数据处理：
- 数据文件目录
- 一级目录：VOC2007
- 二级目录：Annotations #存放标注后的voc格式文件
- ImageSets
- 三级目录：Main #存放分割后的TXT索引文件
- JPEImages #存放图片文件
- train_annotations
- train_JPEImages
- val_annotations
- val_JPEImages
---- 1_split.py
- 一级目录：coco
- 二级目录：annotations #
- train2017
- val2017
---- 2_mv.py
---- 3_xml_json.py//3_v2v_2.py
---- 4_dataset_test.py
- 训练模型：model_train
- output_trainsample
---- 5_trainsample.py
将图片放入JPEImages里，标注好的voc文件放入Annotations里
1_split.py #生成索引文件(trainval.txt、test.txt、train.txt、testval.txt)
2_mv.py #图片标记分割,根据1中生成的索引文件，对图片，xml文件进行分割和复制[先将训练集分离出来]。(train_annotations、train_JPEImages、val_annotations、val_JPEImages)
3_xml_json.py//3_v2v_2.py #将voc格式转json格式用于detectron的训练
4_dataset_test.py #数据集测试
5_trainsample.py #训练模型
--opts MODEL.DEVICE cuda MODEL.WEIGHTS ../model_train/output_Warning_Faster_R-CNN_test/model_final.pth
目标分割检测：
- dataset_17
- JPEGImages #存放图片和标注后的json文件
- 2_json-voc.py
- detectron2
- dataset
- coco_17coco
- annotations
- instances_train2017.json
- instances_val2017.json
- train2017 #图片
- val2017
- 4_dataset_test.py
(1)-Labelme标注图片，生成json文件
(2)-2_json-voc.py #生成train和val两个json文件用于detectron训练
修改自己的类别 classname_to_id = {"clothing":0,……}
图片路径 labelme_path = './dataset_17/JPEGImages';保存路径saved_coco_path = './dateset/coco_17'
(3)-4_dataset_test.py #数据集测试
修改自己的类别 DATASET_CATEGORIES = [{"name": "clothing", "id": 0, "isthing": 1, "color": [220, 20, 60]},……]
数据集路径 DATASET_ROOT = './datasets/coco_17coco'
(4)-5-2-3_trainsample.py #训练模型
修改自己的类别 DATASET_CATEGORIES = [{"name": "clothing", "id": 0, "isthing": 1, "color": [220, 20, 60]},……]
数据集路径 DATASET_ROOT = './datasets/coco_17coco'
修改类别数 cfg.MODEL.ROI_HEADS.NUM_CLASSES = 4
修改训练总批次 cfg.SOLVER.MAX_ITER = 560
1_split.py
'''生成索引文件
划分TXT文件
'''
import os
import random
# 对应位置修改分割比
trainval_percent = 0.1
train_percent = 0.9
xmlfilepath = 'Annotations'
txtsavepath = r'ImageSets\Main'
total_xml = os.listdir(xmlfilepath)
num = len(total_xml)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
ftrainval = open('ImageSets/Main/trainval.txt', 'w')
ftest = open('ImageSets/Main/test.txt', 'w')
ftrain = open('ImageSets/Main/train.txt', 'w')
fval = open('ImageSets/Main/testval.txt', 'w')
for i in list:
name = total_xml[i][:-4] + '\n'
if i in trainval:
ftrainval.write(name)
if i in train:
ftest.write(name)
else:
fval.write(name)
else:
ftrain.write(name)
ftrainval.close()
ftrain.close()
fval.close()
ftest.close()
2_mv.py
'''图片标记分割
根据1中生成的索引文件，对图片，xml文件进行分割和复制
先将训练集分离出来。'''
import os
import shutil
class CopyXml():
def __init__(self):
# 你的xml格式的annotation的路径
# self.xmlpath = r'VOC2007/Annotations'
self.jpgpath = r'VOC2007/JPEGImages'
# 你训练集/测试集xml和jpg存放的路径
# self.newxmlpath = r'VOC2007/val_annotations'
self.newjpgpath = r'VOC2007/train_JPEGImages'
def startcopy(self):
# filelist = os.listdir(self.xmlpath) # file list in this directory
filelist = os.listdir(self.jpgpath) # file list in this directory
# print(len(filelist))
test_list = loadFileList()
# print(len(test_list))
for f in filelist:
# xmldir = os.path.join(self.xmlpath, f)
jpgdir = os.path.join(self.jpgpath, f)
(shotname, extension) = os.path.splitext(f)
if str(shotname) in test_list:
# print('success')
# shutil.copyfile(str(xmldir), os.path.join(self.newxmlpath, f))
shutil.copyfile(str(jpgdir), os.path.join(self.newjpgpath, f))
# load the list of train/test file list
def loadFileList():
filelist = []
f = open("VOC2007/ImageSets/Main/train.txt", "r")
# f = open("VOC2007/ImageSets/Main/trainval.txt", "r")
lines = f.readlines()
for line in lines:
# 去掉文件中每行的结尾字符
line = line.strip('\r\n') # to remove the '\n' for test.txt, '\r\n' for tainval.txt
line = str(line)
filelist.append(line)
f.close()
# print(filelist)
return filelist
if __name__ == '__main__':
demo = CopyXml()
demo.startcopy()3_v2v_2.py
"""
Created on Tue Jun 12 10:24:36 2018
将voc格式转json格式用于caffe2的detectron的训练
在detectron中voc_2007_train.json和voc_2007_val.json中categories的顺序必须保持一致
因此最好事先确定category的顺序，书写在category_set中
@author: yantianwang
"""
import xml.etree.ElementTree as ET
import os
import json
import collections
coco = dict()
coco['images'] = []
coco['type'] = 'instances'
coco['annotations'] = []
coco['categories'] = []
#category_set = dict()
image_set = set()
image_id = 2022000001 #train:2018xxx; val:2019xxx; test:2020xxx
category_item_id = 0
annotation_id = 0
category_set = ['1','2',"0"]
'''
def addCatItem(name):
global category_item_id
category_item = dict()
category_item['supercategory'] = 'none'
category_item_id += 1
category_item['id'] = category_item_id
category_item['name'] = name
coco['categories'].append(category_item)
category_set[name] = category_item_id
return category_item_id
'''
def addCatItem(name):
'''
增加json格式中的categories部分
'''
global category_item_id
category_item = collections.OrderedDict()
category_item['supercategory'] = 'none'
category_item['id'] = category_item_id
category_item['name'] = name
coco['categories'].append(category_item)
category_item_id += 1
def addImgItem(file_name, size):
global image_id
if file_name is None:
raise Exception('Could not find filename tag in xml file.')
if size['width'] is None:
raise Exception('Could not find width tag in xml file.')
if size['height'] is None:
raise Exception('Could not find height tag in xml file.')
#image_item = dict() #按照一定的顺序，这里采用collections.OrderedDict()
image_item = collections.OrderedDict()
print(file_name,"*******")
# jpg_name = os.path.splitext(file_name)[0]+'.png'
jpg_name = file_name
image_item['file_name'] = jpg_name
image_item['width'] = size['width']
image_item['height'] = size['height']
image_item['id'] = image_id
coco['images'].append(image_item)
image_set.add(jpg_name)
image_id = image_id+1
return image_id
def addAnnoItem(object_name, image_id, category_id, bbox):
global annotation_id
#annotation_item = dict()
annotation_item = collections.OrderedDict()
annotation_item['segmentation'] = []
seg = []
# bbox[] is x,y,w,h
# left_top
seg.append(bbox[0])
seg.append(bbox[1])
# left_bottom
seg.append(bbox[0])
seg.append(bbox[1] + bbox[3])
# right_bottom
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1] + bbox[3])
# right_top
seg.append(bbox[0] + bbox[2])
seg.append(bbox[1])
annotation_item['segmentation'].append(seg)
annotation_item['area'] = bbox[2] * bbox[3]
annotation_item['iscrowd'] = 0
annotation_item['image_id'] = image_id
annotation_item['bbox'] = bbox
annotation_item['category_id'] = category_id
annotation_item['id'] = annotation_id
annotation_item['ignore'] = 0
annotation_id += 1
coco['annotations'].append(annotation_item)
def parseXmlFiles(xml_path):
xmllist = os.listdir(xml_path)
xmllist.sort()
for f in xmllist:
if not f.endswith('.xml'):
continue
bndbox = dict()
size = dict()
current_image_id = None
current_category_id = None
file_name = None
size['width'] = None
size['height'] = None
size['depth'] = None
xml_file = os.path.join(xml_path, f)
print(xml_file)
tree = ET.parse(xml_file)
root = tree.getroot() #抓根结点元素
if root.tag != 'annotation': #根节点标签
raise Exception('pascal voc xml root element should be annotation, rather than {}'.format(root.tag))
# elem is <folder>, <filename>, <size>, <object>
for elem in root:
current_parent = elem.tag
current_sub = None
object_name = None
#elem.tag, elem.attrib，elem.text
if elem.tag == 'folder':
continue
if elem.tag == 'filename':
file_name = elem.text
if file_name in category_set:
raise Exception('file_name duplicated')
# add img item only after parse <size> tag
elif current_image_id is None and file_name is not None and size['width'] is not None:
if file_name not in image_set:
current_image_id = addImgItem(file_name, size)#图片信息
print('add image with {} and {}'.format(file_name, size))
else:
raise Exception('duplicated image: {}'.format(file_name))
# subelem is <width>, <height>, <depth>, <name>, <bndbox>
for subelem in elem:
bndbox['xmin'] = None
bndbox['xmax'] = None
bndbox['ymin'] = None
bndbox['ymax'] = None
current_sub = subelem.tag
if current_parent == 'object' and subelem.tag == 'name':
object_name = subelem.text
#if object_name not in category_set:
# current_category_id = addCatItem(object_name)
#else:
#current_category_id = category_set[object_name]
current_category_id = category_set.index(object_name) #index默认从0开始,但是json文件是从1开始，所以+1
elif current_parent == 'size':
if size[subelem.tag] is not None:
raise Exception('xml structure broken at size tag.')
size[subelem.tag] = int(subelem.text)
# option is <xmin>, <ymin>, <xmax>, <ymax>, when subelem is <bndbox>
for option in subelem:
if current_sub == 'bndbox':
if bndbox[option.tag] is not None:
raise Exception('xml structure corrupted at bndbox tag.')
bndbox[option.tag] = int(option.text)
# only after parse the <object> tag
if bndbox['xmin'] is not None:
if object_name is None:
raise Exception('xml structure broken at bndbox tag')
if current_image_id is None:
raise Exception('xml structure broken at bndbox tag')
if current_category_id is None:
raise Exception('xml structure broken at bndbox tag')
bbox = []
# x
bbox.append(bndbox['xmin'])
# y
bbox.append(bndbox['ymin'])
# w
bbox.append(bndbox['xmax'] - bndbox['xmin'])
# h
bbox.append(bndbox['ymax'] - bndbox['ymin'])
print(
'add annotation with {},{},{},{}'.format(object_name, current_image_id-1, current_category_id, bbox))
addAnnoItem(object_name, current_image_id-1, current_category_id, bbox)
#categories部分
for categoryname in category_set:
addCatItem(categoryname)
if __name__ == '__main__':
# xml_path = 'VOC2007/train_annotations'
xml_path = 'VOC2007/val_annotations'
# json_file = 'coco/annotations/_instances_train2022.json'
json_file = 'coco/annotations/_instances_val2022.json'
parseXmlFiles(xml_path)
json.dump(coco, open(json_file, 'w'))4_dataset_test.py
'''数据集测试'''
import os
import cv2
import logging
from collections import OrderedDict
import detectron2.utils.comm as comm
from detectron2.utils.visualizer import Visualizer
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.data.datasets.coco import load_coco_json
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
from detectron2.evaluation import COCOEvaluator, verify_results
from detectron2.modeling import GeneralizedRCNNWithTTA
# 数据集路径
DATASET_ROOT = './datasets/coco_17coco'
ANN_ROOT = os.path.join(DATASET_ROOT, 'annotations')
TRAIN_PATH = os.path.join(DATASET_ROOT, 'train2017')
VAL_PATH = os.path.join(DATASET_ROOT, 'val2017')
TRAIN_JSON = os.path.join(ANN_ROOT, 'instances_train2017.json')
# VAL_JSON = os.path.join(ANN_ROOT, 'val.json')
VAL_JSON = os.path.join(ANN_ROOT, 'instances_val2017.json')
CLASS_NAMES = ['line']
# 数据集类别元数据
DATASET_CATEGORIES = [
# {"name": "background", "id": 0, "isthing": 1, "color": [220, 20, 60]},
{"name": "clothing", "id": 0, "isthing": 1, "color": [220, 20, 60]},
{"name": "face_shield", "id": 1, "isthing": 1, "color": [110, 20, 60]},
{"name": "boot", "id": 2, "isthing": 1, "color": [220, 20, 30]},
# {"name": "clothing_f", "id": 3, "isthing": 1, "color": [220, 10, 30]},
]
# 数据集的子集
PREDEFINED_SPLITS_DATASET = {
"train_2019": (TRAIN_PATH, TRAIN_JSON),
"val_2019": (VAL_PATH, VAL_JSON),
}
def register_dataset():
"""
purpose: register all splits of dataset with PREDEFINED_SPLITS_DATASET
"""
for key, (image_root, json_file) in PREDEFINED_SPLITS_DATASET.items():
register_dataset_instances(name=key,
metadate=get_dataset_instances_meta(),
json_file=json_file,
image_root=image_root)
def get_dataset_instances_meta():
"""
purpose: get metadata of dataset from DATASET_CATEGORIES
return: dict[metadata]
"""
thing_ids = [k["id"] for k in DATASET_CATEGORIES if k["isthing"] == 1]
thing_colors = [k["color"] for k in DATASET_CATEGORIES if k["isthing"] == 1]
# assert len(thing_ids) == 2, len(thing_ids)
thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}
thing_classes = [k["name"] for k in DATASET_CATEGORIES if k["isthing"] == 1]
ret = {
"thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
"thing_classes": thing_classes,
"thing_colors": thing_colors,
}
return ret
def register_dataset_instances(name, metadate, json_file, image_root):
"""
purpose: register dataset to DatasetCatalog,
register metadata to MetadataCatalog and set attribute
"""
DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name))
MetadataCatalog.get(name).set(json_file=json_file,
image_root=image_root,
evaluator_type="coco",
**metadate)
# 注册数据集和元数据
def plain_register_dataset():
DatasetCatalog.register("train_2019", lambda: load_coco_json(TRAIN_JSON, TRAIN_PATH, "train_2019"))
MetadataCatalog.get("train_2019").set(thing_classes=CLASS_NAMES,
json_file=TRAIN_JSON,
image_root=TRAIN_PATH)
DatasetCatalog.register("val_2019", lambda: load_coco_json(VAL_JSON, VAL_PATH, "val_2019"))
MetadataCatalog.get("val_2019").set(thing_classes=CLASS_NAMES,
json_file=VAL_JSON,
image_root=VAL_PATH)
# 查看数据集标注
def checkout_dataset_annotation(name="train_2019"):
dataset_dicts = load_coco_json(TRAIN_JSON, TRAIN_PATH, name)
for d in dataset_dicts:
# img = cv2.imread(d["file_name"])
# visualizer = Visualizer(img[:, :, ::-1], metadata=MetadataCatalog.get(name), scale=1.5)
# vis = visualizer.draw_dataset_dict(d)
# cv2.imshow('show', vis.get_image()[:, :, ::-1])
# cv2.waitKey(0)
print(d['file_name'])
img = cv2.imread(d["file_name"])
visualizer = Visualizer(img[:, :, ::-1], metadata=MetadataCatalog.get(name), scale=1.5)
vis = visualizer.draw_dataset_dict(d)
# # 显示方法一
# cv2.namedWindow('findCorners', 0)
# cv2.resizeWindow('findCorners', 1920, 1080) # 自己设定窗口图片的大小
# 显示方法二
cv2.namedWindow('findCorners', cv2.WINDOW_NORMAL)
cv2.imshow('findCorners', vis.get_image()[:, :, ::-1])
cv2.waitKey(0)
register_dataset()
checkout_dataset_annotation()5_trainsample.py
import os
import cv2
import logging
from collections import OrderedDict
import detectron2.utils.comm as comm
from detectron2.utils.visualizer import Visualizer
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import DatasetCatalog, MetadataCatalog
from detectron2.data.datasets.coco import load_coco_json
from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
from detectron2.evaluation import COCOEvaluator, verify_results
from detectron2.modeling import GeneralizedRCNNWithTTA
from detectron2 import model_zoo
# 数据集路径
DATASET_ROOT = '../datasets/cocococo'
ANN_ROOT = os.path.join(DATASET_ROOT, 'annotations')
TRAIN_PATH = os.path.join(DATASET_ROOT, 'train2017')
VAL_PATH = os.path.join(DATASET_ROOT, 'val2017')
TRAIN_JSON = os.path.join(ANN_ROOT, 'instances_train2017.json')
# VAL_JSON = os.path.join(ANN_ROOT, 'val.json')
VAL_JSON = os.path.join(ANN_ROOT, 'instances_val2017.json')
CLASS_NAMES = ['line']
# 数据集类别元数据
DATASET_CATEGORIES = [
# {"name": "background", "id": 0, "isthing": 1, "color": [220, 20, 60]},
{"name": "line", "id": 0, "isthing": 1, "color": [220, 20, 60]},
]
# 数据集的子集
PREDEFINED_SPLITS_DATASET = {
"train_2019": (TRAIN_PATH, TRAIN_JSON),
"val_2019": (VAL_PATH, VAL_JSON),
}
def register_dataset():
"""
purpose: register all splits of dataset with PREDEFINED_SPLITS_DATASET
"""
for key, (image_root, json_file) in PREDEFINED_SPLITS_DATASET.items():
register_dataset_instances(name=key,
metadate=get_dataset_instances_meta(),
json_file=json_file,
image_root=image_root)
def get_dataset_instances_meta():
"""
purpose: get metadata of dataset from DATASET_CATEGORIES
return: dict[metadata]
"""
thing_ids = [k["id"] for k in DATASET_CATEGORIES if k["isthing"] == 1]
thing_colors = [k["color"] for k in DATASET_CATEGORIES if k["isthing"] == 1]
# assert len(thing_ids) == 2, len(thing_ids)
thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}
thing_classes = [k["name"] for k in DATASET_CATEGORIES if k["isthing"] == 1]
ret = {
"thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
"thing_classes": thing_classes,
"thing_colors": thing_colors,
}
return ret
def register_dataset_instances(name, metadate, json_file, image_root):
"""
purpose: register dataset to DatasetCatalog,
register metadata to MetadataCatalog and set attribute
"""
DatasetCatalog.register(name, lambda: load_coco_json(json_file, image_root, name))
MetadataCatalog.get(name).set(json_file=json_file,
image_root=image_root,
evaluator_type="coco",
**metadate)
# 注册数据集和元数据
def plain_register_dataset():
DatasetCatalog.register("train_2019", lambda: load_coco_json(TRAIN_JSON, TRAIN_PATH, "train_2019"))
MetadataCatalog.get("train_2019").set(thing_classes=CLASS_NAMES,
json_file=TRAIN_JSON,
image_root=TRAIN_PATH)
DatasetCatalog.register("val_2019", lambda: load_coco_json(VAL_JSON, VAL_PATH, "val_2019"))
MetadataCatalog.get("val_2019").set(thing_classes=CLASS_NAMES,
json_file=VAL_JSON,
image_root=VAL_PATH)
class Trainer(DefaultTrainer):
@classmethod
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
if output_folder is None:
output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
return COCOEvaluator(dataset_name, cfg, distributed=False, output_dir=output_folder)
@classmethod
def test_with_TTA(cls, cfg, model):
logger = logging.getLogger("detectron2.trainer")
# In the end of training, run an evaluation with TTA
# Only support some R-CNN models.
logger.info("Running inference with test-time augmentation ...")
model = GeneralizedRCNNWithTTA(cfg, model)
evaluators = [
cls.build_evaluator(
cfg, name, output_folder=os.path.join(cfg.OUTPUT_DIR, "inference_TTA")
)
for name in cfg.DATASETS.TEST
]
res = cls.test(cfg, model, evaluators)
res = OrderedDict({k + "_TTA": v for k, v in res.items()})
return res
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg() # 拷贝default config副本
args.config_file = "../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
cfg.merge_from_file(args.config_file) # 从config file 覆盖配置
cfg.merge_from_list(args.opts) # 从CLI参数 覆盖配置
# 更改配置参数
cfg.DATASETS.TRAIN = ("train_2019",)
cfg.DATASETS.TEST = ("val_2019",)
cfg.DATALOADER.NUM_WORKERS = 2 # 单线程
# cfg.INPUT.MAX_SIZE_TRAIN = 400
# cfg.INPUT.MAX_SIZE_TEST = 400
# cfg.INPUT.MIN_SIZE_TRAIN = (160,)
# cfg.INPUT.MIN_SIZE_TEST = 160
cfg.MODEL.DEVICE = 'cpu'
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 3 # 类别数
# cfg.MODEL.WEIGHTS = "../model/COCO-Detection/faster_rcnn_R_50_FPN_3x/model_final_280758.pkl" # 预训练模型权重
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") # 预训练模型权重
cfg.SOLVER.IMS_PER_BATCH = 6 # 6 batch_size=2; iters_in_one_epoch = dataset_imgs/batch_size
ITERS_IN_ONE_EPOCH = int(340 / cfg.SOLVER.IMS_PER_BATCH)
# (ITERS_IN_ONE_EPOCH * ) - 1 # 12 epochs
cfg.SOLVER.MAX_ITER = 560
cfg.SOLVER.BASE_LR = 0.002
cfg.SOLVER.MOMENTUM = 0.9
cfg.SOLVER.WEIGHT_DECAY = 0.0001
cfg.SOLVER.WEIGHT_DECAY_NORM = 0.0
cfg.SOLVER.GAMMA = 0.1
cfg.SOLVER.STEPS = (500,)
cfg.SOLVER.WARMUP_FACTOR = 1.0 / 1000
cfg.SOLVER.WARMUP_ITERS = 300
cfg.SOLVER.WARMUP_METHOD = "linear"
cfg.SOLVER.CHECKPOINT_PERIOD = ITERS_IN_ONE_EPOCH - 1
cfg.OUTPUT_DIR = "./output_2017_mask_rcnn_test111/"
cfg.freeze()
default_setup(cfg, args)
return cfg
def main(args):
cfg = setup(args)
print(cfg)
# 注册数据集
register_dataset()
if args.eval_only:
model = Trainer.build_model(cfg)
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
cfg.MODEL.WEIGHTS, resume=args.resume
)
res = Trainer.test(cfg, model)
if comm.is_main_process():
verify_results(cfg, res)
return res
trainer = Trainer(cfg)
trainer.resume_or_load(resume=args.resume)
return trainer.train()
if __name__ == "__main__":
args = default_argument_parser().parse_args()
print("Command Line Args:", args)
launch(
main,
args.num_gpus,
num_machines=args.num_machines,
machine_rank=args.machine_rank,
dist_url=args.dist_url,
args=(args,),
)