# HG changeset patch
# User humel
# Date 1272388740 14400
# Node ID ae8102aad5865c6481e098e6c517bd9aaa13d24a
# Parent  4c840798d290f3e84b80a7de09e7ecb9823d41ff
Generation script for AMT data

diff -r 4c840798d290 -r ae8102aad586 data_generation/amt/amt_generate.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/data_generation/amt/amt_generate.py	Tue Apr 27 13:19:00 2010 -0400
@@ -0,0 +1,77 @@
+import numpy, Image
+from pylearn.io import filetensor as ft
+
+
+DATAPATH = '/data/lisa/data/'
+DATASET = 'nist' # nist, p07, pnist
+NUM_BATCHES = 250
+BATCH_SIZE = 10
+IMGSHP = (32,32)
+WHITE_SPACE_THICKNESS = 1
+DATASET_PATH = { 'nist' : [ DATAPATH + 'nist/by_class/all/all_test_data.ft',
+                            DATAPATH + 'nist/by_class/all/all_test_labels.ft' ],
+                 'p07'  : [ DATAPATH + 'ift6266h10/data/P07_test_data.ft',
+                            DATAPATH + 'ift6266h10/data/P07_test_labels.ft' ],
+                 'pnist': [ DATAPATH + 'ift6266h10/data/PNIST07_test_data.ft',
+                            DATAPATH + 'ift6266h10/data/PNIST07_test_labels.ft' ] }
+
+def generate_batches():
+# Generate a directory containing NUM_BATCHES of DATASET
+    total = NUM_BATCHES * BATCH_SIZE
+
+    # Create a matrix of random integers within the range
+    # [0,lenght_dataset-1]  
+
+    f = open(DATASET_PATH[DATASET][0])
+    g = open(DATASET_PATH[DATASET][1])
+    test_data = ft.read(f)
+    test_labels = ft.read(g)
+
+    resulting_data = numpy.zeros((total,IMGSHP[0]*IMGSHP[1]))
+    resulting_labels = numpy.zeros((total,))
+    f.close();g.close()
+
+    ds_size = len(test_data)
+    rand_seq = numpy.random.random_integers(ds_size, size=(NUM_BATCHES,BATCH_SIZE))
+
+    for i in range(NUM_BATCHES):
+       for j in range(BATCH_SIZE):
+           resulting_data[i*BATCH_SIZE+j]=test_data[rand_seq[i,j]]
+           resulting_labels[i*BATCH_SIZE+j] = test_labels[rand_seq[i,j]]
+       image = generate_image(resulting_data[i*BATCH_SIZE:(i+1)*BATCH_SIZE])
+       text = generate_labels(resulting_labels[i*BATCH_SIZE:(i+1)*BATCH_SIZE], rand_seq[i])
+       filename = DATASET + '_' + str("%04d" % int(i+1))
+       image.save(filename+'.jpeg')
+       save_text(text,filename)
+
+    ft_name = 'AMT_'+DATASET+'_'+str(NUM_BATCHES)
+    generate_ft_file(resulting_data,resulting_labels,ft_name)
+
+def save_text(text,filename):
+    f = open(filename+'.txt', 'w')
+    f.write(text)
+    f.close()
+def generate_ft_file(data,labels,ft_name):
+    fdata = open(ft_name+'_data.ft','w')
+    flabels = open(ft_name+'_labels.ft','w')
+    ft.write(fdata,data)
+    ft.write(flabels,labels)
+    fdata.close();flabels.close()
+
+def generate_image(seq):
+    all_images = []
+
+    white_space = numpy.asarray(numpy.zeros((IMGSHP[0],WHITE_SPACE_THICKNESS))+255.,dtype='uint8')
+
+    for i in range(len(seq)):
+        all_images += [numpy.asarray(seq[i].reshape((IMGSHP))*255.,dtype='uint8')]
+
+    all_images_stacked = numpy.hstack(numpy.asarray([numpy.hstack((image,white_space)) for image in all_images]))
+    return Image.fromarray(all_images_stacked)
+
+def generate_labels(seq, indexes):
+    return str(seq) + '\n' + str(indexes)
+
+if __name__ =='__main__':
+    print 'Starting data generation'
+    generate_batches()