--- a
+++ b/test_lung_seg_scan.py
@@ -0,0 +1,58 @@
+import sys
+import lasagne as nn
+import numpy as np
+import theano
+import pathfinder
+import utils
+from configuration import config, set_configuration
+from utils_plots import plot_slice_3d_4
+import theano.tensor as T
+import blobs_detection
+import logger
+import time
+import multiprocessing as mp
+import buffering
+
+theano.config.warn_float64 = 'raise'
+
+if len(sys.argv) < 2:
+    sys.exit("Usage: test_luna_scan.py <configuration_name>")
+
+config_name = sys.argv[1]
+set_configuration('configs_seg_scan', config_name)
+
+# predictions path
+predictions_dir = utils.get_dir_path('model-predictions', pathfinder.METADATA_PATH)
+outputs_path = predictions_dir + '/%s' % config_name
+utils.auto_make_dir(outputs_path)
+
+# logs
+logs_dir = utils.get_dir_path('logs', pathfinder.METADATA_PATH)
+sys.stdout = logger.Logger(logs_dir + '/%s.log' % config_name)
+sys.stderr = sys.stdout
+
+data_iterator = config().train_data_iterator
+
+print
+print 'Data'
+print 'n samples: %d' % data_iterator.nsamples
+
+start_time = time.time()
+n_pos = 0
+tp = 0
+for n, (x, y, lung_mask, annotations, tf_matrix, pid) in enumerate(data_iterator.generate()):
+    print '-------------------------------------'
+    print n, pid
+    n_pos += annotations.shape[0]
+    n_pid_tp = 0
+    annotations = np.int32(annotations)
+    for i in xrange(annotations.shape[0]):
+        if lung_mask[0, 0, annotations[i, 0], annotations[i, 1], annotations[i, 2]] == 1:
+            n_pid_tp += 1
+    tp += n_pid_tp
+    print annotations.shape[0], n_pid_tp
+    if annotations.shape[0] > n_pid_tp:
+        print '----HERE-----!!!!!'
+
+print 'total', n_pos
+print 'detected', tp