输入图片的大小和特征点的像素坐标以及划分的网格大小，返回特征点的分布均匀度指标代码

以下是一个简单的 Python 代码实现：

import numpy as np

def get_uniformity_score(img_size, keypoints, grid_size):
    # 计算网格数量
    grid_rows = int(np.ceil(img_size[0] / grid_size))
    grid_cols = int(np.ceil(img_size[1] / grid_size))
    grid_count = grid_rows * grid_cols
    
    # 将关键点分配到网格中
    grid_points = [[] for _ in range(grid_count)]
    for kp in keypoints:
        row = int(kp[0] / grid_size)
        col = int(kp[1] / grid_size)
        grid_index = row * grid_cols + col
        grid_points[grid_index].append(kp)
    
    # 计算每个网格内特征点数量的平均值
    avg_points_per_grid = len(keypoints) / grid_count
    
    # 计算每个网格内特征点数量与平均值的偏差
    deviations = []
    for gp in grid_points:
        deviation = len(gp) - avg_points_per_grid
        deviations.append(deviation)
    
    # 计算偏差的标准差
    std_deviation = np.std(deviations)
    
    # 计算分布均匀度指标
    uniformity_score = 1 - std_deviation / avg_points_per_grid
    
    return uniformity_score

其中，`img_size` 是输入图片的大小（宽度和高度），`keypoints` 是特征点的像素坐标数组，`grid_size` 是划分的网格大小。函数首先计算出网格数量和每个网格内的特征点，然后计算每个网格内特征点数量的平均值和偏差，并计算偏差的标准差。最后，函数计算分布均匀度指标并返回。