Newer
Older
import os
import json
import pickle
from Workflows.operations import preprocess_s2, run_segmentation
from Learning.ObjectBased import ObjectBasedClassifier
from Postprocessing import Report, MapFormatting
def unroll_file_list(lst):
out_lst = []
for f in lst:
out_lst.extend(glob.glob(f))
return out_lst
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
def process_timeseries(oroot, d, ts_lst_pkl):
ts_lst = []
for ts in d['timeseries']:
print('[MORINGA-INFO] : Preprocessing {} from {}'.format(ts['type'], ts['provider']))
if ts['type'] == 's2':
ots = os.path.join(oroot, 'timeseries/' + ts['type'] + ts['provider'])
os.makedirs(ots, exist_ok=True)
ts_lst.append(preprocess_s2(ts['path'],
ots,
roi=d['roi'],
output_dates_file=ts['output_dates_file'],
provider=ts['provider']))
else:
raise ValueError('TimeSeries type not yet supported.')
with open(ts_lst_pkl, 'wb') as ts_save:
pickle.dump(ts_lst, ts_save)
return
def perform_segmentation(ofn, d):
print('[MORINGA-INFO] : Performing segmentation')
os.makedirs(os.path.dirname(ofn), exist_ok=True)
run_segmentation(d['segmentation']['src'],
d['segmentation']['th'],
d['segmentation']['cw'],
d['segmentation']['sw'],
ofn,
n_first_iter=d['segmentation']['n_first_iter'],
margin=d['segmentation']['margin'],
roi=d['roi'],
n_proc=d['segmentation']['n_proc'],
light=d['segmentation']['lightmode'])
return
def train_valid_workflow(seg, ts_lst_pkl, d, m_file):
assert (os.path.exists(seg))
assert (os.path.exists(ts_lst_pkl))
print('[MORINGA-INFO] : Running Training/Validation Workflow')
with open(ts_lst_pkl, 'rb') as ts_save:
ts_lst = pickle.load(ts_save)
obc = ObjectBasedClassifier(seg,
ts_lst,
unroll_file_list(d['userfeat']),
reference_data=d['ref_db']['path'],
ref_class_field=d['ref_db']['fields'])
obc.gen_k_folds(5, class_field=d['ref_db']['fields'][-1])
for i,cf in enumerate(d['ref_db']['fields']):
if d['training']['classifier'] == 'rf':
m, s, r = obc.train_RF(d['training']['parameters']['n_trees'], class_field=cf, return_true_vs_pred=True)
m_dict = {'model': m, 'results': r, 'summary': s,
'perc2':obc.training_base['perc2'], 'perc98':obc.training_base['perc98']}
os.makedirs(os.path.dirname(m_file[i]), exist_ok=True)
with open(m_file[i], 'wb') as mf:
pickle.dump(m_dict, mf)
return
def classify(seg, ts_lst_pkl, m_files, d, map_files):
assert (os.path.exists(seg))
assert (os.path.exists(ts_lst_pkl))
for m_file in m_files:
assert (os.path.exists(m_file))
print('[MORINGA-INFO] : Performing classification')
with open(ts_lst_pkl, 'rb') as ts_save:
ts_lst = pickle.load(ts_save)
obc = ObjectBasedClassifier(seg,
ts_lst,
unroll_file_list(d['userfeat']))
for m_file, map_file in zip(m_files, map_files):
with open(m_file, 'rb') as mf:
m_dict = pickle.load(mf)
obc.classify(m_dict['model'], perc=[m_dict['perc2'], m_dict['perc98']], output_file=map_file)
return
def report(map_files, m_files, d, report_files):
print('[MORINGA-INFO] : Generating report(s)')
for map_file, palette_fn, m_file, report_file in zip(map_files, d['map_output']['palette_files'], m_files, report_files):
assert os.path.exists(map_file)
assert os.path.exists(m_file)
os.makedirs(os.path.splitext(report_file)[0]+'_figures', exist_ok=True)
with open(m_file, 'rb') as mf:
m_dict = pickle.load(mf)
of = Report.generate_report_figures(
map_file,
palette_fn,
m_dict['results'],
m_dict['summary'],
os.path.splitext(report_file)[0]+'_figures',
d['chain_name'])
Report.generate_pdf(of, report_file, d['chain_name'])
return
def basic(cfg, runlevel=1, single_step=False):
os.environ['OTB_LOGGER_LEVEL'] = 'CRITICAL'
with open(cfg,'r') as f:
d = json.load(f)
oroot = os.path.join(d['output_path'], d['chain_name'])
oside = os.path.join(oroot, '_side')
os.makedirs(oside, exist_ok=True)
step = runlevel
# Preprocess timeseries
ts_lst_pkl = os.path.join(oside, 'time_series_list.pkl')
print("[MORINGA-INFO] : ***** BEGIN STEP {} *****".format(step))
process_timeseries(oroot, d, ts_lst_pkl)
step += 1
if single_step:
return
# Segmentation
seg = os.path.join(oroot, 'segmentation/{}_obj_layer.tif'.format(d['chain_name']))
if step == 2:
print("[MORINGA-INFO] : ***** BEGIN STEP {} *****".format(step))
perform_segmentation(seg, d)
step += 1
if single_step:
return
# Training/Validation Workflow
for cf in d['ref_db']['fields']:
m_files.append(os.path.join(oroot, 'model/model_{}.pkl'.format(cf)))
print("[MORINGA-INFO] : ***** BEGIN STEP {} *****".format(step))
train_valid_workflow(seg, ts_lst_pkl, d, m_files)
step += 1
if single_step:
return
# Classification
map_files = []
for cf in d['ref_db']['fields']:
map_files.append(os.path.join(oroot, 'maps/{}_map_{}.tif'.format(d['chain_name'],cf)))
if step == 4:
print("[MORINGA-INFO] : ***** BEGIN STEP {} *****".format(step))
classify(seg, ts_lst_pkl, m_files, d, map_files)
for m,p in zip(map_files, d['map_output']['palette_files']):
MapFormatting.create_qgs_style(m,p)
step += 1
if single_step:
return
# Report
report_fn = []
for cf in d['ref_db']['fields']:
report_fn.append(os.path.join(oroot, 'reports/{}_report_{}.pdf'.format(d['chain_name'],cf)))
if step == 5:
print("[MORINGA-INFO] : ***** BEGIN STEP {} *****".format(step))
report(map_files, m_files, d, report_fn)
print("[MORINGA-INFO] : ***** PROCESS FINISHED *****".format(step))