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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-12-22 14:19:46 -08:00
parent a0b4d17f7e
commit 0e54731bb8
1 changed files with 12 additions and 14 deletions
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utils/utils.py
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@ -253,21 +253,21 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False):
b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2
# Intersection area # Intersection area
inter_area = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
# Union Area # Union Area
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1
union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area union = (w1 * h1 + 1e-16) + w2 * h2 - inter
iou = inter_area / union_area # iou iou = inter / union # iou
if GIoU or DIoU or CIoU: if GIoU or DIoU or CIoU:
cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width 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 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 if GIoU: # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf
c_area = cw * ch + 1e-16 # convex area c_area = cw * ch + 1e-16 # convex area
return iou - (c_area - union_area) / c_area # GIoU return iou - (c_area - union) / c_area # GIoU
if DIoU or CIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 if DIoU or CIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
# convex diagonal squared # convex diagonal squared
c2 = cw ** 2 + ch ** 2 + 1e-16 c2 = cw ** 2 + ch ** 2 + 1e-16
@ -297,20 +297,18 @@ def box_iou(boxes1, boxes2):
IoU values for every element in boxes1 and boxes2 IoU values for every element in boxes1 and boxes2
""" """
def box_area(boxes): def box_area(box):
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) # box = 4xn
return (box[2] - box[0]) * (box[3] - box[1])
area1 = box_area(boxes1) area1 = box_area(boxes1.t())
area2 = box_area(boxes2) area2 = box_area(boxes2.t())
lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
wh = (rb - lt).clamp(min=0) # [N,M,2] inter = (rb - lt).clamp(min=0).prod(2) # [N,M]
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M] return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)
iou = inter / (area1[:, None] + area2 - inter)
return iou
def wh_iou(wh1, wh2): def wh_iou(wh1, wh2):