In order to reduce the submission file size, our metric uses run-length encoding on the pixel values. Instead of submitting an exhaustive list of indices for your segmentation, you will submit pairs of values that contain a start position and a run length. E.g. '1 3' implies starting at pixel 1 and running a total of 3 pixels (1,2,3).
The competition format requires a space delimited list of pairs. For example, '1 3 10 5' implies pixels 1,2,3,10,11,12,13,14 are to be included in the mask. The pixels are one-indexed and numbered from top to bottom, then left to right: 1 is pixel (1,1), 2 is pixel (2,1), etc. A prediction of of "no ship in image" should have a blank value in the EncodedPixels column.
def rle_decode(mask_rle, shape=(768, 768)): ''' mask_rle: run-length as string formated (start length) shape: (height,width) of array to return Returns numpy array, 1 - mask, 0 - background ''' s = mask_rle.split() starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])] starts -= 1 ends = starts + lengths img = np.zeros(shape*shape, dtype=np.uint8) for lo, hi in zip(starts, ends): img[lo:hi] = 1 return img.reshape(shape).T # Needed to align to RLE direction def rle_encode(img): ''' img: numpy array, 1 - mask, 0 - background Returns run length as string formated ''' pixels = img.T.flatten() # Needed to align to RLE direction # start と end が交互に抽出される pixels = np.concatenate([, pixels, ]) runs = np.where(pixels[1:] != pixels[:-1]) + 1 # end から start を引く。--> length runs[1::2] -= runs[::2] return ' '.join(str(x) for x in runs)