彩票网站建设哪里,嘉兴网站建设优化,网站通栏广告代码,模板建站流程这里主要是搬运一下能找到的 labelme标注的json文件数据转成coco数据集格式#xff08;可处理目标框和实例分割#xff09;的代码#xff0c;以供需要时参考和提供相关帮助。
1、官方labelme实现
如下是labelme官方网址#xff0c;提供了源代码#xff0c;以及相关使用方…这里主要是搬运一下能找到的 labelme标注的json文件数据转成coco数据集格式可处理目标框和实例分割的代码以供需要时参考和提供相关帮助。
1、官方labelme实现
如下是labelme官方网址提供了源代码以及相关使用方法包括数据集格式转换要仔细了解的可以细看。
网址https://github.com/wkentaro/labelme
其中官网也提供了打包成exe可执行文件的方法。 如果自己使用后有其他可改进的想法可以尝试看源码修改增加相关功能 然后打包成exe可执行文件使用会更方便。 可以看到相关工作的介绍里面提供了把实例分割标注文件转成COCO格式的功能。网址https://github.com/wkentaro/labelme/tree/main/examples/instance_segmentation 进入网址如下
labelme提供的 标注文件json 转成coco数据集格式的代码可以包含水平框和实例分割的目标轮廓代码如下
#!/usr/bin/env pythonimport argparse
import collections
import datetime
import glob
import json
import os
import os.path as osp
import sys
import uuidimport imgviz
import numpy as npimport labelmetry:import pycocotools.mask
except ImportError:print(Please install pycocotools:\n\n pip install pycocotools\n)sys.exit(1)def main():parser argparse.ArgumentParser(formatter_classargparse.ArgumentDefaultsHelpFormatter)parser.add_argument(input_dir, helpinput annotated directory)parser.add_argument(output_dir, helpoutput dataset directory)parser.add_argument(--labels, helplabels file, requiredTrue)parser.add_argument(--noviz, helpno visualization, actionstore_true)args parser.parse_args()if osp.exists(args.output_dir):print(Output directory already exists:, args.output_dir)sys.exit(1)os.makedirs(args.output_dir)os.makedirs(osp.join(args.output_dir, JPEGImages))if not args.noviz:os.makedirs(osp.join(args.output_dir, Visualization))print(Creating dataset:, args.output_dir)now datetime.datetime.now()data dict(infodict(descriptionNone,urlNone,versionNone,yearnow.year,contributorNone,date_creatednow.strftime(%Y-%m-%d %H:%M:%S.%f),),licenses[dict(urlNone, id0, nameNone,)],images[# license, url, file_name, height, width, date_captured, id],typeinstances,annotations[# segmentation, area, iscrowd, image_id, bbox, category_id, id],categories[# supercategory, id, name],)class_name_to_id {}for i, line in enumerate(open(args.labels).readlines()):class_id i - 1 # starts with -1class_name line.strip()if class_id -1:assert class_name __ignore__continueclass_name_to_id[class_name] class_iddata[categories].append(dict(supercategoryNone, idclass_id, nameclass_name,))out_ann_file osp.join(args.output_dir, annotations.json)label_files glob.glob(osp.join(args.input_dir, *.json))for image_id, filename in enumerate(label_files):print(Generating dataset from:, filename)label_file labelme.LabelFile(filenamefilename)base osp.splitext(osp.basename(filename))[0]out_img_file osp.join(args.output_dir, JPEGImages, base .jpg)img labelme.utils.img_data_to_arr(label_file.imageData)imgviz.io.imsave(out_img_file, img)data[images].append(dict(license0,urlNone,file_nameosp.relpath(out_img_file, osp.dirname(out_ann_file)),heightimg.shape[0],widthimg.shape[1],date_capturedNone,idimage_id,))masks {} # for areasegmentations collections.defaultdict(list) # for segmentationfor shape in label_file.shapes:points shape[points]label shape[label]group_id shape.get(group_id)shape_type shape.get(shape_type, polygon)mask labelme.utils.shape_to_mask(img.shape[:2], points, shape_type)if group_id is None:group_id uuid.uuid1()instance (label, group_id)if instance in masks:masks[instance] masks[instance] | maskelse:masks[instance] maskif shape_type rectangle:(x1, y1), (x2, y2) pointsx1, x2 sorted([x1, x2])y1, y2 sorted([y1, y2])points [x1, y1, x2, y1, x2, y2, x1, y2]else:points np.asarray(points).flatten().tolist()segmentations[instance].append(points)segmentations dict(segmentations)for instance, mask in masks.items():cls_name, group_id instanceif cls_name not in class_name_to_id:continuecls_id class_name_to_id[cls_name]mask np.asfortranarray(mask.astype(np.uint8))mask pycocotools.mask.encode(mask)area float(pycocotools.mask.area(mask))bbox pycocotools.mask.toBbox(mask).flatten().tolist()data[annotations].append(dict(idlen(data[annotations]),image_idimage_id,category_idcls_id,segmentationsegmentations[instance],areaarea,bboxbbox,iscrowd0,))if not args.noviz:labels, captions, masks zip(*[(class_name_to_id[cnm], cnm, msk)for (cnm, gid), msk in masks.items()if cnm in class_name_to_id])viz imgviz.instances2rgb(imageimg,labelslabels,masksmasks,captionscaptions,font_size15,line_width2,)out_viz_file osp.join(args.output_dir, Visualization, base .jpg)imgviz.io.imsave(out_viz_file, viz)with open(out_ann_file, w) as f:json.dump(data, f)if __name__ __main__:main()代码执行需要导入相关库缺少相关库自行下载安装。然后是看代码执行命令
python ./labelme2coco.py --input_dir xxx --output_dir xxx --labels labels.txt其中 --input_dir 表示输入路径包含标注的 json和图片 --output_dir 表示输出路径用以保存图片和转化的coco文件 --labels 表示标签类别文件
生成文件夹内容 It generates:- data_dataset_coco/JPEGImages- data_dataset_coco/annotations.json2、其他代码实现
代码也很好理解就是把相关功能集成到一起
import os
import argparse
import jsonfrom labelme import utils
import numpy as np
import glob
import PIL.Imageclass labelme2coco(object):def __init__(self, labelme_json[], save_json_path./coco.json)::param labelme_json: the list of all labelme json file paths:param save_json_path: the path to save new jsonself.labelme_json labelme_jsonself.save_json_path save_json_pathself.images []self.categories []self.annotations []self.label []self.annID 1self.height 0self.width 0self.save_json()def data_transfer(self):for num, json_file in enumerate(self.labelme_json):with open(json_file, r) as fp:data json.load(fp)self.images.append(self.image(data, num))for shapes in data[shapes]:label shapes[label].split(_)if label not in self.label:self.label.append(label)points shapes[points]self.annotations.append(self.annotation(points, label, num))self.annID 1# Sort all text labels so they are in the same order across data splits.self.label.sort()for label in self.label:self.categories.append(self.category(label))for annotation in self.annotations:annotation[category_id] self.getcatid(annotation[category_id])def image(self, data, num):image {}img utils.img_b64_to_arr(data[imageData])height, width img.shape[:2]img Noneimage[height] heightimage[width] widthimage[id] numimage[file_name] data[imagePath].split(/)[-1]self.height heightself.width widthreturn imagedef category(self, label):category {}category[supercategory] label[0]category[id] len(self.categories)category[name] label[0]return categorydef annotation(self, points, label, num):annotation {}contour np.array(points)x contour[:, 0]y contour[:, 1]area 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))annotation[segmentation] [list(np.asarray(points).flatten())]annotation[iscrowd] 0annotation[area] areaannotation[image_id] numannotation[bbox] list(map(float, self.getbbox(points)))annotation[category_id] label[0] # self.getcatid(label)annotation[id] self.annIDreturn annotationdef getcatid(self, label):for category in self.categories:if label category[name]:return category[id]print(label: {} not in categories: {}..format(label, self.categories))exit()return -1def getbbox(self, points):polygons pointsmask self.polygons_to_mask([self.height, self.width], polygons)return self.mask2box(mask)def mask2box(self, mask):index np.argwhere(mask 1)rows index[:, 0]clos index[:, 1]left_top_r np.min(rows) # yleft_top_c np.min(clos) # xright_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, dtypenp.uint8)mask PIL.Image.fromarray(mask)xy list(map(tuple, polygons))PIL.ImageDraw.Draw(mask).polygon(xyxy, outline1, fill1)mask np.array(mask, dtypebool)return maskdef data2coco(self):data_coco {}data_coco[images] self.imagesdata_coco[categories] self.categoriesdata_coco[annotations] self.annotationsreturn data_cocodef save_json(self):print(save coco json)self.data_transfer()self.data_coco self.data2coco()print(self.save_json_path)os.makedirs(os.path.dirname(os.path.abspath(self.save_json_path)), exist_okTrue)json.dump(self.data_coco, open(self.save_json_path, w), indent4)if __name__ __main__:import argparseparser argparse.ArgumentParser(descriptionlabelme annotation to coco data json file.)parser.add_argument(labelme_images,helpDirectory to labelme images and annotation json files.,typestr,)parser.add_argument(--output, helpOutput json file path., defaulttrainval.json)args parser.parse_args()labelme_json glob.glob(os.path.join(args.labelme_images, *.json))labelme2coco(labelme_json, args.output)代码执行命令
python labelme2coco.py labelme_images其中labelme_images 表示 放标注文件json和图片的文件夹路径结果默认在当前路径下生成 trainval.json文件
