使用COCO数据集的未压缩RLE编码numpy数组

zqdjd7g9 于 12个月前发布在其他

关注(0)|答案(5)|浏览(110)

要创建带注解图像的COCO数据集，您需要根据对象的类型将二进制掩码转换为多边形或未压缩的游程长度编码表示。
pycocotools库具有对 compressed RLE进行编码和解码的功能，但没有多边形和未压缩RLE的功能。
我可以使用skimage的measure库来generate polygons of masks，但我不确定如何创建未压缩的RLE。
我可以使用this RLE encoder从图像创建RLE的 a 表示，但我不确定COCO期望什么格式。COCO只是提到他们使用“自定义游程编码（RLE）方案”
例如，

ground_truth_binary_mask = np.array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  1,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0]], dtype=np.uint8)

fortran_ground_truth_binary_mask = np.asfortranarray(ground_truth_binary_mask)
rle(fortran_ground_truth_binary_mask)

产出：

(array([26, 36, 46, 56, 61]), array([3, 3, 3, 3, 1]))

这就是Coco RLE的样子：

{
    "segmentation": {
        "counts": [
            272,
            2,
            4,
            4,
            4,
            4,
            2,
            9,
            1,
            2,
            16,
            43,
            143,
            24,
            5,
            8,
            16,
            44,
            141,
            25,
            8,
            5,
            17,
            44,
            140,
            26,
            10,
            2,
            17,
            45,
            129,
            4,
            5,
            27,
            24,
            5,
            1,
            45,
            127,
            38,
            23,
            52,
            125,
            40,
            22,
            53,
            123,
            43,
            20,
            54,
            122,
            46,
            18,
            54,
            121,
            54,
            12,
            53,
            119,
            57,
            11,
            53,
            117,
            59,
            13,
            51,
            117,
            59,
            13,
            51,
            117,
            60,
            11,
            52,
            117,
            60,
            10,
            52,
            118,
            60,
            9,
            53,
            118,
            61,
            8,
            52,
            119,
            62,
            7,
            52,
            119,
            64,
            1,
            2,
            2,
            51,
            120,
            120,
            120,
            101,
            139,
            98,
            142,
            96,
            144,
            93,
            147,
            90,
            150,
            87,
            153,
            85,
            155,
            82,
            158,
            76,
            164,
            66,
            174,
            61,
            179,
            57,
            183,
            54,
            186,
            52,
            188,
            49,
            191,
            47,
            193,
            21,
            8,
            16,
            195,
            20,
            13,
            8,
            199,
            18,
            222,
            17,
            223,
            16,
            224,
            16,
            224,
            15,
            225,
            15,
            225,
            15,
            225,
            15,
            225,
            15,
            225,
            15,
            225,
            15,
            225,
            15,
            225,
            15,
            225,
            14,
            226,
            14,
            226,
            14,
            39,
            1,
            186,
            14,
            39,
            3,
            184,
            14,
            39,
            4,
            183,
            13,
            40,
            6,
            181,
            14,
            39,
            7,
            180,
            14,
            39,
            9,
            178,
            14,
            39,
            10,
            177,
            14,
            39,
            11,
            176,
            14,
            38,
            14,
            174,
            14,
            36,
            19,
            171,
            15,
            33,
            32,
            160,
            16,
            30,
            35,
            159,
            18,
            26,
            38,
            158,
            19,
            23,
            41,
            157,
            20,
            19,
            45,
            156,
            21,
            15,
            48,
            156,
            22,
            10,
            53,
            155,
            23,
            9,
            54,
            154,
            23,
            8,
            55,
            154,
            24,
            7,
            56,
            153,
            24,
            6,
            57,
            153,
            25,
            5,
            57,
            153,
            25,
            5,
            58,
            152,
            25,
            4,
            59,
            152,
            26,
            3,
            59,
            152,
            26,
            3,
            59,
            152,
            27,
            1,
            60,
            152,
            27,
            1,
            60,
            152,
            86,
            154,
            80,
            160,
            79,
            161,
            42,
            8,
            29,
            161,
            41,
            11,
            22,
            2,
            3,
            161,
            40,
            13,
            18,
            5,
            3,
            161,
            40,
            15,
            2,
            5,
            8,
            7,
            2,
            161,
            40,
            24,
            6,
            170,
            35,
            30,
            4,
            171,
            34,
            206,
            34,
            41,
            1,
            164,
            34,
            39,
            3,
            164,
            34,
            37,
            5,
            164,
            34,
            35,
            10,
            161,
            36,
            1,
            3,
            28,
            17,
            155,
            41,
            27,
            16,
            156,
            41,
            26,
            17,
            156,
            41,
            26,
            16,
            157,
            27,
            4,
            10,
            25,
            16,
            158,
            27,
            6,
            8,
            11,
            2,
            12,
            6,
            2,
            7,
            159,
            27,
            7,
            14,
            3,
            4,
            19,
            6,
            160,
            26,
            8,
            22,
            18,
            5,
            161,
            26,
            8,
            22,
            18,
            4,
            162,
            26,
            8,
            23,
            15,
            4,
            164,
            23,
            11,
            23,
            11,
            7,
            165,
            19,
            17,
            22,
            9,
            6,
            167,
            19,
            22,
            18,
            8,
            3,
            170,
            18,
            25,
            16,
            7,
            1,
            173,
            17,
            28,
            15,
            180,
            17,
            30,
            12,
            181,
            16,
            34,
            6,
            184,
            15,
            225,
            14,
            226,
            13,
            227,
            12,
            228,
            11,
            229,
            10,
            230,
            9,
            231,
            9,
            231,
            9,
            231,
            9,
            231,
            8,
            232,
            8,
            232,
            8,
            232,
            8,
            232,
            8,
            232,
            8,
            232,
            7,
            233,
            7,
            233,
            7,
            233,
            7,
            233,
            8,
            232,
            8,
            232,
            8,
            232,
            9,
            231,
            9,
            231,
            9,
            231,
            10,
            230,
            10,
            230,
            11,
            229,
            13,
            227,
            14,
            226,
            16,
            224,
            17,
            223,
            19,
            221,
            23,
            217,
            31,
            3,
            5,
            201,
            39,
            201,
            39,
            201,
            39,
            201,
            39,
            201,
            39,
            201,
            40,
            200,
            40,
            200,
            41,
            199,
            41,
            199,
            41,
            199,
            22,
            8,
            12,
            198,
            22,
            12,
            8,
            198,
            22,
            14,
            6,
            198,
            22,
            15,
            6,
            197,
            22,
            16,
            5,
            197,
            22,
            17,
            5,
            196,
            22,
            18,
            4,
            196,
            22,
            19,
            4,
            195,
            22,
            19,
            5,
            194,
            22,
            20,
            4,
            194,
            25,
            21,
            1,
            193,
            27,
            213,
            29,
            211,
            30,
            210,
            35,
            6,
            6,
            193,
            49,
            191,
            50,
            190,
            50,
            190,
            51,
            189,
            51,
            189,
            52,
            188,
            53,
            187,
            53,
            187,
            54,
            186,
            54,
            186,
            54,
            186,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            55,
            185,
            28,
            1,
            26,
            185,
            23,
            11,
            21,
            185,
            20,
            17,
            17,
            186,
            18,
            21,
            15,
            186,
            16,
            23,
            14,
            187,
            14,
            25,
            14,
            187,
            14,
            26,
            12,
            188,
            14,
            28,
            10,
            188,
            14,
            226,
            14,
            226,
            16,
            224,
            17,
            223,
            19,
            221,
            20,
            220,
            22,
            218,
            24,
            18,
            3,
            12,
            3,
            180,
            25,
            10,
            1,
            4,
            6,
            10,
            6,
            178,
            28,
            7,
            12,
            8,
            8,
            177,
            49,
            3,
            12,
            176,
            65,
            175,
            67,
            173,
            69,
            171,
            53,
            3,
            14,
            170,
            37,
            20,
            9,
            4,
            1,
            169,
            36,
            21,
            8,
            175,
            35,
            22,
            7,
            176,
            34,
            23,
            7,
            176,
            34,
            23,
            6,
            177,
            35,
            22,
            6,
            177,
            35,
            22,
            8,
            175,
            35,
            23,
            9,
            173,
            35,
            205,
            36,
            204,
            39,
            201,
            43,
            197,
            48,
            36,
            1,
            155,
            48,
            35,
            3,
            154,
            49,
            33,
            5,
            154,
            48,
            32,
            6,
            155,
            49,
            27,
            10,
            155,
            51,
            24,
            11,
            154,
            54,
            21,
            11,
            155,
            56,
            19,
            11,
            155,
            56,
            18,
            11,
            156,
            56,
            17,
            11,
            157,
            56,
            16,
            12,
            157,
            56,
            14,
            13,
            159,
            56,
            12,
            13,
            160,
            61,
            5,
            14,
            162,
            78,
            165,
            75,
            167,
            73,
            168,
            72,
            170,
            70,
            171,
            69,
            173,
            67,
            176,
            64,
            179,
            61,
            182,
            58,
            183,
            57,
            185,
            54,
            187,
            53,
            188,
            51,
            191,
            49,
            192,
            47,
            195,
            45,
            196,
            43,
            198,
            42,
            199,
            40,
            201,
            38,
            203,
            36,
            205,
            34,
            207,
            32,
            210,
            28,
            213,
            26,
            216,
            22,
            221,
            16,
            228,
            8,
            10250
        ],
        "size": [
            240,
            320
        ]
    }
}

numpy

来源：https://stackoverflow.com/questions/49494337/encode-numpy-array-using-uncompressed-rle-for-coco-dataset

5条答案

按热度按时间

kqlmhetl1#

有关格式的信息可在此处获得：https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py
RLE是用于存储二进制掩码的简单而有效的格式。RLE首先将矢量（或矢量化图像）划分为一系列分段恒定区域，然后针对每一段简单地存储该段的长度。例如，给定M=[0 0 1 1 1 0 1]，RLE计数将是[2 3 1 1]，或者对于M=[1 1 1 1 1 1 0]，计数将是[0 6 1]（注意奇数计数总是零的数量）。

import numpy as np
from itertools import groupby

def binary_mask_to_rle(binary_mask):
    rle = {'counts': [], 'size': list(binary_mask.shape)}
    counts = rle.get('counts')
    for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order='F'))):
        if i == 0 and value == 1:
            counts.append(0)
        counts.append(len(list(elements)))
    return rle

test_list_1 = np.array([0, 0, 1, 1, 1, 0, 1])
test_list_2 = np.array([1, 1, 1, 1, 1, 1, 0])

print(binary_mask_to_rle(test_list_1))
print(binary_mask_to_rle(test_list_2))

产出：

{'counts': [2, 3, 1, 1], 'size': [7]}
{'counts': [0, 6, 1], 'size': [7]}

您可以使用mask.frPyObjects(rle, size_x, size_y)对RLE进行编码，然后执行所有常见的mask操作。

import json
import numpy as np
from pycocotools import mask
from skimage import measure

ground_truth_binary_mask = np.array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  0,   0,   0,   0,   0,   1,   1,   1,   0,   0],
                                     [  1,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0],
                                     [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0]], dtype=np.uint8)

fortran_ground_truth_binary_mask = np.asfortranarray(ground_truth_binary_mask)

将掩码编码为RLE：

rle = binary_mask_to_rle(fortran_ground_truth_binary_mask)
print(rle)

产出：

{'counts': [6, 1, 40, 4, 5, 4, 5, 4, 21], 'size': [9, 10]}

压缩RLE，然后解码：

compressed_rle = mask.frPyObjects(rle, rle.get('size')[0], rle.get('size')[1])
mask.decode(compressed_rle)

产出：

array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [0, 0, 0, 0, 0, 1, 1, 1, 0, 0],
       [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)

赞(0）回复(0）举报 12个月前

56lgkhnf2#

作为对@waspinator的回答的改进。速度快了35%。

def binary_mask_to_rle(binary_mask):
    rle = {'counts': [], 'size': list(binary_mask.shape)}
    counts = rle.get('counts')

    last_elem = 0
    running_length = 0

    for i, elem in enumerate(binary_mask.ravel(order='F')):
        if elem == last_elem:
            pass
        else:
            counts.append(running_length)
            running_length = 0
            last_elem = elem
        running_length += 1

    counts.append(running_length)

    return rle

赞(0）回复(0）举报 12个月前

44u64gxh3#

为了解码COCO注解中编码的二进制掩码，您需要首先使用COCO's API获得RLE，然后使用opencv获得如下轮廓：

# Import libraries
import numpy as np
import cv2
import json
import mask

# Read the annotations
file_path = "coco/annotations/stuff_annotations_trainval2017/stuff_train2017.json"
with open(file_path, 'r') as f:
    data = json.load(f)

updated_data = []

# For each annotation
for annotation in data['annotations']:

    # Initialize variables
    obj = {}
    segmentation = []
    segmentation_polygons = []

    # Decode the binary mask
    mask_list = mask.decode(annotation['segmentation'])
    mask_list = np.ascontiguousarray(mask_list, dtype=np.uint8)
    mask_new, contours, hierarchy = cv2.findContours((mask_list).astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # Get the contours
    for contour in contours:
        contour = contour.flatten().tolist()
        segmentation.append(contour)
        if len(contour) > 4:
            segmentation.append(contour)
    if len(segmentation) == 0:
        continue

    # Get the polygons as (x, y) coordinates
    for i, segment in enumerate(segmentation):
        poligon = []
        poligons = []
        for j in range(len(segment)):
            poligon.append(segment[j])
            if (j+1)%2 == 0:
                poligons.append(poligon)
                poligon = []
        segmentation_polygons.append(poligons)

    # Save the segmentation and polygons for the current annotation
    obj['segmentation'] = segmentation
    obj['segmentation_polygons'] = segmentation_polygons
    updated_data.append(obj)

注意：只有COCO stuff 2017注解使用二进制掩码，COCO person 2017注解没有，所以你不需要解码后者并找到它们的轮廓。

受此解决方案的启发。

赞(0）回复(0）举报 12个月前

wfveoks04#

这样做：

import numpy as np
from pycocotools import mask as m

# Create bool array
n_a = np.random.normal(size=(10, 10))
b_a = np.array(n_a > 0.5, dtype=np.bool, order='F')

# Encode bool array
e_a = m.encode(b_a)
d_a = m.decode(e_a)

# Decode byte string rle encoded mask
sz = pred['mask']['size']
c = pred['mask']['counts'][2:-1]
es = str.encode(c)
t = {'size': [450, 800], 'counts': es}
dm = m.decode(t)

赞(0）回复(0）举报 12个月前

tquggr8v5#

我用numpy改进了@waspinator和@Carl Cheung的代码，现在比以前快了。灵感来自find consecutive numbers in a list。

import numpy as np

def binary_mask_to_rle_np(binary_mask):
    rle = {"counts": [], "size": list(binary_mask.shape)}

    flattened_mask = binary_mask.ravel(order="F")
    diff_arr = np.diff(flattened_mask)
    nonzero_indices = np.where(diff_arr != 0)[0] + 1
    lengths = np.diff(np.concatenate(([0], nonzero_indices, [len(flattened_mask)])))

    # note that the odd counts are always the numbers of zeros
    if flattened_mask[0] == 1:
        lengths = np.concatenate(([0], lengths))

    rle["counts"] = lengths.tolist()

    return rle

# For test
# def binary_mask_to_rle(binary_mask):
# ...
# M1 = np.array([1, 0, 0, 1])
# M2 = np.array([0, 1, 1, 0, 1, 1, 0])

# assert binary_mask_to_rle_np(M1)["counts"] == binary_mask_to_rle(M1)["counts"]
# assert binary_mask_to_rle_np(M2)["counts"] == binary_mask_to_rle(M2)["counts"]

赞(0）回复(0）举报 12个月前

我来回答

使用COCO数据集的未压缩RLE编码numpy数组

5条答案

相关问题

热门标签

最新问答