GreenTransPowerCalculate/deeplabv3sdRenewable/tools/山东省地貌识别tools/潜力评估阶段/地貌类型矢量转栅格-cropland后处理部分.py

import numpy as np
import sys
import os

# 尝试多种方式导入GDAL
try:
    # 尝试直接导入GDAL
    import gdal
    import ogr
    import osr

    print("成功导入GDAL库")
except ImportError:
    try:
        # 尝试从osgeo导入
        from osgeo import gdal, ogr, osr

        print("从osgeo成功导入GDAL库")
    except ImportError:
        print("无法导入GDAL库，请确保已安装GDAL及其Python绑定")
        sys.exit(1)


def process_rasters(shp_path, raster_path, output_path):
    """
    使用shp文件裁剪栅格，保留不重叠部分设为value=8，然后与原栅格合并

    参数:
    shp_path: shapefile的路径
    raster_path: 原始栅格文件路径 (value 1-7)
    output_path: 输出栅格文件路径
    """
    try:
        # 设置GDAL错误处理
        gdal.UseExceptions()

        # 打开栅格文件
        raster = gdal.Open(raster_path)
        if raster is None:
            print(f"无法打开栅格文件: {raster_path}")
            return None

        # 获取栅格信息
        print(f"栅格大小: {raster.RasterXSize} x {raster.RasterYSize}, {raster.RasterCount} 波段")
        geo_transform = raster.GetGeoTransform()
        projection = raster.GetProjection()
        print(f"栅格投影: {projection}")

        # 读取栅格数据
        band = raster.GetRasterBand(1)
        raster_data = band.ReadAsArray()
        nodata = band.GetNoDataValue()
        if nodata is None:
            nodata = 0

        # 打开shapefile
        driver = ogr.GetDriverByName("ESRI Shapefile")
        try:
            vector = driver.Open(shp_path, 0)  # 0表示只读模式
        except Exception as e:
            print(f"无法打开shapefile: {e}")
            return None

        if vector is None:
            print(f"无法打开shapefile: {shp_path}")
            return None

        # 获取shapefile信息
        layer = vector.GetLayer()
        feature_count = layer.GetFeatureCount()
        print(f"Shapefile有 {feature_count} 个要素")

        # 创建栅格掩码
        # 使用GDAL内置的栅格化功能将shapefile转换为栅格
        print("将shapefile栅格化...")
        memory_driver = gdal.GetDriverByName('MEM')
        mask_ds = memory_driver.Create('', raster.RasterXSize, raster.RasterYSize, 1, gdal.GDT_Byte)
        mask_ds.SetGeoTransform(geo_transform)
        mask_ds.SetProjection(projection)

        # 栅格化
        mask_band = mask_ds.GetRasterBand(1)
        mask_band.Fill(0)  # 初始化为0

        # 将shapefile栅格化到掩码上
        gdal.RasterizeLayer(mask_ds, [1], layer, burn_values=[1])

        # 读取掩码数据
        mask_data = mask_band.ReadAsArray()

        # 栅格有值区域的掩码 (value 1-7)
        valid_data_mask = (raster_data >= 1) & (raster_data <= 7)

        # 获取被shapefile覆盖但不与原始有效栅格区域重叠的部分
        shp_mask = mask_data > 0  # shapefile 覆盖区域
        non_overlapping_mask = shp_mask & (~valid_data_mask)  # shapefile 覆盖但不与原始栅格重叠区域

        # 合并两个栅格
        merged_raster = raster_data.copy()
        merged_raster[non_overlapping_mask] = 8

        # 创建输出栅格
        driver = gdal.GetDriverByName('GTiff')
        out_ds = driver.Create(output_path, raster.RasterXSize, raster.RasterYSize, 1, band.DataType)
        out_ds.SetGeoTransform(geo_transform)
        out_ds.SetProjection(projection)

        # 写入数据
        out_band = out_ds.GetRasterBand(1)
        out_band.WriteArray(merged_raster)
        out_band.SetNoDataValue(nodata)

        # 关闭数据集
        out_ds = None
        mask_ds = None
        raster = None
        vector = None

        print(f"处理完成，结果保存至: {output_path}")

        # 返回处理后的统计信息
        stats = {
            "原始栅格有效区域(value 1-7)像素数": int(np.sum(valid_data_mask)),
            "shapefile覆盖区域像素数": int(np.sum(shp_mask)),
            "不重叠区域(value=8)像素数": int(np.sum(non_overlapping_mask)),
            "结果栅格总有效像素数": int(np.sum(merged_raster > 0))
        }

        return stats
    except Exception as e:
        print(f"发生错误: {e}")
        import traceback
        traceback.print_exc()
        return None


# 使用示例
if __name__ == "__main__":
    # 输入文件路径
    shp_path = r"E:\Data\z18\shandongshp\sd.shp"
    raster_path = r"E:\Data\z18\sd\sd_y10m\sddimao_10m.tif"
    output_path = r"E:\Data\z18\sd\sd_10m\sddimao_10mmerged.tif"

    # 执行处理
    stats = process_rasters(shp_path, raster_path, output_path)

    # 打印统计信息
    if stats:
        for key, value in stats.items():
            print(f"{key}: {value}")
    else:
        print("处理失败，无统计信息可显示")