labelme转COCO数据集(物体检测)
【摘要】
本次采用的数据集是Labelme标注的数据集,地址:链接:https://pan.baidu.com/s/1nxo9-NpNWKK4PwDZqwKxGQ 提取码:kp4e,需要将其转为COCO格式的数据集...
本次采用的数据集是Labelme标注的数据集,地址:链接:https://pan.baidu.com/s/1nxo9-NpNWKK4PwDZqwKxGQ 提取码:kp4e,需要将其转为COCO格式的数据集。转换代码如下:
新建labelme2coco.py
import argparse
import json
import matplotlib.pyplot as plt
import skimage.io as io
import cv2
from labelme import utils
import numpy as np
import glob
import PIL.Image
REQUIRE_MASK = False
labels = {'aircraft': 1, 'oiltank': 2}
class labelme2coco(object):
def __init__(self, labelme_json=[], save_json_path='./new.json'):
'''
:param labelme_json: the list of all labelme json file paths
:param save_json_path: the path to save new json
'''
self.labelme_json = labelme_json
self.save_json_path = save_json_path
self.images = []
self.categories = []
self.annotations = []
# self.data_coco = {}
self.label = []
self.annID = 1
self.height = 0
self.width = 0
self.require_mask = REQUIRE_MASK
self.save_json()
def data_transfer(self):
for num, json_file in enumerate(self.labelme_json):
if not json_file == self.save_json_path:
with open(json_file, 'r') as fp:
data = json.load(fp)
self.images.append(self.image(data, num))
for shapes in data['shapes']:
print("label is ")
print(shapes['label'])
label = shapes['label']
# if label[1] not in self.label:
if label not in self.label:
print("find new category: ")
self.categories.append(self.categorie(label))
print(self.categories)
# self.label.append(label[1])
self.label.append(label)
points = shapes['points']
self.annotations.append(self.annotation(points, label, num))
self.annID += 1
def image(self, data, num):
image = {}
img = utils.img_b64_to_arr(data['imageData'])
height, width = img.shape[:2]
img = None
image['height'] = height
image['width'] = width
image['id'] = num + 1
image['file_name'] = data['imagePath'].split('/')[-1]
self.height = height
self.width = width
return image
def categorie(self, label):
categorie = {}
categorie['supercategory'] = label
# categorie['supercategory'] = label
categorie['id'] = labels[label] # 0 默认为背景
categorie['name'] = label
return categorie
def annotation(self, points, label, num):
annotation = {}
print(points)
x1 = points[0][0]
y1 = points[0][1]
x2 = points[1][0]
y2 = points[1][1]
contour = np.array([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]) # points = [[x1, y1], [x2, y2]] for rectangle
contour = contour.astype(int)
area = cv2.contourArea(contour)
print("contour is ", contour, " area = ", area)
annotation['segmentation'] = [list(np.asarray([[x1, y1], [x2, y1], [x2, y2], [x1, y2]]).flatten())]
# [list(np.asarray(contour).flatten())]
annotation['iscrowd'] = 0
annotation['area'] = area
annotation['image_id'] = num + 1
if self.require_mask:
annotation['bbox'] = list(map(float, self.getbbox(points)))
else:
x1 = points[0][0]
y1 = points[0][1]
width = points[1][0] - x1
height = points[1][1] - y1
annotation['bbox'] = list(np.asarray([x1, y1, width, height]).flatten())
annotation['category_id'] = self.getcatid(label)
annotation['id'] = self.annID
return annotation
def getcatid(self, label):
for categorie in self.categories:
# if label[1]==categorie['name']:
if label == categorie['name']:
return categorie['id']
return -1
def getbbox(self, points):
polygons = points
mask = self.polygons_to_mask([self.height, self.width], polygons)
return self.mask2box(mask)
def mask2box(self, mask):
# np.where(mask==1)
index = np.argwhere(mask == 1)
rows = index[:, 0]
clos = index[:, 1]
left_top_r = np.min(rows) # y
left_top_c = np.min(clos) # x
right_bottom_r = np.max(rows)
right_bottom_c = np.max(clos)
return [left_top_c, left_top_r, right_bottom_c - left_top_c, right_bottom_r - left_top_r]
def polygons_to_mask(self, img_shape, polygons):
mask = np.zeros(img_shape, dtype=np.uint8)
mask = PIL.Image.fromarray(mask)
xy = list(map(tuple, polygons))
PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
mask = np.array(mask, dtype=bool)
return mask
def data2coco(self):
data_coco = {}
data_coco['images'] = self.images
data_coco['categories'] = self.categories
data_coco['annotations'] = self.annotations
return data_coco
def save_json(self):
print("in save_json")
self.data_transfer()
self.data_coco = self.data2coco()
print(self.save_json_path)
json.dump(self.data_coco, open(self.save_json_path, 'w'), indent=4)
labelme_json = glob.glob('LabelmeData/*.json')
from sklearn.model_selection import train_test_split
trainval_files, test_files = train_test_split(labelme_json, test_size=0.2, random_state=55)
import os
if not os.path.exists("datasets/coco/annotations"):
os.makedirs("datasets/coco/annotations/")
if not os.path.exists("/datasets/coco/train2017"):
os.makedirs("datasets/coco/train2017")
if not os.path.exists("datasets/coco/val2017"):
os.makedirs("datasets/coco/val2017")
labelme2coco(trainval_files, 'datasets/coco/annotations/instances_train2017.json')
labelme2coco(test_files, 'datasets/coco/annotations/instances_val2017.json')
import shutil
for file in trainval_files:
shutil.copy(os.path.splitext(file)[0] + ".jpg", "datasets/coco/train2017/")
for file in test_files:
shutil.copy(os.path.splitext(file)[0] + ".jpg", "datasets/coco/val2017/")
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
- 54
- 55
- 56
- 57
- 58
- 59
- 60
- 61
- 62
- 63
- 64
- 65
- 66
- 67
- 68
- 69
- 70
- 71
- 72
- 73
- 74
- 75
- 76
- 77
- 78
- 79
- 80
- 81
- 82
- 83
- 84
- 85
- 86
- 87
- 88
- 89
- 90
- 91
- 92
- 93
- 94
- 95
- 96
- 97
- 98
- 99
- 100
- 101
- 102
- 103
- 104
- 105
- 106
- 107
- 108
- 109
- 110
- 111
- 112
- 113
- 114
- 115
- 116
- 117
- 118
- 119
- 120
- 121
- 122
- 123
- 124
- 125
- 126
- 127
- 128
- 129
- 130
- 131
- 132
- 133
- 134
- 135
- 136
- 137
- 138
- 139
- 140
- 141
- 142
- 143
- 144
- 145
- 146
- 147
- 148
- 149
- 150
- 151
- 152
- 153
- 154
- 155
- 156
- 157
- 158
- 159
- 160
- 161
- 162
- 163
- 164
- 165
- 166
- 167
- 168
- 169
- 170
- 171
- 172
- 173
- 174
- 175
文章来源: wanghao.blog.csdn.net,作者:AI浩,版权归原作者所有,如需转载,请联系作者。
原文链接:wanghao.blog.csdn.net/article/details/120664330
【版权声明】本文为华为云社区用户转载文章,如果您发现本社区中有涉嫌抄袭的内容,欢迎发送邮件进行举报,并提供相关证据,一经查实,本社区将立刻删除涉嫌侵权内容,举报邮箱:
cloudbbs@huaweicloud.com
- 点赞
- 收藏
- 关注作者
评论(0)