def validate_footprints(products_geojson, outpath, index, year):
valid_list = []
for feature in products_geojson['features']:
fname = feature['properties']['title']
footprintgeojson = os.path.join(outpath, f'{fname}.geojson')
with open(footprintgeojson, 'w') as geofile:
json.dump(feature['geometry'], geofile)
footprint = gpd.read_file(footprintgeojson)
footprint_32629 = footprint.to_crs("epsg:32629")
footprint_bounds = footprint_32629.bounds
array = footprint_bounds.to_numpy()
footprint_polygon = box(array[0, 0], array[0, 1], array[0, 2],
array[0, 3])
bbox_incendio = getlargebbox(index, year)
polygon_incendio = box(bbox_incendio[0][0], bbox_incendio[0][1],
bbox_incendio[0][2], bbox_incendio[0][3])
# object.contains(other)
# retorna True se nenhum dos pontos do "other" estiverem no exterior do objecto
# e se pelo menos um ponto do interior do "other" estiver no interior do objecto.
verify = footprint_polygon.contains(polygon_incendio)
if verify:
valid_list.append(fname)
geofile.close()
del_file(footprintgeojson)
return valid_list
def getdem(year="2017"):
pathfolder = "Dataset" + str(year)
num_total_subfolders = [folder for folder in os.listdir(pathfolder)]
for i in range(len(num_total_subfolders)):
pathdem = os.path.join(pathfolder, str(i))
bbox_32629 = getlargebbox(i, year)
_dem_raster = heigths(bbox_32629, pathdem)
def drawimages(year="2017"):
full_path = os.getcwd()
os.chdir(Path(full_path).parents[1])
switcheryear = {2017: "AA2017", 2018: "ardida_2018", 2019: "ardida_2019"}
path = os.path.join(switcheryear[int(year)],
switcheryear[int(year)] + ".shp")
pathimage = "Incendios_Completos_" + str(year)
if year == "2018":
sf = shapefile.Reader(path, encoding="ISO8859-1")
else:
sf = shapefile.Reader(path)
var_image = Imagemap()
i = 0
for shape in sf.iterShapeRecords():
# criar path com respetiva pasta
new_path = os.path.join(pathimage, str(i))
var_image.criarpasta(new_path)
# gerar mascaras binarias de cada shape
# bbox aumentada para a forma como vai entrar na rede
large_bbox = getlargebbox(i, year, initialshapefile=True)[0]
# saída do png será de 256*256
Imagemap.getimage(var_image, shape, 2560, 2560, (256, 256), 'shape',
new_path)
# bbox expandida a partir do centro da shape com 2560 metros de largura e altura
box = Imagemap.bbox(shape, 2560, 2560, modify=True)
# georeferenciar em coordenadas (lat/lon) e converter imagem para geotiff
src = os.path.join(new_path, "shape.png")
dataset = rasterio.open(src, 'r+')
bands = 1
data = dataset.read(bands)
transform = rasterio.transform.from_bounds(box[0], box[1], box[2],
box[3], 256, 256)
dst = os.path.join(pathimage, str(i), "shape.tif")
with rasterio.open(dst,
'w',
driver='GTiff',
width=256,
height=256,
count=bands,
dtype=data.dtype,
nodata=0,
transform=transform,
crs='EPSG:4326') as dst:
dst.write(data, indexes=bands)
# warp para sistema de coordenadas 32629 com 10m por pixel e com tamanho (width e height)
# correcto para entrar na rede
input_raster = gdal.Open(os.path.join(pathimage, str(i), "shape.tif"))
output_raster = os.path.join(pathimage, str(i), "shape32629.tif")
_warp = gdal.Warp(srcDSOrSrcDSTab=input_raster,
destNameOrDestDS=output_raster,
format="GTiff",
outputBounds=[
large_bbox[0], large_bbox[1], large_bbox[2],
large_bbox[3]
],
xRes=10,
yRes=10,
dstSRS='EPSG:32629')
_warp = None
print("Gerado imagem do incendio", format(i))
i += 1
def cropsentinel(year="2017"):
pathdataset = "Dataset" + str(year)
pathproducts = r"Sentinel_Products"
if not os.path.exists(pathproducts):
sys.exit(
"A pasta de produtos não está presente no caminho especificado!")
if len(next(os.walk(pathproducts))[2]) == 0:
sys.exit("A pasta de produtos está vazia!")
num_total_subfolders = [folder for folder in os.listdir(pathdataset)]
for i in range(len(num_total_subfolders)):
pathimages = os.path.join(pathdataset, str(i), "Sentinel_Images")
try:
if not os.path.exists(pathimages):
os.makedirs(pathimages)
if os.path.exists(pathimages):
shutil.rmtree(pathimages)
os.makedirs(pathimages)
except OSError:
print("Erro: Ao criar a pasta " + pathimages)
# para ignorar incêndios que não têm nenhum produto Sentinel associado
empty_filename = os.path.join(pathdataset, str(i), "Sem_Produtos.txt")
my_file_index = Path(empty_filename)
if my_file_index.is_file():
continue
bbox = getlargebbox(i, year)
filetxtpath = os.path.join(pathdataset, str(i), "Final_Product.txt")
match = False
# o ficheiro txt só tem um produto, o que é suposto, se tivesse mais que um produto este conjunto seguinte
# de comandos iria seleccionar o ficheiro zip cujo nome fosse igual ao ultimo produto (linha) presente no txt!!!
with open(filetxtpath, "r") as txt_file:
for line in txt_file:
line = line.rstrip('\n')
for zip_file in os.listdir(pathproducts):
if re.match(f"{line}.zip", zip_file):
zipname = line + ".zip"
match = True
txt_file.close()
if not match:
sys.exit("O produto associado ao incêndio " + f"{i}" + " do ano " +
year + " não está presente na pasta Produtos_Sentinel!")
zip_f = os.path.join(pathproducts, zipname)
zf = ZipFile(zip_f, 'r')
zf.extractall(pathproducts)
zf.close()
extracted = os.path.join(pathproducts, line + ".SAFE")
itr_folder = iter(os.walk(os.path.join(extracted, "GRANULE")))
root, dirs, files = next(itr_folder)
auxpath = os.path.join(os.path.join(extracted, "GRANULE"), dirs[0],
"IMG_DATA")
resolutions = [10, 20, 60]
for k in range(len(resolutions)):
finalpath = os.path.join(auxpath, f"R{resolutions[k]}m")
for file in os.listdir(finalpath):
channel = file.rsplit("_", 2)[1]
list_already_done = os.listdir(pathimages)
verify = any(channel in image for image in list_already_done)
if verify: # se já foi feito o crop com menor resolução para esse canal passa à frente
continue
else:
input_image = os.path.join(finalpath, file)
outpath = os.path.join(pathimages, file[:-8] + ".tif")
sentinelclip(bbox, input_image, outpath)
shutil.rmtree(extracted)
def drawrastershapes(year="2017"):
pathfolder = "Dataset" + str(year)
num_total_subfolders = [folder for folder in os.listdir(pathfolder)]
# Definir tamanho do pixel e o valor no_data para o novo raster
pixel_size = 10
NoData_value = 0
# iterar
for i in range(len(num_total_subfolders)):
# Path para os shapefiles individuais
pathshapefile = os.path.join(pathfolder, str(i), "ShapeIndividual",
"Shape" + str(year[-2:]) + ".shp")
# Nome para os ficheiros raster
pathraster = os.path.join(pathfolder, str(i))
raster_fn = os.path.join(pathraster, "shapebboxincendio.tif")
raster_fn2 = os.path.join(pathraster, "shapebboxaumentada.tif")
# Abrir dados do shp e obter a extensao do shapefile
source_ds = ogr.Open(pathshapefile)
source_layer = source_ds.GetLayer()
xmin, xmax, ymin, ymax = source_layer.GetExtent()
aux_var = getlargebbox(i, year)
box = aux_var[0]
xmin_large = box[0]
xmax_large = box[2]
ymax_large = box[3]
ymin_large = box[1]
# Obter a projeção do raster
epsg = 32629
srs = osr.SpatialReference()
srs.ImportFromEPSG(epsg)
dest_wkt = srs.ExportToWkt()
# Criar os dados de destino
x_res = round((xmax - xmin) / pixel_size)
y_res = round((ymax - ymin) / pixel_size)
target_ds = gdal.GetDriverByName('GTiff').Create(
raster_fn, x_res, y_res, 1, gdal.GDT_Byte)
# Criar os dados de destino
x_res_large = round((xmax_large - xmin_large) / pixel_size)
y_res_large = round((ymax_large - ymin_large) / pixel_size)
target_ds_large = gdal.GetDriverByName('GTiff').Create(
raster_fn2, x_res_large, y_res_large, 1, gdal.GDT_Byte)
# Atribuir projeção
target_ds.SetProjection(dest_wkt)
target_ds_large.SetProjection(dest_wkt)
target_ds.SetGeoTransform((xmin, pixel_size, 0, ymax, 0, -pixel_size))
target_ds_large.SetGeoTransform(
(xmin_large, pixel_size, 0, ymax_large, 0, -pixel_size))
band = target_ds.GetRasterBand(1)
band.SetNoDataValue(NoData_value)
band2 = target_ds_large.GetRasterBand(1)
band2.SetNoDataValue(NoData_value)
gt = [xmin, pixel_size, 0, ymax, 0, -pixel_size]
target_ds.SetGeoTransform(gt)
gt2 = [xmin_large, pixel_size, 0, ymax_large, 0, -pixel_size]
target_ds_large.SetGeoTransform(gt2)
# Rasterizar
gdal.RasterizeLayer(target_ds, [1],
source_layer,
burn_values=[255],
options=["-tr 10 10"])
gdal.RasterizeLayer(target_ds_large, [1],
source_layer,
burn_values=[255],
options=["-tr 10 10"])
# Fechar ficheiro
_target_ds = None
_target_ds_large = None