import numpy as np
import torch
def IOU(box1, box2, wh=False):
if wh == False:
xmin1,ymin1,xmax1,ymax1 = box1
xmin2,ymin2,xmax2,ymax2 = box2
else:
xmin1,ymin1 = int(box1[0] - box1[2] / 2.0), int(box1[1] - box1[3] / 2.0)
xmax1,ymax1 = int(box1[0] + box1[2] / 2.0), int(box1[1] + box1[3] / 2.0)
xmin2,ymin2 = int(box2[0] - box2[2] / 2.0), int(box2[1] - box2[3] / 2.0)
xmax2,ymax2 = int(box2[0] + box2[2] / 2.0), int(box2[1] + box2[3] / 2.0)
xx1 = np.max([xmin1,xmin2])
yy1 = np.max([xmin1,ymin2])
xx2 = np.min([xmax1,xmax2])
yy2 = np.min([ymax1,ymax2])
area1 = (xmax1 - xmin1) * (ymax1 - ymin1)
area2 = (xmax2 - xmin2) * (ymax2 - ymin2)
inter_area = (np.max([0,xx2-xx1]) * np.max([0,yy2-yy1]))
iou = inter_area / (area1 + area2 - inter_area)
return iou
def GIOU(box1,box2):
xmin1,ymin1,xmax1,ymax1 = box1
xmin2,ymin2,xmax2,ymax2 = box2
iou = IOU(box1,box2)
area_C = (max(xmin1,xmax2,xmin2,xmax1) - min(xmin1,xmax2,xmin2,xmax1)) * (max(ymax1,ymax2,ymin1,ymin2) - min(ymax1,ymax2,ymin1,ymin2))
area_1 = (xmax1-xmin1) * (ymin1 - ymax1)
area_2 = (xmax2-xmin1) * (ymin2 - ymax2)
sum_area = area_1 + area_2
w1 = xmax1 - xmin1
w2 = xmax2 - xmin2
h1 = ymin1 - ymax2
h2 = ymin2 - ymax2
W = min(xmin1,xmin2,xmax1,xmax2) - max(xmin1,xmin2,xmax1,xmax2)
H = min(ymin1,ymin2,ymax1,ymax2) - max(ymin1,ymin2,ymax1,ymax2)
Area = W * H
add_area = sum_area - Area
end_area = (area_C - add_area) / area_C
giou = iou - end_area
return giou
def DIOU(box1,box2):
rows = box1.shape[0]
cols = box2.shape[0]
dious = torch.zeros((rows,cols))
if rows * cols == 0:
return dious
exchange = False
if box1.shape[0] > box2.shape[0]:
box1,box2 = box2,box1
dious = torch.zeros((cols,rows))
exchange = True
w1 = box1[:2] - box1[:0]
h1 = box1[:3] - box1[:1]
w2 = box2[:2] - box2[:0]
h2 = box2[:3] - box2[:1]
area1 = w1 * h1
area2 = w2 * h2
center_x1 = (box1[:2]+box1[:0]) / 2.0
center_y1 = (box1[:1]+box1[:3]) / 2.0
center_x2 = (box2[:2]+box2[:0]) / 2.0
center_y2 = (box2[:1]+box2[:3]) / 2.0
inter_max_xy = torch.min(box1[:,2:],box2[:,2:])
inter_min_xy = torch.max(box1[:,:2],box2[:,:2])
out_max_xy = torch.max(box1[:,2:],box2[:,2:])
out_min_xy = torch.min(box1[:,:2],box2[:,:2])
inter = torch.clamp((inter_max_xy - inter_min_xy),min = 0)
inter_area = inter[:,0] * inter[:,1]
inter_diag = (center_x2 - center_x1) ** 2 + (center_y2 - center_y1) ** 2
outer = torch.clamp(out_max_xy-out_min_xy, min = 0)
outer_diag = (outer[:,0] ** 2) + (outer[:,1] ** 2)
union = area1 + area2 - inter_area
dious = inter_area / union - (inter_diag)/outer_diag
dious = torch.clamp(dious, min = -1.0,max = 1.0)
if exchange:
dious = dious.T
return dious
def auc(pred,gt):
for i in range(len(gt) - 1):
for j in range(len(gt)):
if(gt[i] != gt[j]):
d += 1
if(gt[i] > gt[j] and pred[i] > pred[j]) or (gt[i] < gt[j] and pred[i] < pred[j]):
n+=1
return n / d
