coco数据集转换为voc格式

1。coco数据集简介，共标注了90类

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

将coco数据转换成voc格式coco_to_voc.py

'''
把coco数据集合的所有标注转换到voc格式，不改变图片命名方式，
注意，原来有一些图片是黑白照片，检测出不是 RGB 图像，这样的图像不会被放到新的文件夹中
更新日期：2019-11-19
'''
from pycocotools.coco import COCO #这个包可以从git上下载https://github.com/cocodataset/cocoapi/tree/master/PythonAPI，也可以直接用修改后的coco.py
import  os, cv2, shutil
from lxml import etree, objectify
from tqdm import tqdm
from PIL import Image
 
CKimg_dir ='/home/jerry/data/data/object_detect/coco2017_voc/images'
CKanno_dir = '/home/jerry/data/data/object_detect/coco2017_voc/annotations'
#若模型保存文件夹不存在，创建模型保存文件夹，若存在，删除重建
def mkr(path):
    if os.path.exists(path):
        shutil.rmtree(path)
        os.mkdir(path)
    else:
        os.mkdir(path)
 
 
def save_annotations(filename, objs,filepath):
    annopath = CKanno_dir + "/" + filename[:-3] + "xml" #生成的xml文件保存路径
    dst_path = CKimg_dir + "/" + filename
    img_path=filepath
    img = cv2.imread(img_path)
    im = Image.open(img_path)
    if im.mode != "RGB":
        print(filename + " not a RGB image")
        im.close()
        return
    im.close()
    shutil.copy(img_path, dst_path)#把原始图像复制到目标文件夹
    E = objectify.ElementMaker(annotate=False)
    anno_tree = E.annotation(
        E.folder('1'),
        E.filename(filename),
        E.source(
            E.database('CKdemo'),
            E.annotation('VOC'),
            E.image('CK')
        ),
        E.size(
            E.width(img.shape[1]),
            E.height(img.shape[0]),
            E.depth(img.shape[2])
        ),
        E.segmented(0)
    )
    for obj in objs:
        E2 = objectify.ElementMaker(annotate=False)
        anno_tree2 = E2.object(
            E.name(obj[0]),
            E.pose(),
            E.truncated("0"),
            E.difficult(0),
            E.bndbox(
                E.xmin(obj[2]),
                E.ymin(obj[3]),
                E.xmax(obj[4]),
                E.ymax(obj[5])
            )
        )
        anno_tree.append(anno_tree2)
    etree.ElementTree(anno_tree).write(annopath, pretty_print=True)
 
 
def showbycv(coco, dataType, img, classes,origin_image_dir,verbose=False):
    filename = img['file_name']
    filepath=os.path.join(origin_image_dir,dataType,filename)
    I = cv2.imread(filepath)
    annIds = coco.getAnnIds(imgIds=img['id'],  iscrowd=None)
    anns = coco.loadAnns(annIds)
    objs = []
    for ann in anns:
        name = classes[ann['category_id']]
        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 = [name, 1.0, xmin, ymin, xmax, ymax]
            objs.append(obj)
            if verbose:
                cv2.rectangle(I, (xmin, ymin), (xmax, ymax), (255, 0, 0))
                cv2.putText(I, name, (xmin, ymin), 3, 1, (0, 0, 255))
    save_annotations(filename, objs,filepath)
    if verbose:
        cv2.imshow("img", I)
        cv2.waitKey(0)
 
 
def catid2name(coco):#将名字和id号建立一个字典
    classes = dict()
    for cat in coco.dataset['categories']:
        classes[cat['id']] = cat['name']
        # print(str(cat['id'])+":"+cat['name'])
    return classes
 
 
def get_CK5(origin_anno_dir,origin_image_dir,verbose=False):
    dataTypes = ['train2017', 'val2017']
    for dataType in dataTypes:
        annFile = 'instances_{}.json'.format(dataType)
        annpath=os.path.join(origin_anno_dir,annFile)
        coco = COCO(annpath)
        classes = catid2name(coco)
        imgIds = coco.getImgIds()
        # imgIds=imgIds[0:1000]#测试用，抽取10张图片，看下存储效果
        for imgId in tqdm(imgIds):
            img = coco.loadImgs(imgId)[0]
            showbycv(coco, dataType, img, classes,origin_image_dir,verbose=False)
 
def main():
    base_dir='/home/jerry/data/data/object_detect/coco2017_voc'#step1 这里是一个新的文件夹，存放转换后的图片和标注
    image_dir=os.path.join(base_dir,'images')#在上述文件夹中生成images，annotations两个子文件夹
    anno_dir=os.path.join(base_dir,'annotations')
    mkr(image_dir)
    mkr(anno_dir)
    origin_image_dir='/home/jerry/data/data/object_detect/coco2017/images'#step 2原始的coco的图像存放位置
    origin_anno_dir='/home/jerry/data/data/object_detect/coco2017/annotations'#step 3 原始的coco的标注存放位置
    verbose=True #是否需要看下标记是否正确的开关标记，若是true,就会把标记展示到图片上
    get_CK5(origin_anno_dir,origin_image_dir,verbose)
 
if __name__ == "__main__":
    main()
 
    # split_traintest()

一个类别也不放过的COCO数据集转换成VOC

import json
import time
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon
import numpy as np
import copy
import itertools
import os
from collections import defaultdict
import sys
PYTHON_VERSION = sys.version_info[0]
if PYTHON_VERSION == 2:
    from urllib import urlretrieve
elif PYTHON_VERSION == 3:
    from urllib.request import urlretrieve
 
 
def _isArrayLike(obj):
    return hasattr(obj, '__iter__') and hasattr(obj, '__len__')
 
 
class COCO:
    def __init__(self, annotation_file=None):
        """
        Constructor of Microsoft COCO helper class for reading and visualizing annotations.
        :param annotation_file (str): location of annotation file
        :param image_folder (str): location to the folder that hosts images.
        :return:
        """
        # load dataset
        self.dataset,self.anns,self.cats,self.imgs = dict(),dict(),dict(),dict()
        self.imgToAnns, self.catToImgs = defaultdict(list), defaultdict(list)
        if not annotation_file == None:
            print('loading annotations into memory...')
            tic = time.time()
            dataset = json.load(open(annotation_file, 'r'))
            assert type(dataset)==dict, 'annotation file format {} not supported'.format(type(dataset))
            print('Done (t={:0.2f}s)'.format(time.time()- tic))
            self.dataset = dataset
            self.createIndex()
 
    def createIndex(self):
        # create index
        print('creating index...')
        anns, cats, imgs = {}, {}, {}
        imgToAnns,catToImgs = defaultdict(list),defaultdict(list)
        if 'annotations' in self.dataset:
            for ann in self.dataset['annotations']:
                imgToAnns[ann['image_id']].append(ann)
                anns[ann['id']] = ann
 
        if 'images' in self.dataset:
            for img in self.dataset['images']:
                imgs[img['id']] = img
 
        if 'categories' in self.dataset:
            for cat in self.dataset['categories']:
                cats[cat['id']] = cat
 
        if 'annotations' in self.dataset and 'categories' in self.dataset:
            for ann in self.dataset['annotations']:
                catToImgs[ann['category_id']].append(ann['image_id'])
 
        print('index created!')
 
        # create class members
        self.anns = anns
        self.imgToAnns = imgToAnns
        self.catToImgs = catToImgs
        self.imgs = imgs
        self.cats = cats
 
    def info(self):
        """
        Print information about the annotation file.
        :return:
        """
        for key, value in self.dataset['info'].items():
            print('{}: {}'.format(key, value))
 
    def getAnnIds(self, imgIds=[], catIds=[], areaRng=[], iscrowd=None):
        """
        Get ann ids that satisfy given filter conditions. default skips that filter
        :param imgIds  (int array)     : get anns for given imgs
               catIds  (int array)     : get anns for given cats
               areaRng (float array)   : get anns for given area range (e.g. [0 inf])
               iscrowd (boolean)       : get anns for given crowd label (False or True)
        :return: ids (int array)       : integer array of ann ids
        """
        imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
        catIds = catIds if _isArrayLike(catIds) else [catIds]
 
        if len(imgIds) == len(catIds) == len(areaRng) == 0:
            anns = self.dataset['annotations']
        else:
            if not len(imgIds) == 0:
                lists = [self.imgToAnns[imgId] for imgId in imgIds if imgId in self.imgToAnns]
                anns = list(itertools.chain.from_iterable(lists))
            else:
                anns = self.dataset['annotations']
            anns = anns if len(catIds)  == 0 else [ann for ann in anns if ann['category_id'] in catIds]
            anns = anns if len(areaRng) == 0 else [ann for ann in anns if ann['area'] > areaRng[0] and ann['area'] < areaRng[1]]
        if not iscrowd == None:
            ids = [ann['id'] for ann in anns if ann['iscrowd'] == iscrowd]
        else:
            ids = [ann['id'] for ann in anns]
        return ids
 
    def getCatIds(self, catNms=[], supNms=[], catIds=[]):
        """
        filtering parameters. default skips that filter.
        :param catNms (str array)  : get cats for given cat names
        :param supNms (str array)  : get cats for given supercategory names
        :param catIds (int array)  : get cats for given cat ids
        :return: ids (int array)   : integer array of cat ids
        """
        catNms = catNms if _isArrayLike(catNms) else [catNms]
        supNms = supNms if _isArrayLike(supNms) else [supNms]
        catIds = catIds if _isArrayLike(catIds) else [catIds]
 
        if len(catNms) == len(supNms) == len(catIds) == 0:
            cats = self.dataset['categories']
        else:
            cats = self.dataset['categories']
            cats = cats if len(catNms) == 0 else [cat for cat in cats if cat['name']          in catNms]
            cats = cats if len(supNms) == 0 else [cat for cat in cats if cat['supercategory'] in supNms]
            cats = cats if len(catIds) == 0 else [cat for cat in cats if cat['id']            in catIds]
        ids = [cat['id'] for cat in cats]
        return ids
 
    def getImgIds(self, imgIds=[], catIds=[]):
        '''
        Get img ids that satisfy given filter conditions.
        :param imgIds (int array) : get imgs for given ids
        :param catIds (int array) : get imgs with all given cats
        :return: ids (int array)  : integer array of img ids
        '''
        imgIds = imgIds if _isArrayLike(imgIds) else [imgIds]
        catIds = catIds if _isArrayLike(catIds) else [catIds]
 
        if len(imgIds) == len(catIds) == 0:
            ids = self.imgs.keys()
        else:
            ids = set(imgIds)
            for i, catId in enumerate(catIds):
                if i == 0 and len(ids) == 0:
                    ids = set(self.catToImgs[catId])
                else:
                    ids &= set(self.catToImgs[catId])
        return list(ids)
 
    def loadAnns(self, ids=[]):
        """
        Load anns with the specified ids.
        :param ids (int array)       : integer ids specifying anns
        :return: anns (object array) : loaded ann objects
        """
        if _isArrayLike(ids):
            return [self.anns[id] for id in ids]
        elif type(ids) == int:
            return [self.anns[ids]]
 
    def loadCats(self, ids=[]):
        """
        Load cats with the specified ids.
        :param ids (int array)       : integer ids specifying cats
        :return: cats (object array) : loaded cat objects
        """
        if _isArrayLike(ids):
            return [self.cats[id] for id in ids]
        elif type(ids) == int:
            return [self.cats[ids]]
 
    def loadImgs(self, ids=[]):
        """
        Load anns with the specified ids.
        :param ids (int array)       : integer ids specifying img
        :return: imgs (object array) : loaded img objects
        """
        if _isArrayLike(ids):
            return [self.imgs[id] for id in ids]
        elif type(ids) == int:
            return [self.imgs[ids]]
 
    def showAnns(self, anns):
        """
        Display the specified annotations.
        :param anns (array of object): annotations to display
        :return: None
        """
        if len(anns) == 0:
            return 0
        if 'segmentation' in anns[0] or 'keypoints' in anns[0]:
            datasetType = 'instances'
        elif 'caption' in anns[0]:
            datasetType = 'captions'
        else:
            raise Exception('datasetType not supported')
        if datasetType == 'instances':
            ax = plt.gca()
            ax.set_autoscale_on(False)
            polygons = []
            color = []
            for ann in anns:
                c = (np.random.random((1, 3))*0.6+0.4).tolist()[0]
                if 'segmentation' in ann:
                    if type(ann['segmentation']) == list:
                        # polygon
                        for seg in ann['segmentation']:
                            poly = np.array(seg).reshape((int(len(seg)/2), 2))
                            polygons.append(Polygon(poly))
                            color.append(c)
                    else:
                        # mask
                        t = self.imgs[ann['image_id']]
                        if type(ann['segmentation']['counts']) == list:
                            rle = maskUtils.frPyObjects([ann['segmentation']], t['height'], t['width'])
                        else:
                            rle = [ann['segmentation']]
                        m = maskUtils.decode(rle)
                        img = np.ones( (m.shape[0], m.shape[1], 3) )
                        if ann['iscrowd'] == 1:
                            color_mask = np.array([2.0,166.0,101.0])/255
                        if ann['iscrowd'] == 0:
                            color_mask = np.random.random((1, 3)).tolist()[0]
                        for i in range(3):
                            img[:,:,i] = color_mask[i]
                        ax.imshow(np.dstack( (img, m*0.5) ))
                if 'keypoints' in ann and type(ann['keypoints']) == list:
                    # turn skeleton into zero-based index
                    sks = np.array(self.loadCats(ann['category_id'])[0]['skeleton'])-1
                    kp = np.array(ann['keypoints'])
                    x = kp[0::3]
                    y = kp[1::3]
                    v = kp[2::3]
                    for sk in sks:
                        if np.all(v[sk]>0):
                            plt.plot(x[sk],y[sk], linewidth=3, color=c)
                    plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)
                    plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)
            p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
            ax.add_collection(p)
            p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)
            ax.add_collection(p)
        elif datasetType == 'captions':
            for ann in anns:
                print(ann['caption'])
 
    def loadRes(self, resFile):
        """
        Load result file and return a result api object.
        :param   resFile (str)     : file name of result file
        :return: res (obj)         : result api object
        """
        res = COCO()
        res.dataset['images'] = [img for img in self.dataset['images']]
 
        print('Loading and preparing results...')
        tic = time.time()
        if type(resFile) == str or type(resFile) == unicode:
            anns = json.load(open(resFile))
        elif type(resFile) == np.ndarray:
            anns = self.loadNumpyAnnotations(resFile)
        else:
            anns = resFile
        assert type(anns) == list, 'results in not an array of objects'
        annsImgIds = [ann['image_id'] for ann in anns]
        assert set(annsImgIds) == (set(annsImgIds) & set(self.getImgIds())), \
               'Results do not correspond to current coco set'
        if 'caption' in anns[0]:
            imgIds = set([img['id'] for img in res.dataset['images']]) & set([ann['image_id'] for ann in anns])
            res.dataset['images'] = [img for img in res.dataset['images'] if img['id'] in imgIds]
            for id, ann in enumerate(anns):
                ann['id'] = id+1
        elif 'bbox' in anns[0] and not anns[0]['bbox'] == []:
            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
            for id, ann in enumerate(anns):
                bb = ann['bbox']
                x1, x2, y1, y2 = [bb[0], bb[0]+bb[2], bb[1], bb[1]+bb[3]]
                if not 'segmentation' in ann:
                    ann['segmentation'] = [[x1, y1, x1, y2, x2, y2, x2, y1]]
                ann['area'] = bb[2]*bb[3]
                ann['id'] = id+1
                ann['iscrowd'] = 0
        elif 'segmentation' in anns[0]:
            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
            for id, ann in enumerate(anns):
                # now only support compressed RLE format as segmentation results
                ann['area'] = maskUtils.area(ann['segmentation'])
                if not 'bbox' in ann:
                    ann['bbox'] = maskUtils.toBbox(ann['segmentation'])
                ann['id'] = id+1
                ann['iscrowd'] = 0
        elif 'keypoints' in anns[0]:
            res.dataset['categories'] = copy.deepcopy(self.dataset['categories'])
            for id, ann in enumerate(anns):
                s = ann['keypoints']
                x = s[0::3]
                y = s[1::3]
                x0,x1,y0,y1 = np.min(x), np.max(x), np.min(y), np.max(y)
                ann['area'] = (x1-x0)*(y1-y0)
                ann['id'] = id + 1
                ann['bbox'] = [x0,y0,x1-x0,y1-y0]
        print('DONE (t={:0.2f}s)'.format(time.time()- tic))
 
        res.dataset['annotations'] = anns
        res.createIndex()
        return res
 
    def download(self, tarDir = None, imgIds = [] ):
        '''
        Download COCO images from mscoco.org server.
        :param tarDir (str): COCO results directory name
               imgIds (list): images to be downloaded
        :return:
        '''
        if tarDir is None:
            print('Please specify target directory')
            return -1
        if len(imgIds) == 0:
            imgs = self.imgs.values()
        else:
            imgs = self.loadImgs(imgIds)
        N = len(imgs)
        if not os.path.exists(tarDir):
            os.makedirs(tarDir)
        for i, img in enumerate(imgs):
            tic = time.time()
            fname = os.path.join(tarDir, img['file_name'])
            if not os.path.exists(fname):
                urlretrieve(img['coco_url'], fname)
            print('downloaded {}/{} images (t={:0.1f}s)'.format(i, N, time.time()- tic))
 
    def loadNumpyAnnotations(self, data):
        """
        Convert result data from a numpy array [Nx7] where each row contains {imageID,x1,y1,w,h,score,class}
        :param  data (numpy.ndarray)
        :return: annotations (python nested list)
        """
        print('Converting ndarray to lists...')
        assert(type(data) == np.ndarray)
        print(data.shape)
        assert(data.shape[1] == 7)
        N = data.shape[0]
        ann = []
        for i in range(N):
            if i % 1000000 == 0:
                print('{}/{}'.format(i,N))
            ann += [{
                'image_id'  : int(data[i, 0]),
                'bbox'  : [ data[i, 1], data[i, 2], data[i, 3], data[i, 4] ],
                'score' : data[i, 5],
                'category_id': int(data[i, 6]),
                }]
        return ann
 
    def annToRLE(self, ann):
        """
        Convert annotation which can be polygons, uncompressed RLE to RLE.
        :return: binary mask (numpy 2D array)
        """
        t = self.imgs[ann['image_id']]
        h, w = t['height'], t['width']
        segm = ann['segmentation']
        if type(segm) == list:
            # polygon -- a single object might consist of multiple parts
            # we merge all parts into one mask rle code
            rles = maskUtils.frPyObjects(segm, h, w)
            rle = maskUtils.merge(rles)
        elif type(segm['counts']) == list:
            # uncompressed RLE
            rle = maskUtils.frPyObjects(segm, h, w)
        else:
            # rle
            rle = ann['segmentation']
        return rle
 
    def annToMask(self, ann):
        """
        Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask.
        :return: binary mask (numpy 2D array)
        """
        rle = self.annToRLE(ann)
        m = maskUtils.decode(rle)
        return m

这个是上面代码需要的COCO导入 不用导入第一个库了

from pycocotools.coco import COCO #这个包可以从git上下载