Add aps

minigrida/descriptors/aps.py

@@ -0,0 +1,59 @@
+#!/usr/bin/env python
+# file aps.py
+# author Florent Guiotte <florent.guiotte@irisa.fr>
+# version 0.0
+# date 30 mai 2020
+"""Abstract
+
+doc.
+"""
+
+import numpy as np
+import sap
+from sklearn.externals.joblib import Memory
+
+memory = Memory(location='cache/', verbose=0)
+
+
+@memory.cache
+def _attribute_profiles(*kwargs):
+    return sap.attribute_profiles(*kwargs)
+
+
+def run(gt, rasters, coords, remove, attributes, adjacency='4', filtering='direct'):
+    X = []
+    y = []
+    groups = []
+    Xn = None
+
+    for i, (gti, rastersi, coordsi) in enumerate(zip(gt, rasters, coords)):
+        # Compute EAP
+        eap = []
+        for name, raster in rastersi.items():
+            eap += [_attribute_profiles(raster, attributes, adjacency, name, filtering)]
+        eap = sap.concatenate(eap)
+
+        Xn = [' '.join((a['tree']['image_name'], 
+                        a['attribute'], 
+                        *[str(v) for v in p.values()])) 
+                       for a in eap.description for p in a['profiles']] if not Xn else Xn
+
+        # Create vectors
+        X_raw = np.moveaxis(np.array(list(eap.vectorize())), 0, -1)
+        y_raw = gti
+
+        # Remove unwanted label X, y
+        lbl = np.ones_like(y_raw, dtype=np.bool)
+        for l in remove if remove else []:
+            lbl &= y_raw != l
+
+        X += [X_raw[lbl]]
+        y += [y_raw[lbl]]
+        groups += [np.repeat(coordsi, lbl.sum())]
+
+    X = np.concatenate(X)
+    y = np.concatenate(y)
+    groups = np.concatenate(groups)
+    Xn = rasters[0].keys()
+
+    return X, y, groups, Xn