github:https://github.com/zcc720/COCO2VOC.git
原文地址：http://www.manongjc.com/article/28607.html
接上篇VOC数据集提取自己需要的类，这次我们依然从coco数据集中提取我们想要的类，并转为voc格式，用于目标检测。
一、去官网下载数据集
二、安装coco-PythonAPI
linux用户：
pip install cython git clone https://github.com/cocodataset/cocoapi.git cd coco/PythonAPI makewindows用户：
pip install cython git clone https://github.com/cocodataset/cocoapi.git cd coco/PythonAPI python setup.py build_ext --inplace三、get自己想要的类，制作成voc文件
COCO数据集目标检测中有90类：
classes: {1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus', 7: 'train', 8: 'truck', 9: 'boat', 10: 'traffic light', 11: 'fire hydrant', 13: 'stop sign', 14: 'parking meter', 15: 'bench', 16: 'bird', 17: 'cat', 18: 'dog', 19: 'horse', 20: 'sheep', 21: 'cow', 22: 'elephant', 23: 'bear', 24: 'zebra', 25: 'giraffe', 27: 'backpack', 28: 'umbrella', 31: 'handbag', 32: 'tie', 33: 'suitcase', 34: 'frisbee', 35: 'skis', 36: 'snowboard', 37: 'sports ball', 38: 'kite', 39: 'baseball bat', 40: 'baseball glove', 41: 'skateboard', 42: 'surfboard', 43: 'tennis racket', 44: 'bottle', 46: 'wine glass', 47: 'cup', 48: 'fork', 49: 'knife', 50: 'spoon', 51: 'bowl', 52: 'banana', 53: 'apple', 54: 'sandwich', 55: 'orange', 56: 'broccoli', 57: 'carrot', 58: 'hot dog',59: 'pizza', 60: 'donut', 61: 'cake', 62: 'chair', 63: 'couch', 64: 'potted plant', 65: 'bed', 67: 'dining table', 70: 'toilet', 72: 'tv', 73: 'laptop', 74: 'mouse', 75: 'remote', 76: 'keyboard',77: 'cell phone', 78: 'microwave', 79: 'oven', 80: 'toaster', 81: 'sink', 82: 'refrigerator', 84: 'book', 85: 'clock', 86: 'vase', 87: 'scissors', 88: 'teddy bear', 89: 'hair drier', 90: 'toothbrush'} 想要的类
classes_names = ['car', 'bicycle', 'person', 'motorcycle', 'bus', 'truck']from pycocotools.coco import COCO import os import shutil from tqdm import tqdm import skimage.io as io import matplotlib.pyplot as plt import cv2 from PIL import Image, ImageDraw #the path you want to save your results for coco to voc savepath="E:/datasets/COCO/result/" img_dir=savepath+'images/' anno_dir=savepath+'Annotations/' # datasets_list=['train2014', 'val2014'] datasets_list=['train2017'] classes_names = ['car', 'bicycle', 'person', 'motorcycle', 'bus', 'truck'] #Store annotations and train2014/val2014/... in this folder dataDir= 'E:/datasets/COCO/' headstr = """\ <annotation> <folder>VOC</folder> <filename>%s</filename> <source> <database>My Database</database> <annotation>COCO</annotation> <image>flickr</image> <flickrid>NULL</flickrid> </source> <owner> <flickrid>NULL</flickrid> <name>company</name> </owner> <size> <width>%d</width> <height>%d</height> <depth>%d</depth> </size> <segmented>0</segmented> """ objstr = """\ <object> <name>%s</name> <pose>Unspecified</pose> <truncated>0</truncated> <difficult>0</difficult> <bndbox> <xmin>%d</xmin> <ymin>%d</ymin> <xmax>%d</xmax> <ymax>%d</ymax> </bndbox> </object> """ tailstr = '''\ </annotation> ''' #if the dir is not exists,make it,else delete it def mkr(path): if os.path.exists(path): shutil.rmtree(path) os.mkdir(path) else: os.mkdir(path) mkr(img_dir) mkr(anno_dir) def id2name(coco): classes=dict() for cls in coco.dataset['categories']: classes[cls['id']]=cls['name'] return classes def write_xml(anno_path,head, objs, tail): f = open(anno_path, "w") f.write(head) for obj in objs: f.write(objstr%(obj[0],obj[1],obj[2],obj[3],obj[4])) f.write(tail) def save_annotations_and_imgs(coco,dataset,filename,objs): #eg:COCO_train2014_000000196610.jpg-->COCO_train2014_000000196610.xml anno_path=anno_dir+filename[:-3]+'xml' img_path=dataDir+dataset+'/'+filename print(img_path) dst_imgpath=img_dir+filename img=cv2.imread(img_path) if (img.shape[2] == 1): print(filename + " not a RGB image") return shutil.copy(img_path, dst_imgpath) head=headstr % (filename, img.shape[1], img.shape[0], img.shape[2]) tail = tailstr write_xml(anno_path,head, objs, tail) def showimg(coco,dataset,img,classes,cls_id,show=True): global dataDir I=Image.open('%s/%s/%s'%(dataDir,dataset,img['file_name'])) #通过id，得到注释的信息 annIds = coco.getAnnIds(imgIds=img['id'], catIds=cls_id, iscrowd=None) # print(annIds) anns = coco.loadAnns(annIds) # print(anns) # coco.showAnns(anns) objs = [] for ann in anns: class_name=classes[ann['category_id']] if class_name in classes_names: print(class_name) if 'bbox' in ann: bbox=ann['bbox'] xmin = int(bbox[0]) ymin = int(bbox[1]) xmax = int(bbox[2] + bbox[0]) ymax = int(bbox[3] + bbox[1]) obj = [class_name, xmin, ymin, xmax, ymax] objs.append(obj) draw = ImageDraw.Draw(I) draw.rectangle([xmin, ymin, xmax, ymax]) if show: plt.figure() plt.axis('off') plt.imshow(I) plt.show() return objs for dataset in datasets_list: #./COCO/annotations/instances_train2014.json annFile='{}/annotations/instances_{}.json'.format(dataDir,dataset) #COCO API for initializing annotated data coco = COCO(annFile) ''' COCO 对象创建完毕后会输出如下信息: loading annotations into memory... Done (t=0.81s) creating index... index created! 至此, json 脚本解析完毕, 并且将图片和对应的标注数据关联起来. ''' #show all classes in coco classes = id2name(coco) print(classes) #[1, 2, 3, 4, 6, 8] classes_ids = coco.getCatIds(catNms=classes_names) print(classes_ids) for cls in classes_names: #Get ID number of this class cls_id=coco.getCatIds(catNms=[cls]) img_ids=coco.getImgIds(catIds=cls_id) print(cls,len(img_ids)) # imgIds=img_ids[0:10] for imgId in tqdm(img_ids): img = coco.loadImgs(imgId)[0] filename = img['file_name'] # print(filename) objs=showimg(coco, dataset, img, classes,classes_ids,show=False) print(objs) save_annotations_and_imgs(coco, dataset, filename, objs)