def cloudColor(inputpath,inputpathcloud,outputDir):
basename = os.path.basename(inputpath).split('.')[0]
cloudColorTifName = basename + '_cloud_render.tif'
cloudColorTifPath = os.path.join(outputDir, cloudColorTifName)
colorTable = {1: (255, 255, 255)}
UniqueValues.Render(inputpathcloud, colorTable, returnMode='GEOTIFF', outputPath=cloudColorTifPath, isAlpha=True)
#云产品切片
pythonPath = globalCfg['python_path']
gdal2tilesPath = globalCfg['gdal2tiles_path']
tileOutRootDir = globalCfg['tile_server_path']
tileTif=cloudColorTifPath
tileName=basename+'_cloud'
tileOutDir=os.path.join(tileOutRootDir,tileName)
if os.path.exists(tileOutDir):
shutil.rmtree(tileOutDir)
cmd = '%s %s -z %s -w all %s %s' % (pythonPath, gdal2tilesPath, '8-13', tileTif, tileOutDir)
os.system(cmd)
os.remove(tileTif)
def exportImage(info1, info2, searchTime1, searchTime2, outTif1, outTif2):
conn = pymysql.connect(db=globalCfg['database'],
user=globalCfg['database_user'],
password=globalCfg['database_passwd'],
host=globalCfg['database_host'],
port=globalCfg['database_port'])
cursor1 = conn.cursor()
uuidList1 = str(tuple(info1[5]))
sqlStr1 = 'SELECT path FROM ' + globalCfg['database_table_export_image'] + \
' WHERE uuid IN %s;' % uuidList1
cursor1.execute(sqlStr1)
sqlRes1 = cursor1.fetchall()
cursor1.close()
cursor2 = conn.cursor()
uuidList2 = str(tuple(info2[5]))
sqlStr2 = 'SELECT path FROM ' + globalCfg['database_table_export_image'] + \
' WHERE uuid IN %s;' % uuidList2
cursor2.execute(sqlStr2)
sqlRes2 = cursor2.fetchall()
conn.close()
# 加载掩膜数据
maskTifPath = os.path.join(globalCfg['depend_path'], 'taihu', '250',
'taihu_mask_utm[0].tif')
ds = gdal.Open(maskTifPath, gdal.GA_ReadOnly)
maskArray = ds.GetRasterBand(1).ReadAsArray()
ds = None
map_dir = globalCfg['map_dir']
# 去年当月tif
if len(sqlRes2) > 0:
firstTifPath = os.path.join(map_dir, sqlRes2[0][0])
firstDs = gdal.Open(firstTifPath, gdal.GA_ReadOnly)
width = firstDs.RasterXSize
height = firstDs.RasterYSize
trans = firstDs.GetGeoTransform()
proj = firstDs.GetProjection()
frequencyArray = np.zeros((height, width), dtype=np.float)
resultArray = np.zeros((height, width), dtype=np.int)
for each in sqlRes2:
curTifPath = os.path.join(map_dir, each[0])
curDs = gdal.Open(curTifPath, gdal.GA_ReadOnly)
curArray = curDs.GetRasterBand(1).ReadAsArray()
frequencyArray += curArray
curDs = None
# 计算当月天数
datetime1 = datetime.strptime(searchTime1, '%Y-%m-%d %H:%M:%S')
datetime2 = datetime.strptime(searchTime2, '%Y-%m-%d %H:%M:%S')
deltaDays = (datetime2 - datetime1).days + 1
frequencyArray = (frequencyArray / deltaDays) * 100
resultArray[frequencyArray == 0] = 0
resultArray[np.logical_and(frequencyArray > 0,
frequencyArray <= 5)] = 1
resultArray[np.logical_and(frequencyArray > 5,
frequencyArray <= 10)] = 2
resultArray[frequencyArray > 10] = 3
resultArray[maskArray == 0] = 65535
driver = gdal.GetDriverByName("GTiff")
outDs = driver.Create(outTif1, width, height, 1, gdal.GDT_Float32)
outDs.SetGeoTransform(trans)
outDs.SetProjection(proj)
outDs.GetRasterBand(1).WriteArray(resultArray)
outDs = None
else:
print('Not Found Algae.')
# 今年当月tif
if len(sqlRes1) > 0:
firstTifPath = os.path.join(map_dir, sqlRes1[0][0])
firstDs = gdal.Open(firstTifPath, gdal.GA_ReadOnly)
width = firstDs.RasterXSize
height = firstDs.RasterYSize
trans = firstDs.GetGeoTransform()
proj = firstDs.GetProjection()
frequencyArray = np.zeros((height, width), dtype=np.float)
resultArray = np.zeros((height, width), dtype=np.int)
for each in sqlRes1:
curTifPath = os.path.join(map_dir, each[0])
curDs = gdal.Open(curTifPath, gdal.GA_ReadOnly)
curArray = curDs.GetRasterBand(1).ReadAsArray()
frequencyArray += curArray
curDs = None
# 计算当月天数
datetime1 = datetime.strptime(searchTime1, '%Y-%m-%d %H:%M:%S')
datetime2 = datetime.strptime(searchTime2, '%Y-%m-%d %H:%M:%S')
deltaDays = (datetime2 - datetime1).days + 1
frequencyArray = (frequencyArray / deltaDays) * 100
resultArray[frequencyArray == 0] = 0
resultArray[np.logical_and(frequencyArray > 0,
frequencyArray <= 5)] = 1
resultArray[np.logical_and(frequencyArray > 5,
frequencyArray <= 10)] = 2
resultArray[frequencyArray > 10] = 3
resultArray[maskArray == 0] = 65535
driver = gdal.GetDriverByName("GTiff")
outDs = driver.Create(outTif2, width, height, 1, gdal.GDT_Float32)
outDs.SetGeoTransform(trans)
outDs.SetProjection(proj)
outDs.GetRasterBand(1).WriteArray(resultArray)
outDs = None
else:
print('Not Found Algae.')
# 出图
# DateFrame范围
dataFrameBounds = (197833.762, 3419416.355, 277082.543, 3497440.803)
tifFileList = [outTif1, outTif2]
for each in tifFileList:
# 裁切到模板空间大小
clipTifPath = os.path.join(
os.path.dirname(each),
os.path.basename(each).replace('.tif', '_clip.tif'))
ds = gdal.Warp(clipTifPath,
each,
format='GTiff',
outputBounds=dataFrameBounds,
dstNodata=65535,
xRes=250,
yRes=250)
ds = None
# 右下角终止列(2438) 右下角终止行(2407)
activateWidth = 2388 # 有效列数 2004
activateHeight = 2351 # 有效行数 1841
offsetWidth = 51 # 左上角起始列
offsetHeight = 57 # 左上角起始行
colorTable = {
0: (1, 197, 255),
1: (211, 255, 190),
2: (163, 255, 115),
3: (14, 204, 14)
}
classifyRender = UniqueValues.Render(clipTifPath,
colorTable,
returnMode='MEM')
classifyRgbImageObj = Image.fromarray(classifyRender, mode='RGB')
classifyRgbImageObj = classifyRgbImageObj.resize(
(activateWidth, activateHeight), Image.NEAREST)
classifyRgbResize = np.asarray(classifyRgbImageObj)
bgLocation = np.logical_and(
classifyRgbResize[:, :, 0] == 255,
classifyRgbResize[:, :, 1] == 255,
classifyRgbResize[:, :, 2] == 255,
)
modulePngPath = os.path.join(globalCfg['depend_path'], 'taihu',
'mould', 'taihu_algae_month1.png')
moduleArray = skimage.io.imread(modulePngPath)
moduleActivateArray = moduleArray[offsetHeight:offsetHeight +
activateHeight,
offsetWidth:offsetWidth +
activateWidth, :]
classifyRgbResizeCopy = np.copy(classifyRgbResize)
classifyRgbResizeCopy[:, :, 0][
bgLocation] = moduleActivateArray[:, :, 0][bgLocation]
classifyRgbResizeCopy[:, :, 1][
bgLocation] = moduleActivateArray[:, :, 1][bgLocation]
classifyRgbResizeCopy[:, :, 2][
bgLocation] = moduleActivateArray[:, :, 2][bgLocation]
moduleArray[offsetHeight:offsetHeight + activateHeight, offsetWidth:offsetWidth + activateWidth, 0] \
= classifyRgbResizeCopy[:, :, 0]
moduleArray[offsetHeight:offsetHeight + activateHeight, offsetWidth:offsetWidth + activateWidth, 1] \
= classifyRgbResizeCopy[:, :, 1]
moduleArray[offsetHeight:offsetHeight + activateHeight, offsetWidth:offsetWidth + activateWidth, 2] \
= classifyRgbResizeCopy[:, :, 2]
outImagePath = os.path.join(
os.path.dirname(each),
os.path.basename(each).replace('.tif', '.jpg'))
skimage.io.imsave(outImagePath, moduleArray[:, :,
0:3].astype(np.uint8))
def tileServer(self):
"""生成对应切片文件"""
basename = os.path.basename(self.inputPath).split('.')[0]
# 1.假彩色底图切片 falseColor
if self.dataIdentify in FALSE_COLOR_COMPOSE.keys():
if FALSE_COLOR_COMPOSE[self.dataIdentify]:
falseColorTifName = os.path.basename(self.l2TifPath).replace(
'.tif', '_false.tif')
falseColorTifPath = os.path.join(self.outputDir,
falseColorTifName)
if self.dataIdentify in [
'SENTINEL2A_MSI_10', 'SENTINEL2B_MSI_10',
'SENTINEL2A_MSI_20', 'SENTINEL2B_MSI_20',
'SENTINEL3A_OLCI_300', 'SENTINEL3B_OLCI_300',
'TERRA_MODIS_250', 'AQUA_MODIS_250'
]:
RgbComposite.Compose(
self.l2TifPath,
FALSE_COLOR_COMPOSE[self.dataIdentify],
noDataValue=65535,
returnMode='GEOTIFF',
outputPath=falseColorTifPath,
stretchType='LinearPercent',
stretchParam=[0],
isAlpha=True)
else:
RgbComposite.Compose(
self.l2TifPath,
FALSE_COLOR_COMPOSE[self.dataIdentify],
noDataValue=0,
returnMode='GEOTIFF',
outputPath=falseColorTifPath,
stretchType='LinearPercent',
stretchParam=[0],
isAlpha=True)
self.tileDict['falseColor'] = {
'tif': falseColorTifPath,
'name': basename + '_falseColor'
}
# 2.假彩色底图增强切片 falseColor_enhance1
if self.dataIdentify in FALSE_COLOR_COMPOSE.keys():
if FALSE_COLOR_COMPOSE[self.dataIdentify]:
fcEnhanceTifName = os.path.basename(self.l2TifPath).replace(
'.tif', '_falseEnhance.tif')
fcEnhanceTifPath = os.path.join(self.outputDir,
fcEnhanceTifName)
if self.dataIdentify in [
'SENTINEL2A_MSI_10', 'SENTINEL2B_MSI_10',
'SENTINEL2A_MSI_20', 'SENTINEL2B_MSI_20',
'SENTINEL3A_OLCI_300', 'SENTINEL3B_OLCI_300',
'TERRA_MODIS_250', 'AQUA_MODIS_250'
]:
RgbComposite.Compose(
self.l2TifPath,
FALSE_COLOR_COMPOSE[self.dataIdentify],
noDataValue=65535,
returnMode='GEOTIFF',
outputPath=fcEnhanceTifPath,
stretchType='LinearPercent',
stretchParam=[2],
isAlpha=True)
else:
RgbComposite.Compose(
self.l2TifPath,
FALSE_COLOR_COMPOSE[self.dataIdentify],
noDataValue=0,
returnMode='GEOTIFF',
outputPath=fcEnhanceTifPath,
stretchType='LinearPercent',
stretchParam=[2],
isAlpha=True)
self.tileDict['falseColorEnhance'] = {
'tif': fcEnhanceTifPath,
'name': basename + '_falseColor_enhance1'
}
# 3.真彩色底图切片 trueColor
if self.dataIdentify in TRUE_COLOR_COMPOSE.keys():
if TRUE_COLOR_COMPOSE[self.dataIdentify]:
trueColorTifName = os.path.basename(self.l2TifPath).replace(
'.tif', '_true.tif')
trueColorTifPath = os.path.join(self.outputDir,
trueColorTifName)
if self.dataIdentify in [
'SENTINEL2A_MSI_10', 'SENTINEL2B_MSI_10',
'SENTINEL2A_MSI_20', 'SENTINEL2B_MSI_20',
'SENTINEL3A_OLCI_300', 'SENTINEL3B_OLCI_300'
]:
RgbComposite.Compose(self.l2TifPath,
TRUE_COLOR_COMPOSE[self.dataIdentify],
noDataValue=65535,
returnMode='GEOTIFF',
outputPath=trueColorTifPath,
stretchType='LinearPercent',
stretchParam=[0],
isAlpha=True)
else:
RgbComposite.Compose(self.l2TifPath,
TRUE_COLOR_COMPOSE[self.dataIdentify],
noDataValue=0,
returnMode='GEOTIFF',
outputPath=trueColorTifPath,
stretchType='LinearPercent',
stretchParam=[0],
isAlpha=True)
self.tileDict['trueColor'] = {
'tif': trueColorTifPath,
'name': basename + '_trueColor'
}
# 4.真彩色底图增强切片 trueColor_enhance1
if self.dataIdentify in TRUE_COLOR_COMPOSE.keys():
if TRUE_COLOR_COMPOSE[self.dataIdentify]:
tcEnhanceTifName = os.path.basename(self.l2TifPath).replace(
'.tif', '_trueEnhance.tif')
tcEnhanceTifPath = os.path.join(self.outputDir,
tcEnhanceTifName)
if self.dataIdentify in [
'SENTINEL2A_MSI_10', 'SENTINEL2B_MSI_10',
'SENTINEL2A_MSI_20', 'SENTINEL2B_MSI_20',
'SENTINEL3A_OLCI_300', 'SENTINEL3B_OLCI_300'
]:
RgbComposite.Compose(self.l2TifPath,
TRUE_COLOR_COMPOSE[self.dataIdentify],
noDataValue=65535,
returnMode='GEOTIFF',
outputPath=tcEnhanceTifPath,
stretchType='LinearPercent',
stretchParam=[2],
isAlpha=True)
else:
RgbComposite.Compose(self.l2TifPath,
TRUE_COLOR_COMPOSE[self.dataIdentify],
noDataValue=0,
returnMode='GEOTIFF',
outputPath=tcEnhanceTifPath,
stretchType='LinearPercent',
stretchParam=[2],
isAlpha=True)
self.tileDict['trueColorEnhance'] = {
'tif': tcEnhanceTifPath,
'name': basename + '_trueColor_enhance1'
}
# 5.云产品切片 cloud
cloudTifName = os.path.basename(self.cloudTifPath).replace(
'.tif', '_render.tif')
cloudTifPath = os.path.join(self.outputDir, cloudTifName)
colorTable = {1: (255, 255, 255)}
UniqueValues.Render(self.cloudTifPath,
colorTable,
returnMode='GEOTIFF',
outputPath=cloudTifPath,
isAlpha=True)
self.tileDict['cloud'] = {
'tif': cloudTifPath,
'name': basename + '_cloud',
'legendType': '1',
'legendColor': [(255, 255, 255)],
'legendName': ['云']
}
# 6.蓝藻产品切片
algaeTifName = os.path.basename(self.algaeTifPath).replace(
'.tif', '_render.tif')
algaeTifPath = os.path.join(self.outputDir, algaeTifName)
colorTable = {1: (255, 251, 0)}
UniqueValues.Render(self.algaeTifPath,
colorTable,
returnMode='GEOTIFF',
outputPath=algaeTifPath,
isAlpha=True)
self.tileDict['taihu_algae_ndvi'] = {
'tif': algaeTifPath,
'name': basename + '_taihu_algae_ndvi',
'legendType': '1',
'legendColor': [(255, 251, 0)],
'legendName': ['水华']
}
# 7.蓝藻强度产品切片
intensityTifName = os.path.basename(self.intensityTifPath).replace(
'.tif', '_render.tif')
intensityTifPath = os.path.join(self.outputDir, intensityTifName)
colorTable = {1: (0, 255, 102), 2: (255, 255, 0), 3: (255, 153, 0)}
UniqueValues.Render(self.intensityTifPath,
colorTable,
returnMode='GEOTIFF',
outputPath=intensityTifPath,
isAlpha=True)
self.tileDict['algaeClassify'] = {
'tif': intensityTifPath,
'name': basename + '_classify',
'legendType': '1',
'legendColor': [(0, 255, 102), (255, 255, 0), (255, 153, 0)],
'legendName': ['轻度', '中度', '重度']
}
# 调用gdal2tiles工具进行切片
pythonPath = globalCfg['python_path']
gdal2tilesPath = globalCfg['gdal2tiles_path']
tileOutRootDir = globalCfg['tile_server_path']
for key in self.tileDict.keys():
tileTif = self.tileDict[key]['tif']
tileOutDir = os.path.join(tileOutRootDir,
self.tileDict[key]['name'])
if os.path.exists(tileOutDir):
shutil.rmtree(tileOutDir)
cmd = '%s %s -z %s -w all %s %s' % (
pythonPath, gdal2tilesPath, TILE_LEVEL, tileTif, tileOutDir)
os.system(cmd)
os.remove(tileTif)
self.tileDict[key]['path'] = tileOutDir
def exportWord(jsonPath, productUuid):
# 解析当前json中信息
with open(jsonPath, 'r') as f:
jsonData = json.load(f)
cloud = jsonData['cloud'] # 云量信息
totalArea = jsonData['totalArea'] # 蓝藻总面积
# 生成文字所需信息===============================================================
issue = os.path.basename(jsonPath).split('_')[3]
year = int(issue[0:4])
mm = int(issue[4:6])
dd = int(issue[6:8])
hour = int(issue[8:10])
minute = int(issue[10:12])
timeStr = '%d月%d日%d时%d分' % (mm, dd, hour, minute)
totalPercent = jsonData['totalPercent'] # 蓝藻总百分比
lakeStat = jsonData['lakeStat'] # 蓝藻面积分布区域
algaeThreshold = jsonData['algaeThreshold']
lakeRegionList = []
for key in lakeStat.keys():
if lakeStat[key] == 1:
lakeRegionList.append(LAKE_REGION_NAME[key])
if len(lakeRegionList) == 0:
lakeRegionStr = ''
elif len(lakeRegionList) == 1:
lakeRegionStr = lakeRegionList[0]
else:
tempList = lakeRegionList[0:-1]
lakeRegionStr = '、'.join(tempList) + '和' + lakeRegionList[-1]
areaWX = jsonData['adminArea']['wuxi']
areaCZ = jsonData['adminArea']['changzhou']
areaSZ = jsonData['adminArea']['suzhou']
percentWX = jsonData['adminPercent']['wuxi']
percentCZ = jsonData['adminPercent']['changzhou']
percentSZ = jsonData['adminPercent']['suzhou']
areaH = jsonData['highArea']
areaM = jsonData['midArea']
areaL = jsonData['lowArea']
percentH = jsonData['highPercent']
percentM = jsonData['midPercent']
percentL = jsonData['lowPercent']
# 计算期号
nowDatetime = datetime.datetime.strptime(issue[0:8] + '0000', '%Y%m%d%H%M')
# 3月以前算上一年期号
if mm < 3:
startDatetime = datetime.datetime.strptime(str(year) + '01010000', '%Y%m%d%H%M')
else:
startDatetime = datetime.datetime.strptime(str(year) + '03010000', '%Y%m%d%H%M')
num = (nowDatetime - startDatetime).days + 1 # 期号
label2 = ''
label3 = ''
# 蓝藻日报文字部分===================================================
# 1.全云
if cloud >= 95:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖全部被云层覆盖，无法判断蓝藻聚集情况。' % timeStr
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖全部被云层覆盖，无法判断蓝藻聚集情况。' % timeStr
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
templateID = 1
typeID = 1
# 2.有云无藻
elif 5 < cloud < 95 and totalArea == 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内未发现蓝藻聚集现象。' % timeStr
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内未发现蓝藻聚集现象。' % timeStr
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
templateID = 1
typeID = 2
# 3.无云无藻
elif cloud <= 5 and totalArea == 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖未发现蓝藻聚集现象。' % timeStr
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖未发现蓝藻聚集现象。' % timeStr
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
templateID = 1
typeID = 3
# 4.有云有藻 面积不大于300
elif 5 < cloud < 95 and 0 < totalArea <= 300:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米（图2），' \
'占全湖总面积的%.1f%%，主要分布在%s。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'占全湖总面积的%.1f%%，主要分布在%s。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
templateID = 2
typeID = 4
# 5.无云有藻 面积不大于300
elif cloud <= 5 and 0 < totalArea <= 300:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖发现蓝藻聚集面积约%d平方千米（图2），占全湖总面积的%.1f%%，' \
'主要分布在%s。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，占%d%%；苏州水域%d平方千米，' \
'占%d%%。'\
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，占全湖总面积的%.1f%%，' \
'主要分布在%s。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，占%d%%；苏州水域%d平方千米，' \
'占%d%%。'\
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
templateID = 2
typeID = 5
# 6.无云有藻 面积大于300 有高中低聚集区
elif cloud <= 5 and totalArea > 300 and areaH > 0 and areaM > 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖发现蓝藻聚集面积约%d平方千米（图2），占全湖总面积的%.1f%%，' \
'主要分布在%s。其中，高、中、低聚集区面积分别约为%d平方千米、%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%、%d%%和%d%%（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaH, areaM, areaL, percentH, percentM,
percentL, areaWX, percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，占全湖总面积的%.1f%%，' \
'主要分布在%s。其中，高、中、低聚集区面积分别约为%d平方千米、%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%、%d%%和%d%%。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaH, areaM, areaL, percentH, percentM,
percentL, areaWX, percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 5
# 7.无云有藻 面积大于300 无高聚集区 有中低聚集区
elif cloud <= 5 and totalArea > 300 and areaH == 0 and areaM > 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖发现蓝藻聚集面积约%d平方千米（图2），占全湖总面积的%.1f%%，' \
'主要分布在%s。其中，无高聚集区，中、低聚集区面积分别约为%d平方千米和%d平方千米，占蓝藻总聚集面积的%d%%和%d%%' \
'（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，占%d%%；苏州水域%d平方千米，' \
'占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaM, areaL, percentM, percentL, areaWX,
percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，占全湖总面积的%.1f%%，' \
'主要分布在%s。其中，无高聚集区，中、低聚集区面积分别约为%d平方千米和%d平方千米，占蓝藻总聚集面积的%d%%和%d%%' \
'。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，占%d%%；苏州水域%d平方千米，' \
'占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaM, areaL, percentM, percentL, areaWX,
percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 5
# 8.无云有藻 面积大于300 无高中聚集区 有低聚集区
elif cloud <= 5 and totalArea > 300 and areaH == 0 and areaM == 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖发现蓝藻聚集面积约%d平方千米（图2），占全湖总面积的%.1f%%，' \
'主要分布在%s，全部为低聚集区（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，占全湖总面积的%.1f%%，' \
'主要分布在%s，全部为低聚集区。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 5
# 9.有云有藻 面积大于300 有高中低聚集区
elif 5 < cloud < 95 and totalArea > 300 and areaH > 0 and areaM > 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米（图2），' \
'占全湖总面积的%.1f%%，主要分布在%s。其中，高、中、低聚集区面积分别约为%d平方千米、%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%、%d%%和%d%%（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaH, areaM, areaL, percentH, percentM,
percentL, areaWX, percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'占全湖总面积的%.1f%%，主要分布在%s。其中，高、中、低聚集区面积分别约为%d平方千米、%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%、%d%%和%d%%。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaH, areaM, areaL, percentH, percentM,
percentL, areaWX, percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 4
# 10.有云有藻 面积大于300 无高聚集区 有中低聚集区
elif 5 < cloud < 95 and totalArea > 300 and areaH == 0 and areaM > 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米（图2），' \
'占全湖总面积的%.1f%%，主要分布在%s。其中，无高聚集区，中、低聚集区面积约分别约为%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%和%d%%（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaM, areaL, percentM, percentL, areaWX,
percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'占全湖总面积的%.1f%%，主要分布在%s。其中，无高聚集区，中、低聚集区面积约分别约为%d平方千米和%d平方千米，' \
'占蓝藻总聚集面积的%d%%和%d%%。按行政边界划分，无锡水域%d平方千米，占%d%%；常州水域%d平方千米，' \
'占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaM, areaL, percentM, percentL, areaWX,
percentWX, areaCZ, percentCZ, areaSZ, percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 4
# 11.有云有藻 面积大于300 无高中聚集区 有低聚集区
elif 5 < cloud < 95 and totalArea > 300 and areaH == 0 and areaM == 0 and areaL > 0:
description = '%sEOS/MODIS卫星遥感影像显示（图1），太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米（图2），' \
'占全湖总面积的%.1f%%，主要分布在%s，全部为低聚集区（表1、图3）。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
description2 = '%sEOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'占全湖总面积的%.1f%%，主要分布在%s，全部为低聚集区。按行政边界划分，无锡水域%d平方千米，占%d%%；' \
'常州水域%d平方千米，占%d%%；苏州水域%d平方千米，占%d%%。' \
% (timeStr, totalArea, totalPercent, lakeRegionStr, areaWX, percentWX, areaCZ, percentCZ, areaSZ,
percentSZ)
label1 = '图1 %d年%d月%d日太湖区域卫星遥感影像' % (year, mm, dd)
label2 = '图2 %d年%d月%d日太湖蓝藻遥感监测' % (year, mm, dd)
label3 = '图3 %d年%d月%d日太湖蓝藻聚集强度分级' % (year, mm, dd)
templateID = 3
typeID = 4
else:
print('No Match Found!!!')
return
print(description)
# 生成文件====================================================
# 1.生成日报
replaceText = {'year': year, 'num': num, 'mm': mm, 'dd': dd, 'description': description, 'label1': label1,
'label2': label2, 'label3': label3, 'areaH': areaH, 'areaM': areaM, 'areaL': areaL,
'totalArea': totalArea, 'percentH': percentH, 'percentM': percentM, 'percentL': percentL}
dependDir = globalCfg['depend_path']
templateDir = os.path.join(dependDir, 'word')
templatePath = os.path.join(templateDir, 'report_daily' + str(templateID) + '.docx')
tpl = DocxTemplate(templatePath)
tpl.render(replaceText)
jsonBaseName = os.path.basename(jsonPath)
outputDir = os.path.dirname(jsonPath)
outWordName = jsonBaseName.replace('.json', '.docx')
outWordPath = os.path.join(outputDir, outWordName)
picturePath1 = os.path.join(outputDir, jsonBaseName.replace('.json', '_reportImg1_noPoints.jpg'))
picturePath2 = os.path.join(outputDir, jsonBaseName.replace('.json', '_reportImg2.jpg'))
picturePath3 = os.path.join(outputDir, jsonBaseName.replace('.json', '_reportImg3.jpg'))
if not (os.path.exists(picturePath1) and os.path.exists(picturePath2) and os.path.exists(picturePath2)):
print('Cannot Find JPG File!!!')
return
if templateID == 1:
replacePic = {"template_picture1.jpg": picturePath1}
elif templateID == 2:
replacePic = {"template_picture1.jpg": picturePath1, "template_picture2.jpg": picturePath2}
elif templateID == 3:
replacePic = {"template_picture1.jpg": picturePath1, "template_picture2.jpg": picturePath2,
"template_picture3.jpg": picturePath3}
else:
replacePic = {}
for key in replacePic.keys():
tpl.replace_pic(key, replacePic[key])
if os.path.exists(outWordPath):
os.remove(outWordPath)
tpl.save(outWordPath)
# 2.生成推送所需txt第一段文字，剩余两段文字后续添加
outTxtName = jsonBaseName.replace('.json', '.txt')
outTxtPath = os.path.join(outputDir, outTxtName)
with open(outTxtPath, 'w') as f:
f.write(description2)
# 3.生成EXCEL
xls_num = num
xls_date = str(year) + '/' + str(mm) + '/' + str(dd)
xls_time = '%s时%s分' % (str(hour), str(minute))
xls_threshold = str(algaeThreshold)
xls_ndviMax = str(jsonData['ndviMax'])
xls_ndviMin = str(jsonData['ndviMin'])
xls_ndviMean = str(jsonData['ndviMean'])
xls_boundary = str(jsonData['boundaryThreshold'])
xls_area = ''
if typeID == 4 or typeID == 5:
xls_area = str(totalArea)
xls_algae_area = ''
if typeID == 2 or typeID == 3:
xls_algae_area = '0'
elif typeID == 4 or typeID == 5:
xls_algae_area = str(totalArea)
xls_high = ''
if totalArea >= 300 and areaH > 0:
xls_high = str(areaH)
xls_mid = ''
if totalArea >= 300 and areaM > 0:
xls_mid = str(areaM)
xls_low = ''
if totalArea >= 300 and areaL > 0:
xls_low = str(areaL)
xls_region = lakeRegionStr
xls_cloud = str(cloud)
if cloud > 50 and totalArea == 0:
xls_activate = '0'
else:
xls_activate = '1'
xls_explain = str(typeID)
xls_weather = ''
if cloud <= 5:
xls_cloud_cover = '无覆盖'
elif cloud >= 95:
xls_cloud_cover = '全部覆盖'
else:
xls_cloud_cover = '部分覆盖'
xls_total_percent = '%.2f%%' % totalPercent
xls_intensity_threshold = ''
if totalArea >= 300:
xls_intensity_threshold = '%.3f-%.3f,%.3f-%.3f,%.3f-%.3f' \
% (jsonData['ndviMin'], jsonData['insThreshold1'], jsonData['insThreshold1'],
jsonData['insThreshold2'], jsonData['insThreshold2'], jsonData['ndviMax'])
outXlsxName = jsonBaseName.replace('.json', '.xlsx')
outXlsxPath = os.path.join(outputDir, outXlsxName)
if os.path.exists(outXlsxPath):
os.remove(outXlsxPath)
workBook = xlsxwriter.Workbook(outXlsxPath)
sheet = workBook.add_worksheet()
writeTable = {'A1': '报告期数', 'A2': xls_num,
'B1': '日期', 'B2': xls_date,
'C1': '时间', 'C2': xls_time,
'D1': 'NDVI阈值', 'D2': xls_threshold,
'E1': 'NDVI最大值（蓝藻区域）', 'E2': xls_ndviMax,
'F1': 'NDVI最小值（蓝藻区域）', 'F2': xls_ndviMin,
'G1': 'NDVI均值（蓝藻区域）', 'G2': xls_ndviMean,
'H1': '边界缩放', 'H2': xls_boundary,
'I1': '面积(km2)无云无藻不填', 'I2': xls_area,
'J1': '蓝藻面积（无云无藻填0，全云不填，其他按面积填）', 'J2': xls_algae_area,
'K1': '高聚区面积', 'K2': xls_high,
'L1': '中聚区面积', 'L2': xls_mid,
'M1': '低聚区面积', 'M2': xls_low,
'N1': '分布范围（竺山湖、梅梁湖、贡湖、西部沿岸、南部沿岸、东部沿岸和湖心区）', 'N2': xls_region,
'O1': '云量', 'O2': xls_cloud,
'P1': '是否为有效监测（云量超过50%并且没有监测到蓝藻算无效,1为有效，0为无效）', 'P2': xls_activate,
'Q1': '说明（1全云；2有云无藻；3无云无藻；4有云有藻；5无云有藻）', 'Q2': xls_explain,
'R1': '天气', 'R2': xls_weather,
'S1': '是否被云覆盖（无覆盖、全部覆盖、部分覆盖）', 'S2': xls_cloud_cover,
'T1': '水华面积百分比', 'T2': xls_total_percent,
'U1': 'NDVI分级阈值', 'U2': xls_intensity_threshold
}
format1 = workBook.add_format({'align': 'center', 'font_size': 10,
'valign': 'vcenter', 'text_wrap': 1})
for key in writeTable.keys():
sheet.write(key, writeTable[key], format1)
sheet.set_row(0, 60)
sheet.set_column('A:M', 8.85)
sheet.set_column('N:N', 73)
sheet.set_column('P:P', 73)
sheet.set_column('Q:Q', 60)
sheet.set_column('S:S', 44)
sheet.set_column('T:T', 15)
sheet.set_column('U:U', 40)
workBook.close()
# 4.生成EXCEL_WX
writeTable2 = {'A1': '报告期数', 'A2': xls_num,
'B1': '日期', 'B2': xls_date,
'C1': '时间', 'C2': xls_time,
'D1': 'NDVI阈值', 'D2': xls_threshold,
'E1': '边界缩放', 'E2': xls_boundary,
'F1': '面积(km2)无云无藻不填', 'F2': xls_area,
}
outXlsxWxName = jsonBaseName.replace('.json', '_wx.xlsx')
outXlsxWxPath = os.path.join(outputDir, outXlsxWxName)
if os.path.exists(outXlsxWxPath):
os.remove(outXlsxWxPath)
workBook2 = xlsxwriter.Workbook(outXlsxWxPath)
sheet2 = workBook2.add_worksheet()
format2 = workBook2.add_format({'align': 'center', 'font_size': 10,
'valign': 'vcenter', 'text_wrap': 1})
for key in writeTable2.keys():
sheet2.write(key, writeTable2[key], format2)
sheet.set_row(0, 60)
sheet.set_column('A:F', 9)
workBook2.close()
# 转pdf供前端查看 Windows无法测试===================================================
outPdfDir = os.path.join(globalCfg['taihu_report_remote'], issue[0:8])
if not os.path.exists(outPdfDir):
os.makedirs(outPdfDir)
cmdStr = 'libreoffice6.3 --headless --convert-to pdf:writer_pdf_Export ' + outWordPath + ' --outdir ' + outPdfDir
print(cmdStr)
try:
os.system(cmdStr)
print('Convert PDF Success.')
except Exception as e:
print(e)
# 信息入库=========================================================
conn = pymysql.connect(
db=globalCfg['database'],
user=globalCfg['database_user'],
password=globalCfg['database_passwd'],
host=globalCfg['database_host'],
port=globalCfg['database_port']
)
# t_water_report_taihu
cursor = conn.cursor()
algaeTifName = os.path.basename(jsonPath).replace('.json', '.tif')
db_uuid = str(uuid.uuid4())
db_date = issue[0:4] + '-' + issue[4:6] + '-' + issue[6:8]
db_number = str(num)
db_description = description
db_label1 = ''
db_label2 = ''
db_label3 = ''
db_image1 = ''
db_image2 = ''
db_image3 = ''
db_high_area = ''
db_mid_area = ''
db_low_area = ''
db_total_area = ''
db_high_percent = ''
db_mid_percent = ''
db_low_percent = ''
db_total_percent = ''
db_image = algaeTifName
db_title = '太湖蓝藻水华卫星遥感监测日报'
db_time_modify = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
if templateID == 1:
db_label1 = label1
db_image1 = picturePath1.replace('\\', '/').replace('/mnt/resource/', '')
elif templateID == 2:
db_label1 = label1
db_image1 = picturePath1.replace('\\', '/').replace('/mnt/resource/', '')
db_label2 = label2
db_image2 = picturePath2.replace('\\', '/').replace('/mnt/resource/', '')
elif templateID == 3:
db_label1 = label1
db_image1 = picturePath1.replace('\\', '/').replace('/mnt/resource/', '')
db_label2 = label2
db_image2 = picturePath2.replace('\\', '/').replace('/mnt/resource/', '')
db_label3 = label3
db_image3 = picturePath3.replace('\\', '/').replace('/mnt/resource/', '')
db_high_area = str(areaH)
db_mid_area = str(areaM)
db_low_area = str(areaL)
db_total_area = str(totalArea)
db_total_percent = '100.0'
db_high_percent = str(percentH)
db_mid_percent = str(percentM)
db_low_percent = str(percentL)
else:
pass
# 查找是否已存在
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_report_taihu'] + \
' WHERE image=%s and is_deleted=0;'
cursor.execute(sqlStr, algaeTifName)
sqlRes = cursor.fetchall()
if len(sqlRes) > 0:
# 更新
sqlStr = 'UPDATE ' + globalCfg['database_table_report_taihu'] + \
' SET date=%s,number=%s,description=%s,image1=%s,image2=%s,image3=%s,label1=%s,label2=%s,label3=%s,' \
'high_area=%s,mid_area=%s,low_area=%s,total_area=%s,high_percent=%s,mid_percent=%s,low_percent=%s,' \
'total_percent=%s,title=%s,time_modify=%s WHERE image=%s;'
sqlData = (db_date, db_number, db_description, db_image1, db_image2, db_image3, db_label1, db_label2, db_label3,
db_high_area, db_mid_area, db_low_area, db_total_area, db_high_percent, db_mid_percent,
db_low_percent, db_total_percent, db_title, db_time_modify, db_image)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
# 插入
sqlStr = 'INSERT INTO ' + globalCfg['database_table_report_taihu'] + \
' (uuid,date,number,description,image1,image2,image3,label1,label2,label3,high_area,' \
'mid_area,low_area,total_area,high_percent,mid_percent,low_percent,total_percent,is_deleted,' \
'is_default,image,title,time_modify) VALUES ' \
'(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);'
sqlData = (db_uuid, db_date, db_number, db_description, db_image1, db_image2, db_image3, db_label1, db_label2,
db_label3, db_high_area, db_mid_area, db_low_area, db_total_area, db_high_percent,
db_mid_percent, db_low_percent, db_total_percent, 0, 0, db_image, db_title, db_time_modify)
cursor.execute(sqlStr, sqlData)
conn.commit()
# t_water_taihu_modis
# 查找是否已存在
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_report_taihu_info'] + \
' WHERE image_uuid=%s;'
cursor.execute(sqlStr, productUuid)
sqlRes = cursor.fetchall()
db_date = '%s-%s-%s %s:%s' % (issue[0:4], issue[4:6], issue[6:8], issue[8:10], issue[10:12])
regionArea = jsonData['regionArea']
area_zsh = str(regionArea['zhushanhu'])
area_mlh = str(regionArea['meilianghu'])
area_gh = str(regionArea['gonghu'])
area_xbya = str(regionArea['westCoast'])
area_nbya = str(regionArea['southCoast'])
area_hxq = str(regionArea['centerLake'])
area_dbya = str(regionArea['eastCoast'])
area_dth = str(regionArea['eastTaihu'])
db_region_area = ','.join([area_zsh, area_mlh, area_gh, area_xbya, area_nbya, area_hxq, area_dbya, area_dth])
if len(sqlRes) > 0:
# 更新
sqlStr = 'UPDATE ' + globalCfg['database_table_report_taihu_info'] + \
' SET number=%s,date=%s,ndvi_threshold=%s,ndvi_max=%s,ndvi_min=%s,ndvi_mean=%s,boundary=%s,area=%s,' \
'region_area=%s,high_area=%s,mid_area=%s,low_area=%s,cloud=%s,type=%s,is_activate=%s,ndvi_grade=%s ' \
'WHERE image_uuid=%s;'
sqlData = (xls_num, db_date, xls_threshold, xls_ndviMax, xls_ndviMin, xls_ndviMean, xls_boundary,
str(totalArea), db_region_area, str(areaH), str(areaM), str(areaL), xls_cloud, xls_explain,
xls_activate, xls_intensity_threshold, productUuid)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
sqlStr = 'INSERT INTO ' + globalCfg['database_table_report_taihu_info'] + \
' (number,date,ndvi_threshold,ndvi_max,ndvi_min,ndvi_mean,boundary,area,region_area,high_area,' \
'mid_area,low_area,cloud,type,is_activate,ndvi_grade,image_uuid) ' \
'VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s);'
sqlData = (xls_num, db_date, xls_threshold, xls_ndviMax, xls_ndviMin, xls_ndviMean, xls_boundary,
str(totalArea), db_region_area, str(areaH), str(areaM), str(areaL), xls_cloud, xls_explain,
xls_activate, xls_intensity_threshold, productUuid)
cursor.execute(sqlStr, sqlData)
conn.commit()
# 更新t_export_image信息
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_export_image'] + \
' WHERE uuid=%s and is_deleted=0;'
cursor.execute(sqlStr, productUuid)
sqlRes = cursor.fetchall()
if len(sqlRes) > 0:
sqlStr = 'UPDATE ' + globalCfg['database_table_export_image'] + \
' SET area=%s,threshold=%s WHERE uuid=%s;'
sqlData = (totalArea, algaeThreshold, productUuid)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
pass
cursor.close()
conn.close()
# 更新切片==============================================================
tileDict = {}
basename = '_'.join(jsonBaseName.split('_')[0:7])
# 1.蓝藻产品切片
algaeTifPath = os.path.join(outputDir, jsonBaseName.replace('.json', '.tif'))
algaeTifRender = os.path.join(outputDir, jsonBaseName.replace('.json', '_render.tif'))
colorTable = {1: (255, 251, 0)}
UniqueValues.Render(algaeTifPath, colorTable, returnMode='GEOTIFF', outputPath=algaeTifRender, isAlpha=True)
tileDict['taihu_algae_ndvi'] = {'tif': algaeTifRender, 'name': basename + '_taihu_algae_ndvi',
'legendType': '1', 'legendColor': [(255, 251, 0)], 'legendName': ['水华']}
# 2.蓝藻强度产品切片
intensityTifPath = os.path.join(outputDir, jsonBaseName.replace('_ndvi.json', '_intensity.tif'))
intensityTifRender = os.path.join(outputDir, jsonBaseName.replace('_ndvi.json', '_intensity_render.tif'))
colorTable = {1: (0, 255, 102), 2: (255, 255, 0), 3: (255, 153, 0)}
UniqueValues.Render(intensityTifPath, colorTable, returnMode='GEOTIFF', outputPath=intensityTifRender, isAlpha=True)
tileDict['algaeClassify'] = {'tif': intensityTifRender, 'name': basename + '_classify', 'legendType': '1',
'legendColor': [(0, 255, 102), (255, 255, 0), (255, 153, 0)],
'legendName': ['轻度', '中度', '重度']}
# 调用gdal2tiles工具进行切片
pythonPath = globalCfg['python_path']
gdal2tilesPath = globalCfg['gdal2tiles_path']
tileOutRootDir = globalCfg['tile_server_path']
for key in tileDict.keys():
tileTif = tileDict[key]['tif']
tileOutDir = os.path.join(tileOutRootDir, tileDict[key]['name'])
if os.path.exists(tileOutDir):
shutil.rmtree(tileOutDir)
cmd = '%s %s -z %s -w all %s %s' % (pythonPath, gdal2tilesPath, TILE_LEVEL, tileTif, tileOutDir)
os.system(cmd)
os.remove(tileTif)
tileDict[key]['path'] = tileOutDir
def exportImage(jsonPath):
"""生成后续所需所有专题图"""
tempDir = os.path.dirname(jsonPath)
jsonBaseName = os.path.basename(jsonPath)
# 1.出专题图
# 专题图相关文件
l2TifPath = os.path.join(tempDir,
jsonBaseName.replace('ndvi.json',
'ndvi.l2.tif')) # 原始数据
algaeTifPath = os.path.join(tempDir,
jsonBaseName.replace('ndvi.json',
'ndvi.tif')) # 蓝藻产品
intensityTifPath = os.path.join(tempDir,
jsonBaseName.replace(
'ndvi.json', 'intensity.tif')) # 强度产品
if not os.path.isfile(l2TifPath):
print('Cannot Find %s' % l2TifPath)
return
if not os.path.isfile(algaeTifPath):
print('Cannot Find %s' % algaeTifPath)
return
if not os.path.isfile(intensityTifPath):
print('Cannot Find %s' % intensityTifPath)
return
# mxd数据框范围 (195203.762, 3420416.355, 280082.543, 3498433.803) utm_zone_51N
# mxd数据框范围 (12284297.415195609, 3516083.924476728, 12369297.415195609, 3438583.924476728) Albers
dataFrameBounds = (195203.762, 3420416.355, 280082.543, 3498433.803)
# 相关文件以数据框进行放缩 TODO 取消写临时文件到硬盘
l2ClipPath = os.path.join(
tempDir, jsonBaseName.replace('ndvi.json', 'ndvi.l2_tmp.tif'))
algaeClipPath = os.path.join(
tempDir, jsonBaseName.replace('ndvi.json', 'ndvi_tmp.tif'))
intensityClipPath = os.path.join(
tempDir, jsonBaseName.replace('ndvi.json', 'intensity_tmp.tif'))
if os.path.exists(l2ClipPath):
os.remove(l2ClipPath)
if os.path.exists(algaeClipPath):
os.remove(algaeClipPath)
if os.path.exists(intensityClipPath):
os.remove(intensityClipPath)
# 裁切到模板空间大小
ds = gdal.Warp(l2ClipPath,
l2TifPath,
format='GTiff',
outputBounds=dataFrameBounds,
dstNodata=65535,
xRes=250,
yRes=250)
ds = None
ds = gdal.Warp(algaeClipPath,
algaeTifPath,
format='GTiff',
outputBounds=dataFrameBounds,
dstNodata=0,
xRes=250,
yRes=250)
ds = None
ds = gdal.Warp(intensityClipPath,
intensityTifPath,
format='GTiff',
outputBounds=dataFrameBounds,
dstNodata=0,
xRes=250,
yRes=250)
ds = None
# 太湖蓝藻监测的模板有效嵌入信息：
# 右下角终止列(2055) 右下角终止行(1893)
activateWidth = 2004 # 有效列数 2004
activateHeight = 1841 # 有效行数 1841
offsetWidth = 52 # 左上角起始列
offsetHeight = 53 # 左上角起始行
# 运算三种模板所需的有效数据范围内rgb数组 1.假彩色 2.蓝藻二值 3.聚集强度
# 假彩色
falseColorRgb = RgbComposite.Compose(l2ClipPath, (1, 2, 1),
noDataValue=65535,
returnMode='MEM')
falseColorImageObj = Image.fromarray(falseColorRgb, mode='RGB')
falseColorImageObj = falseColorImageObj.resize(
(activateWidth, activateHeight), Image.NEAREST)
falseColorRgbResize = np.asarray(falseColorImageObj)
# 蓝藻rgb
colorTable = {1: (229, 250, 5)}
algaeRgb = np.copy(falseColorRgb) # 拷贝背景数组
algaeRender = UniqueValues.Render(algaeClipPath,
colorTable,
returnMode='MEM')
invalidLoc = np.logical_and(algaeRender[:, :, 0] == 255,
algaeRender[:, :, 1] == 255,
algaeRender[:, :, 2] == 255)
algaeRgb[:, :, 0][~invalidLoc] = algaeRender[:, :, 0][~invalidLoc]
algaeRgb[:, :, 1][~invalidLoc] = algaeRender[:, :, 1][~invalidLoc]
algaeRgb[:, :, 2][~invalidLoc] = algaeRender[:, :, 2][~invalidLoc]
algaeRgbImageObj = Image.fromarray(algaeRgb, mode='RGB')
algaeRgbImageObj = algaeRgbImageObj.resize((activateWidth, activateHeight),
Image.NEAREST)
algaeRgbResize = np.asarray(algaeRgbImageObj)
# 聚集强度
colorTable = {1: (0, 255, 102), 2: (255, 254, 0), 3: (255, 153, 0)}
intensityRgb = np.copy(falseColorRgb) # 拷贝背景数组
intensityRender = UniqueValues.Render(intensityClipPath,
colorTable,
returnMode='MEM')
invalidLoc = np.logical_and(intensityRender[:, :, 0] == 255,
intensityRender[:, :, 1] == 255,
intensityRender[:, :, 2] == 255)
intensityRgb[:, :, 0][~invalidLoc] = intensityRender[:, :, 0][~invalidLoc]
intensityRgb[:, :, 1][~invalidLoc] = intensityRender[:, :, 1][~invalidLoc]
intensityRgb[:, :, 2][~invalidLoc] = intensityRender[:, :, 2][~invalidLoc]
intensityRgbImageObj = Image.fromarray(intensityRgb, mode='RGB')
intensityRgbImageObj = intensityRgbImageObj.resize(
(activateWidth, activateHeight), Image.NEAREST)
intensityRgbResize = np.asarray(intensityRgbImageObj)
# 循环出图 出图信息存储在字典中
outImagePath1 = os.path.join(
tempDir,
jsonBaseName.replace('ndvi.json', 'ndvi_reportImg1_noPoints.jpg'))
outImagePath2 = os.path.join(
tempDir, jsonBaseName.replace('ndvi.json', 'ndvi_reportImg1.jpg'))
outImagePath3 = os.path.join(
tempDir,
jsonBaseName.replace('ndvi.json', 'ndvi_reportImg2_noPoints.jpg'))
outImagePath4 = os.path.join(
tempDir, jsonBaseName.replace('ndvi.json', 'ndvi_reportImg2.jpg'))
outImagePath5 = os.path.join(
tempDir,
jsonBaseName.replace('ndvi.json', 'ndvi_reportImg3_noPoints.jpg'))
outImagePath6 = os.path.join(
tempDir, jsonBaseName.replace('ndvi.json', 'ndvi_reportImg3.jpg'))
mouldDependDir = os.path.join(globalCfg['depend_path'], 'taihu', 'mould')
mouldPath1 = os.path.join(mouldDependDir, 'taihu_algae_mould1.png')
mouldPath2 = os.path.join(mouldDependDir, 'taihu_algae_mould1_point.png')
mouldPath3 = os.path.join(mouldDependDir, 'taihu_algae_mould2.png')
mouldPath4 = os.path.join(mouldDependDir, 'taihu_algae_mould2_point.png')
mouldPath5 = os.path.join(mouldDependDir, 'taihu_algae_mould3.png')
mouldPath6 = os.path.join(mouldDependDir, 'taihu_algae_mould3_point.png')
mouldDict = {
'mould1': {
'modulePath': mouldPath1,
'activateArray': falseColorRgbResize,
'outImagePath': outImagePath1
},
'mould2': {
'modulePath': mouldPath2,
'activateArray': falseColorRgbResize,
'outImagePath': outImagePath2
},
'mould3': {
'modulePath': mouldPath3,
'activateArray': algaeRgbResize,
'outImagePath': outImagePath3
},
'mould4': {
'modulePath': mouldPath4,
'activateArray': algaeRgbResize,
'outImagePath': outImagePath4
},
'mould5': {
'modulePath': mouldPath5,
'activateArray': intensityRgbResize,
'outImagePath': outImagePath5
},
'mould6': {
'modulePath': mouldPath6,
'activateArray': intensityRgbResize,
'outImagePath': outImagePath6
}
}
for each in mouldDict.keys():
moduleArray = skimage.io.imread(mouldDict[each]['modulePath'])
# 模板中的非透明位置
alphaLocation = moduleArray[:, :, 3] != 0
moduleArrayBackup = np.copy(moduleArray) # 拷贝模板数组
moduleArray[offsetHeight:offsetHeight + activateHeight, offsetWidth:offsetWidth + activateWidth, 0] \
= mouldDict[each]['activateArray'][:, :, 0]
moduleArray[offsetHeight:offsetHeight + activateHeight, offsetWidth:offsetWidth + activateWidth, 1] \
= mouldDict[each]['activateArray'][:, :, 1]
moduleArray[offsetHeight:offsetHeight + activateHeight, offsetWidth:offsetWidth + activateWidth, 2] \
= mouldDict[each]['activateArray'][:, :, 2]
moduleArray[:, :,
0][alphaLocation] = moduleArrayBackup[:, :,
0][alphaLocation]
moduleArray[:, :,
1][alphaLocation] = moduleArrayBackup[:, :,
1][alphaLocation]
moduleArray[:, :,
2][alphaLocation] = moduleArrayBackup[:, :,
2][alphaLocation]
outImagePath = mouldDict[each]['outImagePath']
skimage.io.imsave(outImagePath, moduleArray[:, :,
0:3].astype(np.uint8))
# 添加左下角水印
fontPath = os.path.join(globalCfg['depend_path'], 'ttf',
'simhei.ttf') # 字体
font = ImageFont.truetype(fontPath, 50)
# 水印文本
issue = jsonBaseName.split('_')[3]
textMark = '%d年%d月%d日%d时%d分 EOS/MODIS 1B' % \
(int(issue[0:4]), int(issue[4:6]), int(issue[6:8]), int(issue[8:10]), int(issue[10:12]))
image = Image.open(outImagePath)
draw = ImageDraw.Draw(image)
text_location = [300, 1960]
draw.text(text_location,
textMark,
font=font,
fill="#000000",
spacing=0,
align='left')
image.save(outImagePath, 'jpeg')
# 卫星中心模板大小与以上模板不同
activateWidth = 2004
activateHeight = 1841
offsetWidth = 52
offsetHeight = 63
# 假彩色
falseColorRgb = RgbComposite.Compose(l2ClipPath, (1, 2, 1),
noDataValue=65535,
returnMode='MEM')
falseColorImageObj = Image.fromarray(falseColorRgb, mode='RGB')
falseColorImageObj = falseColorImageObj.resize(
(activateWidth, activateHeight), Image.NEAREST)
falseColorRgbResize = np.asarray(falseColorImageObj)
# 蓝藻rgb
colorTable = {1: (229, 250, 5)}
algaeRgb = np.copy(falseColorRgb) # 拷贝背景数组
algaeRender = UniqueValues.Render(algaeClipPath,
colorTable,
returnMode='MEM')
invalidLoc = np.logical_and(algaeRender[:, :, 0] == 255,
algaeRender[:, :, 1] == 255,
algaeRender[:, :, 2] == 255)
algaeRgb[:, :, 0][~invalidLoc] = algaeRender[:, :, 0][~invalidLoc]
algaeRgb[:, :, 1][~invalidLoc] = algaeRender[:, :, 1][~invalidLoc]
algaeRgb[:, :, 2][~invalidLoc] = algaeRender[:, :, 2][~invalidLoc]
algaeRgbImageObj = Image.fromarray(algaeRgb, mode='RGB')
algaeRgbImageObj = algaeRgbImageObj.resize((activateWidth, activateHeight),
Image.NEAREST)
algaeRgbResize = np.asarray(algaeRgbImageObj)
# 聚集强度
colorTable = {1: (0, 255, 102), 2: (255, 254, 0), 3: (255, 153, 0)}
intensityRgb = np.copy(falseColorRgb) # 拷贝背景数组
intensityRender = UniqueValues.Render(intensityClipPath,
colorTable,
returnMode='MEM')
invalidLoc = np.logical_and(intensityRender[:, :, 0] == 255,
intensityRender[:, :, 1] == 255,
intensityRender[:, :, 2] == 255)
intensityRgb[:, :, 0][~invalidLoc] = intensityRender[:, :, 0][~invalidLoc]
intensityRgb[:, :, 1][~invalidLoc] = intensityRender[:, :, 1][~invalidLoc]
intensityRgb[:, :, 2][~invalidLoc] = intensityRender[:, :, 2][~invalidLoc]
intensityRgbImageObj = Image.fromarray(intensityRgb, mode='RGB')
intensityRgbImageObj = intensityRgbImageObj.resize(
(activateWidth, activateHeight), Image.NEAREST)
intensityRgbResize = np.asarray(intensityRgbImageObj)
outImagePath7 = os.path.join(
tempDir, jsonBaseName.replace('ndvi.json', 'ndvi_reportImg1_wxzx.jpg'))
outImagePath8 = os.path.join(
tempDir, jsonBaseName.replace('ndvi.json', 'ndvi_reportImg2_wxzx.jpg'))
outImagePath9 = os.path.join(
tempDir, jsonBaseName.replace('ndvi.json', 'ndvi_reportImg3_wxzx.jpg'))
mouldPath7 = os.path.join(mouldDependDir, 'taihu_algae_mould1_wxzx.png')
mouldPath8 = os.path.join(mouldDependDir, 'taihu_algae_mould2_wxzx.png')
mouldPath9 = os.path.join(mouldDependDir, 'taihu_algae_mould3_wxzx.png')
mouldDict = {
'mould7': {
'modulePath': mouldPath7,
'activateArray': falseColorRgbResize,
'outImagePath': outImagePath7
},
'mould8': {
'modulePath': mouldPath8,
'activateArray': algaeRgbResize,
'outImagePath': outImagePath8
},
'mould9': {
'modulePath': mouldPath9,
'activateArray': intensityRgbResize,
'outImagePath': outImagePath9
}
}
for each in mouldDict.keys():
moduleArray = skimage.io.imread(mouldDict[each]['modulePath'])
# 模板中的非透明位置
alphaLocation = moduleArray[:, :, 3] != 0
moduleArrayBackup = np.copy(moduleArray) # 拷贝模板数组
moduleArray[offsetHeight:offsetHeight + activateHeight, offsetWidth:offsetWidth + activateWidth, 0] \
= mouldDict[each]['activateArray'][:, :, 0]
moduleArray[offsetHeight:offsetHeight + activateHeight, offsetWidth:offsetWidth + activateWidth, 1] \
= mouldDict[each]['activateArray'][:, :, 1]
moduleArray[offsetHeight:offsetHeight + activateHeight, offsetWidth:offsetWidth + activateWidth, 2] \
= mouldDict[each]['activateArray'][:, :, 2]
moduleArray[:, :,
0][alphaLocation] = moduleArrayBackup[:, :,
0][alphaLocation]
moduleArray[:, :,
1][alphaLocation] = moduleArrayBackup[:, :,
1][alphaLocation]
moduleArray[:, :,
2][alphaLocation] = moduleArrayBackup[:, :,
2][alphaLocation]
outImagePath = mouldDict[each]['outImagePath']
skimage.io.imsave(outImagePath, moduleArray[:, :,
0:3].astype(np.uint8))
# 添加左下角水印
fontPath = os.path.join(globalCfg['depend_path'], 'ttf',
'simhei.ttf') # 字体
font = ImageFont.truetype(fontPath, 50)
# 水印文本
issue = jsonBaseName.split('_')[3]
textMark = '%d年%d月%d日%d时%d分 EOS/MODIS 1B' % \
(int(issue[0:4]), int(issue[4:6]), int(issue[6:8]), int(issue[8:10]), int(issue[10:12]))
image = Image.open(outImagePath)
draw = ImageDraw.Draw(image)
text_location = [300, 1973]
draw.text(text_location,
textMark,
font=font,
fill="#000000",
spacing=0,
align='left')
image.save(outImagePath, 'jpeg')
# 删除临时文件
if os.path.exists(l2ClipPath):
os.remove(l2ClipPath)
if os.path.exists(algaeClipPath):
os.remove(algaeClipPath)
if os.path.exists(intensityClipPath):
os.remove(intensityClipPath)
def exportWord(jsonPath, productUuid):
# 解析当前json中信息
with open(jsonPath, 'r') as f:
jsonData = json.load(f)
cloud = jsonData['cloud'] # 云量信息
totalArea = jsonData['totalArea'] # 蓝藻总面积
# 生成文字所需信息===============================================================
issue = os.path.basename(jsonPath).split('_')[3]
year = int(issue[0:4])
month = int(issue[4:6])
day = int(issue[6:8])
hour = int(issue[8:10])
minute = int(issue[10:12])
if hour < 12:
apStr = '上午'
else:
apStr = '下午'
totalPercent = jsonData['totalPercent'] # 蓝藻总占比
lakeStat = jsonData['lakeStat'] # 蓝藻面积分布区域
algaeThreshold = jsonData['algaeThreshold']
lakeRegionList = []
lakeRegionStr = '' # 区域描述1
for key in lakeStat.keys():
if lakeStat[key] == 1:
lakeRegionList.append(LAKE_REGION_NAME[key])
if len(lakeStat) > 0:
lakeRegionStr = '、'.join(lakeRegionList)
# 区域描述2
if len(lakeRegionList) == 0:
lakeRegionStr2 = ''
elif len(lakeRegionList) == 1:
lakeRegionStr2 = lakeRegionList[0]
else:
tempList = lakeRegionList[0:-1]
lakeRegionStr2 = '、'.join(tempList) + '和' + lakeRegionList[-1]
description = '' # 未会商报告文字部分
description2 = '' # 监测中心每日一报文字部分
# 1.全云
if cloud >= 95:
description = '%d月%d日遥感监测结果显示，太湖%s全部被云层覆盖，无法判断蓝藻聚集情况，未会商。' % (
month, day, apStr)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖全部被云层覆盖，无法判断蓝藻聚集情况。' \
% (month, day, hour, minute)
# 2.有云无藻
elif 5 < cloud < 95 and totalArea == 0:
description = '%d月%d日遥感监测结果显示，太湖%s部分被云层覆盖，无云区域内未发现蓝藻聚集现象，未会商。' % (
month, day, apStr)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内未发现蓝藻聚集现象。' \
% (month, day, hour, minute)
# 3.无云无藻
elif cloud <= 5 and totalArea == 0:
description = '%d月%d日遥感监测结果显示，太湖%s未发现蓝藻聚集现象，未会商。' % (month, day, apStr)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖未发现蓝藻聚集现象。' % (
month, day, hour, minute)
# 4.有云有藻
elif 5 < cloud < 95 and totalArea > 0:
description = '%d月%d日遥感监测结果显示，太湖%s部分被云层覆盖，在%s发现蓝藻聚集现象，面积约%d平方千米，占太湖总面积的%.1f%%，' \
'未会商。' % (month, day, apStr, lakeRegionStr, totalArea, totalPercent)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖部分湖区被云层覆盖，无云区域内发现蓝藻聚集面积约%d平方千米，' \
'主要分布在%s。' % (month, day, hour, minute, totalArea, lakeRegionStr2)
# 5.无云有藻
elif cloud <= 5 and totalArea > 0:
description = '%d月%d日遥感监测结果显示，太湖%s在%s发现蓝藻聚集现象，面积约%d平方千米，占太湖总面积的%.1f%%，未会商。' \
% (month, day, apStr, lakeRegionStr, totalArea, totalPercent)
description2 = '%d月%d日%d时%d分EOS/MODIS卫星遥感影像显示，太湖发现蓝藻聚集面积约%d平方千米，' \
'主要分布%s。' % (month, day, hour, minute, totalArea, lakeRegionStr2)
else:
pass
print(description)
print(description2)
# 生成word====================================================
# 简报word生成
dependDir = globalCfg['depend_path']
templateDir = os.path.join(dependDir, 'word')
templatePath = os.path.join(templateDir, 'report_quick.docx')
tpl = DocxTemplate(templatePath)
replaceText = {"content": description}
tpl.render(replaceText)
outputDir = os.path.dirname(jsonPath)
jsonBaseName = os.path.basename(jsonPath)
outWordName = jsonBaseName.replace('.json', '_quick.docx')
outWordPath = os.path.join(outputDir, outWordName)
picturePath1 = os.path.join(
outputDir, jsonBaseName.replace('.json', '_reportImg1_noPoints.jpg'))
picturePath2 = os.path.join(
outputDir, jsonBaseName.replace('.json', '_reportImg2.jpg'))
if not (os.path.exists(picturePath1) and os.path.exists(picturePath2)):
print('Cannot Find JPG File!!!')
return
replacePic = {
"template_picture1.jpg": picturePath1,
"template_picture2.jpg": picturePath2
}
for key in replacePic.keys():
tpl.replace_pic(key, replacePic[key])
if os.path.exists(outWordPath):
os.remove(outWordPath)
tpl.save(outWordPath)
# 每日一报word生成
templatePath2 = os.path.join(templateDir, 'report_jsem.docx')
tpl2 = DocxTemplate(templatePath2)
replaceText2 = {
"content": description2,
"year": str(year),
"mm": str(month),
"dd": str(day)
}
tpl2.render(replaceText2)
outWordName2 = jsonBaseName.replace('.json', '_jsem.docx')
outWordPath2 = os.path.join(outputDir, outWordName2)
replacePic2 = {
"template_picture1.jpg": picturePath1,
}
for key in replacePic2.keys():
tpl2.replace_pic(key, replacePic2[key])
if os.path.exists(outWordPath2):
os.remove(outWordPath2)
tpl2.save(outWordPath2)
# 信息入库t_water_report_taihu_quick====================================================
conn = pymysql.connect(db=globalCfg['database'],
user=globalCfg['database_user'],
password=globalCfg['database_passwd'],
host=globalCfg['database_host'],
port=globalCfg['database_port'])
# 先查询数据库是否有这期数据
cursor = conn.cursor()
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_report_taihu_quick'] + \
' WHERE image=%s and is_deleted=0;'
algaeTifName = os.path.basename(jsonPath).replace('.json', '.tif')
cursor.execute(sqlStr, algaeTifName)
sqlRes = cursor.fetchall()
if len(sqlRes) > 0:
# 更新
db_image = algaeTifName
db_description = description
db_image1 = picturePath1.replace('\\',
'/').replace('/mnt/resource/', '')
db_image2 = picturePath2.replace('\\',
'/').replace('/mnt/resource/', '')
db_time_modify = time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time()))
sqlStr = 'UPDATE ' + globalCfg['database_table_report_taihu_quick'] + \
' SET description=%s,image1=%s,image2=%s,time_modify=%s WHERE image=%s;'
sqlData = (db_description, db_image1, db_image2, db_time_modify,
db_image)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
# 插入
db_uuid = str(uuid.uuid4())
db_description = description
db_image1 = picturePath1.replace('\\',
'/').replace('/mnt/resource/', '')
db_image2 = picturePath2.replace('\\',
'/').replace('/mnt/resource/', '')
db_image = algaeTifName
sqlStr = 'INSERT INTO ' + globalCfg['database_table_report_taihu_quick'] + \
' (uuid,description,image1,image2,image) VALUES (%s,%s,%s,%s,%s);'
sqlData = (db_uuid, db_description, db_image1, db_image2, db_image)
cursor.execute(sqlStr, sqlData)
conn.commit()
# 更新t_export_image表信息
sqlStr = 'SELECT * FROM ' + globalCfg['database_table_export_image'] + \
' WHERE uuid=%s and is_deleted=0;'
cursor.execute(sqlStr, productUuid)
sqlRes = cursor.fetchall()
if len(sqlRes) > 0:
sqlStr = 'UPDATE ' + globalCfg['database_table_export_image'] + \
' SET area=%s,threshold=%s WHERE uuid=%s;'
sqlData = (totalArea, algaeThreshold, productUuid)
cursor.execute(sqlStr, sqlData)
conn.commit()
else:
pass
cursor.close()
conn.close()
# 更新切片==============================================================
tileDict = {}
basename = '_'.join(jsonBaseName.split('_')[0:7])
# 1.蓝藻产品切片
algaeTifPath = os.path.join(outputDir,
jsonBaseName.replace('.json', '.tif'))
algaeTifRender = os.path.join(outputDir,
jsonBaseName.replace('.json', '_render.tif'))
colorTable = {1: (255, 251, 0)}
UniqueValues.Render(algaeTifPath,
colorTable,
returnMode='GEOTIFF',
outputPath=algaeTifRender,
isAlpha=True)
tileDict['taihu_algae_ndvi'] = {
'tif': algaeTifRender,
'name': basename + '_taihu_algae_ndvi',
'legendType': '1',
'legendColor': [(255, 251, 0)],
'legendName': ['水华']
}
# 2.蓝藻强度产品切片
intensityTifPath = os.path.join(
outputDir, jsonBaseName.replace('_ndvi.json', '_intensity.tif'))
intensityTifRender = os.path.join(
outputDir, jsonBaseName.replace('_ndvi.json', '_intensity_render.tif'))
colorTable = {1: (0, 255, 102), 2: (255, 255, 0), 3: (255, 153, 0)}
UniqueValues.Render(intensityTifPath,
colorTable,
returnMode='GEOTIFF',
outputPath=intensityTifRender,
isAlpha=True)
tileDict['algaeClassify'] = {
'tif': intensityTifRender,
'name': basename + '_classify',
'legendType': '1',
'legendColor': [(0, 255, 102), (255, 255, 0), (255, 153, 0)],
'legendName': ['轻度', '中度', '重度']
}
# 调用gdal2tiles工具进行切片
pythonPath = globalCfg['python_path']
gdal2tilesPath = globalCfg['gdal2tiles_path']
tileOutRootDir = globalCfg['tile_server_path']
for key in tileDict.keys():
tileTif = tileDict[key]['tif']
tileOutDir = os.path.join(tileOutRootDir, tileDict[key]['name'])
if os.path.exists(tileOutDir):
shutil.rmtree(tileOutDir)
cmd = '%s %s -z %s -w all %s %s' % (pythonPath, gdal2tilesPath,
TILE_LEVEL, tileTif, tileOutDir)
os.system(cmd)
os.remove(tileTif)
tileDict[key]['path'] = tileOutDir