我正在尝试将所选参数设置为react-datepicker的DatePicker的默认日期。从数据库中获取的日期基本上是以下格式：
event_date: "2019-06-15"
当我设置状态时，该日期以这种方式显示-event_date: "2019-06-15T00:00:00"
我已尝试new Date()将其转换为JavaScript兼容日期。还尝试了MomentJS，但同时抛出了同样的错误。当我在new Date()参数中调用selected时，一切运行正常。我的意思是，DatePicker显示默认的今天日期。但是，当我尝试将自定义日期值设置为DatePicker时，会引发错误-RangeError: Invalid time value。
有人可以告诉我DatePicker设置自定义日期需要哪种类型的数据？
答案 0 :(得分：3)
您的日期似乎是import cv2
import os
import json
import numpy as np
from IPython import embed
import tensorflow as tf
from object_detection.utils import dataset_util
json_path = "/home/deploy/ved/train.json"
out_path = "/home/deploy/ved/out"
commodity_lst = ["tomato"]
writer = tf.python_io.TFRecordWriter("train.record")
target_size = 1200
label_map = "/home/deploy/ved/label_map.pbtxt"
write_label_map = True
category = ['tomato']
category_index = {k:v for k,v in zip(range(1,len(category)+1), category)}
with open(json_path, 'r') as f:
data = json.load(f)
if write_label_map:
with open(label_map, "w") as f_pbtxt:
for k,v in category_index.items():
f_pbtxt.write("item{{\n\tid: {}\n\tname: '{}'\n}}\n".format(k,v))
def checkJPG(fn):
with tf.Graph().as_default():
try:
image_contents = tf.read_file(fn)
image = tf.image.decode_jpeg(image_contents, channels=3)
init_op = tf.initialize_all_tables()
with tf.Session() as sess:
sess.run(init_op)
tmp = sess.run(image)
except:
print("Corrupted file: ", fn)
return False
return True
def getSquareImage( img, target_width = 500 ):
width = img.shape[1]
height = img.shape[0]
square = np.zeros( (target_width, target_width, 3), dtype=np.uint8 )
max_dim = width if width >= height else height
scale = target_width / max_dim
if ( width >= height ):
width = target_width
x = 0
height = int(height * scale)
y = int(( target_width - height ) / 2)
else:
y = 0
height = target_width
width = int(width * scale)
x = int(( target_width - width ) / 2)
# embed()
square[y:y+height, x:x+width] = cv2.resize( img , (width, height) )
return square, x, y, scale
for key in data:
img = cv2.imread(os.path.join("/home/deploy/ved/dataset", data[key]["filename"]))
if checkJPG(os.path.join("/home/deploy/ved/dataset", data[key]["filename"])):
_img, _x, _y, _scale = getSquareImage(img, target_size)
encoded_jpg = cv2.imencode('.jpg', _img)[1].tostring()
filename = tf.compat.as_bytes(data[key]["filename"])
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
width = _img.shape[1]
height = _img.shape[0]
for j in range(len(data[key]["regions"])):
x_lst = np.asarray(data[key]["regions"][j]["shape_attributes"]["all_points_x"])
y_lst = np.asarray(data[key]["regions"][j]["shape_attributes"]["all_points_y"])
xmin = int(np.amin(x_lst)*_scale) + _x
xmax = int(np.amax(x_lst)*_scale) + _x
ymin = int(np.amin(y_lst)*_scale) + _y
ymax = int(np.amax(y_lst)*_scale) + _y
cv2.rectangle(_img, (xmin, ymin), (xmax, ymax), (255,0,0), 2)
try:
_commodity = [i for i in commodity_lst if i in data[key]["regions"][j]["region_attributes"]][0]
except:
print("commodity name not in list")
embed()
if _commodity == data[key]["regions"][j]["region_attributes"][_commodity]:
text = _commodity
try:
index = [k for k,v in category_index.items() if v == text.strip()][0]
except:
print("one tagged catogory not in list")
embed()
xmins.append( xmin / width)
xmaxs.append( xmax / width)
ymins.append( ymin / height)
ymaxs.append( ymax / height)
classes_text.append(tf.compat.as_bytes(text))
classes.append(index)
# cv2.putText(_img, text, (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 255, 255), lineType=cv2.LINE_AA)
# _path = os.path.join(out_path, os.path.splitext(data[key]["filename"])[0]+"_ann"+os.path.splitext(data[key]["filename"])[1])
# cv2.imwrite(_path, _img)
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename),
'image/source_id': dataset_util.bytes_feature(filename),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
writer.write(tf_example.SerializeToString())
writer.close()
格式。 string不接受字符串日期。
您需要使用parseISO
将字符串日期解析为实际日期Datepicker用法
import { parseISO } from 'date-fns'
答案 1 :(得分：2)
React-datepicker需要传递Date的实例以用于配置值，例如startDate等（或者可能还包括时间戳整数，不确定）。
您可以使用
new Date(Date.parse("2019-06-15T00:00:00"));
创建日期实例。 Date.parse()可以识别许多日期字符串格式，并将它们转换为Date()构造函数可以接受的时间戳值。
答案 2 :(得分：0)
您可以在 Javascript 中使用最新的 date-fns 库并使用下面的格式
import { format, parseISO } from 'date-fns'
format(parseISO(DateinISOformat), "MMM dd h:m a")
下面的例子 -
format(parseISO('2019-06-15T00:00:00'), "MMM dd h:m a")