updates

utils/utils.py

@@ -11,6 +11,7 @@ import numpy as np
 import torch
 import torch.nn as nn
 from tqdm import tqdm
+import math
 
 from . import torch_utils  # , google_utils
 
@@ -234,7 +235,7 @@ def compute_ap(recall, precision):
     return ap
 
 
-def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False):
+def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False):
     # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
     box2 = box2.t()
 
@@ -255,15 +256,31 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False):
                  (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
 
     # Union Area
-    union_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1) + 1e-16) + \
-                 (b2_x2 - b2_x1) * (b2_y2 - b2_y1) - inter_area
+    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1
+    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1
+    union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area
 
     iou = inter_area / union_area  # iou
-    if GIoU:  # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf
-        c_x1, c_x2 = torch.min(b1_x1, b2_x1), torch.max(b1_x2, b2_x2)
-        c_y1, c_y2 = torch.min(b1_y1, b2_y1), torch.max(b1_y2, b2_y2)
-        c_area = (c_x2 - c_x1) * (c_y2 - c_y1) + 1e-16  # convex area
-        return iou - (c_area - union_area) / c_area  # GIoU
+    if GIoU or DIoU or CIoU:
+        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
+        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
+        if GIoU:  # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf
+            c_area = cw * ch + 1e-16  # convex area
+            return iou - (c_area - union_area) / c_area  # GIoU
+        if DIoU or CIoU:  # Distance IoU https://arxiv.org/abs/1911.08287v1
+            c2 = cw ** 2 + ch ** 2 + 1e-16  # convex diagonal squared
+            # b1_xc, b1_yc = (b1_x1 + b1_x2) / 2, (b1_y1 + b1_y2) / 2
+            # b2_xc, b2_yc = (b2_x1 + b2_x2) / 2, (b2_y1 + b2_y2) / 2
+            # rho2 = (b2_xc - b1_xc) ** 2 + (b2_yc - b1_yc) ** 2  # centerpoint distance squared
+            rho2 = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2)) ** 2 / 4 + ((b2_y1 + b2_y2) - (b1_y1 + b1_y2)) ** 2 / 4
+            if DIoU:
+                return iou - rho2 / c2  # DIoU
+            elif CIoU:
+                atan = torch.atan(w2 / h2) - torch.atan(w1 / h1)
+                v = (4 / math.pi ** 2) * torch.pow(atan, 2)
+                alpha = v / (1 - iou + v)
+                # ar = - (8 / (math.pi ** 2)) * atan * (w1 * h1)
+                return iou - (rho2 / c2 + alpha * v)  # CIoU
 
     return iou