remove utils

box-metrics.py

@@ -3,7 +3,6 @@ import datasets
 import numpy as np
 from seametrics.payload import Payload
 import torch
-from utils import bbox_iou, bbox_bep
 import datasets
 
 _CITATION = """\
@@ -290,4 +289,86 @@ class box_metrics(evaluate.Metric):
         return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
     
 
-    
+def bbox_bep(box1, box2, xywh=False, eps=1e-7, bep1 = True):
+    """
+    Calculates bottom edge proximity between two boxes
+    
+    Input shapes are box1(1,4) to box2(n,4)
+    
+    Implementation of bep2 from 
+        Are object detection assessment criteria ready for maritime computer vision?
+    """
+
+    # Get the coordinates of bounding boxes
+    if xywh:  # transform from xywh to xyxy
+        (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
+        w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
+        b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
+        b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
+    else:  # x1, y1, x2, y2 = box1
+        b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
+        b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
+        w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps)
+        w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps)
+
+    # Bottom edge distance (absolute value)
+    # xb = torch.abs(b2_x2 - b1_x1)
+    xb = torch.min(b2_x2-b1_x1, b1_x2-b2_x1)
+    xa = w2 - xb
+    xc = w1 - xb
+    ybe = torch.abs(b2_y2 - b1_y2)
+
+    X2 = xb/(xb+xa)
+    Y2 = 1-ybe/h2
+
+    X1 = xb/(xb+xa+xc+eps)
+    Y1 = 1-ybe/(torch.max(h2,h1)+eps)
+
+    bep = X1*Y1 if bep1 else X2*Y2
+
+    return bep
+
+def bbox_iou(box1, box2, xywh=False, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
+    """
+    Calculates IoU, GIoU, DIoU, or CIoU between two boxes, supporting xywh/xyxy formats.
+
+    Input shapes are box1(1,4) to box2(n,4).
+    """
+
+    # Get the coordinates of bounding boxes
+    if xywh:  # transform from xywh to xyxy
+        (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
+        w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
+        b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
+        b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
+    else:  # x1, y1, x2, y2 = box1
+        b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
+        b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
+        w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps)
+        w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps)
+
+    # Intersection area
+    inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * (
+        b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)
+    ).clamp(0)
+
+    # Union Area
+    union = w1 * h1 + w2 * h2 - inter + eps
+
+    # IoU
+    iou = inter / union
+    if CIoU or DIoU or GIoU:
+        cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1)  # convex (smallest enclosing box) width
+        ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1)  # convex height
+        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
+            c2 = cw**2 + ch**2 + eps  # convex diagonal squared
+            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center dist ** 2
+            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+                v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
+                with torch.no_grad():
+                    alpha = v / (v - iou + (1 + eps))
+                return iou - (rho2 / c2 + v * alpha)  # CIoU
+            return iou - rho2 / c2  # DIoU
+        c_area = cw * ch + eps  # convex area
+        return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf
+    return iou  # IoU