使用Tensorflow数据集创建RLE（行程编码）蒙版

2024-04-30 • 问答

我一直在尝试使用Tensorflow数据集，但无法弄清楚如何有效地创建RLE蒙版。仅供参考，我正在使用Kaggle的空客船舶检测挑战赛中的dat：https://www.kaggle.com/c/airbus-ship-detection/data

我知道我的RLE解码功能可以从以下一种内核中工作（借用）：

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
'''
if not isinstance(mask_rle,str):
    img = np.zeros(shape[0]*shape[1],dtype=np.uint8)
    return img.reshape(shape).T

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[0]*shape[1],dtype=np.uint8)
for lo,hi in zip(starts,ends):
    img[lo:hi] = 1
return img.reshape(shape).T

....但是它似乎不能很好地与管道配合使用：

list_ds = tf.data.Dataset.list_files(train_paths_abs)
ds = list_ds.map(parse_img)

使用以下解析功能，一切正常：

def parse_img(file_path,new_size=[128,128]):    
    img_content = tf.io.read_file(file_path)
    img = tf.image.decode_jpeg(img_content)
    img = tf.image.convert_image_dtype(img,tf.float32)    
    img = tf.image.resize(img,new_size)
    return img

但是如果我戴上口罩，事情就会变得很糟糕

def parse_img(file_path,128]):

    # Image
    img_content = tf.io.read_file(file_path)
    img = tf.image.decode_jpeg(img_content)
    img = tf.image.convert_image_dtype(img,new_size)

    # Mask
    file_id = tf.strings.split(file_path,'/')[-1]
    objects = [rle_decode(m) for m in df2[df.ImageId==file_id]]
    mask = np.sum(objects,axis=0)
    mask = np.expand_dims(mask,3)   # Force mask to have 3 channels,necessary for resize step
    mask = tf.image.convert_image_dtype(mask,tf.int8)
    mask = tf.clip_by_value(mask,1)
    mask = tf.image.resize(mask,new_size)
    mask = tf.squeeze(mask)     # squeeze back
    mask = tf.image.convert_image_dtype(mask,tf.int8)

    return img,mask

尽管我的parse_img函数工作正常（我已经在一个样本上对其进行了检查，但是每次运行需要271 µs±67.9 µs）； list_ds.map步骤将永远挂起（> 5分钟），然后再挂起。我不知道怎么了，这让我发疯！ 有什么想法吗？

import tensorflow as tf def rle_decode_tf(mask_rle,shape): shape = tf.convert_to_tensor(shape,tf.int64) size = tf.math.reduce_prod(shape) # Split string s = tf.strings.split(mask_rle) s = tf.strings.to_number(s,tf.int64) # Get starts and lengths starts = s[::2] - 1 lens = s[1::2] # Make ones to be scattered total_ones = tf.reduce_sum(lens) ones = tf.ones([total_ones],tf.uint8) # Make scattering indices r = tf.range(total_ones) lens_cum = tf.math.cumsum(lens) s = tf.searchsorted(lens_cum,r,'right') idx = r + tf.gather(starts - tf.pad(lens_cum[:-1],[(1,0)]),s) # Scatter ones into flattened mask mask_flat = tf.scatter_nd(tf.expand_dims(idx,1),ones,[size]) # Reshape into mask return tf.reshape(mask_flat,shape)

mask_rle = '1 2 4 3 9 4 15 5' shape = [4,6] # Original NumPy function print(rle_decode(mask_rle,shape)) # [[1 0 0 1] # [1 0 0 0] # [0 1 1 0] # [1 1 1 0] # [1 1 1 0] # [1 1 1 0]] # TensorFlow function (transposing is done out of the function) tf.print(tf.transpose(rle_decode_tf(mask_rle,shape))) # [[1 0 0 1] # [1 0 0 0] # [0 1 1 0] # [1 1 1 0] # [1 1 1 0] # [1 1 1 0]]

使用Tensorflow数据集创建RLE（行程编码）蒙版

ksjfhvqkugt9a 回答：使用Tensorflow数据集创建RLE（行程编码）蒙版

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