def test_example(self):
self.assertEqual("0100101111101110", compress(example_image))
def test_do(self):
img = numpy.array([[0]])
self.assertEqual("10", compress(img))
img = numpy.array([[1]])
self.assertEqual("11", compress(img))
def test(self):
self.assertEqual(main.compress([]), [])
self.assertEqual(main.compress([1, 2, 3, 1, 4, 5]), [1, 2, 3, 1, 4, 5])
self.assertEqual(
main.compress([1, 1, 1, 1, 2, 3, 3, 1, 1, 4, 5, 5, 5, 5]),
[1, 2, 3, 1, 4, 5])
def main(InputFolder, OutputFolder, DEM, _6S, pansharpen, pyramid, pid, cache,
InputFile_list):
if InputFolder:
files = glob.glob(join(InputFolder, "GF*"))
elif InputFile_list:
files = InputFile_list
else:
raise ValueError("Please specify --InputFolder or --InputFile_list")
os.environ["GDAL_CACHEMAX"] = cache
# Batch preprocess begins
for i, file in enumerate(files):
print("进程%d: 处理第%d景数据中，共%d景数据" % (pid, i + 1, len(files)))
try:
# Extract archive
if ".tar.gz" in file:
# Extract to current directory
datapath = file.replace(".tar.gz", "")
try:
untar(file, datapath)
except:
continue
else:
datapath = file
# Get spectral rasters and pancromatic rasters
M, P = get_rasters_name(datapath)
# RPC Orthorectification for spectral rasters
m_out_list = list()
for m in M:
try:
m_in = join(datapath, m)
m_out = join(OutputFolder, m)
rpc_ortho(m_in, DEM, m_out)
temp = correction(m_out).radiometric()
# Compress and delete temp file
compress(temp, m_out)
m_out_list.append(m_out)
except:
continue
# Mosaic GF6_WFV-1,2,3
if "GF6_WFV" in M[0]:
m = join(OutputFolder, M[0])
mosaic_file = re.sub("-[0-9].tiff", "-mosaic.tiff", m)
vrt = re.sub("-[0-9].tiff", ".vrt", m)
txt = re.sub("-[0-9].tiff", ".txt", m)
[save_txt(txt, m_out) for m_out in m_out_list]
mosaic(mosaic_file, txt, vrt)
[os.remove(m_out) for m_out in m_out_list]
# 6S atmospheric correction
if _6S:
if "GF6_WFV" in M[0]:
metadata = re.sub("-[0-9].tiff", ".xml",
join(datapath, M[0]))
temp = correction(mosaic_file, metadata).atmospheric()
compress(temp, mosaic_file)
else:
for m_out in m_out_list:
metadata = re.sub(
".tiff", ".xml",
join(datapath, os.path.basename(m_out)))
temp = correction(m_out, metadata).atmospheric()
compress(temp, m_out)
# Pansharpening
if pansharpen and len(P) > 0:
fusion_list = list()
for p in P:
p_in = join(datapath, p)
p_out = join(OutputFolder, p)
rpc_ortho(p_in, DEM, p_out)
temp = correction(p_out).radiometric()
compress(temp, p_out)
fusion = p_out.replace("PAN", "FUS")
fusion_list.append(fusion)
# Trying to find corresponding spectral dataset
m = glob.glob(
p_out.rsplit("PAN", 1)[0] + "M*" +
p_out.rsplit("PAN", 1)[1])[0]
pansharpening(m, p_out, fusion)
os.remove(m)
os.remove(p_out)
# Build pyramid, we only build for the final raster
if pyramid:
if "GF6_WFV" in M[0]:
build_pyramid(mosaic_file)
elif pansharpen and len(P) > 0:
[build_pyramid(fusion) for fusion in fusion_list]
else:
[build_pyramid(m_out) for m_out in m_out_list]
except:
continue
def test_compress(self):
self.assertEqual(main.compress("test"), 81.25)