45、实例分割的labelme数据集转coco数据集以及coco数据集转labelme数据集

基本思想：需要自己标注一些数据集和coco单类别数据集进行混合训练，写了脚本提取coco部分数据集　train2017 实例分割数据集　

第一步：如果你用labelme标注了数据集，需要转成coco数据集进行实例分割训练，可以用下面脚本进行转换

labelme数据转coco数据集　代码来自这labelme/examples/instance_segmentation at master · zsffuture/labelme · GitHub

 
import argparse
import collections
import datetime
import glob
import json
import os
import os.path as osp
import sys
import uuid
 
import imgviz
import numpy as np
 
import labelme
 
try:
    import pycocotools.mask
except ImportError:
    print("Please install pycocotools:\n\n    pip install pycocotools\n")
    sys.exit(1)
 
 
def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument("--input_dir",default="/home/ubuntu/Downloads/dest", help="input annotated directory")
    parser.add_argument("--output_dir", default="/home/ubuntu/Downloads/coco",help="output dataset directory")
    parser.add_argument("--labels",default="/home/ubuntu/Downloads/labels.txt", help="labels file")
    parser.add_argument(
        "--noviz", help="no visualization", action="store_true"
    )
    args = parser.parse_args()
 
    if not os.path.exists(args.output_dir):
        print("Output directory already exists:", args.output_dir)
 
        os.makedirs(args.output_dir)
        os.makedirs(osp.join(args.output_dir, "JPEGImages"))
 
    print("Creating dataset:", args.output_dir)
 
    now = datetime.datetime.now()
 
    data = dict(
        info=dict(
            description=None,
            url=None,
            version=None,
            year=now.year,
            contributor=None,
            date_created=now.strftime("%Y-%m-%d %H:%M:%S.%f"),
        ),
        licenses=[dict(url=None, id=0, name=None,)],
        images=[
            # license, url, file_name, height, width, date_captured, id
        ],
        type="instances",
        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 -1
        class_name = line.strip()
        if class_id == -1:
            assert class_name == "__ignore__"
            continue
        class_name_to_id[class_name] = class_id
        data["categories"].append(
            dict(supercategory=None, id=class_id, name=class_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(filename=filename)
        print(label_file)
        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(
                license=0,
                url=None,
                file_name=osp.relpath(out_img_file, osp.dirname(out_ann_file)),
                height=img.shape[0],
                width=img.shape[1],
                date_captured=None,
                id=image_id,
            )
        )
 
        masks = {}  # for area
        segmentations = collections.defaultdict(list)  # for segmentation
        for 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] | mask
            else:
                masks[instance] = mask
 
            if shape_type == "rectangle":
                (x1, y1), (x2, y2) = points
                x1, 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 = instance
            if cls_name not in class_name_to_id:
                continue
            cls_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(
                    id=len(data["annotations"]),
                    image_id=image_id,
                    category_id=cls_id,
                    segmentation=segmentations[instance],
                    area=area,
                    bbox=bbox,
                    iscrowd=0,
                )
            )
 
        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(
                image=img,
                labels=labels,
                masks=masks,
                captions=captions,
                font_size=15,
                line_width=2,
            )
            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()

标签定义　labels.txt

__ignore__
__background__
person
bicycle
car
motorbike
aeroplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
sofa
pottedplant
bed
diningtable
toilet
tvmonitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush

利用上面脚本就可以转成对应的coco数据集

第二步：要是需要上述或者别人生成的coco数据集转成labelme数据集，可以用下面的脚本（可以将下列脚本修改用于目标检测的数据集生成）

# -*- coding: utf-8 -*-
import glob
import os
import cv2
import json
import io

coco=["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
        "fire hydrant","" ,"stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
        "elephant", "bear", "zebra", "giraffe", "","backpack", "umbrella","", "","handbag", "tie", "suitcase", "frisbee",
        "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
        "tennis racket", "bottle","", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
        "potted plant", "bed","", "dining table", "","","toilet", "","tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
        "microwave", "oven", "toaster", "sink", "refrigerator", "book", "","clock", "vase", "scissors", "teddy bear",
        "hair drier", "toothbrush"]

label=dict()
for idx,item in enumerate(coco):
    label.update({idx:item})
labelme_path='/home/ubuntu/Downloads/test/total1'
coco_json_path = '/home/ubuntu/Downloads/test/human_save_2'
jpg_path='/home/ubuntu/Downloads/test/human_save_2'
coco_json=glob.glob(os.path.join(coco_json_path,"*.json"))[0]
file_json = io.open(coco_json,'r',encoding='utf-8')
m_json_data = file_json.read()
m_data = json.loads(m_json_data)
#m_type=m_data['type']

for item in m_data['images']:
    flag=False
    m_images_file_name = item['file_name']
    (filename_path, m_filename) = os.path.split(m_images_file_name)
    (m_name, extension) = os.path.splitext(m_filename)
    m_image=cv2.imread(os.path.join(jpg_path,m_name+".jpg"))
    m_images_height = item['height']
    m_images_width = item['width']
    m_images_id = item['id']
    data = {}
    data['imagePath'] = m_filename
    data['flags'] = {}
    data['imageWidth'] = m_images_width
    data['imageHeight'] = m_images_height
    data['imageData'] = None
    data['version'] = "5.0.1"
    data["shapes"] = []
    for annit in m_data['annotations']:
        m_image_id=annit['image_id']
        m_category_id=annit['category_id']
        if m_image_id==m_images_id and label[m_category_id-1]=='person':
            flag = True
            for segitem in annit['segmentation']:
                points = []
                for idx in range(0,len(segitem),2):
                    x,y=segitem[idx],segitem[idx+1]
                    if str(x).isalpha() or str(y).isalpha():
                        flag = False
                        break
                    points.append([x,y])
                itemData = {'points': []}
                if len(points)==0:
                    flag = False
                    break
                itemData['points'].extend(points)
                itemData["flag"] = {}
                itemData["group_id"] = None
                itemData["shape_type"] = "polygon"
                itemData["label"] =label[m_category_id-1]
                data["shapes"].append(itemData)
    if flag:
        jsonName = ".".join([m_name, "json"])
        jpgName = ".".join([m_name, "jpg"])
        print(labelme_path, jsonName)
        jsonPath = os.path.join(labelme_path, jsonName)
        jpgPath = os.path.join(labelme_path, jpgName)
        with open(jsonPath, "w") as f:
            json.dump(data, f)
        cv2.imwrite(jpgPath,m_image)
        print("加载入文件完成...")

第三步：如果要将官方的coco数据集转成labelme，稍微有点不同，因为官方的coco的实际标签是排列到90，当将coco数据集转成labelme数据集时候，需要修改一下标签，插入空标签

分别要在（从１开始）　12 26 29 30 45 66 68 69 71 83 插入空labelme

# -*- coding: utf-8 -*-
import glob
import os
import cv2
import json
import io

coco=["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
        "fire hydrant","" ,"stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
        "elephant", "bear", "zebra", "giraffe", "","backpack", "umbrella","", "","handbag", "tie", "suitcase", "frisbee",
        "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
        "tennis racket", "bottle","", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
        "potted plant", "bed","", "dining table", "","","toilet", "","tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
        "microwave", "oven", "toaster", "sink", "refrigerator", "book", "","clock", "vase", "scissors", "teddy bear",
        "hair drier", "toothbrush"]

label=dict()
for idx,item in enumerate(coco):
    label.update({idx:item})
labelme_path="/home/ubuntu/Downloads/coco_all"
coco_json_path = '/home/ubuntu/Downloads/coco2017/train_ann'
jpg_path="/home/ubuntu/Downloads/coco2017/train2017"
coco_json=glob.glob(os.path.join(coco_json_path,"*.json"))[0]
file_json = io.open(coco_json,'r',encoding='utf-8')
m_json_data = file_json.read()
m_data = json.loads(m_json_data)
#m_type=m_data['type']

for item in m_data['images']:
    m_images_file_name = item['file_name']
    (filename_path, m_filename) = os.path.split(m_images_file_name)
    (m_name, extension) = os.path.splitext(m_filename)
    m_image=cv2.imread(os.path.join(jpg_path,m_name+".jpg"))
    m_images_height = item['height']
    m_images_width = item['width']
    m_images_id = item['id']
    data = {}
    data['imagePath'] = m_filename
    data['flags'] = {}
    data['imageWidth'] = m_images_width
    data['imageHeight'] = m_images_height
    data['imageData'] = None
    data['version'] = "5.0.1"
    data["shapes"] = []
    for annit in m_data['annotations']:
        m_image_id=annit['image_id']
        m_category_id=annit['category_id']

        if m_image_id==m_images_id :
            for segitem in annit['segmentation']:
                points = []
                for idx in range(0,len(segitem),2):
                    x,y=segitem[idx],segitem[idx+1]
                    if str(x).isalpha() or str(y).isalpha():
                        break
                    points.append([x,y])
                itemData = {'points': []}
                if len(points)==0:
                    flag = False
                    break
                itemData['points'].extend(points)
                itemData["flag"] = {}
                itemData["group_id"] = None
                itemData["shape_type"] = "polygon"
                itemData["label"] =label[m_category_id-1]
                data["shapes"].append(itemData)
    jsonName = ".".join([m_name, "json"])
    jpgName = ".".join([m_name, "jpg"])
    print(labelme_path, jsonName)
    jsonPath = os.path.join(labelme_path, jsonName)
    jpgPath = os.path.join(labelme_path, jpgName)
    with open(jsonPath, "w") as f:
        json.dump(data, f)
    cv2.imwrite(jpgPath,m_image)
    print("加载入文件完成...")

然后自己需要哪个类别的实例分割数据集，自己写个if条件筛选就行

比如我要提取cup的数据集

# -*- coding: utf-8 -*-
import glob
import os
import cv2
import json
import io

coco=["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
        "fire hydrant","" ,"stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
        "elephant", "bear", "zebra", "giraffe", "","backpack", "umbrella","", "","handbag", "tie", "suitcase", "frisbee",
        "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
        "tennis racket", "bottle","", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
        "potted plant", "bed","", "dining table", "","","toilet", "","tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
        "microwave", "oven", "toaster", "sink", "refrigerator", "book", "","clock", "vase", "scissors", "teddy bear",
        "hair drier", "toothbrush"]

label=dict()
for idx,item in enumerate(coco):
    label.update({idx:item})
labelme_path="/home/ubuntu/Downloads/coco_all"
coco_json_path = '/home/ubuntu/Downloads/coco2017/train_ann'
jpg_path="/home/ubuntu/Downloads/coco2017/train2017"
coco_json=glob.glob(os.path.join(coco_json_path,"*.json"))[0]
file_json = io.open(coco_json,'r',encoding='utf-8')
m_json_data = file_json.read()
m_data = json.loads(m_json_data)
#m_type=m_data['type']

for item in m_data['images']:
    flag=False
    m_images_file_name = item['file_name']
    (filename_path, m_filename) = os.path.split(m_images_file_name)
    (m_name, extension) = os.path.splitext(m_filename)
    m_image=cv2.imread(os.path.join(jpg_path,m_name+".jpg"))
    m_images_height = item['height']
    m_images_width = item['width']
    m_images_id = item['id']
    data = {}
    data['imagePath'] = m_filename
    data['flags'] = {}
    data['imageWidth'] = m_images_width
    data['imageHeight'] = m_images_height
    data['imageData'] = None
    data['version'] = "5.0.1"
    data["shapes"] = []
    for annit in m_data['annotations']:
        m_image_id=annit['image_id']
        m_category_id=annit['category_id']
        if m_image_id==m_images_id and label[m_category_id-1]=='cup':
            flag = True
            for segitem in annit['segmentation']:
                points = []
                for idx in range(0,len(segitem),2):
                    x,y=segitem[idx],segitem[idx+1]
                    if str(x).isalpha() or str(y).isalpha():
                        break
                    points.append([x,y])
                itemData = {'points': []}
                itemData['points'].extend(points)
                itemData["flag"] = {}
                itemData["group_id"] = None
                itemData["shape_type"] = "polygon"
                itemData["label"] =label[m_category_id-1]
                data["shapes"].append(itemData)
    if flag:
        jsonName = ".".join([m_name, "json"])
        jpgName = ".".join([m_name, "jpg"])
        print(labelme_path, jsonName)
        jsonPath = os.path.join(labelme_path, jsonName)
        jpgPath = os.path.join(labelme_path, jpgName)
        with open(jsonPath, "w") as f:
            json.dump(data, f)
        cv2.imwrite(jpgPath,m_image)
        print("加载入文件完成...")

然后在和自己的数据集中和一下，转到coco数据集就ok