# HG changeset patch
# User James Bergstra <bergstrj@iro.umontreal.ca>
# Date 1256251996 14400
# Node ID 3c1fb6f14a14f2f39d5e7165980e02836450c4f0
# Parent  d7ee9c906d7eb8bc3878bb6ecc40f819304e20d4
moved COIL100 code to sandbox

diff -r d7ee9c906d7e -r 3c1fb6f14a14 pylearn/dataset_ops/COIL100.py
--- a/pylearn/dataset_ops/COIL100.py	Thu Oct 22 18:52:47 2009 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,62 +0,0 @@
-
-"""
-http://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php
-
-"Columbia Object Image Library (COIL-100),"
-    S. A. Nene, S. K. Nayar and H. Murase, 
-        Technical Report CUCS-006-96, February 1996. 
-
-"""
-
-import os, cPickle
-import Image, numpy
-from pylearn.datasets.config import data_root # config
-
-from .memo import memo
-
-def filenames():
-    root = os.path.join(data_root(), 'COIL-100', 'coil-100', )
-    for filename in os.listdir(root):
-        yield filename, os.path.join(root,filename )
-
-def filenameidx_imgidx(filename):
-    if filename.startswith("obj"):
-        obj_idx = int(filename[3:filename.index("_")])
-        img_idx = int(filename[filename.index("_")+2:filename.index(".")])
-        return obj_idx, img_idx
-    else:
-        raise ValueError(filename)
-
-_32x32grey_path = os.path.join(data_root(), "COIL-100", "dct_32x32_grey.pkl")
-_32x32grey_header = "Dictionary of COIL-100 dataset at 32x32 resolution, greyscale"
-def build_32x32_grey():
-    f = file(_32x32grey_path, "w")
-    cPickle.dump(_32x32grey_header, f, protocol=cPickle.HIGHEST_PROTOCOL)
-
-    dct = {}
-    for filename, fullname in filenames():
-        if filename.startswith('obj'):
-            obj_idx, img_idx = filenameidx_imgidx(filename)
-            img = numpy.asarray(Image.open(fullname))
-            dct.setdefault(obj_idx, {})[img_idx] = img.mean(axis=2)[::4,::4]
-    rval = numpy.empty((100, 72, 32, 32), dtype='float32')
-    rval[...] = -1
-    for obj_id, dd in dct.iteritems():
-        for img_id, v in dd.iteritems():
-            rval[obj_id, img_id, :, :] = v
-    assert numpy.all(rval >= 0.0)
-
-    cPickle.dump(rval, f, protocol=cPickle.HIGHEST_PROTOCOL)
-    f.close()
-
-@memo
-def get_32x32_grey():
-    f = file(_path_32x32_grey)
-    if _32x32grey_header != cPickle.load(f):
-        raise ValueError('wrong pickle file')
-    rval = cPickle.load(f)
-    f.close()
-    return rval
-
-
-
diff -r d7ee9c906d7e -r 3c1fb6f14a14 pylearn/dataset_ops/sandbox/COIL100.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pylearn/dataset_ops/sandbox/COIL100.py	Thu Oct 22 18:53:16 2009 -0400
@@ -0,0 +1,62 @@
+
+"""
+http://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php
+
+"Columbia Object Image Library (COIL-100),"
+    S. A. Nene, S. K. Nayar and H. Murase, 
+        Technical Report CUCS-006-96, February 1996. 
+
+"""
+
+import os, cPickle
+import Image, numpy
+from pylearn.datasets.config import data_root # config
+
+from .memo import memo
+
+def filenames():
+    root = os.path.join(data_root(), 'COIL-100', 'coil-100', )
+    for filename in os.listdir(root):
+        yield filename, os.path.join(root,filename )
+
+def filenameidx_imgidx(filename):
+    if filename.startswith("obj"):
+        obj_idx = int(filename[3:filename.index("_")])
+        img_idx = int(filename[filename.index("_")+2:filename.index(".")])
+        return obj_idx, img_idx
+    else:
+        raise ValueError(filename)
+
+_32x32grey_path = os.path.join(data_root(), "COIL-100", "dct_32x32_grey.pkl")
+_32x32grey_header = "Dictionary of COIL-100 dataset at 32x32 resolution, greyscale"
+def build_32x32_grey():
+    f = file(_32x32grey_path, "w")
+    cPickle.dump(_32x32grey_header, f, protocol=cPickle.HIGHEST_PROTOCOL)
+
+    dct = {}
+    for filename, fullname in filenames():
+        if filename.startswith('obj'):
+            obj_idx, img_idx = filenameidx_imgidx(filename)
+            img = numpy.asarray(Image.open(fullname))
+            dct.setdefault(obj_idx, {})[img_idx] = img.mean(axis=2)[::4,::4]
+    rval = numpy.empty((100, 72, 32, 32), dtype='float32')
+    rval[...] = -1
+    for obj_id, dd in dct.iteritems():
+        for img_id, v in dd.iteritems():
+            rval[obj_id, img_id, :, :] = v
+    assert numpy.all(rval >= 0.0)
+
+    cPickle.dump(rval, f, protocol=cPickle.HIGHEST_PROTOCOL)
+    f.close()
+
+@memo
+def get_32x32_grey():
+    f = file(_path_32x32_grey)
+    if _32x32grey_header != cPickle.load(f):
+        raise ValueError('wrong pickle file')
+    rval = cPickle.load(f)
+    f.close()
+    return rval
+
+
+