from osgeo import gdal import numpy as np 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 z = val self.iteSize += 1 return x, y, z 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 = spiral_iterator.get() print(f'Value at ({x},{y}):{z}')这段代码怎么改可以)依据共线方程将地面点(X,Y,Z)反算其在原始航 片中的像素值行列号( r,c),当 img1 该位置像素值为 0 值,修改其像素值为 255,当 img1 该( r,c) 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。

这段代码需要进行修改，添加反算像素行列号的函数，并在循环中调用该函数，判断是否需要修改像素值或标记遮蔽。以下是修改后的代码：

from osgeo import gdal 
import numpy as np 

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] 
            
            # 反算像素行列号
            r, c = self.pixel2colrow(j, i)
            
            # 判断是否需要修改像素值或标记遮蔽
            if img1[r][c] == 0:
                img1[r][c] = 255 
            elif img1[r][c] == 255:
                # 标记遮蔽
                self.source[i][j] = -1 
            
            # 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 
            self.checkIndex()
            
            # 返回像素坐标和高程值
            x = self.x_origin + (j + 0.5) * self.pixel_width 
            y = self.y_origin + (i + 0.5) * self.pixel_height 
            z = val 
            self.iteSize += 1 
            return x, y, z 
        
    def pixel2colrow(self, x, y):
        c = int((x - self.x_origin) / self.pixel_width)
        r = int((y - self.y_origin) / self.pixel_height)
        return r, c
    
    def checkIndex(self):
        if self.i >= self.row:
            self.i = self.row - 1
        elif self.i < 0:
            self.i = 0
        if self.j >= self.col:
            self.j = self.col - 1
        elif self.j < 0:
            self.j = 0

dsm_path = 'C:\sanwei\jianmo\Productions\Production_2\Production_2_DSM_part_2_2.tif' 
img1_path = 'path/to/img1.tif'

dsm_data = gdal.Open(dsm_path) 
dsm_array = dsm_data.ReadAsArray() 

# 读取待修改的图像
img1_data = gdal.Open(img1_path)
img1 = img1_data.ReadAsArray()

spiral_iterator = SpiralIterator(dsm_array, x=810, y=500) 
while spiral_iterator.hasNext(): 
    x, y, z = spiral_iterator.get() 
    # 打印高程值
    print(f'Value at ({x},{y}):{z}') 

# 保存修改后的图像
driver = gdal.GetDriverByName('GTiff')
out_ds = driver.Create('path/to/output.tif', img1.shape[1], img1.shape[0], 1, gdal.GDT_Byte)
out_ds.SetGeoTransform(img1_data.GetGeoTransform())
out_band = out_ds.GetRasterBand(1)
out_band.WriteArray(img1)
out_band.FlushCache()

注意：这段代码中的 `img1` 是待修改的图像，需要根据实际情况进行替换。另外，修改像素值和标记遮蔽的判断条件可能需要根据具体需求进行调整。