New DFC scripts with Split First

descriptors/dfc_aps.py

@@ -1,8 +1,4 @@
 import numpy as np
-import yaml
-
-import sys
-sys.path.append('..')
 import ld2dap
 
 def run(rasters, treshold=1e4, areas=None, sd=None, moi=None, split=1, split_dim=0):

descriptors/dfc_daps.py

@@ -9,33 +9,66 @@
 # TODO details
 
 import numpy as np
-
-import sys
-sys.path.append('..')
 import ld2dap
 
-def run(rasters, treshold=1e4, areas=None, sd=None, moi=None):
+def run(rasters, treshold=1e4, areas=None, sd=None, moi=None, split=1, split_dim=0):
-    # Parse parameters type
+    """DFC Differential Attribute Profiles
+
+    Compute description vectors for parameters. Rasters can be splitted along
+    `split_dim` before description proceeds.
+
+    """
+
+    # Parse attribute type
     treshold = float(treshold)
     areas = None if areas is None else np.array(areas).astype(np.float).astype(np.int)
     sd = None if sd is None else np.array(sd).astype(np.float)
     moi = None if moi is None else np.array(moi).astype(np.float)
 
-    # Pipelines
+    # Load and filter 
     loader = ld2dap.LoadTIFF(rasters)
     dfc_filter = ld2dap.Treshold(treshold)
+    normalize = ld2dap.Normalize(dtype=np.uint8)
+    raw_out = ld2dap.RawOutput()
+
+    raw_out.input = normalize
+    normalize.input = dfc_filter
     dfc_filter.input = loader
-    aps = ld2dap.AttributeProfiles(area=areas, sd=sd, moi=moi)
+    raw_out.run()
-    aps.input = dfc_filter
-    differential = ld2dap.Differential()
-    differential.input = aps
-    out_vectors = ld2dap.RawOutput()
-    out_vectors.input = differential
 
-    # Compute vectors
+    # Split
-    out_vectors.run()
+    n = split; d = split_dim
 
-    return out_vectors.data
+    step = int(raw_out.data.shape[d] / n)
+    view = np.moveaxis(raw_out.data, d, 0)
+    cuts = list()
+    for i in range(n):
+        cut = np.moveaxis(view[i*step:(i+1)*step+1], 0, d)
+        cuts.append(cut)
+
+    # Describe
+    dcuts = list()
+    for cut in cuts:
+        rinp = ld2dap.RawInput(cut, raw_out.metadata)
+        aps = ld2dap.AttributeProfiles(areas, sd, moi, normalize_to_dtype=False)
+        diff = ld2dap.Differential()
+        vout = ld2dap.RawOutput()
+        
+        vout.input = diff
+        diff.input = aps
+        aps.input = rinp
+        vout.run()
+        
+        dcuts.append(vout.data)
+
+    # Merge
+    descriptors = np.zeros(raw_out.data.shape[:2] + (dcuts[0].shape[-1],))
+    view = np.moveaxis(descriptors, d, 0)
+
+    for i, cut in enumerate(dcuts):
+        view[i*step:(i+1)*step+1] = np.moveaxis(cut, 0, d)
+
+    return descriptors
 
 def version():
     return 'v0.0'

descriptors/dfc_dsdaps.py

@@ -1,41 +1,74 @@
 #!/usr/bin/python
 # -*- coding: utf-8 -*-
-# \file dfc_dsdaps.py
+# \file dfc_daps.py
 # \brief TODO
 # \author Florent Guiotte <florent.guiotte@gmail.com>
 # \version 0.1
-# \date 28 août 2018
+# \date 27 août 2018
 #
 # TODO details
 
 import numpy as np
-
-import sys
-sys.path.append('..')
 import ld2dap
 
-def run(rasters, treshold=1e4, areas=None, sd=None, moi=None):
+def run(rasters, treshold=1e4, areas=None, sd=None, moi=None, split=1, split_dim=0):
-    # Parse parameters type
+    """DFC Differential Self Dual Attribute Profiles
+
+    Compute description vectors for parameters. Rasters can be splitted along
+    `split_dim` before description proceeds.
+
+    """
+
+    # Parse attribute type
     treshold = float(treshold)
     areas = None if areas is None else np.array(areas).astype(np.float).astype(np.int)
     sd = None if sd is None else np.array(sd).astype(np.float)
     moi = None if moi is None else np.array(moi).astype(np.float)
 
-    # Pipelines
+    # Load and filter 
     loader = ld2dap.LoadTIFF(rasters)
     dfc_filter = ld2dap.Treshold(treshold)
+    normalize = ld2dap.Normalize(dtype=np.uint8)
+    raw_out = ld2dap.RawOutput()
+
+    raw_out.input = normalize
+    normalize.input = dfc_filter
     dfc_filter.input = loader
-    sdaps = ld2dap.SelfDualAttributeProfiles(area=areas, sd=sd, moi=moi)
+    raw_out.run()
-    sdaps.input = dfc_filter
-    differential = ld2dap.Differential()
-    differential.input = sdaps
-    out_vectors = ld2dap.RawOutput()
-    out_vectors.input = differential
 
-    # Compute vectors
+    # Split
-    out_vectors.run()
+    n = split; d = split_dim
 
-    return out_vectors.data
+    step = int(raw_out.data.shape[d] / n)
+    view = np.moveaxis(raw_out.data, d, 0)
+    cuts = list()
+    for i in range(n):
+        cut = np.moveaxis(view[i*step:(i+1)*step+1], 0, d)
+        cuts.append(cut)
+
+    # Describe
+    dcuts = list()
+    for cut in cuts:
+        rinp = ld2dap.RawInput(cut, raw_out.metadata)
+        aps = ld2dap.SelfDualAttributeProfiles(areas, sd, moi, normalize_to_dtype=False)
+        diff = ld2dap.Differential()
+        vout = ld2dap.RawOutput()
+        
+        vout.input = diff
+        diff.input = aps
+        aps.input = rinp
+        vout.run()
+        
+        dcuts.append(vout.data)
+
+    # Merge
+    descriptors = np.zeros(raw_out.data.shape[:2] + (dcuts[0].shape[-1],))
+    view = np.moveaxis(descriptors, d, 0)
+
+    for i, cut in enumerate(dcuts):
+        view[i*step:(i+1)*step+1] = np.moveaxis(cut, 0, d)
+
+    return descriptors
 
 def version():
     return 'v0.0'

descriptors/dfc_sdaps.py

@@ -9,32 +9,64 @@
 # TODO details
 
 import numpy as np
-
-import sys
-sys.path.append('..')
 import ld2dap
 
-def run(rasters, treshold=1e4, areas=None, sd=None, moi=None):
+def run(rasters, treshold=1e4, areas=None, sd=None, moi=None, split=1, split_dim=0):
-    # Parse parameters type
+    """DFC Self Dual Attribute Profiles
+
+    Compute description vectors for parameters. Rasters can be splitted along
+    `split_dim` before description proceeds.
+
+    """
+
+    # Parse attribute type
     treshold = float(treshold)
     areas = None if areas is None else np.array(areas).astype(np.float).astype(np.int)
     sd = None if sd is None else np.array(sd).astype(np.float)
     moi = None if moi is None else np.array(moi).astype(np.float)
 
-    # Pipelines
+    # Load and filter 
     loader = ld2dap.LoadTIFF(rasters)
     dfc_filter = ld2dap.Treshold(treshold)
+    normalize = ld2dap.Normalize(dtype=np.uint8)
+    raw_out = ld2dap.RawOutput()
+
+    raw_out.input = normalize
+    normalize.input = dfc_filter
     dfc_filter.input = loader
-    sdaps = ld2dap.SelfDualAttributeProfiles(area=areas, sd=sd, moi=moi)
+    raw_out.run()
-    sdaps.input = dfc_filter
-    out_vectors = ld2dap.RawOutput()
-    out_vectors.input = sdaps
 
-    # Compute vectors
+    # Split
-    out_vectors.run()
+    n = split; d = split_dim
 
-    return out_vectors.data
+    step = int(raw_out.data.shape[d] / n)
+    view = np.moveaxis(raw_out.data, d, 0)
+    cuts = list()
+    for i in range(n):
+        cut = np.moveaxis(view[i*step:(i+1)*step+1], 0, d)
+        cuts.append(cut)
+
+    # Describe
+    dcuts = list()
+    for cut in cuts:
+        rinp = ld2dap.RawInput(cut, raw_out.metadata)
+        aps = ld2dap.SelfDualAttributeProfiles(areas, sd, moi, normalize_to_dtype=False)
+        vout = ld2dap.RawOutput()
+        
+        vout.input = aps
+        aps.input = rinp
+        vout.run()
+        
+        dcuts.append(vout.data)
+
+    # Merge
+    descriptors = np.zeros(raw_out.data.shape[:2] + (dcuts[0].shape[-1],))
+    view = np.moveaxis(descriptors, d, 0)
+
+    for i, cut in enumerate(dcuts):
+        view[i*step:(i+1)*step+1] = np.moveaxis(cut, 0, d)
+
+    return descriptors
 
 def version():
     return 'v0.0'
-

test_mockup.yml

@@ -15,14 +15,16 @@ expe:
     raster: ./Data/ground_truth/2018_IEEE_GRSS_DFC_GT_TR.tif
     meta_labels: ./Data/ground_truth/jurse_meta_idx.csv
   descriptors_script:
-    name: descriptors.dfc_aps
+    name: descriptors.dfc_sdaps
     parameters:
-      split: 5
+      split: 4
       areas:
       - 100
       - 1000
+      - 1e4
       moi:
       - 0.5
+      - 0.7
       - 0.9
       rasters:
       - ./Data/dfc_rasters/DEM+B_C123/UH17_GEM051_TR.tif
@@ -32,12 +34,12 @@ expe:
     name: Split
     package: cvgenerators.jurse
     parameters:
-      n_test: 5
+      n_test: 4
   classifier:
     name: RandomForestClassifier
     package: sklearn.ensemble
     parameters:
       min_samples_leaf: 10
-      n_estimators: 10
+      n_estimators: 100
       n_jobs: -1
       random_state: 0