diff --git a/Learning/ObjectBased.py b/Learning/ObjectBased.py
index 1fa7787723341ec8e7e831372e958d22c568cad4..aa7aa9636c2bdbe71b5364b66050ac91d2a8ea59 100644
--- a/Learning/ObjectBased.py
+++ b/Learning/ObjectBased.py
@@ -94,7 +94,7 @@ class ObjectBasedClassifier:
}
return models, summary, results
- def classify(self, model):
+ def classify(self, model, output_file=None, compress='NONE'):
if isinstance(model, list):
for t, L, X in self.obia_base.tiled_data(normalize=[self.training_base['perc2'],
self.training_base['perc98']]):
@@ -103,12 +103,12 @@ class ObjectBasedClassifier:
prob.append(m.predict_proba(X))
prob = np.prod(prob, axis=0)
c = model[0].classes_[np.argmax(prob, axis=1)]
- self.obia_base.populate_map(t, L, c)
+ self.obia_base.populate_map(t, L, c, output_file, compress)
else:
for t,L,X in self.obia_base.tiled_data(normalize=[self.training_base['perc2'],
self.training_base['perc98']]):
c = model.predict(X)
- self.obia_base.populate_map(t, L, c)
+ self.obia_base.populate_map(t, L, c, output_file, compress)
return
#TEST CODE
@@ -128,6 +128,5 @@ def run_test():
m,s,r = obc.train_RF(100)
print(s)
print('Performing Classification...')
- obc.classify(m)
- obc.obia_base.export_raster_map('/DATA/Benin/OBSYDYA_data/MORINGAv2/firstmap.tif')
+ obc.classify(m, '/DATA/Benin/OBSYDYA_data/MORINGAv2/firstmap.tif')
return obc
diff --git a/OBIA/OBIABase.py b/OBIA/OBIABase.py
index 3e264e49b4b97bd6b7c715037f5a1c9e434445ef..a5e33f726873bf3fb35d1c24d8295a44694b5efe 100644
--- a/OBIA/OBIABase.py
+++ b/OBIA/OBIABase.py
@@ -1,6 +1,7 @@
import os.path
import sys
import rasterio as rio
+from rasterio.windows import Window
from rasterio.features import shapes
import numpy as np
import geopandas as gpd
@@ -27,6 +28,9 @@ class OBIABase:
ref_data=None, ref_id_field='id', ref_class_field='class',
nominal_tile_size=5000, background_label=0):
self.object_layer = object_layer
+ with rio.open(self.object_layer, 'r') as src:
+ self.crs = src.crs
+ self.transform = src.transform
self.background_label = background_label
if type(nominal_tile_size) is tuple:
self.nominal_tile_size = nominal_tile_size
@@ -110,15 +114,15 @@ class OBIABase:
def generate_tile_info(self, nominal_tile_size, background_label):
in_seg = to_otb_pipeline(self.object_layer)
- W, H = in_seg.GetImageSize('out')
+ self.W, self.H = in_seg.GetImageSize('out')
r = otb.itkRegion()
obj_bbox = {}
- rw, rh = range(0, W, nominal_tile_size[0]), range(0, H, nominal_tile_size[1])
+ rw, rh = range(0, self.W, nominal_tile_size[0]), range(0, self.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)
+ r['size'][0], r['size'][1] = min(nominal_tile_size[0], self.W-i), min(nominal_tile_size[1], self.H-j)
in_seg.PropagateRequestedRegion('out', r)
tile = in_seg.GetImageAsNumpyArray('out')
rp = regionprops(tile.astype(np.uint32))
@@ -135,7 +139,7 @@ class OBIABase:
mn, mx = np.min(obj_bbox[o], axis=0), np.max(obj_bbox[o], axis=0)
obj_bbox[o] = [mn[0], mn[1], mx[2], mx[3]]
- tx, ty = int(W / nominal_tile_size[0]) + 1, int(H / nominal_tile_size[1]) + 1
+ tx, ty = int(self.W / nominal_tile_size[0]) + 1, int(self.H / nominal_tile_size[1]) + 1
obj_to_tile = {}
tiles = dict.fromkeys(range(tx * ty))
@@ -154,8 +158,6 @@ class OBIABase:
tiles = {k: [v[0],v[1],v[2]-v[0],v[3]-v[1]] for k,v in tiles.items() if not np.isinf(v[0])}
- self.W, self.H = W, H
-
return tiles, obj_to_tile
def compute_stats_on_tile(self, tile_num, input_image, stats=[OStats.MEAN], on_ref=False):
@@ -256,7 +258,7 @@ class OBIABase:
def populate_ref_db(self):
assert(self.ref_db is not None)
- for t in tqdm(self.tiles.keys(), desc="Comp. zonal stats on DB", total=len(self.tiles)):
+ for t in tqdm(self.tiles.keys(), desc="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:
@@ -307,9 +309,7 @@ class OBIABase:
X = (X - normalize[0]) / (normalize[1] - normalize[0])
yield tilenum,L,X
- def populate_map(self, tilenum, obj_id, classes):
- if self.output_map is None:
- self.output_map = np.zeros((self.H,self.W), dtype=np.int)
+ def populate_map(self, tilenum, obj_id, classes, output_file=None, compress='NONE'):
r = otb.itkRegion()
r['index'][0], r['index'][1], r['size'][0], r['size'][1] = self.tiles[tilenum]
obj = to_otb_pipeline(self.object_layer)
@@ -318,8 +318,21 @@ class OBIABase:
tile_obj = np.squeeze(clip_obj['array']).astype(np.uint32)
tmp = np.zeros(np.max(tile_obj) + 1, dtype=int)
tmp[obj_id] = classes
- self.output_map[self.tiles[tilenum][1]:self.tiles[tilenum][1]+self.tiles[tilenum][3],
- self.tiles[tilenum][0]:self.tiles[tilenum][0]+self.tiles[tilenum][2]] += tmp[tile_obj]
+ if output_file is None:
+ if self.output_map is None:
+ self.output_map = np.zeros((self.H,self.W), dtype=np.int)
+ self.output_map[self.tiles[tilenum][1]:self.tiles[tilenum][1]+self.tiles[tilenum][3],
+ self.tiles[tilenum][0]:self.tiles[tilenum][0]+self.tiles[tilenum][2]] += tmp[tile_obj]
+ else:
+ if tilenum == 0 and os.path.exists(output_file):
+ os.remove(output_file)
+ mode = 'w+' if not os.path.exists(output_file) else 'r+'
+ with rio.open(output_file, mode, driver='GTiff', width=self.W, height=self.H, crs=self.crs,
+ transform=self.transform, count=1, dtype=np.int16, compress=compress) as dst:
+ win = Window(self.tiles[tilenum][0], self.tiles[tilenum][1],
+ self.tiles[tilenum][2], self.tiles[tilenum][3])
+ x = dst.read(1, window=win)
+ dst.write(x + tmp[tile_obj], window=win, indexes=1)
return
def export_raster_map(self, output_file, compress=False):
@@ -336,6 +349,10 @@ class OBIABase:
obj.SetParameterOutputImagePixelType('out', otb.ImagePixelType_uint16)
obj.ExecuteAndWriteOutput()
+ def populate_export_raster_map(self, obj_id, classes, output_file, compress='NONE'):
+ for tn in tqdm(self.tiles.keys(), desc="Writing output map"):
+ self.populate_map(tn,obj_id,classes,output_file,compress)
+
def true_pred_bypixel(self, labels, predicted_classes):
pred_c = np.zeros(np.max(self.ref_db['orig_label']).astype(int)+1)
pred_c[labels] = predicted_classes