Added heuristic for bounding bbox ordering (#835)

sahi/predict.py

@@ -12,6 +12,8 @@
 if is_available("torch"):
     import torch
 
+from functools import cmp_to_key
+
 import numpy as np
 from tqdm import tqdm
 
@@ -289,6 +291,43 @@ def get_sliced_prediction(
     )
 
 
+def bbox_sort(a, b, thresh):
+    """
+    a, b  - function receives two bounding bboxes
+
+    thresh - the threshold takes into account how far two bounding bboxes differ in
+    Y where thresh is the threshold we set for the
+    minimum allowable difference in height between adjacent bboxes
+    and sorts them by the X coordinate
+    """
+
+    bbox_a = a
+    bbox_b = b
+
+    if abs(bbox_a[1] - bbox_b[1]) <= thresh:
+        return bbox_a[0] - bbox_b[0]
+
+    return bbox_a[1] - bbox_b[1]
+
+
+def agg_prediction(result: PredictionResult, thresh):
+    coord_list = []
+    res = result.to_coco_annotations()
+    for ann in res:
+        current_bbox = ann["bbox"]
+        x = current_bbox[0]
+        y = current_bbox[1]
+        w = current_bbox[2]
+        h = current_bbox[3]
+
+        coord_list.append((x, y, w, h))
+    cnts = sorted(coord_list, key=cmp_to_key(lambda a, b: bbox_sort(a, b, thresh)))
+    for pred in range(len(res) - 1):
+        res[pred]["image_id"] = cnts.index(tuple(res[pred]["bbox"]))
+
+    return res
+
+
 def predict(
     detection_model: DetectionModel = None,
     model_type: str = "mmdet",