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For fixing the problem `Evaluation Exception: Index was outside the bounds of the array. `

functions.py

@@ -0,0 +1,93 @@
+import numpy as np
+import pandas as pd
+from skimage import morphology
+from skimage.morphology import binary_closing, binary_opening, disk, binary_dilation
+
+def run_length_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
+    run_lengths = ' '.join([str(r) for r in run_lengths])
+    return run_lengths
+
+def numpy2encoding(predicts, img_name):
+    """predicts: [H, W, N] instance binary masks
+    注意： 中间可能有洞
+    """
+    ImageId = []
+    EncodedPixels = []
+    for i in range(predicts.shape[2]): 
+        rle = run_length_encoding(predicts[:,:,i])
+        ImageId.append(img_name)
+        EncodedPixels.append(rle)    
+    return ImageId, EncodedPixels
+
+def numpy2encoding_no_overlap(predicts, img_name):
+    """predicts: [H, W, N] instance binary masks
+    注意： 中间可能有洞
+    remove overlapping parts
+    """
+    sum_predicts = np.sum(predicts, axis=2)
+    sum_predicts[sum_predicts>=2] = 0
+    sum_predicts = np.expand_dims(sum_predicts, axis=-1)
+    predicts = predicts * sum_predicts
+
+    ImageId = []
+    EncodedPixels = []
+    for i in range(predicts.shape[2]): 
+        rle = run_length_encoding(predicts[:,:,i])
+        if len(rle)>0:
+            ImageId.append(img_name)
+            EncodedPixels.append(rle)    
+    return ImageId, EncodedPixels
+
+def numpy2encoding_no_overlap2(predicts, img_name, scores):
+    """predicts: [H, W, N] instance binary masks
+    注意： 中间可能有洞
+    overlapping parts are given to the instance of highest score (i.e. DETECTION CONFIDENCE)
+    """
+    # refine your masks here !
+#    predicts = np.apply_along_axis(refineMasks, 2, predicts)
+#    for i in range(predicts.shape[2]-1):
+#        predicts[:,:,i] = refineMasks(predicts[:,:,i])
+#    
+    sum_predicts = np.sum(predicts, axis=2)
+    rows, cols = np.where(sum_predicts>=2)
+
+    for i in zip(rows, cols):
+        instance_indicies = np.where(np.any(predicts[i[0],i[1],:]))[0]
+        highest = instance_indicies[0]
+        predicts[i[0],i[1],:] = predicts[i[0],i[1],:]*0
+        predicts[i[0],i[1],highest] = 1
+
+    ImageId = []
+    EncodedPixels = []
+    for i in range(predicts.shape[2]): 
+        rle = run_length_encoding(predicts[:,:,i])
+        if len(rle)>0:
+            ImageId.append(img_name)
+            EncodedPixels.append(rle)    
+    return ImageId, EncodedPixels
+
+def refineMasks(mask):
+    return binary_dilation(mask, disk(1))
+
+
+def write2csv(file, ImageId, EncodedPixels):
+    df = pd.DataFrame({ 'ImageId' : ImageId , 'EncodedPixels' : EncodedPixels})
+    df.to_csv(file, index=False, columns=['ImageId', 'EncodedPixels'])
+
+
+
+def clean_img(x):
+    """http://blog.csdn.net/haoji007/article/details/52063306
+        closing 先膨胀再腐蚀，可用来填充孔洞
+        opening 先腐蚀再膨胀，可以消除小物体或小斑块
+    https://www.kaggle.com/kmader/nuclei-overview-to-submission: 
+        remove single pixels, connect nearby regions
+    """
+    return binary_opening(binary_closing(x, disk(1)), disk(3))