private void StatRasterTemplate <T>(IRasterDataProvider raster, IRasterDataProvider tempRaster, Func <T, bool> func, ref SortedDictionary <string, double> statResult)//计算最大覆盖
{
try
{
//计算待统计栅格与分类栅格的相交区域,以创建相同的虚拟栅格。
CoordEnvelope jxEnv = raster.CoordEnvelope.Intersect(tempRaster.CoordEnvelope);
VirtualRasterHeader vHeader = VirtualRasterHeader.Create(jxEnv, raster.Width, raster.Height);
VirtualRaster vRaster = new VirtualRaster(raster, vHeader);
VirtualRaster template = new VirtualRaster(tempRaster, vHeader);
//2、依据逻辑，计算初步统计结果,这一步对于大数据，可考虑分块处理
T[] vdata = vRaster.ReadData <T>(1, 0, 0, vHeader.Width, vHeader.Height);
int[] typeData = template.ReadData <int>(1, 0, 0, vHeader.Width, vHeader.Height);
int length = typeData.Length;
for (int i = 0; i < length; i++)
{
UpdateProgress((int)(i * 100f / length), "正在计算统计数据");
//统计满足条件的栅格，并将计数值加入字典。
if (func(vdata[i]))
{
int tvalue = typeData[i];
AddUpToDic(statResult, tvalue.ToString(), _perAreas);
}
}
//3、直接返回初步统计结果，或者直接进一步处理
//SortedDictionary<int, double> newDic = ToSSXLevel(statResult);
//return newDic;
}
finally
{
UpdateProgress(100, "统计数据计算完毕");
}
}
/// <summary>
/// 多条件面积统计,[当前区域]
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="raster"></param>
/// <param name="multiFilter"></param>
/// <returns></returns>
private Dictionary <string, SortedDictionary <string, double> > StatRaster <T>(IRasterDataProvider raster, Dictionary <string, Func <T, bool> > multiFilter, bool weight, float weightZoom) where T : struct, IConvertible
{
try
{
int filterLength = multiFilter.Count;
string[] filterKeys = multiFilter.Keys.ToArray();
Func <T, bool>[] filters = new Func <T, bool> [filterLength];
double[] areas = new double[filterLength];
for (int i = 0; i < filterLength; i++)
{
filters[i] = multiFilter[filterKeys[i]];
}
//计算待统计栅格与分类栅格的相交区域,以创建相同的虚拟栅格。
CoordEnvelope jxEnv = raster.CoordEnvelope;
VirtualRasterHeader vHeader = VirtualRasterHeader.Create(jxEnv, raster.Width, raster.Height);
VirtualRaster vRaster = new VirtualRaster(raster, vHeader);
//2、依据逻辑，计算初步统计结果,这一步对于大数据，可考虑分块处理
T[] vdata = vRaster.ReadData <T>(1, 0, 0, vHeader.Width, vHeader.Height);
int length = vdata.Length;
for (int i = 0; i < length; i++)
{
UpdateProgress((int)(i * 100f / length), "正在计算统计数据");
//统计满足条件的栅格，并将计数值加入字典。
for (int f = 0; f < filterLength; f++)
{
if (filters[f](vdata[i]))
{
areas[f] += weight ? _perAreas * Convert.ToSingle(vdata[i]) / weightZoom : _perAreas;
//break;
}
}
}
SortedDictionary <string, double> dic = new SortedDictionary <string, double>();
for (int i = 0; i < filterLength; i++)
{
dic.Add(filterKeys[i], areas[i]);
}
Dictionary <string, SortedDictionary <string, double> > result = new Dictionary <string, SortedDictionary <string, double> >();
result.Add("当前区域", dic);
return(result);
}
finally
{
UpdateProgress(100, "统计数据计算完毕");
}
}
public static void totest2()
{
string file = @"D:\WorkFolder\Smart二期\1.联机开发@Smart\SMART\bin\Release\SystemData\RasterTemplate\China_XjRaster.dat";
IRasterDataProvider inRaster = RasterDataDriver.Open(file) as IRasterDataProvider;
VirtualRasterHeader vrh = VirtualRasterHeader.Create(new CoordEnvelope(120, 140, 10, 30), 0.005f, 0.005f);
VirtualRaster vin = new VirtualRaster(inRaster, vrh);
int[] data = vin.ReadData <int>(1, 10, 10, vrh.Width - 10, vrh.Height - 10);
using (IRasterDataProvider raster = VirtualRaster.CreateVirtureData(vrh, @"d:\pppppp2.ldf", enumDataType.Int32))
{
GCHandle buffer = VirtualRaster.GetHandles(data);
IntPtr bufferPtr = buffer.AddrOfPinnedObject();
raster.GetRasterBand(1).Write(10, 10, vrh.Width - 10, vrh.Height - 10, bufferPtr, enumDataType.Int32, vrh.Width - 10, vrh.Height - 10);
buffer.Free();
}
}
/// <summary>
/// 设置处理对象（输入和输出）
/// 输入和输出文件范围可以不一致，但是必须投影方式一样，分辨率一致。
/// </summary>
/// <param name="fileIn">一组创建好的输入对象，具有范围定义（每个文件的范围可以不一致,暂时要求分辨率和输出文件保持一致）</param>
/// <param name="fileOut">一组创建好的输出对象，具有范围定义（每个文件的范围,大小，分辨率必须一致）</param>
public void SetRaster(RasterMaper[] fileIn, RasterMaper[] fileOut)
{
if (fileOut == null || fileIn == null || fileIn.Length == 0 || fileOut.Length == 0)//获取为空时候使用其他逻辑，比如输入的并集，但我觉得还是放在外面好。
{
return;
}
//1、检查所有文件投影坐标都一致（这里暂时不考虑栅格坐标）
//2、检查所有输出文件范围、坐标、分辨率等都一致
//3、计算每个输入文件的偏移（相对输出文件）
_fileInOffsets = new RasterOffset[fileIn.Length];
_fileOutOffsets = new RasterOffset[fileIn.Length];
for (int i = 0; i < fileIn.Length; i++)
{
RasterOffset fileOffset;
RasterOffset fileOutOffset;
CalcFileOffset(fileIn[i].Raster, fileOut[0].Raster, out fileOffset, out fileOutOffset);
_fileInOffsets[i] = fileOffset;
_fileOutOffsets[i] = fileOutOffset;
}
//4、设置通道映射表
CheckBandMap(fileIn);
CheckBandMap(fileOut);
_fileIn = fileIn;
_fileOut = fileOut;
//建立虚拟栅格
VirtualRasterHeader vHeader = VirtualRasterHeader.Create(fileOut[0].Raster);
VirtualRaster[] vRasters = new VirtualRaster[fileIn.Length];
for (int i = 0; i < fileIn.Length; i++)
{
VirtualRaster vr = new VirtualRaster(fileIn[i].Raster, vHeader);
vRasters[i] = vr;
}
_vRasterIn = vRasters;
}
private Dictionary <string, SortedDictionary <string, double> > StatByRasterTemplate <T>(IRasterDataProvider raster, IRasterDataProvider tempRaster, Dictionary <string, Func <T, bool> > multiFilter, bool weight, float weightZoom, string vectorTemplate) where T : struct, IConvertible
{
try
{
Dictionary <string, SortedDictionary <string, double> > result = new Dictionary <string, SortedDictionary <string, double> >();
string[] filterKeys = multiFilter.Keys.ToArray();
foreach (string kye in filterKeys)
{
result.Add(kye, new SortedDictionary <string, double>());
}
//计算待统计栅格与分类栅格的相交区域,以创建相同的虚拟栅格。
CoordEnvelope jxEnv = raster.CoordEnvelope.Intersect(tempRaster.CoordEnvelope);
VirtualRasterHeader vHeader = VirtualRasterHeader.Create(jxEnv, raster.Width, raster.Height);
VirtualRaster vRaster = new VirtualRaster(raster, vHeader);
VirtualRaster template = new VirtualRaster(tempRaster, vHeader);
//2、依据逻辑，计算初步统计结果,这一步对于大数据，可考虑分块处理
T[] vdata = vRaster.ReadData <T>(1, 0, 0, vHeader.Width, vHeader.Height);
int[] typeData = template.ReadData <int>(1, 0, 0, vHeader.Width, vHeader.Height);
int length = typeData.Length;
if (string.IsNullOrEmpty(vectorTemplate))
{
for (int i = 0; i < length; i++)
{
UpdateProgress((int)(i * 100f / length), "正在计算统计数据");
//统计满足条件的栅格，并将计数值加入字典。
foreach (string key in filterKeys)
{
Func <T, bool> func = multiFilter[key];
if (func(vdata[i]))
{
int tvalue = typeData[i];
AddUpToDic(result[key], tvalue.ToString(), weight ? _perAreas * Convert.ToSingle(vdata[i]) / weightZoom : _perAreas);
}
}
}
}
else
{
Size size = new Size(raster.Width, raster.Height);
string[] typeNames = null;
byte[] typeValue = null;
string templateName;
string[] featureNames = null;
if (vectorTemplate.IndexOf(":") != -1)
{
templateName = vectorTemplate.Substring(0, vectorTemplate.IndexOf(":"));
featureNames = vectorTemplate.Substring(vectorTemplate.IndexOf(":") + 1).Split(new char[] { ',' });
}
else
{
templateName = vectorTemplate;
}
Action act2 = new Action(() => { typeValue = GetLanduseTypeRaster(raster, size, out typeNames, templateName); });
Parallel.Invoke(act2);
for (int i = 0; i < length; i++)
{
UpdateProgress((int)(i * 100f / length), "正在计算统计数据");
//统计满足条件的栅格，并将计数值加入字典。
foreach (string key in filterKeys)
{
Func <T, bool> func = multiFilter[key];
if (func(vdata[i]))
{
int tvalue = typeData[i];
AddUpToDic(result[key], featureNames, tvalue.ToString(), typeNames[typeValue[i]], weight ? _perAreas * Convert.ToSingle(vdata[i]) / weightZoom : _perAreas);
}
}
}
}
return(result);
}
finally
{
UpdateProgress(100, "统计数据计算完毕");
}
}
private SortedDictionary <string, StatAreaItem> StatTemplate <T>(IRasterDataProvider[] rasters, IRasterDataProvider tempRaster, Func <T, bool> func)
{
try
{
//01、计算所有输入栅格的的范围并集
CoordEnvelope maxEnv = UnionEnv(rasters);
//计算待统计栅格与分类栅格的相交区域,以创建相同的虚拟栅格
CoordEnvelope virtureEnv = maxEnv.Intersect(tempRaster.CoordEnvelope);
VirtualRasterHeader vHeader = VirtualRasterHeader.Create(virtureEnv, rasters[0].ResolutionX, rasters[0].ResolutionX);
List <VirtualRaster> vRasters = new List <VirtualRaster>();
for (int i = 0; i < rasters.Length; i++)
{
VirtualRaster vRaster = new VirtualRaster(rasters[i], vHeader);
vRasters.Add(vRaster);
}
VirtualRaster template = new VirtualRaster(tempRaster, vHeader);
int[] tdata = template.ReadData <int>(1, 0, 0, vHeader.Width, vHeader.Height);
//2、依据逻辑，计算初步统计结果,这一步对于大数据，可考虑分块处理
int calcLength = vHeader.Width * vHeader.Height * vRasters.Count;
int dataLength = vHeader.Width * vHeader.Height;
byte[] valueCalced = new byte[dataLength];
SortedDictionary <string, StatAreaItem> result = new SortedDictionary <string, StatAreaItem>();
for (int i = 0; i < vRasters.Count; i++)
{
T[] datas = vRasters[i].ReadData <T>(1, 0, 0, vHeader.Width, vHeader.Height);
for (int j = 0; j < dataLength; j++)
{
UpdateProgress((int)((j + (i * dataLength)) * 100f / calcLength), "正在计算统计数据");
if (func(datas[j]))
{
string key = tdata[j].ToString();
if (result.ContainsKey(key))//累计计数
{
result[key].GrandTotal += 1;
}
else
{
result.Add(key, new StatAreaItem()
{
GrandTotal = 1
});
}
if (valueCalced[j] == 0)//覆盖计数
{
valueCalced[j] = 1;
result[key].Cover += 1;
}
}
}
}
double perAreas = AreaCountHelper.CalcArea(vHeader.CoordEnvelope.Center.X, vHeader.CoordEnvelope.Center.Y, vHeader.ResolutionX, vHeader.ResolutionY) * Math.Pow(10, -6);
foreach (string key in result.Keys)
{
result[key].GrandTotal *= perAreas;
result[key].Cover *= perAreas;
}
return(result);
}
finally
{
UpdateProgress(100, "统计数据计算完毕");
}
}
/// <summary>
/// 覆盖面积：指的是覆盖到的面积，
/// 累计面积：即累计覆盖面积，相同区域不同时次的累计计算
/// </summary>
/// <typeparam name="T"></typeparam>
/// <param name="rasters"></param>
/// <param name="rasterTemplate"></param>
/// <param name="func"></param>
/// <returns></returns>
private SortedDictionary <string, StatAreaItem> StatTemplate <T>(IRasterDataProvider[] rasters, IRasterDataProvider typeRaster, Func <T, bool> func, bool isCombinSameDay)
{
try
{
//01、计算所有输入栅格的的范围并集
CoordEnvelope maxEnv = UnionEnv(rasters);
//计算待统计栅格与分类栅格的相交区域,以创建相同的虚拟栅格。
CoordEnvelope virtureEnv = maxEnv.Intersect(typeRaster.CoordEnvelope);
VirtualRasterHeader vHeader = VirtualRasterHeader.Create(virtureEnv, rasters[0].ResolutionX, rasters[0].ResolutionX);
VirtualRaster typelate = new VirtualRaster(typeRaster, vHeader);
int[] tdata = typelate.ReadData <int>(1, 0, 0, vHeader.Width, vHeader.Height);
//2、依据逻辑，计算初步统计结果,这一步对于大数据，可考虑分块处理
int calcLength = vHeader.Width * vHeader.Height * rasters.Length;
int dataLength = vHeader.Width * vHeader.Height;
byte[] valueCalced = new byte[dataLength];
SortedDictionary <string, StatAreaItem> result = new SortedDictionary <string, StatAreaItem>();
if (isCombinSameDay)
{
//预处理同天数据
Dictionary <DateTime, List <IRasterDataProvider> > dic = new Dictionary <DateTime, List <IRasterDataProvider> >();
foreach (IRasterDataProvider raster in rasters)
{
if (raster.DataIdentify.OrbitDateTime == DateTime.MinValue)
{
RasterIdentify identify = new RasterIdentify(raster.fileName);
raster.DataIdentify.OrbitDateTime = identify.OrbitDateTime;
}
List <IRasterDataProvider> lst;
if (dic.TryGetValue(raster.DataIdentify.OrbitDateTime, out lst))
{
dic[raster.DataIdentify.OrbitDateTime].Add(raster);
}
else
{
lst = new List <IRasterDataProvider>();
lst.Add(raster);
dic.Add(raster.DataIdentify.OrbitDateTime, lst);
}
}
foreach (DateTime dateKey in dic.Keys)
{
IRasterDataProvider[] rastersSameDay = dic[dateKey].ToArray();
if (rastersSameDay.Length == 1 || dateKey == DateTime.MinValue)
{
for (int i = 0; i < rastersSameDay.Length; i++)
{
VirtualRaster vRaster = new VirtualRaster(rastersSameDay[i], vHeader);
T[] datas = vRaster.ReadData <T>(1, 0, 0, vHeader.Width, vHeader.Height);
for (int j = 0; j < dataLength; j++)
{
UpdateProgress((int)((j + (i * dataLength)) * 100f / calcLength), "正在计算统计数据");
if (func(datas[j]))
{
string key = tdata[j].ToString();
if (result.ContainsKey(key))//累计计数
{
result[key].GrandTotal += 1;
}
else
{
result.Add(key, new StatAreaItem()
{
GrandTotal = 1
});
}
if (valueCalced[j] == 0)//覆盖计数
{
valueCalced[j] = 1;
result[key].Cover += 1;
}
}
}
}
}
else//同天数据处理,
{
byte[] sameDayCalced = new byte[vHeader.Width * vHeader.Height];
for (int i = 0; i < rasters.Length; i++)
{
VirtualRaster vRaster = new VirtualRaster(rasters[i], vHeader);
T[] datas = vRaster.ReadData <T>(1, 0, 0, vHeader.Width, vHeader.Height);
for (int j = 0; j < dataLength; j++)
{
UpdateProgress((int)((j + (i * dataLength)) * 100f / calcLength), "正在计算统计数据");
if (func(datas[j]))
{
if (sameDayCalced[j] == 0)//
{
sameDayCalced[j] = 1;
string key = tdata[j].ToString();
if (result.ContainsKey(key))//累计计数
{
result[key].GrandTotal += 1;
}
else
{
result.Add(key, new StatAreaItem()
{
GrandTotal = 1
});
}
if (valueCalced[j] == 0)//覆盖计数
{
valueCalced[j] = 1;
result[key].Cover += 1;
}
}
}
}
}
}
}
}
else
{
for (int i = 0; i < rasters.Length; i++)
{
VirtualRaster vRaster = new VirtualRaster(rasters[i], vHeader);
T[] datas = vRaster.ReadData <T>(1, 0, 0, vHeader.Width, vHeader.Height);
for (int j = 0; j < dataLength; j++)
{
UpdateProgress((int)((j + (i * dataLength)) * 100f / calcLength), "正在计算统计数据");
if (func(datas[j]))
{
string key = tdata[j].ToString();
if (result.ContainsKey(key))//累计计数
{
result[key].GrandTotal += 1;
}
else
{
result.Add(key, new StatAreaItem()
{
GrandTotal = 1
});
}
if (valueCalced[j] == 0)//覆盖计数
{
valueCalced[j] = 1;
result[key].Cover += 1;
}
}
}
}
}
double perAreas = AreaCountHelper.CalcArea(vHeader.CoordEnvelope.Center.X, vHeader.CoordEnvelope.Center.Y, vHeader.ResolutionX, vHeader.ResolutionY) * Math.Pow(10, -6);
foreach (string key in result.Keys)
{
result[key].GrandTotal *= perAreas;
result[key].Cover *= perAreas;
}
return(result);
}
finally
{
UpdateProgress(100, "统计数据计算完毕");
}
}