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from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
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 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)):
image = load_file(band_path).astype(np.float32)
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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