Task02 目标检测基础和VOC数据集

目标检测基础

什么是目标检测

图像分类：只需要判断输入的图像中是否包含感兴趣物体。
目标检测：需要在识别出图片中目标类别的基础上，还要精确定位到目标的具体位置，并用外接矩形框标出。

边界框（bounding box）

# 注意坐标轴原点是图片的左上角。bbox 是 bounding box 的缩写。
dog_bbox = [300, 200, 600, 450]
cat_bbox = [30, 30, 230, 200]
# 格式为x左上，y左上，x右下，y右下

而转换格式为：

def xy_to_cxcy(xy):
    """
    Convert bounding boxes from boundary coordinates (x_min, y_min, x_max, y_max) to center-size coordinates (c_x, c_y, w, h).

    :param xy: bounding boxes in boundary coordinates, a tensor of size (n_boxes, 4)
    :return: bounding boxes in center-size coordinates, a tensor of size (n_boxes, 4)
    """
    return torch.cat([(xy[:, 2:] + xy[:, :2]) / 2,  # c_x, c_y
                      xy[:, 2:] - xy[:, :2]], 1)  # w, h


def cxcy_to_xy(cxcy):
    """
    Convert bounding boxes from center-size coordinates (c_x, c_y, w, h) to boundary coordinates (x_min, y_min, x_max, y_max).

    :param cxcy: bounding boxes in center-size coordinates, a tensor of size (n_boxes, 4)
    :return: bounding boxes in boundary coordinates, a tensor of size (n_boxes, 4)
    """
    return torch.cat([cxcy[:, :2] - (cxcy[:, 2:] / 2),  # x_min, y_min
                      cxcy[:, :2] + (cxcy[:, 2:] / 2)], 1)  # x_max, y_max

交并比(IOU)

简单来说，就是将两者的交集比上两者的并集

计算过程是将左上方的最大值(max(red_x1, green_x1), max(red_y1, green_y1)), 和右下点坐标最小值:(min(red_x2, green_x2), min(red_y2, green_y2))

具体代码如下

def find_intersection(set_1, set_2):
    """ 
    Find the intersection of every box combination between two sets of boxes that are in boundary coordinates.

    :param set_1: set 1, a tensor of dimensions (n1, 4)                                                                                                           
    :param set_2: set 2, a tensor of dimensions (n2, 4)
    :return: intersection of each of the boxes in set 1 with respect to each of the boxes in set 2, a tensor of dimensions (n1, n2)
    """

    # PyTorch auto-broadcasts singleton dimensions
    lower_bounds = torch.max(set_1[:, :2].unsqueeze(1), set_2[:, :2].unsqueeze(0))  # (n1, n2, 2)
    upper_bounds = torch.min(set_1[:, 2:].unsqueeze(1), set_2[:, 2:].unsqueeze(0))  # (n1, n2, 2)
    intersection_dims = torch.clamp(upper_bounds - lower_bounds, min=0)  # (n1, n2, 2)
    return intersection_dims[:, :, 0] * intersection_dims[:, :, 1]  # (n1, n2)


def find_jaccard_overlap(set_1, set_2):
    """ 
    Find the Jaccard Overlap (IoU) of every box combination between two sets of boxes that are in boundary coordinates.

    :param set_1: set 1, a tensor of dimensions (n1, 4)
    :param set_2: set 2, a tensor of dimensions (n2, 4)
    :return: Jaccard Overlap of each of the boxes in set 1 with respect to each of the boxes in set 2, a tensor of dimensions (n1, n2)
    """

    # Find intersections
    intersection = find_intersection(set_1, set_2)  # (n1, n2)

    # Find areas of each box in both sets
    areas_set_1 = (set_1[:, 2] - set_1[:, 0]) * (set_1[:, 3] - set_1[:, 1])  # (n1)
    areas_set_2 = (set_2[:, 2] - set_2[:, 0]) * (set_2[:, 3] - set_2[:, 1])  # (n2)

    # Find the union
    # PyTorch auto-broadcasts singleton dimensions
    union = areas_set_1.unsqueeze(1) + areas_set_2.unsqueeze(0) - intersection  # (n1, n2)

    return intersection / union  # (n1, n2)

VOC数据集

VOC数据集介绍

JPEGImages

这个文件夹中存放所有的图片，包括训练验证测试用到的所有图片。
这个文件夹中包含三个子文件夹，Layout、Main、Segmentation

ImageSets

• Layout文件夹中存放的是train，valid，test和train+valid数据集的文件名

• Segmentation文件夹中存放的是分割所用train，valid，test和train+valid数据集的文件名

• Main文件夹中存放的是各个类别所在图片的文件名，比如cow_val，表示valid数据集中，包含有cow类别目标的图片名称。

Annotation
Annotation文件夹中存放着每张图片相关的标注信息，以xml格式的文件存储

总结

这次介绍了关于目标检测的目标框的基础知识和目标检测的概念，并了解了VOC数据集的格式，而目标检测的数据部分已经了解完毕，后面将要了解相关的模型来深入目标检测的精髓。