Python实现RLE格式与PNG格式互转

目录

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.PNG2RLE

PNG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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