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from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datetime import datetime
from glob import glob
import os
import utm
import rasterio
from tqdm import tqdm
#from xml.etree import ElementTree as et
import xmltodict
##
def cloud_masking(image,cld):
cloud_mask = cld > 30
band_mean = image.mean()
image[cloud_mask] = band_mean
return image
##
def load_file(fp):
"""Takes a PosixPath object or string filepath
and returns np array"""
return np.array(Image.open(fp.__str__()))
def paths (name):
fold_band_10 = glob(name+"/GRANULE/*/IMG_DATA/R10m")[0]
fold_band_20 = glob(name+"/GRANULE/*/IMG_DATA/R20m")[0]
fold_band_60 = glob(name+"/GRANULE/*/IMG_DATA/R60m")[0]
path = name+"/GRANULE/*/IMG_DATA/R10m"+"/*.jp2"
x = glob(path)
lists = x[0].split("/")[-1].split("_")
fixe = lists[0]+'_'+lists[1]
band_10 = ['B02', 'B03', 'B04','B08']
band_20 = ['B05', 'B06', 'B07','B8A','B11', 'B12']
band_60 = ['B01','B09']
images_name_10m = [fixe+"_"+band+"_10m.jp2" for band in band_10 ]
images_name_20m = [fixe+"_"+band+"_20m.jp2" for band in band_20 ]
images_name_60m = [fixe+"_"+band+"_60m.jp2" for band in band_60 ]
#
bandes_path_10 = [os.path.join(fold_band_10,img) for img in images_name_10m]
bandes_path_20 = [os.path.join(fold_band_20,img) for img in images_name_20m]
bandes_path_60 = [os.path.join(fold_band_60,img) for img in images_name_60m]
#
tile_path = name+"/INSPIRE.xml"
path_cld_20 = glob(name+"/GRANULE/*/QI_DATA/MSK_CLDPRB_20m.jp2")[0]
path_cld_60 = glob(name+"/GRANULE/*/QI_DATA/MSK_CLDPRB_60m.jp2")[0]
return bandes_path_10,bandes_path_20,bandes_path_60,tile_path,path_cld_20,path_cld_60
##
def coords_to_pixels(ref, utm, m=10):
""" Convert UTM coordinates to pixel coordinates"""
x = int((utm[0] - ref[0])/m)
y = int((ref[1] - utm[1])/m)
return x, y
##
def timer(message,start_time=None):
if not start_time:
start_time = datetime.now()
return start_time
elif start_time:
thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600)
tmin, tsec = divmod(temp_sec, 60)
print('\n'+message+' Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2)))
##
def extract_sub_image(bandes_path,tile_path,area,resolution=10, d= 3, cld_path = None):
xml_file=open(tile_path,"r")
xml_string=xml_file.read()
python_dict=xmltodict.parse(xml_string)
tile_coordonnates = python_dict["gmd:MD_Metadata"]["gmd:identificationInfo"]["gmd:MD_DataIdentification"]["gmd:abstract"]["gco:CharacterString"].split()
# S2 tile coordonnates
lat,lon = float(tile_coordonnates[0]),float(tile_coordonnates[1])
tile_coordonnate = [lat,lon]
refx, refy, _, _ = utm.from_latlon(tile_coordonnate[0], tile_coordonnate[1])
ax,ay,_,_ = utm.from_latlon(area[1],area[0]) # lat,lon
ref = [refx, refy]
utm_cord = [ax,ay]
x,y = coords_to_pixels(ref,utm_cord,resolution)
images = []
# sub_image_extraction
for band_path in tqdm(bandes_path, total=len(bandes_path)):
start_time_loading = timer('load image',start_time=None)
image = load_file(band_path).astype(np.float32)
start_time_loading = timer('load image',start_time_loading)
if resolution==60:
sub_image = image[y,x]
images.append(sub_image)
else:
sub_image = image[y-d:y+d,x-d:x+d]
images.append(sub_image)
images = np.array(images)
# verify if the study are is cloudy
if cld_path is not None:
cld_mask = load_file(cld_path).astype(np.float32)
cld = cld_mask[y-d:y+d,x-d:x+d]
# cloud removing
images = cloud_masking(images,cld)
if resolution==60:
return images
else:
return images.mean((1,2))
def ndvi(area, tile_name):
"""
polygone: (lon,lat) format
tile_name: name of tile with the most low cloud coverage
"""
#Extract tile coordonnates (lat,long)
tile_path = tile_name+"/INSPIRE.xml"
xml_file=open(tile_path,"r")
xml_string=xml_file.read()
python_dict=xmltodict.parse(xml_string)
tile_coordonnates = python_dict["gmd:MD_Metadata"]["gmd:identificationInfo"]["gmd:MD_DataIdentification"]["gmd:abstract"]["gco:CharacterString"].split()
# S2 tile coordonnates
lat,lon = float(tile_coordonnates[0]),float(tile_coordonnates[1])
tile_coordonnate = [lat,lon]
refx, refy, _, _ = utm.from_latlon(tile_coordonnate[0], tile_coordonnate[1])
ax,ay,_,_ = utm.from_latlon(area[1],area[0]) # lat,lon
ref = [refx, refy]
utm_cord = [ax,ay]
x,y = coords_to_pixels(ref,utm_cord)
# read images
path_4 = tile_name+"/GRANULE/*/IMG_DATA/R10m/*_B04_10m.jp2"
path_8 = tile_name+"/GRANULE/*/IMG_DATA/R10m/*_B08_10m.jp2"
red_object = rasterio.open(glob(path_4)[0])
nir_object = rasterio.open(glob(path_8)[0])
red = red_object.read()
nir = nir_object.read()
red,nir = red[0],nir[0]
# extract area and remove unsigne
sub_red = red[y-3:y+3,x-3:x+3].astype(np.float16)
sub_nir = nir[y-3:y+3,x-3:x+3].astype(np.float16)
# NDVI
ndvi_image = ((sub_nir - sub_red)/(sub_nir+sub_red))
ndvi_mean_value = ndvi_image.mean()
return ndvi_mean_value
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