让yolo输出目标中心点坐标python代码

以下是基于Python的代码实现获取YOLO输出的目标中心点坐标的方法：

import numpy as np

def decode_output(output, anchors, num_classes, input_shape, score_threshold):
    output = np.array(output)
    anchors = np.array(anchors)
    num_anchors = len(anchors)
    grid_size = output.shape[1:3]
    num_classes = int(num_classes)

    # 对YOLO输出进行解码，得到边界框坐标、类别概率和置信度
    output = np.reshape(output, [-1, num_anchors * (5 + num_classes)])
    xy_offset = np.sigmoid(output[..., :2])  # 边界框中心点坐标偏移量
    wh_scale = np.exp(output[..., 2:4]) * anchors  # 边界框宽高缩放比例
    obj_score = np.sigmoid(output[..., 4])  # 边界框置信度
    class_prob = np.sigmoid(output[..., 5:])  # 类别概率

    # 对边界框坐标进行还原，得到实际图像中的坐标
    grid_y = np.tile(np.arange(grid_size[0])[:, np.newaxis], [1, grid_size[1]])
    grid_x = np.tile(np.arange(grid_size[1])[np.newaxis, :], [grid_size[0], 1])
    xy_offset = np.stack([grid_x, grid_y], axis=-1) + xy_offset
    xy_offset = xy_offset / np.array(grid_size)[::-1]  # 归一化
    wh_scale = wh_scale / np.array(input_shape)[::-1]  # 归一化
    xy_min = xy_offset - wh_scale / 2
    xy_max = xy_offset + wh_scale / 2
    boxes = np.concatenate([xy_min, xy_max], axis=-1)

    # 对边界框置信度和类别概率进行筛选和排序
    mask = obj_score >= score_threshold
    boxes = boxes[mask]
    class_prob = class_prob[mask]
    scores = obj_score[mask] * class_prob.max(axis=-1)

    # 对边界框按照置信度进行排序，选择置信度最高的边界框作为最终的检测结果
    order = scores.argsort()[::-1]
    boxes = boxes[order]
    scores = scores[order]
    classes = class_prob.argmax(axis=-1)[order]

    # 计算每个目标的中心点坐标
    centers = np.stack([(boxes[:, 0] + boxes[:, 2]) / 2, (boxes[:, 1] + boxes[:, 3]) / 2], axis=-1)

    return boxes, scores, classes, centers

其中，`output`是YOLO的输出，`anchors`是预定义的锚框，`num_classes`是类别数量，`input_shape`是输入图像的大小，`score_threshold`是置信度阈值。函数返回值包括边界框坐标、置信度、类别和中心点坐标。可以通过调用该函数来获取YOLO的输出的目标中心点坐标。