# HG changeset patch
# User bergstra@mlp4.ais.sandbox
# Date 1234334546 18000
# Node ID 37a259d6946a4b7afbe63f010563f7fd8951b2c0
# Parent  d8af9c8a4bbbbfd67dd198d33175a8fcd4179984
added image_tiling

diff -r d8af9c8a4bbb -r 37a259d6946a pylearn/io/image_tiling.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pylearn/io/image_tiling.py	Wed Feb 11 01:42:26 2009 -0500
@@ -0,0 +1,84 @@
+"""
+Illustrate filters (or data) in a grid of small image-shaped tiles.
+"""
+
+import numpy
+from PIL import Image
+
+def scale_to_unit_interval(ndar):
+    ndar = ndar.copy()
+    ndar -= ndar.min()
+    ndar *= 1.0 / ndar.max()
+    return ndar
+
+def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0,0),
+        scale_rows_to_unit_interval=True, 
+        output_pixel_vals=True
+        ):
+    """
+    Transform an array with one flattened image per row, into an array in which images are
+    reshaped and layed out like tiles on a floor.
+
+    This function is useful for visualizing datasets whose rows are images, and also columns of
+    matrices for transforming those rows (such as the first layer of a neural net).
+
+    :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can be 2-D ndarrays or None
+    :param X: a 2-D array in which every row is a flattened image.
+    :type img_shape: tuple; (height, width)
+    :param img_shape: the original shape of each image
+    :type tile_shape: tuple; (rows, cols)
+    :param tile_shape: the number of images to tile (rows, cols)
+
+    :returns: array suitable for viewing as an image.  (See:`PIL.Image.fromarray`.)
+    :rtype: a 2-d array with same dtype as X.
+
+    """
+    assert len(img_shape) == 2
+    assert len(tile_shape) == 2
+    assert len(tile_spacing) == 2
+
+    out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp 
+        in zip(img_shape, tile_shape, tile_spacing)]
+
+    if isinstance(X, tuple):
+        assert len(X) == 4
+        if output_pixel_vals:
+            out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype='uint8')
+        else:
+            out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype)
+
+        #colors default to 0, alpha defaults to 1 (opaque)
+        if output_pixel_vals:
+            channel_defaults = [0,0,0,255]
+        else:
+            channel_defaults = [0.,0.,0.,1.]
+
+        for i in xrange(4):
+            if X[i] is None:
+                out_array[:,:,i] = numpy.zeros(out_shape,
+                        dtype='uint8' if output_pixel_vals else out_array.dtype
+                        )+channel_defaults[i]
+            else:
+                out_array[:,:,i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)
+        return out_array
+
+    else:
+        H, W = img_shape
+        Hs, Ws = tile_spacing
+
+        out_array = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)
+        for tile_row in xrange(tile_shape[0]):
+            for tile_col in xrange(tile_shape[1]):
+                if tile_row * tile_shape[1] + tile_col < X.shape[0]:
+                    if scale_rows_to_unit_interval:
+                        this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape))
+                    else:
+                        this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)
+                    out_array[
+                        tile_row * (H+Hs):tile_row*(H+Hs)+H,
+                        tile_col * (W+Ws):tile_col*(W+Ws)+W
+                        ] \
+                        = this_img * (255 if output_pixel_vals else 1)
+        return out_array
+
+