KD = [] for i in range(0, 7): p1 = points[i] for j in range(0, 7): p2 = points[j] D = [] for k in range(0, 7): p3 = points[k] if p1 != p2 and p1 != p3 and p2 != p3: d = abs((p2[1] - p1[1]) * p3[0] - (p2[0] - p1[0]) * p3[1] + p2[0] * p1[1] - p2[1] * p1[0]) / distance(p1, p2) D.append(d) print(f"选取顶点 :{p3} 直线: ({p2}, {p1}) 距离 :{d}") print(D) K = max(D) KD.append(K) print(KD)这个代码中D列表为啥是空值
时间: 2024-03-04 10:53:27 浏览: 161
在这段代码中,D 列表初始为空值,是因为在每个点 p2 和 p1 的组合下,只有当满足条件 p1 != p2 and p1 != p3 and p2 != p3 时,才会往 D 列表中添加距离值 d。如果 p1、p2 或 p3 中有任意两个点相同,就不会往 D 列表中添加距离值,因此 D 列表可能为空值。
另外,可以在循环开始前添加语句 `KD = []`,这样可以保证每次运行循环时,KD 列表都是空的,不会受到上一次运行循环的影响。
相关问题
for i in range(0,7): p1 = points[i] for j in range(0, 7): p2 = points[j] D=[] for k in range(0, 7): p3 = points[k] if p1 != p2 and p1 != p3 and p2 != p3: d = abs((p2[1] - p1[1]) * p3[0] - (p2[0] - p1[0]) * p3[1] + p2[0] * p1[1] - p2[1] * p1[0]) / distance(p1, p2) D.append(d) print(f"选取顶点 :{p3} 直线: ({p2}, {p1}) 距离 :{d}") print(D) K=max(D) KD.append(K)这个代码为啥错了
这段代码缺少一些变量的定义,例如 distance 函数,KD 列表等。另外,如果没有缩进错误的话,最后一行没有结束符号。可以试着修改为如下代码:
```
# 假设已经定义了 distance 函数和 points 列表
KD = []
for i in range(0, 7):
p1 = points[i]
for j in range(0, 7):
p2 = points[j]
D = []
for k in range(0, 7):
p3 = points[k]
if p1 != p2 and p1 != p3 and p2 != p3:
d = abs((p2[1] - p1[1]) * p3[0] - (p2[0] - p1[0]) * p3[1] + p2[0] * p1[1] - p2[1] * p1[0]) / distance(p1, p2)
D.append(d)
print(f"选取顶点 :{p3} 直线: ({p2}, {p1}) 距离 :{d}")
print(D)
K = max(D)
KD.append(K)
print(KD)
```
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:
```python
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。