--- a
+++ b/dsb2018_topcoders/albu/src/submit.py
@@ -0,0 +1,166 @@
+import numpy as np
+import pandas as pd
+from scipy.misc import imread
+import cv2
+import os
+from scipy import ndimage as ndi
+from skimage.morphology import remove_small_objects, watershed, remove_small_holes
+from skimage import measure
+# Run-length encoding stolen from https://www.kaggle.com/rakhlin/fast-run-length-encoding-python
+def rle_encoding(x):
+    dots = np.where(x.T.flatten() == 1)[0]
+    run_lengths = []
+    prev = -2
+    for b in dots:
+        if (b>prev+1): run_lengths.extend((b + 1, 0))
+        run_lengths[-1] += 1
+        prev = b
+    return run_lengths
+
+
+def prob_to_rles(lab_img):
+    # lab_img = label(x > cutoff)
+    for i in range(1, lab_img.max() + 1):
+        yield rle_encoding(lab_img == i)
+
+
+def my_watershed(what, mask1, mask2):
+    # markers = ndi.label(mask2, output=np.uint32)[0]
+    # big_seeds = watershed(what, markers, mask=mask1, watershed_line=False)
+    # m2 = mask1 - (big_seeds > 0)
+    # mask2 = mask2 | m2
+
+    markers = ndi.label(mask2, output=np.uint32)[0]
+    labels = watershed(what, markers, mask=mask1, watershed_line=True)
+    # labels = watershed(what, markers, mask=mask1, watershed_line=False)
+    return labels
+
+
+def calc_score(labels, y_pred):
+    true_objects = len(np.unique(labels))
+    pred_objects = len(np.unique(y_pred))
+    #    print("Number of true objects:", true_objects)
+    #    print("Number of predicted objects:", pred_objects)
+    # Compute intersection between all objects
+    intersection = np.histogram2d(labels.flatten(), y_pred.flatten(), bins=(true_objects, pred_objects))[0]
+
+    # Compute areas (needed for finding the union between all objects)
+    area_true = np.histogram(labels, bins=true_objects)[0]
+    area_pred = np.histogram(y_pred, bins=pred_objects)[0]
+    area_true = np.expand_dims(area_true, -1)
+    area_pred = np.expand_dims(area_pred, 0)
+
+    # Compute union
+    union = area_true + area_pred - intersection
+
+    # Exclude background from the analysis
+    intersection = intersection[1:, 1:]
+    union = union[1:, 1:]
+    union[union == 0] = 1e-9
+
+    # Compute the intersection over union
+    iou = intersection / union
+
+    # Precision helper function
+    def precision_at(threshold, iou):
+        matches = iou > threshold
+        true_positives = np.sum(matches, axis=1) == 1  # Correct objects
+        false_positives = np.sum(matches, axis=0) == 0  # Missed objects
+        false_negatives = np.sum(matches, axis=1) == 0  # Extra objects
+        tp, fp, fn = np.sum(true_positives), np.sum(false_positives), np.sum(false_negatives)
+        return tp, fp, fn
+
+    # Loop over IoU thresholds
+    prec = []
+    #    print("Thresh\tTP\tFP\tFN\tPrec.")
+    for t in np.arange(0.5, 1.0, 0.05):
+        tp, fp, fn = precision_at(t, iou)
+        p = tp / (tp + fp + fn)
+        #        print("{:1.3f}\t{}\t{}\t{}\t{:1.3f}".format(t, tp, fp, fn, p))
+        prec.append(p)
+    #    print("AP\t-\t-\t-\t{:1.3f}".format(np.mean(prec)))
+    return np.mean(prec), prec
+
+
+def wsh(mask_img, threshold, border_img, seeds):
+    img_copy = np.copy(mask_img)
+    m = seeds * border_img# * dt
+    img_copy[m <= threshold + 0.35] = 0
+    img_copy[m > threshold + 0.35] = 1
+    img_copy = img_copy.astype(np.bool)
+    img_copy = remove_small_objects(img_copy, 10).astype(np.uint8)
+
+    mask_img[mask_img <= threshold] = 0
+    mask_img[mask_img > threshold] = 1
+    mask_img = mask_img.astype(np.bool)
+    mask_img = remove_small_holes(mask_img, 1000)
+    mask_img = remove_small_objects(mask_img, 8).astype(np.uint8)
+    # cv2.imwrite('t.png', (mask_img * 255).astype(np.uint8))
+    # cv2.imwrite('t2.png', (img_copy * 255).astype(np.uint8))
+    labeled_array = my_watershed(mask_img, mask_img, img_copy)
+    return labeled_array
+
+def postprocess_victor(pred):
+    av_pred = pred / 255.
+    av_pred = av_pred[..., 2] * (1 - av_pred[..., 1])
+    av_pred = 1 * (av_pred > 0.5)
+    av_pred = av_pred.astype(np.uint8)
+
+    y_pred = measure.label(av_pred, neighbors=8, background=0)
+    props = measure.regionprops(y_pred)
+    for i in range(len(props)):
+        if props[i].area < 12:
+            y_pred[y_pred == i + 1] = 0
+    y_pred = measure.label(y_pred, neighbors=8, background=0)
+
+    nucl_msk = (255 - pred[..., 2])
+    nucl_msk = nucl_msk.astype('uint8')
+    y_pred = watershed(nucl_msk, y_pred, mask=((pred[..., 2] > 80)), watershed_line=True)
+    return y_pred
+
+# test_dir = r'C:\dev\dsbowl\results_test\bowl_remap3\merged'
+# borders_dir = r'C:\dev\dsbowl\results_test\bowl_remap_border2\merged'
+
+oof = True
+# borders_dir = r'd:\tmp\bowl\results_test\s4b\merged'
+if oof:
+    # test_dir = r'/home/albu/dev/bowl/results/dpn_sigm_3channel'
+    test_dir = r'c:\dev\dsbowl\results\dpn_sigm_f0'
+else:
+    test_dir = r'd:\tmp\bowl\results_test\dpn_softmax3\merged'
+# labels_dir = r'/home/albu/dev/bowl/train_imgs/labels_all6'
+labels_dir = r'D:\dsbowl\train_imgs\labels_all6'
+# vgg = r'D:\tmp\bowl\vgg2folds\predict'
+new_test_ids = []
+rles = []
+im_names = os.listdir(test_dir)
+# im_names = [im for im in im_names if not im.startswith('jw-')]
+test_ids = [os.path.splitext(i)[0] for i in im_names]
+preds_test = [imread(os.path.join(test_dir, im), mode='RGB') for im in im_names]
+# vgg_data = [imread(os.path.join(vgg, im), mode='RGB') for im in im_names]
+# dts = [imread(os.path.join(dt_dir, im), mode='L') for im in im_names]
+if oof:
+    pred_labels = [cv2.imread(os.path.join(labels_dir, os.path.splitext(im)[0] + '.tif'), cv2.IMREAD_UNCHANGED) for im in im_names]
+scores = []
+for n, id_ in enumerate(test_ids):
+    # cv2.imshow('b', preds_test[n][...,2])
+    # cv2.imshow('r', preds_test[n][...,0])
+    # cv2.waitKey()
+    test_img = wsh(preds_test[n][...,2] / 255., 0.3, 1 - preds_test[n][...,1] / 255., preds_test[n][...,2] / 255)
+    # test_img = postprocess_victor(preds_test[n])
+    if oof:
+        test_img = ndi.label(test_img, output=np.uint32)[0]
+        score = calc_score(pred_labels[n], test_img)[0]
+        scores.append(score)
+    else:
+        cv2.imwrite(os.path.join(r'D:\tmp\bowl\res_bin', im_names[n]), (test_img > 0).astype(np.uint8) * 255)
+    rle = list(prob_to_rles(test_img))
+    rles.extend(rle)
+    new_test_ids.extend([id_] * len(rle))
+if oof:
+    print(np.mean(scores))
+else:
+    sub = pd.DataFrame()
+    sub['ImageId'] = new_test_ids
+    sub['EncodedPixels'] = pd.Series(rles).apply(lambda x: ' '.join(str(y) for y in x))
+    sub.to_csv('sub-dsbowl2018-1.csv', index=False)