import pickle
import cv2
import numpy as np
import tensorflow as tf
import random
import torch
import traceback
import json
import time
#os.chdir('/mnt/oss/luci-hangzhou/junxuan/notebooks/cvml-kit')
from utils import draw_bbox_on_img, TqdmLogger
from options.test_options import TestOptions
from models.models import create_model
from data.data_loader import CreateDataLoader
from datatools.tfrecord import feature
logger = TqdmLogger('data_aug')
import torchvision.transforms as transforms
import torch
from data.base_dataset import BaseDataset
from data.image_folder import make_dataset
from PIL import Image
from tensorflow.python.framework.ops import disable_eager_execution
disable_eager_execution()
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
# Example:
# python inspect_tfrecord.py /data/train.tfrecord-00000-of-00010 10
import os
import sys
import pickle
import cv2
import numpy as np
import tensorflow as tf
import json
from utils import draw_bbox_on_img, TqdmLogger
#from grab import algo_grabcut
import random
from maskrcnn_benchmark.config import cfg
from predictor import COCODemo
logger = TqdmLogger('inspector')
def all_path(dirname):
result = []#所有的文件
for maindir, subdir, file_name_list in os.walk(dirname):
for filename in file_name_list:
_,type=filename.split('.')
if type=='tfrecord':
apath = os.path.join(maindir, filename)#合并成一个完整路径
result.append(apath)
# Example:
# python inspect_tfrecord.py /data/train.tfrecord-00000-of-00010 10
import os
import sys
import pickle
import cv2
import numpy as np
import tensorflow as tf
os.chdir('/mnt/oss/luci-hangzhou/junxuan/notebooks/cvml-kit')
from utils import draw_bbox_on_img, TqdmLogger
#from grab import algo_grabcut
import random
from maskrcnn_benchmark.config import cfg
from predictor import COCODemo
logger = TqdmLogger('inspector')
from tensorflow.python.framework.ops import disable_eager_execution
disable_eager_execution()
def extract_fn(tfrecord):
image_feature_description = {
'image/height': tf.io.FixedLenFeature([], tf.int64),
'image/width': tf.io.FixedLenFeature([], tf.int64),
#'image/channels': tf.io.FixedLenFeature([], tf.int64),
'image/object/bbox/xmin': tf.io.VarLenFeature(tf.float32),
'image/object/bbox/xmax': tf.io.VarLenFeature(tf.float32),
'image/object/bbox/ymin': tf.io.VarLenFeature(tf.float32),
'image/object/bbox/ymax': tf.io.VarLenFeature(tf.float32),
'image/object/class/label': tf.io.VarLenFeature(tf.int64),
'image/object/class/text': tf.io.VarLenFeature(tf.string),