介绍
1.PNG2RLE
2.RLE2PNG
3.示例
4.完整代码如下
介绍在机器视觉领域的深度学习中，每个数据集都有一份标注好的数据用于训练神经网络。
为了节省空间，很多数据集的标注文件使用RLE的格式。
但是神经网络的输入一定是一张图片，为此必须把RLE格式的文件转变为图像格式。
图像格式主要又分为 .webp 和 .webp 两种格式，其中label数据一定不能使用 .webp，因为它因为压缩算算法的原因，会造成图像失真，图像各个像素的值可能会发生变化。分割任务的数据集的 label 图像中每一个像素都代表了该像素点所属的类别，所以这样的失真是无法接受的。为此只能使用 .webp 格式作为label，pascol voc 和 coco 数据集正是这样做的。
1.PNG2RLEPNG格式转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格式转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.webp'
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.完整代码如下
'''
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.webp'
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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