Python實現(xiàn)RLE格式與PNG格式互轉(zhuǎn)

更新時間：2022年08月18日 08:49:34 作者：Livingbody

在機器視覺領域的深度學習中，很多數(shù)據(jù)集的標注文件使用RLE的格式。但是神經(jīng)網(wǎng)絡的輸入一定是一張圖片，為此必須把RLE格式的文件轉(zhuǎn)變?yōu)閳D像格式。本文將利用Python實現(xiàn)RLE格式與PNG格式互轉(zhuǎn)，感興趣的可以了解一下

介紹

在機器視覺領域的深度學習中，每個數(shù)據(jù)集都有一份標注好的數(shù)據(jù)用于訓練神經(jīng)網(wǎng)絡。

為了節(jié)省空間，很多數(shù)據(jù)集的標注文件使用RLE的格式。

但是神經(jīng)網(wǎng)絡的輸入一定是一張圖片，為此必須把RLE格式的文件轉(zhuǎn)變?yōu)閳D像格式。

圖像格式主要又分為 .jpg 和 .png 兩種格式，其中l(wèi)abel數(shù)據(jù)一定不能使用 .jpg，因為它因為壓縮算算法的原因，會造成圖像失真，圖像各個像素的值可能會發(fā)生變化。分割任務的數(shù)據(jù)集的 label 圖像中每一個像素都代表了該像素點所屬的類別，所以這樣的失真是無法接受的。為此只能使用 .png 格式作為label，pascol voc 和 coco 數(shù)據(jù)集正是這樣做的。

1.PNG2RLE

PNG格式轉(zhuǎn)RLE格式

#!---- coding: utf- ---- import numpy as np

def rle_encode(binary_mask):
    '''
    binary_mask: numpy array, 1 - mask, 0 - background
    Returns run length as string formated
    '''
    pixels = binary_mask.flatten()
    pixels = np.concatenate([[0], pixels, [0]])
    runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
    runs[1::2] -= runs[::2]
    return ' '.join(str(x) for x in runs)

2.RLE2PNG

RLE格式轉(zhuǎn)PNG格式

#!--*-- coding: utf- --*--
import numpy as np

def rle_decode(mask_rle, shape):
    '''
    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
    binary_mask = np.zeros(shape[0] * shape[1], dtype=np.uint8)
    for lo, hi in zip(starts, ends):
        binary_mask[lo:hi] = 1
    return binary_mask.reshape(shape)

3.示例

'''
RLE: Run-Length Encode
'''
from PIL import Image
import numpy as np 

def __main__():
    maskfile = '/path/to/test.png'
    mask = np.array(Image.open(maskfile))
    binary_mask = mask.copy()
    binary_mask[binary_mask <= 127] = 0
    binary_mask[binary_mask > 127] = 1

    # encode
    rle_mask = rle_encode(binary_mask)
    
    # decode
    binary_mask_decode = self.rle_decode(rle_mask, binary_mask.shape[:2])

4.完整代碼如下

'''
RLE: Run-Length Encode
'''
#!--*-- coding: utf- --*--
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt


# M1:
class general_rle(object):
    '''
    ref.: https://www.kaggle.com/stainsby/fast-tested-rle
    '''
    def __init__(self):
        pass


    def rle_encode(self, binary_mask):
        pixels = binary_mask.flatten()
        # We avoid issues with '1' at the start or end (at the corners of
        # the original image) by setting those pixels to '0' explicitly.
        # We do not expect these to be non-zero for an accurate mask,
        # so this should not harm the score.
        pixels[0] = 0
        pixels[-1] = 0
        runs = np.where(pixels[1:] != pixels[:-1])[0] + 2
        runs[1::2] = runs[1::2] - runs[:-1:2]
        return runs


    def rle_to_string(self, runs):
        return ' '.join(str(x) for x in runs)


    def check(self):
        test_mask = np.asarray([[0, 0, 0, 0],
                                [0, 0, 1, 1],
                                [0, 0, 1, 1],
                                [0, 0, 0, 0]])
        assert rle_to_string(rle_encode(test_mask)) == '7 2 11 2'


# M2:
class binary_mask_rle(object):
    '''
    ref.: https://www.kaggle.com/paulorzp/run-length-encode-and-decode
    '''
    def __init__(self):
        pass

    def rle_encode(self, binary_mask):
        '''
        binary_mask: numpy array, 1 - mask, 0 - background
        Returns run length as string formated
        '''
        pixels = binary_mask.flatten()
        pixels = np.concatenate([[0], pixels, [0]])
        runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
        runs[1::2] -= runs[::2]
        return ' '.join(str(x) for x in runs)


    def rle_decode(self, mask_rle, shape):
        '''
        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
        binary_mask = np.zeros(shape[0] * shape[1], dtype=np.uint8)
        for lo, hi in zip(starts, ends):
            binary_mask[lo:hi] = 1
        return binary_mask.reshape(shape)

    def check(self):
        maskfile = '/path/to/test.png'
        mask = np.array(Image.open(maskfile))
        binary_mask = mask.copy()
        binary_mask[binary_mask <= 127] = 0
        binary_mask[binary_mask > 127] = 1

        # encode
        rle_mask = self.rle_encode(binary_mask)

        # decode
        binary_mask2 = self.rle_decode(rle_mask, binary_mask.shape[:2])

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