--- a
+++ b/scripts/submission.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+
+from __future__ import division, print_function
+
+import os
+import glob
+
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+
+from rvseg import opts, patient, dataset, models
+
+
+def load_patient_images(path, normalize=True):
+    p = patient.PatientData(path)
+
+    # reshape to account for channel dimension
+    images = np.asarray(p.images, dtype='float64')[:,:,:,None]
+
+    # maybe normalize images
+    if normalize:
+        dataset.normalize(images, axis=(1,2))
+
+    return images, p.index, p.labeled, p.rotated
+
+def get_contours(mask):
+    mask_image = np.where(mask > 0.5, 255, 0).astype('uint8')
+    im2, coords, hierarchy = cv2.findContours(mask_image, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
+
+    if not coords:
+        print("No contour detected.")
+        coords = np.ones((1, 1, 1, 2), dtype='int')
+    if len(coords) > 1:
+        print("Multiple contours detected.")
+        lengths = [len(coord) for coord in coords]
+        coords = [coords[np.argmax(lengths)]]
+
+    coords = np.squeeze(coords[0], axis=(1,))
+    coords = np.append(coords, coords[:1], axis=0)
+
+    return coords
+
+def save_image(figname, image, mask_pred, alpha=0.3):
+    cmap = plt.cm.gray
+    plt.figure(figsize=(8, 3.75))
+    plt.subplot(1, 2, 1)
+    plt.axis("off")
+    plt.imshow(image, cmap=cmap)
+    plt.subplot(1, 2, 2)
+    plt.axis("off")
+    plt.imshow(image, cmap=cmap)
+    plt.imshow(mask_pred, cmap=cmap, alpha=alpha)
+    plt.savefig(figname, bbox_inches='tight')
+    plt.close()
+
+def main():
+    # Sort of a hack:
+    # args.checkpoint = turns on saving of images
+    args = opts.parse_arguments()
+    args.checkpoint = False     # override for now
+
+    glob_search = os.path.join(args.datadir, "patient*")
+    patient_dirs = sorted(glob.glob(glob_search))
+    if len(patient_dirs) == 0:
+        raise Exception("No patient directors found in {}".format(data_dir))
+
+    # get image dimensions from first patient
+    images, _, _, _ = load_patient_images(patient_dirs[0], args.normalize)
+    _, height, width, channels = images.shape
+    classes = 2                 # hard coded for now
+    contour_type = {'inner': 'i', 'outer': 'o'}[args.classes]
+
+    print("Building model...")
+    string_to_model = {
+        "unet": models.unet,
+        "dilated-unet": models.dilated_unet,
+        "dilated-densenet": models.dilated_densenet,
+        "dilated-densenet2": models.dilated_densenet2,
+        "dilated-densenet3": models.dilated_densenet3,
+    }
+    model = string_to_model[args.model]
+
+    m = model(height=height, width=width, channels=channels, classes=classes,
+              features=args.features, depth=args.depth, padding=args.padding,
+              temperature=args.temperature, batchnorm=args.batchnorm,
+              dropout=args.dropout)
+
+    m.load_weights(args.load_weights)
+
+    for path in patient_dirs:
+        ret = load_patient_images(path, args.normalize)
+        images, patient_number, frame_indices, rotated = ret
+
+        predictions = []
+        for image in images:
+            mask_pred = m.predict(image[None,:,:,:]) # feed one at a time
+            predictions.append((image[:,:,0], mask_pred[0,:,:,1]))
+
+        for (image, mask), frame_index in zip(predictions, frame_indices):
+            filename = "P{:02d}-{:04d}-{}contour-auto.txt".format(
+                patient_number, frame_index, contour_type)
+            outpath = os.path.join(args.outdir, filename)
+            print(filename)
+
+            contour = get_contours(mask)
+            if rotated:
+                height, width = image.shape
+                x, y = contour.T
+                x, y = height - y, x
+                contour = np.vstack((x, y)).T
+
+            np.savetxt(outpath, contour, fmt='%i', delimiter=' ')
+
+            if args.checkpoint:
+                filename = "P{:02d}-{:04d}-{}contour-auto.png".format(
+                    patient_number, frame_index, contour_type)
+                outpath = os.path.join(args.outdir, filename)
+                save_image(outpath, image, np.round(mask))
+
+if __name__ == '__main__':
+    main()