diff --git a/Learning/ObjectBased.py b/Learning/ObjectBased.py
index 46c459e8a4dd697f0e7ad827eeff77f79b957382..1fa7787723341ec8e7e831372e958d22c568cad4 100644
--- a/Learning/ObjectBased.py
+++ b/Learning/ObjectBased.py
@@ -8,8 +8,8 @@ from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, cohen_kappa_score, precision_recall_fscore_support
class ObjectBasedClassifier:
- def __init__(self, object_layer, reference_data, time_series_patterns, user_feature_list):
- self.obia_base = OBIABase(object_layer, ref_data = reference_data)
+ def __init__(self, object_layer, reference_data, time_series_patterns, user_feature_list, ref_class_field='class'):
+ self.obia_base = OBIABase(object_layer, ref_data = reference_data, ref_class_field=ref_class_field)
for ptrn in time_series_patterns:
lst = sorted(glob.glob(ptrn))
self.obia_base.add_raster_time_series_for_stats(lst)
@@ -113,20 +113,19 @@ class ObjectBasedClassifier:
#TEST CODE
def run_test():
- '''
obc = ObjectBasedClassifier('/DATA/Moringa_Sample/Parakou/output/segmentation/segmentation.tif',
'/DATA/Moringa_Sample/Parakou/input/REF/ref.shp',
['/DATA/Moringa_Sample/Parakou/output/S2_processed/T31PDL/*/*FEAT.tif'],
['/DATA/Moringa_Sample/Parakou/input/THR/THR_SPOT6.tif'])
'''
- print('Initializing the OBIA training base...')
obc = ObjectBasedClassifier('/DATA/Benin/OBSYDYA_data/MORINGA/SEGMENTATION/segmentation.tif',
'/DATA/Benin/OBSYDYA_data/MORINGA/reference/BD_OBSYDYA_2022_ParakouNdali_v0.2.shp',
['/DATA/Benin/OBSYDYA_data/MORINGA/basefolder/FEAT/S2_THEIA_FEAT/S2_THEIA_MOSAIC_*.tif'],
glob.glob('/DATA/Benin/OBSYDYA_data/MORINGA/ext_features'))
+ '''
obc.gen_k_folds(5)
print('Performing Training and Cross-Validation...')
- m,s,r = obc.train_RF(400)
+ m,s,r = obc.train_RF(100)
print(s)
print('Performing Classification...')
obc.classify(m)
diff --git a/OBIA/OBIABase.py b/OBIA/OBIABase.py
index ff0bd8d8f78e1a9a116e2411c1190b0a1f9ee6be..3e264e49b4b97bd6b7c715037f5a1c9e434445ef 100644
--- a/OBIA/OBIABase.py
+++ b/OBIA/OBIABase.py
@@ -9,6 +9,7 @@ from enum import Enum
from Common.otb_numpy_proc import to_otb_pipeline
from skimage.measure import regionprops, label
import otbApplication as otb
+from tqdm import tqdm
class OStats(Enum):
MEAN = 'mean'
@@ -88,7 +89,7 @@ class OBIABase:
columns=['area', 'orig_label', 'polygon_id', 'class'],
index=[])
r = otb.itkRegion()
- for tn, t in self.tiles.items():
+ for tn, t in tqdm(self.tiles.items(), desc='Init. Ref. DB', total=len(self.tiles)):
r['index'][0], r['index'][1] = t[0], t[1]
r['size'][0], r['size'][1] = t[2], t[3]
ref_ol.PropagateRequestedRegion('out', r)
@@ -99,9 +100,9 @@ class OBIABase:
self.ref_db = pd.concat([
self.ref_db,
pd.DataFrame(
- data=[np.insert(o.intensity_min, 0, o.area) for o in rp],
+ data=[np.insert(o.intensity_min, 0, o.area) for o in rp if self.obj_to_tile[o.label] == tn],
columns=self.ref_db.columns,
- index=[o.label for o in rp]
+ index=[o.label for o in rp if self.obj_to_tile[o.label] == tn]
)]
)
@@ -112,19 +113,22 @@ class OBIABase:
W, H = in_seg.GetImageSize('out')
r = otb.itkRegion()
obj_bbox = {}
- for i in range(0, W, nominal_tile_size[0]):
- for j in range(0, H, nominal_tile_size[1]):
- r['index'][0], r['index'][1] = i, j
- r['size'][0], r['size'][1] = min(nominal_tile_size[0], W-i), min(nominal_tile_size[1], H-j)
- in_seg.PropagateRequestedRegion('out', r)
- tile = in_seg.GetImageAsNumpyArray('out')
- rp = regionprops(tile.astype(np.uint32))
- for o in rp:
- bbox = np.array([o.bbox[0]+j, o.bbox[1]+i, o.bbox[2]+j, o.bbox[3]+i])
- if o.label in obj_bbox.keys():
- obj_bbox[o.label].append(bbox)
- else:
- obj_bbox[o.label] = [bbox]
+ rw, rh = range(0, W, nominal_tile_size[0]), range(0, H, nominal_tile_size[1])
+ with tqdm(total=len(rw)*len(rh), desc='Gen. tile info') as pb:
+ for i in rw:
+ for j in rh:
+ r['index'][0], r['index'][1] = i, j
+ r['size'][0], r['size'][1] = min(nominal_tile_size[0], W-i), min(nominal_tile_size[1], H-j)
+ in_seg.PropagateRequestedRegion('out', r)
+ tile = in_seg.GetImageAsNumpyArray('out')
+ rp = regionprops(tile.astype(np.uint32))
+ for o in rp:
+ bbox = np.array([o.bbox[0]+j, o.bbox[1]+i, o.bbox[2]+j, o.bbox[3]+i])
+ if o.label in obj_bbox.keys():
+ obj_bbox[o.label].append(bbox)
+ else:
+ obj_bbox[o.label] = [bbox]
+ pb.update(1)
for o in obj_bbox.keys():
obj_bbox[o] = np.array(obj_bbox[o])
@@ -252,7 +256,7 @@ class OBIABase:
def populate_ref_db(self):
assert(self.ref_db is not None)
- for t in self.tiles.keys():
+ for t in tqdm(self.tiles.keys(), desc="Comp. zonal stats on DB", total=len(self.tiles)):
for rf, rs, rv in zip(self.raster_files, self.raster_stats, self.raster_var_names):
k, v = self.compute_stats_on_tile(t, rf, rs, on_ref=True)
if k is not None: