# HG changeset patch
# User goldfinger
# Date 1272390332 14400
# Node ID f61a04074723c1f3e8c8abb11a739be360811705
# Parent  ae8102aad5865c6481e098e6c517bd9aaa13d24a
code for amazon MT

diff -r ae8102aad586 -r f61a04074723 AMT/gene_images.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/AMT/gene_images.py	Tue Apr 27 13:45:32 2010 -0400
@@ -0,0 +1,52 @@
+import PIL
+import numpy, cPickle, gzip
+from numpy import *
+from utils import *
+#from logistic_sgd import load_data
+
+def generate_img(source, n=1):
+
+    
+    #param : source = the path to the pickled data 
+    #param : n = the number of tiled image to create 
+    
+    #loading the dataset contaning images and labels (we gonna sample from the test set)
+    f = gzip.open(source,'rb')
+    train_set, valid_set, test_set = cPickle.load(f)
+    test_set_x, test_set_y = test_set
+    f.close()
+
+    max = test_set_x.shape[0]    
+
+    # creating n images containing each 10 randomly pickes caraters from the test set
+
+    for i in range(n):
+ 
+        #picking randomly 10 images in the test set with their labels
+        rng = numpy.random.RandomState(None)
+        sample_idx = rng.randint(max)
+        samples = numpy.array(test_set_x[sample_idx: sample_idx + 10 ])
+        samples_labels = numpy.array(test_set_y[sample_idx: sample_idx + 10 ])
+
+
+        #tiling images into a PIL images and saving it
+        image = PIL.Image.fromarray(tile_raster_images( samples,
+                     img_shape = (28,28), tile_shape = (1,10), 
+                     tile_spacing=(1,1)))
+
+        print ' ... is saving images'
+        img_name = source + str(i)
+        image.save(img_name+'.png')
+
+        #saving the corresponding labels : todo after testing if the saving works
+        print '... is saving labels'
+        numpy.savetxt(img_name+'.txt', samples_labels)
+
+    print n, 'images saved'
+    
+if __name__ == '__main__':
+
+    print '... is generating samples'
+    generate_img('mnist.pkl.gz', n=5)
+    print 'done'
+