out_ds = driver.Create('C:/yingxiang/output.tif', width=4096,height=4096, 1, gdal.GDT_Float32)这句话报错

这个错误信息可能是因为在创建输出数据集时，传递的参数个数不正确。根据你提供的代码，应该将 `width` 和 `height` 这两个参数放在前面，然后才是数据集的数量和数据类型，如下所示：

out_ds = driver.Create('C:/yingxiang/output.tif', width=4096, height=4096, bands=1, eType=gdal.GDT_Float32)

这应该可以正确创建输出数据集了。请注意，`bands` 和 `eType` 这两个参数需要指定名称，否则会引起语法错误。希望这可以帮助你解决问题。

相关问题

def extract_building_shadow(image, dsm, ground_spacing, radius): shadow_mask = np.zeros_like(image, dtype=np.bool) for i in range(0, image.shape[0], ground_spacing): for j in range(0, image.shape[1], ground_spacing): if not np.any(shadow_mask[i, j]): center = (i, j) ground_height = dsm[i, j] for x, y in spiral_search(center, radius, ground_spacing): if x < 0 or x >= image.shape[0] or y < 0 or y >= image.shape[1]: continue if np.any(shadow_mask[x, y:]): continue height = dsm[x, y] if height > ground_height: shadow_mask[x, y] = True elif height == ground_height: if np.linalg.norm(np.array([x, y]) - np.array(center)) < \ np.linalg.norm(np.array([i, j]) - np.array(center)): shadow_mask[x, y] = True cv2.imwrite(output_path, shadow_mask.astype(np.uint8) * 255) return shadow_mask image = cv2.imread('C:\yingxiang\DJI_20230322140516_0026_V.JPG') dsm_path = 'C:/sanwei/jianmo/Productions/Production_2/Production_2_DSM_part_2_2.tif' dsm_dataset = gdal.Open(dsm_path) output_path = 'C:\yingxiang\mask.png'这段代码为什么运行后不能将掩膜结果保存

这可能是由于在保存 PNG 文件之前，尝试在代码中访问 `output_path` 变量，而此时该变量尚未定义。您可以将 `output_path` 变量的定义移动到函数之前，以确保在访问变量之前它已经存在。例如：

output_path = 'C:\yingxiang\mask.png'
def extract_building_shadow(image, dsm, ground_spacing, radius):
    shadow_mask = np.zeros_like(image, dtype=np.bool)
    for i in range(0, image.shape[0], ground_spacing):
        for j in range(0, image.shape[1], ground_spacing):
            if not np.any(shadow_mask[i, j]):
                center = (i, j)
                ground_height = dsm[i, j]
                for x, y in spiral_search(center, radius, ground_spacing):
                    if x < 0 or x >= image.shape[0] or y < 0 or y >= image.shape[1]:
                        continue
                    if np.any(shadow_mask[x, y:]):
                        continue
                    height = dsm[x, y]
                    if height > ground_height:
                        shadow_mask[x, y] = True
                    elif height == ground_height:
                        if np.linalg.norm(np.array([x, y]) - np.array(center)) < \
                                np.linalg.norm(np.array([i, j]) - np.array(center)):
                            shadow_mask[x, y] = True
    cv2.imwrite(output_path, shadow_mask.astype(np.uint8) * 255)
    return shadow_mask

image = cv2.imread('C:\yingxiang\DJI_20230322140516_0026_V.JPG')
dsm_path = 'C:/sanwei/jianmo/Productions/Production_2/Production_2_DSM_part_2_2.tif'
dsm_dataset = gdal.Open(dsm_path)

# 调用函数
extract_building_shadow(image, dsm_dataset.ReadAsArray(), 10, 10)

请注意，此代码还将 `dsm` 数据集转换为数组，并将其传递给函数。

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 ''' 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。