ENH: object based training pipeline complete.

Learning/ObjectBased.py

+import glob
+from OBIA.OBIABase import *
+from sklearn.model_selection import StratifiedGroupKFold
+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)
+        for ptrn in time_series_patterns:
+            lst = sorted(glob.glob(ptrn))
+            self.obia_base.add_raster_time_series_for_stats(lst)
+        for ras in user_feature_list:
+            self.obia_base.add_raster_for_stats(ras)
+        self.obia_base.populate_ref_db()
+
+    def train_RF(self, n_estimators, n_folds):
+        L, X, Y, G, p2, p98 = self.obia_base.get_reference_db_as_training_base()
+        sgk = StratifiedGroupKFold(n_splits=n_folds, shuffle=True)
+        folds_indices = []
+        models = []
+        results = []
+        for tr_i, ts_i in sgk.split(X, Y, G):
+            folds_indices.append((tr_i, ts_i))
+            models.append(RandomForestClassifier(n_estimators=n_estimators))
+            models[-1].fit(X[tr_i], Y[tr_i])
+            l, c = L[ts_i], models[-1].predict(X[ts_i])
+            y_true, y_pred = self.obia_base.true_pred_bypixel(l, c)
+            results.append(
+                {
+                    'conf_matrix': confusion_matrix(y_true, y_pred),
+                    'accuracy': accuracy_score(y_true, y_pred),
+                    'kappa' : cohen_kappa_score(y_true, y_pred),
+                    'p_r_f1': precision_recall_fscore_support(y_true, y_pred)
+                }
+            )
+        return folds_indices, models, results

OBIA/OBIABase.py

@@ -69,7 +69,10 @@ class OBIABase:
         obj = in_seg.GetImageAsNumpyArray('out')
 
         mask = ids>0
-        self.ref_obj_layer = label(obj * mask, connectivity=1).astype(np.uint32)
+        # This was to relabel object being disconnected by intersecting the GT
+        # Re-enable if needed...
+        # self.ref_obj_layer = label(obj * mask, connectivity=1).astype(np.uint32)
+        self.ref_obj_layer = (obj * mask).astype(np.uint32)
         rp = regionprops(self.ref_obj_layer, intensity_image=np.dstack((obj,ids,cls)).astype(np.int))
         self.ref_db = pd.DataFrame(data=[np.insert(o.intensity_min,0,o.area) for o in rp],
                                    columns=['area','orig_label','polygon_id','class'],
@@ -219,6 +222,7 @@ class OBIABase:
     def get_reference_db_as_training_base(self):
         assert(self.ref_db is not None and len(self.raster_var_names)>0)
         vars = [item for sublist in self.raster_var_names for item in sublist]
+        L = self.ref_db['orig_label'].to_numpy(dtype=int)
         X = self.ref_db[vars].to_numpy()
         # compute percentiles and normalize
         p2 = np.zeros(X.shape[1])
@@ -236,7 +240,7 @@ class OBIABase:
             X[:,g] = (tmp - m)/(M - m)
         Y = self.ref_db['class'].to_numpy(dtype=int)
         G = self.ref_db['polygon_id'].to_numpy(dtype=int)
-        return X,Y,G,p2,p98
+        return L,X,Y,G,p2,p98
 
     def tiled_data(self, normalize=None):
         vars = [item for sublist in self.raster_var_names for item in sublist]
@@ -249,4 +253,14 @@ class OBIABase:
             yield L,X
 
     def populate_map(self, tilenum, labels):
-        return
\ No newline at end of file
+        return
+
+    def true_pred_bypixel(self, labels, predicted_classes):
+        pred_c = np.zeros(np.max(self.ref_obj_layer)+1)
+        pred_c[labels] = predicted_classes
+        pred = pred_c[self.ref_obj_layer]
+        true_c = np.zeros(np.max(self.ref_db['orig_label']).astype(int)+1)
+        # ATTENTION: works if "labels" is sorted (as provided by get_reference_...)
+        true_c[labels] = self.ref_db.loc[self.ref_db['orig_label'].isin(labels),'class'].to_numpy(dtype=int)
+        true = true_c[self.ref_obj_layer]
+        return pred[pred>0], true[pred>0]