前言

在遥感目标检测中，由于DOTA数据集本身类别的样本数量是极度不均衡的；所以，使用合适的数据增强，可以在一定程度上提升模型的最终检测性能。而在之前的一篇博客中，我提到过使用随机旋转的方法进行增强，但是代码运行完毕之后最好还是需要进行可视化操作验证更为稳妥，所以便有了cv2.polylines这一函数的出现，该函数可以画任意的多边形。

正文

这里使用python版本的函数举例，可以发现该函数所需要的参数主要为

polylines(img, pts, isClosed, color, thickness=None, lineType=None, shift=None)

参数：
img（array）：为ndarray类型（可以为cv.imread）直接读取的数据
pts（array）：为所画多边形的顶点坐标，举个简单的例子：当一张图片需要有多个四边形时，该数组ndarray的shape应该为（N，4，2）
isClosed（bool）：所画四边形是否闭合，通常为True
color（tuple）：BGR三个通道的值
thickness（int）：画线的粗细
shift：顶点坐标中小数的位数

接下来便是实战来观察实际的效果：
首先准备的图片为：(个人认为这张图片十分经典)

而这张图片对应的label如下：

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518.0 407.0 526.0 415.0 499.0 444.0 491.0 436.0 ship 0
573.0 117.0 585.0 107.0 1003.0 530.0 993.0 540.0 harbor 0

利用程序将标注文件的坐标读入（给出代码块仅供参考）：

filelist = glob.glob('./*.txt')
    for filename in filelist:
        # basename = osp.basename(filename)
        with open(filename, 'r') as fread:
            lines = fread.readlines()
            nplines = []
            # read lines
            for line in lines:
                line = line.split()
                npline = np.array(line[:8], dtype=np.float32).astype(np.int32)
                nplines.append(npline[np.newaxis])
            nplines = np.concatenate(nplines, 0).reshape(-1, 4, 2)

此时读入的nplines结果为：

array([[[ 807,  331],
        [ 800,  324],
        [ 817,  309],
        [ 823,  316]],

       [[ 879,  366],
        [ 888,  373],
        [ 870,  393],
        [ 861,  386]],

       [[ 862,  384],
        [ 855,  377],
        [ 873,  356],
        [ 882,  363]],

       ...,

       [[ 525,  417],
        [ 536,  426],
        [ 510,  456],
        [ 499,  446]],

       [[ 518,  407],
        [ 526,  415],
        [ 499,  444],
        [ 491,  436]],

       [[ 573,  117],
        [ 585,  107],
        [1003,  530],
        [ 993,  540]]])

接下来的核心代码为：

    imgori1 = imgori.copy()
    img = cv.polylines(imgori1, nplines, isClosed=True, color=(255, 125, 125), thickness=1)

最后的img结果为：

注意！！！：这里其实有一个坑，polylines函数其实可以不需要将返回值赋给新的变量，它是直接将输入变量上进行操作，所以上面两行代码，我特意进行了一次copy操作，以防将最初的图片覆盖掉。

结束

以上便是对于该函数的介绍，如有疏漏欢迎指正！！！！