对COCO数据使用未压缩RLE对numpy数组进行编码

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)

{ "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 ] } }

2条回答

网友

1楼 · 编辑于 2024-05-14 23:44:06

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 is a simple yet efficient format for storing binary masks. RLE first divides a vector (or vectorized image) into a series of piecewise constant regions and then for each piece simply stores the length of that piece. For example, given M=[0 0 1 1 1 0 1] the RLE counts would be [2 3 1 1], or for M=[1 1 1 1 1 1 0] the counts would be [0 6 1] (note that the odd counts are always the numbers of zeros).

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))

输出：

^{pr2}$

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。在

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)

输出：

{'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)

输出：

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)

网友

2楼 · 编辑于 2024-05-14 23:44:06

为了解码在COCO注释中编码的二进制掩码，首先需要使用COCO's API来获得RLE，然后使用opencv来获得如下轮廓：

# 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注释使用了二进制掩码，COCO person 2017注释没有使用，因此您不需要解码后者并找到它们的轮廓。

灵感来自this解决方案。在

相关问题更多 >

编程相关推荐

热门问题

热门文章