Utils for Computer Vision Deep Learning research',
 
     long_description=long_description,