目标检测yolo, voc, coco的BBox格式转换

yolo, voc, coco bbox格式互转函数

yolo: [xmid, ymid, w, h],归一化到0-1

voc: [x1, y1, x2, y2]

coco: [xmin, ymin, w, h]

 

def voc2yolo(bboxes, image_height=720, image_width=1280):
    """
    voc  => [x1, y1, x2, y2]
    yolo => [xmid, ymid, w, h] (normalized)
    """
    
    bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int
    
    bboxes[..., [0, 2]] = bboxes[..., [0, 2]]/ image_width
    bboxes[..., [1, 3]] = bboxes[..., [1, 3]]/ image_height
    
    w = bboxes[..., 2] - bboxes[..., 0]
    h = bboxes[..., 3] - bboxes[..., 1]
    
    bboxes[..., 0] = bboxes[..., 0] + w/2
    bboxes[..., 1] = bboxes[..., 1] + h/2
    bboxes[..., 2] = w
    bboxes[..., 3] = h
    
    return bboxes

def yolo2voc(bboxes, image_height=720, image_width=1280):
    """
    yolo => [xmid, ymid, w, h] (normalized)
    voc  => [x1, y1, x2, y2]
    
    """ 
    bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int
    
    bboxes[..., [0, 2]] = bboxes[..., [0, 2]]* image_width
    bboxes[..., [1, 3]] = bboxes[..., [1, 3]]* image_height
    
    bboxes[..., [0, 1]] = bboxes[..., [0, 1]] - bboxes[..., [2, 3]]/2
    bboxes[..., [2, 3]] = bboxes[..., [0, 1]] + bboxes[..., [2, 3]]
    
    return bboxes

def coco2yolo(bboxes, image_height=720, image_width=1280):
    """
    coco => [xmin, ymin, w, h]
    yolo => [xmid, ymid, w, h] (normalized)
    """
    
    bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int
    
    # normolizinig
    bboxes[..., [0, 2]]= bboxes[..., [0, 2]]/ image_width
    bboxes[..., [1, 3]]= bboxes[..., [1, 3]]/ image_height
    
    # converstion (xmin, ymin) => (xmid, ymid)
    bboxes[..., [0, 1]] = bboxes[..., [0, 1]] + bboxes[..., [2, 3]]/2
    
    return bboxes

def yolo2coco(bboxes, image_height=720, image_width=1280):
    """
    yolo => [xmid, ymid, w, h] (normalized)
    coco => [xmin, ymin, w, h]
    
    """ 
    bboxes = bboxes.copy().astype(float) # otherwise all value will be 0 as voc_pascal dtype is np.int
    
    # denormalizing
    bboxes[..., [0, 2]]= bboxes[..., [0, 2]]* image_width
    bboxes[..., [1, 3]]= bboxes[..., [1, 3]]* image_height
    
    # converstion (xmid, ymid) => (xmin, ymin) 
    bboxes[..., [0, 1]] = bboxes[..., [0, 1]] - bboxes[..., [2, 3]]/2
    
    return bboxes

def voc2coco(bboxes, image_height=720, image_width=1280):
    bboxes  = voc2yolo(bboxes, image_height, image_width)
    bboxes  = yolo2coco(bboxes, image_height, image_width)
    return bboxes

版权声明:本文为CSDN博主「fwu11」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。
原文链接:https://blog.csdn.net/weixin_42552135/article/details/122533582

fwu11

我还没有学会写个人说明!

暂无评论

发表评论

相关推荐

【目标检测】YOLO、SSD、CornerNet原理介绍

目标检测是计算机视觉中比较简单的任务,用来在一张图篇中找到某些特定的物体,目标检测不仅要求我们识别这些物体的种类,同时要求我们标出这些物体的位置。其中类别是离散数据,位置是连续数据。 目

目标检测篇之---YOLO系列

YOLO系列 首先先说一下目标检测之one-stage和two-stage网络是什么意思?有什么区别? 刚开始看目标检测的时候总能看见单阶段(one-stage)和两阶段(

分享 | 物体检测和数据集

因为最近学习任务比较紧(但也不妨碍元旦摆烂三天),所以中间有几个实战Kaggle比赛就跳过了,等以后有时间再回头来看看。物体检测和数据集这一节花了有一天的时间,一直有一个bug困扰,后来改了代码把box