def read_tif(inputfile, band):
tifObj = GeoTiffFile(inputfile)
tifObj.readTif()
data = tifObj.getBandData(band)
width = tifObj.getWidth()
height = tifObj.getHeight()
proj = tifObj.getProj()
geotrans = tifObj.getGeoTrans()
return data, width, height, proj, geotrans
def delete3():
pass
tif_path1 = r'E:\NPP\temp\svi01.tif'
lat_path = r'E:\NPP\temp\Latitude.tif'
lon_path = r'E:\NPP\temp\Longitude.tif'
tif_obj1 = GeoTiffFile(lat_path)
tif_obj1.readTif()
lat_data = tif_obj1.getBandData(0)
tif_obj2 = GeoTiffFile(lon_path)
tif_obj2.readTif()
lon_data = tif_obj2.getBandData(0)
dataset = gdal.Open(tif_path1, gdal.GA_Update)
geo_line = 7680 # 地理信息行数
geo_sample = 6400 # 地理信息列数
# 采样间隔
resample_step = 100
resample_line_num = int(geo_line / resample_step)
resample_sample_num = int(geo_sample / resample_step)
position_list = []
for i in range(resample_line_num):
for j in range(resample_sample_num):
position_list.append(
((i + 1) * resample_step - 1, (j + 1) * resample_step - 1))
gcps_list = []
for each in position_list:
lie = each[1]
hang = each[0]
lon = float(lon_data[hang][lie])
lat = float(lat_data[hang][lie])
gdal_gcp = gdal.GCP(lon, lat, 0, lie, hang)
gcps_list.append(gdal_gcp)
sr = osr.SpatialReference()
sr.SetWellKnownGeogCS('WGS84')
# 添加控制点
dataset.SetGCPs(gcps_list, sr.ExportToWkt())
out_tif_path = r'E:\NPP\temp\svi01_proj.tif.'
dst_ds = gdal.Warp(out_tif_path,
dataset,
format='GTiff',
tps=True,
xRes=0.0037,
yRes=0.0037,
dstNodata=65533,
resampleAlg=gdal.GRIORA_NearestNeighbour,
outputType=gdal.GDT_UInt16)
"""
def raster_clip_by_shp(in_path, out_path, shp_path, nodata_val=-9999):
"""
依据shp边界对栅格数据进行裁切，输出TIF投影和输入TIF投影一致
:param in_path: str: 栅格文件路径
:param out_path: str: 输出栅格文件路径
:param shp_path: str: 矢量文件路径
:param nodata_val: float: 背景值
:return:
"""
try:
shp_obj = ShapeFile(shp_path)
shp_obj.readShp()
shp_extend = shp_obj.getExtent() # 获取矢量经纬度范围
tif_obj = GeoTiffFile(in_path)
tif_obj.readTif()
xres = tif_obj.getXRes() # 获取栅格分辨率
yres = tif_obj.getYRes()
out_bounds = (shp_extend["xMin"], shp_extend["yMin"],
shp_extend["xMax"], shp_extend["yMax"])
# 方案1：裁切结果与shp左、上相切，强制分辨率与原始保持一致
ds = gdal.Warp(out_path,
in_path,
format='GTiff',
outputBounds=out_bounds,
dstNodata=nodata_val,
cutlineDSName=shp_path,
cropToCutline=False,
xRes=xres,
yRes=yres)
# 方案2：裁切结果不会偏移，取shp内部像元，分辨率不变
# ds = gdal.Warp(out_path, in_path, format='GTiff', outputBounds=out_bounds, dstNodata=nodata_val,
# cutlineDSName=shp_path, cropToCutline=True)
if not ds:
return False
del ds
return True
except Exception as e:
print(e)
return False
def write_tif(data, band, width, height, proj, geotrans, outpath):
outTifObj = GeoTiffFile(outpath)
outTifObj.setDims(width, height, band)
outTifObj.setProj(proj)
outTifObj.setGeoTrans(geotrans)
outTifObj.setData(data)
outTifObj.writeTif()
def rasterSynthesis(inputFileList,
outputPath,
mode,
valueRange,
bgValue=-9999,
extent=None,
res=None):
"""
单波段栅格文件最大/最小/均值合成功能
默认输入文件分辨率和经纬度范围一致
也可指定经纬度范围和分辨率进行重采样，参数(extent, res)
:param inputFileList: list 输入文件路径列表，文件之间以","隔开
:param outputPath: str 合成输出文件路径
:param mode: str 合成方法 可选参数 ("mean", "max", "min")
:param valueRange: list 合成有效值范围 e.g [-1,1]
:param bgValue: float 合成结果中无效值设定 默认-9999
:param extent: 输出文件经纬度范围 默认与第一个文件保持一致 {"xMin": lonMin, "xMax": lonMax, "yMin": latMin, "yMax": latMax}
:param res: float 输出文件分辨率 默认与第一个文件保持一致
:return:
"""
try:
sampleTifObj = GeoTiffFile(inputFileList[0])
sampleTifObj.readTif()
# 判断参数是否指定经纬度范围和分辨率
if extent is None:
extentInfo = sampleTifObj.getGeoExtent()
else:
extentInfo = extent
if res is None:
resInfo = sampleTifObj.getXRes()
else:
resInfo = res
# 获取输出行列号
warpDs = GdalUtil.raster_warp(inputFileList[0],
"",
extentInfo,
resInfo,
mode="MEM")
if warpDs is None:
return None
width = warpDs.RasterXSize
height = warpDs.RasterYSize
trans = warpDs.GetGeoTransform()
proj = warpDs.GetProjection()
del warpDs
# 三种合成方式max/min/mean
# 1.mean
if mode.upper() == "MEAN":
countArray = np.zeros((height, width), dtype=np.uint16) # 计数器
sumArray = np.zeros((height, width), dtype=np.float32) # 总和
for f in inputFileList:
warpDs = GdalUtil.raster_warp(f,
"",
extentInfo,
resInfo,
mode="MEM")
array = warpDs.GetRasterBand(1).ReadAsArray()
loc = np.logical_and(array > valueRange[0],
array <= valueRange[1])
countArray[loc] = countArray[loc] + 1
sumArray[loc] = sumArray[loc] + array[loc]
outArray = sumArray / countArray
outArray[countArray == 0] = bgValue
# 2.max
elif mode.upper() == "MAX":
outArray = np.ones(
(height, width), dtype=np.float32) * valueRange[0]
for f in inputFileList:
warpDs = GdalUtil.raster_warp(f,
"",
extentInfo,
resInfo,
mode="MEM")
array = warpDs.GetRasterBand(1).ReadAsArray()
array = array.astype(np.float32)
nanLoc = np.logical_or(array <= valueRange[0],
array > valueRange[1])
array[nanLoc] = np.nan
loc = array > outArray
outArray[loc] = array[loc]
nanLoc = np.logical_or(outArray <= valueRange[0],
outArray > valueRange[1])
outArray[nanLoc] = bgValue
# 3.min
elif mode.upper() == "MIN":
outArray = np.ones(
(height, width), dtype=np.float32) * (valueRange[1] + 1)
for f in inputFileList:
warpDs = GdalUtil.raster_warp(f,
"",
extentInfo,
resInfo,
mode="MEM")
array = warpDs.GetRasterBand(1).ReadAsArray()
array = array.astype(np.float32)
nanLoc = np.logical_or(array <= valueRange[0],
array > valueRange[1])
array[nanLoc] = np.nan
loc = array < outArray
outArray[loc] = array[loc]
nanLoc = np.logical_or(outArray <= valueRange[0],
outArray > valueRange[1])
outArray[nanLoc] = bgValue
else:
print("Cannot calculate with mode :" + mode)
outTifObj = GeoTiffFile(outputPath)
outTifObj.setDims(width, height, 1)
outTifObj.setGeoTrans(trans)
outTifObj.setProj(proj)
outTifObj.setData(outArray)
outTifObj.writeTif()
except Exception as e:
return None
def get_sixs_param(warp_path, temp_dir, file_basename):
sixs_exe_path = r'C:\6S\6s.exe'
in_txt_path = os.path.join(os.path.dirname(sixs_exe_path), 'in.txt')
angle = {'SolarZenithAngle': None, 'SolarAzimuthAngle': None,
'SatelliteZenithAngle': None, 'SatelliteAzimuthAngle': None}
extent = {'xMin': 119.89, 'xMax': 120.64, 'yMin': 30.93, 'yMax': 31.55}
for each in angle.keys():
clip_tif_path = os.path.join(temp_dir, os.path.basename(warp_path[each]).replace('.tif', '_clip.tif'))
GdalUtil.raster_warp(warp_path[each], clip_tif_path, extent, 0.004667, nodata_val=65533)
tif_obj = GeoTiffFile(clip_tif_path)
tif_obj.readTif()
extent = tif_obj.getGeoExtent()
data_array = tif_obj.getBandData(0)
data_array[data_array == 65533] = np.nan
angle_mean = abs(float(np.nanmean(data_array)))
angle[each] = angle_mean
# 月 日
month = int(file_basename.split('_')[2][5:7])
day = int(file_basename.split('_')[2][7:9])
# 大气模式
center_lat = (extent['yMax'] + extent['yMin']) / 2
if center_lat < 23.5 and month < 9 and month > 2:
atmo_model = 1
elif center_lat >= 23.5 and center_lat < 66.5 and month < 9 and month > 2:
atmo_model = 2
elif center_lat >= 23.5 and center_lat < 66.5 and (month >= 9 or month <= 2):
atmo_model = 3
elif center_lat >= 66.5 and month < 9 and month > 2:
atmo_model = 4
elif center_lat >= 66.5 and (month >= 9 or month <= 2):
atmo_model = 5
else:
# 错误
atmo_model = 0
# 波长范围
wave_range = {'svi01': (0.6025, 0.6775),
'svi02': (0.8455, 0.8845),
'svi03': (1.58, 1.64),
'svi04': (3.55, 3.93)}
atmo_param = {'svi01': None, 'svi02': None, 'svi03': None, 'svi04': None}
for each in atmo_param.keys():
# 写入in.txt
with open(in_txt_path, 'w') as f:
f.write('%d\n' % 0) # 自定义几何
f.write('%.2f\n' % angle['SolarZenithAngle']) # 太阳天顶角
f.write('%.2f\n' % angle['SolarAzimuthAngle']) # 太阳方位角
f.write('%.2f\n' % angle['SatelliteZenithAngle']) # 卫星天顶角
f.write('%.2f\n' % angle['SatelliteAzimuthAngle']) # 卫星方位角
f.write('%d\n' % month) # 月
f.write('%d\n' % day) # 日
f.write('%d\n' % atmo_model) # 大气模式
f.write('%d\n' % 1) # 气溶胶类型
f.write('%.1f\n' % 15.0) # 气溶胶参数
f.write('%.3f\n' % -0.010) # 目标高度
f.write('%d\n' % -1000) # 传感器高度
f.write('%d\n' % 0) # 自定义输入波长
f.write('%.4f\n' % wave_range[each][0]) # 波长起始
f.write('%.4f\n' % wave_range[each][1]) # 波长终止
f.write('%d\n' % 0) # 无方向反射
f.write('%d\n' % 0) # 朗伯体假设
f.write('%d\n' % 4) # 湖泊水体
f.write('%.1f\n' % -0.4) # 进行大气校正
sixs_exe_dir = os.path.dirname(sixs_exe_path)
sixs_exe_name = os.path.basename(sixs_exe_path)
cmd_str = 'cd %s && %s<%s>log.txt' % (sixs_exe_dir, sixs_exe_name, 'in.txt')
os.system(cmd_str)
sixs_out_path = os.path.join(sixs_exe_dir, 'sixs.out')
with open(sixs_out_path, 'r') as f1:
line_str = f1.readline()
while line_str:
line_str = f1.readline()
# print(line_str)
if 'coefficients xa xb xc' in line_str:
atmo_param_str = line_str.split(':')[1]
xa = float(atmo_param_str.split(' ')[1])
xb = float(atmo_param_str.split(' ')[2])
xc = float(atmo_param_str.split(' ')[3])
print(xa, xb, xc)
atmo_param[each] = (xa, xb, xc)
return atmo_param
def cal_radiance(warp_path, atmo_param, out_tif_path):
radiance_factor = {'svi01': (0.013155036, -0.41), 'svi02': (0.0063949213, -0.24),
'svi03': (0.0013309018, -0.21), 'svi04': (5.52444e-05, -0.01)}
# 先获取行列号
tif_path = warp_path['svi01']
tif_obj = GeoTiffFile(tif_path)
tif_obj.readTif()
width = tif_obj.getWidth()
height = tif_obj.getHeight()
proj = tif_obj.getProj()
geo_trans = tif_obj.getGeoTrans()
del tif_obj
out_tif_obj = GeoTiffFile(out_tif_path)
out_tif_data = np.zeros((4, height, width), dtype=np.float)
i = 0
for key in radiance_factor.keys():
tif_path = warp_path[key]
tif_obj = GeoTiffFile(tif_path)
tif_obj.readTif()
tif_data = tif_obj.getBandData(0)
nan_loc = tif_data >= 65530
# 辐射定标计算
tif_data = tif_data * radiance_factor[key][0] + radiance_factor[key][1]
# 大气校正计算
# y=xa*(measured radiance)-xb; acr=y/(1.+xc*y)
y = atmo_param[key][0] * tif_data - atmo_param[key][1]
ref_data = y / (1 + atmo_param[key][2] * y) * 1000
# ref_data = ref_data.astype(np.uint16)
ref_data[nan_loc] = 65533
out_tif_data[i, :, :] = ref_data
i += 1
out_tif_obj.setDims(width, height, 4)
out_tif_obj.setData(out_tif_data)
out_tif_obj.setProj(proj)
out_tif_obj.setGeoTrans(geo_trans)
out_tif_obj.writeTif()
print("finish")