try:
os.remove(r"test_outputs/composite_test.tif")
except FileNotFoundError:
pass
test_data = [
r"test_data/20180103T172709.tif", r"test_data/20180319T172021.tif",
r"test_data/20180329T171921.tif"
]
out_file = r"test_outputs/composite_test.tif"
pyeo.composite_images_with_mask(test_data, out_file)
def test_ml_masking():
os.chdir(os.path.dirname(os.path.abspath(__file__)))
pyeo.create_mask_from_model(
r"test_data/20180103T172709.tif",
r"test_data/cloud_model_v0.1.pkl",
)
shutil.copy(r"test_data/20180103T172709.msk",
r"test_outputs/model_mask.tif")
if __name__ == "__main__":
print(sys.path)
os.chdir(os.path.dirname(os.path.abspath(__file__)))
log = pyeo.init_log("test_log.log")
pyeo.create_model_from_signatures(
r"test_data/cloud_training_outlier_removal.csv",
r"test_data/cloud_model_v0.1.pkl")
test_ml_masking()
conf.read(args.config_path)
sen_user = conf['sent_2']['user']
sen_pass = conf['sent_2']['pass']
project_root = conf['forest_sentinel']['root_dir']
aoi_path = conf['forest_sentinel']['aoi_path']
start_date = conf['forest_sentinel']['start_date']
end_date = conf['forest_sentinel']['end_date']
log_path = conf['forest_sentinel']['log_path']
cloud_cover = conf['forest_sentinel']['cloud_cover']
cloud_certainty_threshold = int(
conf['forest_sentinel']['cloud_certainty_threshold'])
model_path = conf['forest_sentinel']['model']
sen2cor_path = conf['sen2cor']['path']
pyeo.create_file_structure(project_root)
log = pyeo.init_log(log_path)
l1_image_path = os.path.join(project_root, r"images/L1")
l2_image_path = os.path.join(project_root, r"images/L2")
planet_image_path = os.path.join(project_root, r"images/planet")
merged_image_path = os.path.join(project_root, r"images/merged")
stacked_image_path = os.path.join(project_root, r"images/stacked")
catagorised_image_path = os.path.join(project_root, r"output/categories")
probability_image_path = os.path.join(project_root,
r"output/probabilities")
# Query and download
if args.do_download or do_all:
products = pyeo.check_for_s2_data_by_date(aoi_path, start_date,
end_date, conf)
log.info("Downloading")
if __name__ == "__main__":
# Reading in config file
parser = argparse.ArgumentParser(
description='Compare old_image with new_image using model')
parser.add_argument("old_image")
parser.add_argument("new_image")
parser.add_argument("model")
parser.add_argument("output")
parser.add_argument("-l",
"--log_path",
default=os.path.join(os.getcwd(), "comparison.log"))
parser.add_argument("-c", "--chunks", default=16)
parser.add_argument("-m", "--mask", action="store_true")
args = parser.parse_args()
log = pyeo.init_log(args.log_path)
with TemporaryDirectory() as td:
stacked_path = os.path.join(td, "stacked.tif")
pyeo.stack_images([args.old_image, args.new_image],
stacked_path,
geometry_mode="intersect")
pyeo.classify_image(stacked_path,
args.model,
args.output,
prob_out_path=None,
num_chunks=args.chunks,
apply_mask=args.mask)
def setup_module():
os.chdir(os.path.dirname(os.path.abspath(__file__)))
pyeo.init_log("test_log.log")
dest='config_path',
action='store',
default=r'model_creation.ini',
help="Path to the .ini file specifying the job.")
args = parser.parse_args()
conf = configparser.ConfigParser()
conf.read(args.config_path)
training_shape_path = conf["pyeo"]["shapefile"]
training_raster_path = conf["pyeo"]["raster"]
model_out_path = conf["pyeo"]["model"]
class_field = conf["pyeo"]["class_field"]
log_path = conf["pyeo"]["log_path"]
log = pc.init_log(log_path)
# This will be changed in the near future as I'm planning to refactor core soon
# to make the ML model building functions more granular
learning_data, classes = pc.get_training_data(training_shape_path,
training_raster_path,
class_field)
model = ens.ExtraTreesClassifier(bootstrap=False,
criterion="gini",
max_features=0.55,
min_samples_leaf=2,
min_samples_split=16,
n_estimators=100,
n_jobs=4,
class_weight='balanced')
model.fit(learning_data, classes)
import os, sys
sys.path.insert(
0, os.path.abspath(os.path.join(__file__, '..', '..', '..', '..')))
import argparse
import pyeo.core as pyeo
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description=
"Removes any SAFEfile in directory without the 2,3,4 or 8m imagery"
"in the 10m folder")
parser.add_argument("l2_dir",
help="Path to the directory that needs cleaning")
parser.add_argument("-d",
"--disable_warning",
dest="do_warning",
action='store_false',
default=True,
help="If present, do not prompt before removing files")
parser.add_argument("-r",
"--resolution",
dest="resolution",
action="store",
default="10m",
help="Resolution to check (10m, 20m or 60m")
args = parser.parse_args()
pyeo.init_log("clean_log.log")
pyeo.clean_l2_dir(args.l2_dir, args.resolution, args.do_warning)
import sys, os
sys.path.insert(0, os.path.abspath(os.path.join(__file__, '..', '..', '..', '..')))
import pyeo.core as pyeo
import configparser
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Produces a trained model from a filder containing '
'rasters and shapefiles')
parser.add_argument("region_path", type=str, action='store',
help="Path to the folder containing the region data")
parser.add_argument("training_class", type=str, action='store',
help="Attribute field holding the class to train on")
parser.add_argument("out_path", type=str, action="store",
help="Path for the output .pkl file")
args = parser.parse_args()
conf = configparser.ConfigParser()
log = pyeo.init_log("model.log")
log.info("***MODEL CREATION START***")
pyeo.create_model_for_region(args.region_path, args.out_path,
args.training_class.rsplit('.')[0]+"_scores.txt",
args.training_class)
log.info("***MODEL CREATION END***")