class SpiralIterator: def init(self, source, x=810, y=500, length=None): self.source = source self.row = np.shape(self.source)[0] #第一个元素是行数 self.col = np.shape(self.source)[1] #第二个元素是列数 if length: self.length = min(length, np.size(self.source)) else: self.length = np.size(self.source) if x: self.x = x else: self.x = self.row // 2 if y: self.y = y else: self.y = self.col // 2 self.i = self.x self.j = self.y self.iteSize = 0 geo_transform = dsm_data.GetGeoTransform() self.x_origin = geo_transform[0] self.y_origin = geo_transform[3] self.pixel_width = geo_transform[1] self.pixel_height = geo_transform def hasNext(self): return self.iteSize < self.length # 不能取更多值了 def get(self): if self.hasNext(): # 还能再取一个值 # 先记录当前坐标的值 —— 准备返回 i = self.i j = self.j val = self.source[i][j] # 计算下一个值的坐标 relI = self.i - self.x # 相对坐标 relJ = self.j - self.y # 相对坐标 if relJ > 0 and abs(relI) < relJ: self.i -= 1 # 上 elif relI < 0 and relJ > relI: self.j -= 1 # 左 elif relJ < 0 and abs(relJ) > relI: self.i += 1 # 下 elif relI >= 0 and relI >= relJ: self.j += 1 # 右 #判断索引是否在矩阵内 x = self.x_origin + (j + 0.5) * self.pixel_width y = self.y_origin + (i + 0.5) * self.pixel_height col = int((x - dsm_data.GetGeoTransform()[0]) / dsm_data.GetGeoTransform()[1]) row = int((y - dsm_data.GetGeoTransform()[3]) / dsm_data.GetGeoTransform()[5]) z = val self.iteSize += 1 return x, y, z, row, col dsm_path = 'C:\sanwei\jianmo\Productions\Production_2\Production_2_DSM_part_2_2.tif' dsm_data = gdal.Open(dsm_path) dsm_array = dsm_data.ReadAsArray() spiral_iterator = SpiralIterator(dsm_array,x=810,y=500) while spiral_iterator.hasNext(): x, y, z, row, col = spiral_iterator.get() print(f'Value at ({x},{y}) ({row},{col}):{z}')这段代码怎么改可以当原始航片该位置像素值为 0 值,修改其像素值为 255,当 原始航片该位置像素值为 255 时,说明此点已被占用,则对地面点(X,Y,Z)标记此点位被遮蔽

您可以在SpiralIterator类中添加一个方法，用于修改像素值并标记被遮蔽的点。具体代码如下：

class SpiralIterator:
    def __init__(self, source, x=810, y=500, length=None):
        self.source = source
        self.row = np.shape(self.source)[0] #第一个元素是行数
        self.col = np.shape(self.source)[1] #第二个元素是列数
        if length:
            self.length = min(length, np.size(self.source))
        else:
            self.length = np.size(self.source)
        if x:
            self.x = x
        else:
            self.x = self.row // 2
        if y:
            self.y = y
        else:
            self.y = self.col // 2
        self.i = self.x
        self.j = self.y
        self.iteSize = 0
        geo_transform = dsm_data.GetGeoTransform()
        self.x_origin = geo_transform[0]
        self.y_origin = geo_transform[3]
        self.pixel_width = geo_transform[1]
        self.pixel_height = geo_transform[5]

    def hasNext(self):
        return self.iteSize < self.length # 不能取更多值了

    def get(self):
        if self.hasNext(): # 还能再取一个值
            # 先记录当前坐标的值 —— 准备返回
            i = self.i
            j = self.j
            val = self.source[i][j]
            
            # 计算下一个值的坐标
            relI = self.i - self.x # 相对坐标
            relJ = self.j - self.y # 相对坐标
            if relJ > 0 and abs(relI) < relJ:
                self.i -= 1 # 上
            elif relI < 0 and relJ > relI:
                self.j -= 1 # 左
            elif relJ < 0 and abs(relJ) > relI:
                self.i += 1 # 下
            elif relI >= 0 and relI >= relJ:
                self.j += 1 # 右
            
            # 判断索引是否在矩阵内
            x = self.x_origin + (j + 0.5) * self.pixel_width
            y = self.y_origin + (i + 0.5) * self.pixel_height
            col = int((x - dsm_data.GetGeoTransform()[0]) / dsm_data.GetGeoTransform()[1])
            row = int((y - dsm_data.GetGeoTransform()[3]) / dsm_data.GetGeoTransform()[5])
            if val == 0:
                self.source[i][j] = 255 # 修改像素值为255
            elif val == 255:
                Z = -9999 # 设置被遮蔽点的高程值为-9999
                print(f'点({x},{y})被遮蔽，标记为({row},{col}):{Z}')
                
            self.iteSize += 1
            return x, y, val, row, col
        
dsm_path = 'C:\sanwei\jianmo\Productions\Production_2\Production_2_DSM_part_2_2.tif'
dsm_data = gdal.Open(dsm_path)
dsm_array = dsm_data.ReadAsArray()
spiral_iterator = SpiralIterator(dsm_array,x=810,y=500)
while spiral_iterator.hasNext():
    x, y, z, row, col = spiral_iterator.get()
    print(f'Value at ({x},{y}) ({row},{col}):{z}')

这样，在遍历时，如果遇到原始航片该位置像素值为0，就会将其修改为255；如果遇到原始航片该位置像素值为255，就会标记被遮蔽点，并将其高程值设为-9999。需要注意的是，由于被遮蔽点的高程值为-9999，因此在打印输出时，可能会出现一些奇怪的结果，需要根据实际情况进行调整。