def DSM_grid_sorting_masking_check(DSM,grid_size,threshold_angle): width = DSM.RasterXSize height = DSM.RasterYSize #计算网格数量 grid_num_y =int(np.ceil(height/grid_size)) grid_num_x =int(np.ceil(width/grid_size)) #初始化遮蔽检测结果矩阵 result = np.ones((grid_num_y,grid_num_x),dtype=bool) #计算每个格网进行遮蔽检测 for i in range(grid_num_y): for j in range(grid_num_x): #当前格网内的点坐标 y_min = i*grid_size y_max = min((i+1)*grid_size,height) x_min = j * grid_size x_max = min((j+1)*grid_size,width) coords = np.argwhere(DSM.ReadAsArray(x_min, y_min, x_max - x_min, y_max - y_min) > 0) coords[:, 0] += y_min coords[:, 1] += x_min # 构建KD树 tree = cKDTree(coords) # 查询每个点的最邻近点 k = 2 dist, ind = tree.query(coords, k=k) # 计算每个点的法向量 normals = np.zeros(coords.shape) for l in range(coords.shape[0]): if k == 2: p1 = coords[l, :] p2 = coords[ind[l, 1], :] else: p1 = coords[l, :] p2 = coords[ind[l, 1], :] normals[l, :] = np.cross(p1 - p2, p1 - DSM.ReadAsArray(p1[1], p1[0], 1, 1)) # 计算每个点的可见性 visibilities = np.zeros(coords.shape[0]) for l in range(coords.shape[0]): if k == 2: p1 = coords[l, :] p2 = coords[ind[l, 1], :] else: p1 = coords[l, :] p2 = coords[ind[l, 1], :] angle = np.cross(np.dot(normals[l, :], (p2 - p1) / dist[l, 1])) * 180 / np.pi if angle <= threshold_angle: visibilities[l] = 1 # 判断当前格网是否遮蔽 if np.sum(visibilities) == 0: result[i, j] = False else: result[i, j] = True return result dsm_path = 'C:/yingxiang/output.tif' DSM = gdal.Open(dsm_path) result = DSM_grid_sorting_masking_check(DSM,grid_size=10,threshold_angle=40) print(result)这段代码怎么改可以没有以下错误in method 'BandRasterIONumPy', argument 3 of type 'double'

这个错误通常是因为传入的参数类型和函数参数类型不匹配导致的。在这段代码中，可能是以下这一行代码引起的问题：

coords = np.argwhere(DSM.ReadAsArray(x_min, y_min, x_max - x_min, y_max - y_min) > 0)

这里传入到ReadAsArray函数中的参数是x_min, y_min, x_max - x_min, y_max - y_min，这些参数的类型需要是int型的像素坐标。如果这些参数的类型不正确，就会导致函数调用出错，进而导致BandRasterIONumPy函数出错。

要解决这个问题，可以先将输入的像素坐标转换为int类型，代码如下：

x_min = int(x_min)
y_min = int(y_min)

需要在x_min和y_min的计算后立即进行类型转换。

同样地，还需要检查其他传入函数的参数类型是否正确，确保类型匹配。

相关问题

def DSM_grid_sorting_masking_check(DSM,grid_size,threshold_angle): ''' 进行基于DSM格网排序的遮蔽检测方法 :param DSM: 输入的数字高程模型 :param grid_size: 格网大小 :param threshold_angle: 实现遮蔽的最大角度 :return: 遮蔽检测结果。True表示不遮蔽，False表示遮蔽 ''' width = DSM.RasterXSize height = DSM.RasterYSize #计算网格数量 grid_num_y =int(np.ceil(height/grid_size)) grid_num_x =int(np.ceil(width/grid_size)) #初始化遮蔽检测结果矩阵 result = np.ones((grid_num_y,grid_num_x),dtype=bool) #计算每个格网进行遮蔽检测 for i in range(grid_num_y): for j in range(grid_num_x): #当前格网内的点坐标 y_min = i*grid_size y_max = min((i+1)*grid_size,height) x_min = j * grid_size x_max = min((j+1)*grid_size,width) coords = np.argwhere(DSM.ReadAsArray(x_min, y_min, x_max - x_min, y_max - y_min) > 0) coords[:, 0] += y_min coords[:, 1] += x_min # 构建KD树 tree = cKDTree(coords) # 查询每个点的最邻近点 k = 2 dist, ind = tree.query(coords, k=k) # 计算每个点的法向量 normals = np.zeros(coords.shape) for l in range(coords.shape[0]): if k == 2: p1 = coords[l, :] p2 = coords[ind[l, 1], :] else: p1 = coords[l, :] p2 = coords[ind[l, 1], :] normals[l, :] = np.cross(p1 - p2, p1 - DSM.ReadAsArray(p1[1], p1[0], 1, 1)) # 计算每个点的可见性 visibilities = np.zeros(coords.shape[0]) for l in range(coords.shape[0]): if k == 2: p1 = coords[l, :] p2 = coords[ind[l, 1], :] else: p1 = coords[l, :] p2 = coords[ind[l, 1], :] angle = np.cross(np.dot(normals[l, :], (p2 - p1) / dist[l, 1])) * 180 / np.pi if angle <= threshold_angle: visibilities[l] = 1 # 判断当前格网是否遮蔽 if np.sum(visibilities) == 0: result[i, j] = False else: result[i, j] = True return result dsm_path = 'C:/yingxiang/output.tif' DSM = gdal.Open(dsm_path) result = DSM_grid_sorting_masking_check(DSM,grid_size=10,threshold_angle=10) print(result.shape)这段代码怎么改可以输出每个点是否被遮蔽

可以在函数中添加一个返回值，记录每个点是否被遮蔽。可以按照以下方式修改函数：

def DSM_grid_sorting_masking_check(DSM,grid_size,threshold_angle):
    ''' 进行基于DSM格网排序的遮蔽检测方法
    :param DSM: 输入的数字高程模型
    :param grid_size: 格网大小
    :param threshold_angle: 实现遮蔽的最大角度
    :return: 遮蔽检测结果。True表示不遮蔽，False表示遮蔽，以及每个点是否被遮蔽的矩阵，True表示不遮蔽，False表示遮蔽
    '''
    width = DSM.RasterXSize
    height = DSM.RasterYSize

    #计算网格数量
    grid_num_y =int(np.ceil(height/grid_size))
    grid_num_x =int(np.ceil(width/grid_size))

    #初始化遮蔽检测结果矩阵
    result = np.ones((grid_num_y,grid_num_x),dtype=bool)
    mask = np.zeros((height, width), dtype=bool)

    #计算每个格网进行遮蔽检测
    for i in range(grid_num_y):
        for j in range(grid_num_x):
            #当前格网内的点坐标
            y_min = i*grid_size
            y_max = min((i+1)*grid_size,height)
            x_min = j * grid_size
            x_max = min((j+1)*grid_size,width)

            coords = np.argwhere(DSM.ReadAsArray(x_min, y_min, x_max - x_min, y_max - y_min) > 0)
            coords[:, 0] += y_min
            coords[:, 1] += x_min

            # 构建KD树
            tree = cKDTree(coords)

            # 查询每个点的最邻近点
            k = 2
            dist, ind = tree.query(coords, k=k)

            # 计算每个点的法向量
            normals = np.zeros(coords.shape)
            for l in range(coords.shape[0]):
                if k == 2:
                    p1 = coords[l, :]
                    p2 = coords[ind[l, 1], :]
                else:
                    p1 = coords[l, :]
                    p2 = coords[ind[l, 1], :]

                normals[l, :] = np.cross(p1 - p2, p1 - DSM.ReadAsArray(p1[1], p1[0], 1, 1))

            # 计算每个点的可见性
            visibilities = np.zeros(coords.shape[0])
            for l in range(coords.shape[0]):
                if k == 2:
                    p1 = coords[l, :]
                    p2 = coords[ind[l, 1], :]
                else:
                    p1 = coords[l, :]
                    p2 = coords[ind[l, 1], :]

                angle = np.cross(np.dot(normals[l, :], (p2 - p1) / dist[l, 1])) * 180 / np.pi
                if angle <= threshold_angle:
                    visibilities[l] = 1

            # 判断当前格网是否遮蔽
            if np.sum(visibilities) == 0:
                result[i, j] = False
                mask[y_min:y_max, x_min:x_max] = True
            else:
                result[i, j] = True

    return result, mask

dsm_path = 'C:/yingxiang/output.tif'
DSM = gdal.Open(dsm_path)
result, mask = DSM_grid_sorting_masking_check(DSM,grid_size=10,threshold_angle=10)
print(result.shape)

使用该函数后，`mask` 矩阵的值即表示每个点是否被遮蔽，True 表示被遮蔽，False 表示没有被遮蔽。

key=functools.cmp_to_key

The `functools.cmp_to_key` function is a utility function in Python's `functools` module that helps convert a comparison function to a key function.

In Python, sorting a list of objects requires a way to compare the objects. This is done by providing a comparison function that takes two objects as input and returns `-1`, `0`, or `1` depending on whether the first object is less than, equal to, or greater than the second object.

However, some sorting functions in Python (such as the `sorted` function) require a key function instead of a comparison function. A key function takes a single object as input and returns a value that can be used for sorting.

The `cmp_to_key` function helps convert a comparison function to a key function by returning a new function that takes a single object as input and returns a tuple `(comparison_result, object)` where `comparison_result` is the result of calling the original comparison function with the input object and another object, and `object` is the input object itself. This tuple can then be used for sorting.

Here's an example of how to use `cmp_to_key`:

from functools import cmp_to_key

def compare_length(str1, str2):
    if len(str1) < len(str2):
        return -1
    elif len(str1) > len(str2):
        return 1
    else:
        return 0
        
strings = ['cat', 'dog', 'elephant', 'a', 'zebra']
sorted_strings = sorted(strings, key=cmp_to_key(compare_length))
print(sorted_strings)
# Output: ['a', 'cat', 'dog', 'zebra', 'elephant']

In this example, we define a comparison function `compare_length` that compares the lengths of two strings. We then use `cmp_to_key` to convert this comparison function to a key function, and pass it to the `sorted` function to sort a list of strings by length. The resulting sorted list is `['a', 'cat', 'dog', 'zebra', 'elephant']`.