def trigger_radio_processing(product):
global PROCESSING_STATUS
PROCESSING_STATUS = 'PASSED' #to be pass in this interface (as parameter)
infra = main_infra.mainInfra()
stat_table = []
stat_table_sort = []
roi_list = [rec for rec in glob.glob(os.path.join(product, 'ROI*'))]
for roi in roi_list:
log.infog(' -- Processing of ' + roi + ' : ')
mtl = metadata_extraction.LandsatMTL(roi)
mtl.set_test_site_information(
infra.configuration_site_description_file)
log.info(' -- Convert to RAD / TOA')
mtl.display_mtl_info()
r = rad.radiometric_calibration(product, mtl)
log.info(' -- Extract / store statistics : ')
image_file_list = mtl.rhotoa_image_list
output_txt_file = RADIOMETRIC_STABILITY_RHO_RESULTS
#infra.result_radiometricStability
roi_id = os.path.basename(roi)
gainList = [str(rec) for rec in mtl.rescaling_gain]
#print mtl.band_sequence
for image_file in image_file_list:
a = None
a = Image.Statistics(image_file, roi_id)
r = a.get_statistics()
stat_table.append(r)
stat_table_sort = sorted(stat_table,
key=lambda x: x[1]) #sort by band_number
infra.update_text_file(mtl, stat_table_sort)
def trigger_productSubview(product):
global PROCESSING_STATUS
PROCESSING_STATUS = 'PASSED'
infra = main_infra.mainInfra()
#infra.checkDirectoryPresence()
mtl = metadata_extraction.LandsatMTL(product)
if mtl.mtl_file_name == '':
print ' -- Missing MTL File'
return
if mtl.missing_image_in_list == 'Missing_image':
print ' -- Missing Image files'
return
mtl.set_test_site_information(infra.configuration_site_description_file)
if str(len(mtl.test_site)) == '0':
print ' -- No Test site Found'
return
print " -- site " + mtl.test_site[0]
mtl.display_mtl_info()
infra.makeProductSubview(mtl)
return True
def trigger_radio_processing():
infra = main_infra.mainInfra()
product_id = 'LC81810402013218LGN00'
#Hypothese : nous sommes dans le repertoire stabilityMonitoring
interest = 'stabilityMonitoring'
product_list = [
rec for rec in glob.glob(
os.path.join(infra.processing_location, interest, 'input',
product_id, 'ROI*'))
]
for product in product_list:
log.infog(' -- Processing of ' + product + ' : ')
mtl = metadata_extraction.LandsatMTL(product)
log.info(' -- Convert to RAD / TOA')
#r=rad.radiometric_calibration(product,mtl)
log.info(' -- Extract / store statistics ')
mtl.update_image_file_list()
#mtl.display_mtl_info()
image_file_list = mtl.rhotoa_image_list
output_txt_file = infra.result_radiometricStability
roi_id = os.path.basename(product)
gainList = [str(rec) for rec in mtl.rescaling_gain]
#print mtl.band_sequence
extract_reflectance_in_roy.reduction_on_roi(mtl, image_file_list,
output_txt_file, roi_id)
print '\n'
print output_txt_file
#Move Product From input to done
input_product = os.path.join(infra.processing_location, interest, 'input',
product_id)
output_rep = os.path.join(infra.processing_location, interest, 'done')
cmd = ' '.join(['mv', input_product, output_rep])
os.system(cmd)
def trigger_productSubview():
infra = main_infra.mainInfra()
infra.checkDirectoryPresence()
product_id = 'LC81810402013218LGN00'
product = os.path.join(infra.input_data_location, product_id)
mtl = metadata_extraction.LandsatMTL(product)
mtl.get_test_site_information(infra.configuration_site_description_file)
infra.makeProductSubview(mtl)
def test_thresholdImage():
infra = main_infra.mainInfra()
infra.checkDirectoryPresence()
#trigger_productSubview()
#trigger_radio_processing()
cor_product = os.path.join(
infra.processing_location, 'interbandRegistration', 'wd_pan',
'LC81960302017110MTI00_ROI_prism_roi_BD_REF8_BD_WORK10')
land_sea_mask = os.path.join(infra.processing_location,
'interbandRegistration', 'input',
'LC81960302017110MTI00', 'ROI_prism_roi',
'land_sea_mask.tif')
cor_conf_image = os.path.join(
cor_product, 'LC81960302017110MTI00_B10_dc-confidence.TIF')
dst_file_name = os.path.join(
cor_product, 'LC81960302017110MTI00_B10_dc-displacement.TIF')
infra.threshold_confidence_image(land_sea_mask, cor_conf_image,
dst_file_name)
#threshold_displacement_image(self,cor_conf_image,d_image):
dx_image = os.path.join(cor_product,
'LC81960302017110MTI00_B10_dx-displacement.TIF')
def trigger_directLocation_processing(product,interband):
global PROCESSING_STATUS
PROCESSING_STATUS='PASSED' #to be pass in this interface (as parameter)
infra=main_infra.mainInfra()
mtl = metadata_extraction.LandsatMTL(product)
scene_id = mtl.landsat_scene_id
mtl.set_test_site_information(infra.configuration_site_description_file)
country_name=((mtl.test_site[0]).split())[0]
site_name=((mtl.test_site[0]).split())[1]
repo_ref=os.path.join(infra.reference_data_raster_file_location,country_name,site_name,'ROI');
#Access to assessment manager ?
band_list=assessmentManager_get_band_list(infra,mtl)
mtl=None
#http class : interface with web server
http = http_side.performance_report(product)
#-- MLAB output file initialisation
file_label = interband.file_label
band_type = interband.band_type
#-- Loop on all image roi, perform correlation and statistics
#LOOP OVER ROIs
roi_list=[rec for rec in glob.glob(os.path.join(product,'ROI*'))]
for roi in roi_list:
roi_name=os.path.basename(roi).split('_')[1]
print ' '
print ' -------------------------'
log.infog(' -- Processing of '+roi_name+' : ')
mtl = metadata_extraction.LandsatMTL(roi)
mtl.set_test_site_information(infra.configuration_site_description_file)
mtl.add_roi_name_information(roi_name)
#START Image Matching Loop
wd_list = []
interband.roi = roi
for band_twin in band_combination[band_type]:
interband.set_ref_channel(band_twin)
ref_channel = interband.ref_channel
work_channel = interband.work_channel
#Define the working directory
wd_name=mtl.landsat_scene_id+'_ROI_'+roi_name+'_BD_'+ref_channel
productWorkingDirectory=os.path.join(infra.processing_location,interest,'wd',wd_name)
ref_channel_reg = os.path.join(os.path.join(repo_ref,roi_name,'*B0'+ref_channel+'*.tiff'))
work_channel_reg = os.path.join(os.path.join(product,'ROI_'+roi_name,'*B'+work_channel+'*.TIF'))
if len(glob.glob(ref_channel_reg)) > 0 :
interband.refImage = glob.glob(ref_channel_reg)[0]
interband.refImageTrue = True
else :
log.err('Missing reference product ')
return
if len(glob.glob(work_channel_reg)) > 0 :
interband.workImage = glob.glob(work_channel_reg)[0]
interband.workImageTrue = True
else :
log.err('Missing one product as input ')
log.err(work_channel_reg)
return
#CORREL
log.infog(" -- Prepare Parameters")
cor = correl_image(os.path.join(productWorkingDirectory,'grille.hdf'))
log.infog(" -- Start Matching")
productWorkingDirectory = image_matching(mtl,
infra,
interband,
cor)
#productWorkingDirectory = '/home/saunier/DEV/LS08_ideas_cyclic_report__processing/PROCESSING/directLocation/wd/LC81980332017172MTI00_ROI_ibiza_BD_8'
wd_list = (productWorkingDirectory)
log.infog(" -- Processing of : "+productWorkingDirectory)
# cor = correl_image(os.path.join(productWorkingDirectory,'grille.hdf'))
#GEOCODING
log.infog(" -- Geocoded Medicis Results")
inputImage = interband.workImage
refimageName = interband.workImage #?
#Size of input Full Image (correlation undersampled input images ...)
src_filename = inputImage
src_ds = gdal.Open(str(src_filename))
image_width = src_ds.RasterXSize
image_length = src_ds.RasterYSize
src_ds = None
cor.geocoded(inputImage,refimageName,productWorkingDirectory)
if (cor.geocoded_valid) :
dx = cor.dx
dy = cor.dy
dc = cor.dc
mask = cor.mask
else :
log.warning(" -- [ERROR ] No Correlation Results in ")
log.warning(" -- [ERROR ] No Correlation Results in ")
return
#FILTERING : IF MASK AVAILABLE - APPLIED
log.infog(" -- Mask Confidence Image with land_sea mask \n")
land_sea_mask = os.path.join(product,roi,'land_sea_mask.tif')
if os.path.exists(land_sea_mask):
log.infog(" --- Land Sea Mask Exist : "+land_sea_mask)
input_image = dc
mask = land_sea_mask
log.infog(" --- Applied Land Sea Mask to confidence image")
list_image = []
list_image.append(input_image)
im_st = im_p.image(list_image)
im_st.maskImage(mask,productWorkingDirectory)
log.info(" -- Create "+im_st.masked_image+' \n')
cmd = ' '.join(['mv', im_st.masked_image,dc])
os.system(cmd)
im_st = None
#FILTERING : APPLIED CONFIDENCE THRESHOLD ON DC - can applied to DX,DY,DC
list_image = []
list_image.append(dc)
im_st = im_p.image(list_image)
im_st.histerysisThreshold(confidence,productWorkingDirectory)
dc = im_st.masked_image
dc_mask = im_st.mask_image #BINARY MASK
im_st = None
#UPDATE DC / DC MASK WITH 3 SIGMA THRESHOLD ON DX , DY
im_st = im_p.image([dc,dc_mask])
n_value = 3.5 #SIGMA VALUE
threshold = confidence #CONFIDENCE VALUE -
im_st.sigma_threshold(dx,n_value,confidence)
im_st.sigma_threshold(dy,n_value,confidence)
dc = im_st.masked_image
dc_mask = im_st.mask_image #BINARY MASK
cmd = ' '.join(['mv', im_st.masked_image,cor.dc]) #Ecrase DC - original in "OLD"
os.system(cmd)
im_st = None
#APPLIED DC BINARY MASK TO DX, DY
mask = dc_mask
list_image = []
list_image.append(dx)
im_st = im_p.image(list_image)
im_st.maskImage(mask,productWorkingDirectory)
log.info(" -- Create "+im_st.masked_image+' \n')
dx = im_st.masked_image
im_st = None
list_image = []
list_image.append(dy)
im_st = im_p.image(list_image)
im_st.maskImage(mask,productWorkingDirectory)
log.info(" -- Create "+im_st.masked_image+' \n')
dy = im_st.masked_image
im_st = None
log.infog(" -- Mask :"+dc_mask)
log.infog(" -- DX :"+dx)
log.infog(" -- DY :"+dy)
log.infog(" -- DC :"+dc+'\n')
#COMPUTE DX²+ DY² - RADIAL ERROR
list_image = [dx,dy]
dst_file_name = os.path.join(productWorkingDirectory,scene_id+
'_B'+interband.ref_channel+
'_B'+interband.work_channel+
'_radialError_'+
str(np.int(confidence*100))+'.tif',)
im_st = im_p.image(list_image)
if (im_st.computeRadialError(dst_file_name)):
cor.radial_error = dst_file_name
cor.radial_error_valid = True
im_st = None
#STATISTICS DX
#STATISTICS DY
#STATISTICS DC
#-- QL Radial Error - Overlayed Image Band with B1 B2 B3 Image
#Rescale QL Radial Error to size of B1 , B2 , B3
list_image = [interband.workImage]
im1 = cor.radial_error
dst_filename = im1.replace('.tif','_rescale.tif')
im_st = im_p.image(list_image)
im_st.rescaleImage(im1,dst_filename)
cor.radial_error_rescaled = dst_filename
im1 = dc_mask
dst_filename = im1.replace('.TIF','_rescale.tif')
im_st.rescaleImage(im1,dst_filename) #Geometric Rescaling
cor.dc_mask_rescaled = dst_filename
im_st = None
#BURN TIF IMAGES WITH RADIAL ERRORS
#Convert each image to 8 Bits
list_image = []
list_image.append(glob.glob(os.path.join(product,roi,'*B8.TIF'))[0]) # QL Generation
list_image.append(glob.glob(os.path.join(product,roi,'*B8.TIF'))[0]) # QL Generation
list_image.append(glob.glob(os.path.join(product,roi,'*B8.TIF'))[0]) # QL Generation
im_st = im_p.image(list_image)
dst_repo = productWorkingDirectory
output_list = im_st.byte_rescaling(dst_repo )
im_st = None
#Burn each one tif images
list_image = output_list
output_list = []
for image in list_image:
output_list.append(image.replace('.tif','_burn.tif'))
im_st = im_p.image(list_image)
im_filename = cor.radial_error_rescaled #Radial Error Value map to QL
mask_filename = cor.dc_mask_rescaled #DC Mask to select pixel
im_st.burn_image(output_list,im_filename,mask_filename)
im_st = None
#Create the quick look
image_data_list = output_list
ql_name = os.path.join(productWorkingDirectory,scene_id+
'_B'+interband.ref_channel+
'_B'+interband.work_channel+
'_Radial_Error_QL.jpg')
outputdir = productWorkingDirectory
quick_look_resolution = 30
im_st = im_p.image(image_data_list) # Create Object instance
im_st.createimageQuicklook(ql_name,outputdir,quick_look_resolution)
#-- MLAB Production
#result file is matlab file (.mat)
src_ds = gdal.Open(str(interband.refImage))
pixelSpacing= (src_ds.GetGeoTransform())[1] #Pixel spacing of the orginal Image
src_ds = None
#-- Option
#createimageQuicklook(inputImage,productWorkingDirectory)
#addDigitalElevationImage(inputImage,productWorkingDirectory,demReference)
#-- End Option
# ext = '.mat'
# result_mat_file= interband.get_output_filename(mtl,infra,ext)
#-- Summary Statistisque file_label - same level of mat file but
# just list results in a CSV.
# ext = '.txt'
# result_sta_file= interband.get_output_filename(mtl,infra,ext)
log.infog(" -- Update Multi Temporal MAT File \n")
metadataFile=mtl
# Set Filename of ".mat" file
filename = site_name+'_multiTemp.mat'
result_mat_file=os.path.join(infra.result_location,interest,filename)
medicisDirectory=productWorkingDirectory
#[NFO] Mlab_run - attente des fichiers dx,dy,dc normés
# M=dir([obj.repName filesep '*_dx-displacement.TIF']);
# M=dir([obj.repName filesep '*_dy-displacement.TIF']);
# M=dir([obj.repName filesep '*_dc-confidence.TIF']);
Mlab_run(mtl,
result_mat_file,
productWorkingDirectory,
pixelSpacing)
##TEMPS MININIMUM A 30 BIEN ATTENDRE LA FIN DE TRAITEMENT
print (' ')
log.warn('TIME SLEEP ACTIVIATED - 30 s - Wait end of M LAB PROCESSING')
log.warn('TIME SLEEP ACTIVIATED - 30 s - Important to let Completion')
log.warn('TIME SLEEP ACTIVIATED - 30 s - of the mat file ')
print (' ')
time.sleep(30)
#--- 2. Repatriate BD2 BD3 BD4 Images
#--- 3. Open Images and Burn Values
#--- 4. Create QL
#-- CP to the HTTP
src_directory = productWorkingDirectory
#-- Copy MLAB Results (PNG) to repository
#-- Copy DC mask image (PNG)to http repository
#-- Copy Radial Error image (JPG)to http repository
http.updateContent(productWorkingDirectory,interest,band_type)
#Generate A single stat/csv for all Report WD in this processing
result_sta_file = os.path.join(productWorkingDirectory,'stat.txt')
log.infog(" - [STA File] STA File : "+result_sta_file)
#PARTIE EXPORT SUR HTTP A SEPARER DE CETTE FONCTION
print ( ' ')
log.infog(" - Populate HTTP \n")
#-- STA FILE :
dst_file_name = ''.join([mtl.landsat_scene_id,'_','roi_',roi_name,'_multiTemp_',band_type,'.txt'])
http.updateContentWith_FILE(result_sta_file,dst_file_name,interest,band_type)
log.infog(" - CP Matlab File \n")
http.updateWithDirectLocationMATFile(result_mat_file)
input_product=product
output_rep=os.path.join(product,'../../done')
cmd = ' '.join(['mv',input_product,output_rep])
log.info(' - [CMD ] '+cmd)
os.system(cmd)
input_product=product
output_rep=os.path.join(product,'../../done')
cmd = ' '.join(['mv',input_product,output_rep])
log.info(' - [CMD ] '+cmd)
os.system(cmd)
if __name__ == '__main__':
band_type = 'pan' #MS OR PAN band_type to create specific WD.
#IF Directlocation the WD is 'WD'
interband = interband(band_type);
if len(sys.argv) == 2 :
infra=main_infra.mainInfra()
infra.checkDirectoryPresence()
interest='directLocation'
product = sys.argv[1]
log.info(" Processing of : "+product)
mtl = metadata_extraction.LandsatMTL(product)
dst_filename = os.path.join(product,'land_sea_mask.tif')
if (mtl.bqa) :
get_qa_land_sea_mask(mtl, dst_filename)
#product = glob.glob(product)
trigger_directLocation_processing(product,interb)
else :
infra=main_infra.mainInfra()
def trigger_interband_processing(product,interband):
#Recall Object
infra=main_infra.mainInfra()
mtl = metadata_extraction.LandsatMTL(product)
scene_id = mtl.landsat_scene_id
mtl.set_test_site_information(infra.configuration_site_description_file)
country_name=((mtl.test_site[0]).split())[0]
site_name=((mtl.test_site[0]).split())[1]
repo_ref=os.path.join(infra.reference_data_raster_file_location,country_name,site_name,'ROI');
#Access to assessment manager ?
band_list=assessmentManager_get_band_list(infra,mtl)
# mtl=None ? cette commande
roi_list=[rec for rec in glob.glob(os.path.join(product,'ROI*'))]
#http class : interface with web server
http = http_side.performance_report(product)
band_type = interband.band_type
interband.set_main_wd(infra,band_type)
#-- MLAB output file initialisation
file_label = interband.file_label
ext = '.mat'
result_mat_file= interband.get_output_filename(mtl,infra,ext)
#-- Summary Statistisque file_label - same level of mat file but
# just list results in a CSV.
ext = '.txt'
result_sta_file= interband.get_output_filename(mtl,infra,ext)
#-- Loop on all image roi, perform correlation and statistics
for roi in roi_list:
interband.roi = roi
roi_name=os.path.basename(roi).split('_')[1]
print ' '
print ' -------------------------'
log.infog(' -- Processing of '+roi_name+' : ')
mtl = metadata_extraction.LandsatMTL(roi)
mtl.set_test_site_information(infra.configuration_site_description_file)
mtl.add_roi_name_information(roi_name)
#START Image Matching Loop
wd_list = []
for band_twin in band_combination[band_type]:
interband.set_ref_channel(band_twin)
test1=interband.search_image_twin(product)
if test1 :
if (interband.ref_channel == '8') : #Et mission Landsat alors rescaling a 30 m
if interband.ref_pixel_size == 15 :
log.infog(' -- Input Image scale is '+str(interband.ref_pixel_size)+' m --')
log.infog(' -- Input Image is rescaled '+str(interband.work_pixel_size)+' m --')
px_size = str(interband.work_pixel_size)
i_file = interband.refImage
o_file = interband.refImage.replace('B8.TIF','B8_30.TIF')
if not os.path.exists(o_file) :
cmd = ' '.join(['gdalwarp -tr ', px_size,px_size, i_file, o_file])
os.system(cmd)
interband.refImage = o_file
src_ds = None
band_type = interband.band_type
roi = interband.roi
ref_channel = interband.ref_channel
work_channel = interband.work_channel
wd_name = (mtl.landsat_scene_id+'_'+os.path.basename(roi)+'_BD_REF'+ref_channel+'_BD_WORK'+work_channel)
productWorkingDirectory=os.path.join(infra.processing_location,interest,'wd_'+band_type,wd_name)
cor = correl_image(os.path.join(productWorkingDirectory,'grille.hdf'))
print cor.grille
print interband.refImage
productWorkingDirectory = image_matching(mtl,
infra,
interband,
cor)
wd_list.append(productWorkingDirectory)
cor = correl_image(os.path.join(productWorkingDirectory,'grille.hdf'))
#GEOCODING
log.infog(" -- Geocoded Medicis Results")
inputImage = interband.workImage
refimageName = interband.workImage #?
#Size of input Full Image (correlation undersampled input images ...)
src_filename = inputImage
src_ds = gdal.Open(str(src_filename))
image_width = src_ds.RasterXSize
image_length = src_ds.RasterYSize
src_ds = None
cor.geocoded(inputImage,refimageName,productWorkingDirectory)
if (cor.geocoded_valid) :
dx = cor.dx
dy = cor.dy
dc = cor.dc
mask = cor.mask
else :
log.warning(" -- [ERROR ] No Correlation Results in ")
log.warning(" -- [ERROR ] No Correlation Results in ")
return
#FILTERING : IF MASK AVAILABLE - APPLIED
log.infog(" -- Mask Confidence Image with land_sea mask \n")
land_sea_mask = os.path.join(product,roi,'land_sea_mask.tif')
if os.path.exists(land_sea_mask):
log.infog(" --- Land Sea Mask Exist : "+land_sea_mask)
input_image = dc
mask = land_sea_mask
log.infog(" --- Applied Land Sea Mask to confidence image")
list_image = []
list_image.append(input_image)
im_st = im_p.image(list_image)
im_st.maskImage(mask,productWorkingDirectory)
log.info(" -- Create "+im_st.masked_image+' \n')
cmd = ' '.join(['mv', im_st.masked_image,dc])
os.system(cmd)
im_st = None
#FILTERING : APPLIED CONFIDENCE THRESHOLD ON DC - can applied to DX,DY,DC
list_image = []
list_image.append(dc)
im_st = im_p.image(list_image)
im_st.histerysisThreshold(confidence,productWorkingDirectory)
dc = im_st.masked_image
dc_mask = im_st.mask_image #BINARY MASK
im_st = None
#UPDATE DC / DC MASK WITH 3 SIGMA THRESHOLD ON DX , DY
im_st = im_p.image([dc,dc_mask])
n_value = 3 #SIGMA VALUE
threshold = confidence #CONFIDENCE VALUE -
log.infog(" --- Applied Sigma Threshold to DX ")
DX_proc_flag = im_st.sigma_threshold(dx,n_value,confidence)
log.infog(" --- Applied Sigma Threshold to DY ")
DY_proc_flag = im_st.sigma_threshold(dy,n_value,confidence)
dc = im_st.masked_image
dc_mask = im_st.mask_image #BINARY MASK
cmd = ' '.join(['mv', im_st.masked_image,cor.dc]) #Ecrase DC - original in "OLD"
os.system(cmd)
im_st = None
print "Exit Processing if cannot applied 3 sigma threshold "
if ((not DX_proc_flag) or (not DY_proc_flag)) :
log.err(" -- Correlation is NOT successfull")
log.err(" -- After 3 Sigma threshold ")
log.err(" -- There is no sufficient point above the confidence threshold ")
log.err(" -- Processing Abort and record not added to the m lab structure ")
return
#APPLIED DC BINARY MASK TO DX, DY
mask = dc_mask
list_image = []
list_image.append(dx)
im_st = im_p.image(list_image)
im_st.maskImage(mask,productWorkingDirectory)
log.info(" -- Create "+im_st.masked_image+' \n')
dx = im_st.masked_image
im_st = None
list_image = []
list_image.append(dy)
im_st = im_p.image(list_image)
im_st.maskImage(mask,productWorkingDirectory)
log.info(" -- Create "+im_st.masked_image+' \n')
dy = im_st.masked_image
im_st = None
log.infog(" -- Mask :"+dc_mask)
log.infog(" -- DX :"+dx)
log.infog(" -- DY :"+dy)
log.infog(" -- DC :"+dc+'\n')
#COMPUTE DX²+ DY² - RADIAL ERROR
list_image = [dx,dy]
dst_file_name = os.path.join(productWorkingDirectory,scene_id+
'_B'+interband.ref_channel+
'_B'+interband.work_channel+
'_radialError_'+
str(np.int(confidence*100))+'.tif',)
im_st = im_p.image(list_image)
if (im_st.computeRadialError(dst_file_name)):
cor.radial_error = dst_file_name
cor.radial_error_valid = True
im_st = None
#STATISTICS DX
#STATISTICS DY
#STATISTICS DC
#-- QL Radial Error - Overlayed Image Band with B1 B2 B3 Image
#Rescale QL Radial Error to size of B1 , B2 , B3
list_image = [interband.workImage]
im1 = cor.radial_error
dst_filename = im1.replace('.tif','_rescale.tif')
im_st = im_p.image(list_image)
im_st.rescaleImage(im1,dst_filename)
cor.radial_error_rescaled = dst_filename
im1 = dc_mask
dst_filename = im1.replace('.TIF','_rescale.tif')
im_st.rescaleImage(im1,dst_filename) #Geometric Rescaling
cor.dc_mask_rescaled = dst_filename
im_st = None
#BURN TIF IMAGES WITH RADIAL ERRORS
#Convert each image to 8 Bits
list_image = []
list_image.append(glob.glob(os.path.join(product,roi,'*B2.TIF'))[0]) # QL Generation
list_image.append(glob.glob(os.path.join(product,roi,'*B3.TIF'))[0]) # QL Generation
list_image.append(glob.glob(os.path.join(product,roi,'*B4.TIF'))[0]) # QL Generation
im_st = im_p.image(list_image)
dst_repo = productWorkingDirectory
output_list = im_st.byte_rescaling(dst_repo )
im_st = None
#Burn each one tif images
list_image = output_list
output_list = []
for image in list_image:
output_list.append(image.replace('.tif','_burn.tif'))
im_st = im_p.image(list_image)
im_filename = cor.radial_error_rescaled #Radial Error Value map to QL
mask_filename = cor.dc_mask_rescaled #DC Mask to select pixel
im_st.burn_image(output_list,im_filename,mask_filename)
im_st = None
#Create the quick look
image_data_list = output_list
ql_name = os.path.join(productWorkingDirectory,scene_id+
'_B'+interband.ref_channel+
'_B'+interband.work_channel+
'_Radial_Error_QL.jpg')
outputdir = productWorkingDirectory
quick_look_resolution = 30
im_st = im_p.image(image_data_list) # Create Object instance
im_st.createimageQuicklook(ql_name,outputdir,quick_look_resolution)
#-- MLAB Production
#result file is matlab file (.mat)
pixelSpacing=interband.ref_pixel_size #Pixel spacing of the orginal Image
#[NFO] Mlab_run - attente des fichiers dx,dy,dc normés
# M=dir([obj.repName filesep '*_dx-displacement.TIF']);
# M=dir([obj.repName filesep '*_dy-displacement.TIF']);
# M=dir([obj.repName filesep '*_dc-confidence.TIF']);
Mlab_run(mtl,
result_mat_file,
productWorkingDirectory,
pixelSpacing)
##TEMPS MININIMUM A 30 BIEN ATTENDRE LA FIN DE TRAITEMENT
print (' ')
log.warn('TIME SLEEP ACTIVIATED - 30 s - Wait end of M LAB PROCESSING')
log.warn('TIME SLEEP ACTIVIATED - 30 s - Important to let Completion')
log.warn('TIME SLEEP ACTIVIATED - 30 s - of the mat file ')
print (' ')
time.sleep(30)
#--- 2. Repatriate BD2 BD3 BD4 Images
#--- 3. Open Images and Burn Values
#--- 4. Create QL
#-- CP to the HTTP
src_directory = productWorkingDirectory
#-- Copy MLAB Results (PNG) to repository
#-- Copy DC mask image (PNG)to http repository
#-- Copy Radial Error image (JPG)to http repository
http.updateContent(productWorkingDirectory,interest,band_type)
#Remove the hdf file as output from medicis
hdf_grille = os.path.join(productWorkingDirectory,'grille.hdf')
if os.path.exists(hdf_grille):
cmd = ' '.join(['rm -f ',hdf_grille])
os.system(cmd)
#[END] of processing the band twin LOOP
log.infog(" - End Loop on WD LIST \n")
#Generate A single stat/csv for all Report WD in this processing
log.infog(" - [STA File] Create STA File : "+result_sta_file)
create_statistics_report(wd_list,result_sta_file)
#PARTIE EXPORT SUR HTTP A SEPARER DE CETTE FONCTION
print ( ' ')
log.infog(" - Populate HTTP \n")
# POPULATE HTTP WITH ALL RESULTS :
#-- MAT FILE :
dst_file_name = ''.join([mtl.landsat_scene_id,'_','roi_',roi_name,'_inter_',band_type,'.mat'])
if not (http.updateContentWith_FILE(result_mat_file,dst_file_name,interest,band_type)):
# log.info(" -- [MAT File] Copy From : "+result_mat_file)
# log.info(" -- [MAT File] to : "+dst_file_name)
# else :
log.err(" -- [ERROR ] Input / Output MAT FILE Missing+'\n' ")
#-- STA FILE :
dst_file_name = ''.join([mtl.landsat_scene_id,'_','roi_',roi_name,'_inter_',band_type,'.txt'])
if not (http.updateContentWith_FILE(result_sta_file,dst_file_name,interest,band_type)):
# log.info(" -- [MAT File] Copy From : "+result_sta_file)
# log.info(" -- [MAT File] to : "+dst_file_name)
# else :
log.err(" - [ERROR ] Input / Output TXT-STA FILE Missing+'\n' ")
#-- QL
listql=[]
listql.append(glob.glob(os.path.join(product,roi,'*B2.TIF'))[0]) # QL Generation
listql.append(glob.glob(os.path.join(product,roi,'*B3.TIF'))[0]) # QL Generation
listql.append(glob.glob(os.path.join(product,roi,'*B4.TIF'))[0]) # QL Generation
image_data_list = listql
ql_file_name = ''.join([mtl.landsat_scene_id,'_','roi_',roi_name,'_bd234.jpg'])
ql_name = os.path.join(interband.main_wd,ql_file_name)
log.info(" -- [QL ] Create ROI QL : "+ql_name)
dst_dir = productWorkingDirectory
quick_look_resolution = 30
outputdir = interband.main_wd
im_st = im_p.image(listql) # Create Object instance
im_st.createimageQuicklook(ql_name,outputdir,quick_look_resolution)
http.updateContentWitQL(infra,interest,band_type,ql_name)
#End of processing each ROI
log.infog(" - [PROC ] End ")
def trigger_interband_processing_with_ms(product, interband):
#Recall Object
infra = main_infra.mainInfra()
mtl = metadata_extraction.LandsatMTL(product)
scene_id = mtl.landsat_scene_id
mtl.set_test_site_information(infra.configuration_site_description_file)
country_name = ((mtl.test_site[0]).split())[0]
site_name = ((mtl.test_site[0]).split())[1]
repo_ref = os.path.join(infra.reference_data_raster_file_location,
country_name, site_name, 'ROI')
#Access to assessment manager ?
band_list = assessmentManager_get_band_list(infra, mtl)
# mtl=None ? cette commande
roi_list = [rec for rec in glob.glob(os.path.join(product, 'ROI*'))]
#http class : interface with web server
http = http_side.performance_report(product)
band_type = interband.band_type
interband.set_main_wd(infra, band_type)
#-- MLAB output file initialisation
file_label = interband.file_label
ext = '.mat'
result_mat_file = interband.get_output_filename(mtl, infra, ext)
#-- Summary Statistisque file_label - same level of mat file but
# just list results in a CSV.
ext = '.txt'
result_sta_file = interband.get_output_filename(mtl, infra, ext)
#-- Loop on all image roi, perform correlation and statistics
for roi in roi_list:
print 'roi ' + roi
interband.roi = roi
roi_name = os.path.basename(roi).split('_')[1]
print ' '
log.infog(' -------------------------')
log.infog(' -- Processing of ' + roi_name + ' -- ')
log.infog(' -------------------------')
mtl = metadata_extraction.LandsatMTL(roi)
mtl.set_test_site_information(
infra.configuration_site_description_file)
mtl.add_roi_name_information(roi_name)
#START Image Matching Loop
wd_list = []
for band_twin in band_combination[band_type]:
interband.set_ref_channel(band_twin)
test1 = interband.search_image_twin(product)
if (interband.ref_channel == '8'
): #Et mission Landsat alors rescaling a 30 m
if interband.ref_pixel_size == 15:
log.infog(' -- Input Image scale is ' +
str(interband.ref_pixel_size) + ' m --')
log.infog(' -- Input Image is rescaled ' +
str(interband.work_pixel_size) + ' m --')
px_size = str(interband.work_pixel_size)
i_file = interband.refImage
o_file = interband.refImage.replace('B8.TIF', 'B8_30.TIF')
if not os.path.exists(o_file):
cmd = ' '.join([
'gdalwarp -tr ', px_size, px_size, i_file, o_file
])
os.system(cmd)
interband.refImage = o_file
src_ds = None