使用 google 外部 css 时，输入类型=复选框不确定状态未可视化（谷歌css样式加载不了）

25-05-01 1

本文将分享使用google外部css时，输入类型=复选框不确定状态未可视化的详细内容，并且还将对谷歌css样式加载不了进行详尽解释，此外，我们还将为大家带来关于2个纪元后出现imageai错误：Val

本文将分享使用 google 外部 css 时，输入类型=复选框不确定状态未可视化的详细内容，并且还将对谷歌css样式加载不了进行详尽解释，此外，我们还将为大家带来关于2个纪元后出现imageai错误：ValueError：确保您指定了正确的输入图像，输入类型，输出类型和/或输出图像路径、android – 输入类型=数字键盘在Galaxy Tab中禁用了点、Android 之设置 EditText 输入类型、asp.net-mvc – 输入类型=“隐藏”vs htmlhelper奇怪的问题的相关知识，希望对你有所帮助。

本文目录一览：

使用 google 外部 css 时，输入类型=复选框不确定状态未可视化（谷歌css样式加载不了）
2个纪元后出现imageai错误：ValueError：确保您指定了正确的输入图像，输入类型，输出类型和/或输出图像路径
android – 输入类型=数字键盘在Galaxy Tab中禁用了点
Android 之设置 EditText 输入类型
asp.net-mvc – 输入类型=“隐藏”vs htmlhelper奇怪的问题

使用 google 外部 css 时，输入类型=复选框不确定状态未可视化（谷歌css样式加载不了）

如何解决使用 google 外部 css 时，输入类型=复选框不确定状态未可视化

我正在开发谷歌表侧栏并使用谷歌插件 css。

我想显示状态不确定的复选框，但插件阻止了它。有关说明，请参阅代码片段。 带有谷歌链接

<html>
    <head>
        <link rel="stylesheet" href="https://ssl.gstatic.com/docs/script/css/add-ons1.css">  
    </head>
    <body>
        With google css
        <br>
        <label>
            <input type="checkBox" id="cond1">indeterminate checkBox
        </label><br>
      
        <span id="span" style="font-weight: bold;">With google css</span>
        <script type="text/javascript">
         //Set indeterminate 
            document.getElementById("cond1").indeterminate = true;
        </script>        
    </body>
</html>

没有

<html>
    <head>
        <!-- <link rel="stylesheet" href="https://ssl.gstatic.com/docs/script/css/add-ons1.css">   -->
    </head>
    <body>
        Without google css
        <br>
        <label>
            <input type="checkBox" id="cond1">indeterminate checkBox
        </label><br>
      
        <span id="span" style="font-weight: bold;">With google css</span>
        <script type="text/javascript">
         //Set indeterminate 
            document.getElementById("cond1").indeterminate = true;
        </script>        
    </body>
</html>

可以做什么？

解决方法

Google CSS 有 appearance: none;... 未选中时用 appearance: auto !important; 覆盖它。

input[type="checkbox"]{
  appearance: auto !important;
}
input[type="checkbox"]:checked{
  appearance: none !important;
}

<html>
    <head>
        <link rel="stylesheet" href="https://ssl.gstatic.com/docs/script/css/add-ons1.css">  
    </head>
    <body>
        With google css
        <br>
        <label>
            <input type="checkbox" id="cond1">indeterminate checkbox
        </label><br>
      
        <span id="span" style="font-weight: bold;">With google css</span>
        <script type="text/javascript">
         //Set indeterminate 
            document.getElementById("cond1").indeterminate = true;
        </script>        
    </body>
</html>

2个纪元后出现imageai错误：ValueError：确保您指定了正确的输入图像，输入类型，输出类型和/或输出图像路径

如何解决2个纪元后出现imageai错误：ValueError：确保您指定了正确的输入图像，输入类型，输出类型和/或输出图像路径

这是我收到的错误，我不知道为什么会这样，因为第一个纪元运行顺利，我给了包含整个代码的笔记本文件。我在kaggle笔记本电脑上执行此操作，同时启用了Internet和GPU。

我正在使用tensorflow 1，这是一个动作识别任务，我从头开始构建了模型，因此您可能必须查看详细信息，以下是我的整个代码，只是缺少一个函数，但它并不重要，我还给了nexxt评论，以便您了解它的作用

代码 ''''''

!cp -r ../input/imageai/imageai/imageai/ imageai

!python3 -c ''import tensorflow as tf; print(tf.__version__)''  # for Python 3

!python -c ''import keras; print(keras.__version__)''
1.12.0
Using TensorFlow backend.
2.2.4

import numpy as np # linear algebra
import pandas as pd # data processing,CSV file I/O (e.g. pd.read_csv)
 under the input directory

import os

from keras.preprocessing.image import ImageDataGenerator
from keras.applications.inception_v3 import preprocess_input
from keras.utils.data_utils import GeneratorEnqueuer
import matplotlib.pyplot as plt
import pandas as pd 
import numpy as np 
import math,os
from imageai.Detection import ObjectDetection
import warnings
from keras.callbacks import ModelCheckpoint
from imageai.Detection import ObjectDetection
import os
import cv2
import numpy as np
import shutil

from keras.models import Sequential
from keras.callbacks import Callback
from keras.layers import Conv2D,MaxPooling2D,Dropout,Dense,Flatten,UpSampling2D
from keras import backend as K

import random
import glob
import subprocess
import os
from PIL import Image
import numpy as np
from matplotlib.pyplot import imshow,figure

from keras.layers import Lambda,Reshape,Permute,Input,add,Conv3D,GaussianNoise,concatenate
from keras.layers import ConvLSTM2D,Batchnormalization,Timedistributed,Add
from keras.models import Model

warnings.filterwarnings("ignore")
%matplotlib inline


OBJECT_DETECTION_SPEED = "fastest"
PRETRAINED_MODEL_PATH = "/kaggle/input/imageai/resnet50_coco_best_v2.0.1.h5"
FONT = cv2.FONT_HERShey_SIMPLEX
IMG_SIZE = 128
FRAME_BATCH_SIZE = 4
THRESHOLD_DIFF_TOLERANCE = 80
batch_size = 8
EPOCHS = 5
Using TensorFlow backend.
detector = ObjectDetection()
detector.setModelTypeAsRetinaNet()
detector.setModelPath(PRETRAINED_MODEL_PATH)
detector.loadModel(detection_speed = OBJECT_DETECTION_SPEED) #change parameter to adjust accuracy and speed
custom = detector.CustomObjects(person=True)
train_dir = "/kaggle/input/violence-final-2/welp"
val_dir = "/kaggle/input/moviesviolencenonviolence/movies/"

total_number_frames_train = 0
total_number_frames_valid = 0
def get_Boxes(frame):
    _,detections = detector.detectCustomObjectsFromImage(
        custom_objects=custom,input_type="array",input_image= frame,output_type="array"
        )
    return detections

def frame_preprocessing(frame):
    frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
    frame = cv2.resize(frame,dsize=(IMG_SIZE,IMG_SIZE),interpolation=cv2.INTER_CUBIC)

    return frame

def get_frame_difference(frame_1,frame_2):
    frame = cv2.absdiff(frame_1,frame_2)
    ret,thresh1 = cv2.threshold(frame,THRESHOLD_DIFF_TOLERANCE,255,cv2.THRESH_BINARY)   #127 is threshold
    return thresh1

def mask_frame(frame,detections,detections_temp):
    mask = np.zeros(frame.shape,dtype=np.uint8)

    for eachObject in detections:
        x1,y1,x2,y2 = eachObject["Box_points"]
        mask = cv2.rectangle(mask,(x1,y1),(x2,y2),(255,255),-1) 

    for eachObject in detections_temp:
        x1,-1) 

    result = cv2.bitwise_and(frame,mask)   # Mask input image with binary mask
    result[mask==0] = 255   # Optional : set background -> Now white/ by default black

    return result
            
def my_generater(batch_size,in_dir,videos_per_category):           
    
    total_number_frames_train = 0
    total_number_frames_valid = 0

    list_fight=os.listdir(os.path.join(in_dir,"Violence"))
    list_no_fight=os.listdir(os.path.join(in_dir,"NonViolence"))

    fight_final=random.sample(list_fight,videos_per_category)
    no_fight_final=random.sample(list_no_fight,videos_per_category)

    fight_labels = []
    no_fight_labels = []

    for i in range (videos_per_category):
        fight_labels.append([1,0])
        no_fight_labels.append([0,1])

    final = fight_final + no_fight_final
    labels = fight_labels + no_fight_labels

    c = list(zip(final,labels))
    random.shuffle(c)
    names,labels = zip(*c)
    
    images_batches=[]
    labelsss=[]
    images_batches=[]
    labelss=[]
    counter = 0
    
#     print("check")
    while True:
        for i in range(len(names)): ##no. of videos loop
            
#             print("check1")
            if labels[i]==[1,0]:
                in_file = os.path.join(in_dir,"Violence")
            else:
                in_file = os.path.join(in_dir,"NonViolence")
                
            in_file = os.path.join(in_file,names[i])
            vidcap = cv2.VideoCapture(in_file)
            length = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))  #frames in a video
            if in_dir == "/kaggle/input/violence-final-2/welp/":
                total_number_frames_train = total_number_frames_train + length
            else:
                total_number_frames_valid = total_number_frames_valid + length
        
            for j in range(int(length/FRAME_BATCH_SIZE)):
                detections_temp_2=[]
                success,frame_temp = vidcap.read()
                frame_temp = cv2.resize(frame_temp,interpolation=cv2.INTER_CUBIC)
                detections_temp = get_Boxes(frame_temp) 
                frame_temp = mask_frame(frame_temp,detections_temp,detections_temp_2)                    
                frame_temp = cv2.cvtColor(frame_temp,cv2.COLOR_BGR2GRAY)
                
                images_frame_batches=[]

                for k in range(FRAME_BATCH_SIZE - 1):
                    
                    success,frame = vidcap.read()
                    frame = cv2.resize(frame,interpolation=cv2.INTER_CUBIC)
                    detections = get_Boxes(frame)                   
                    frame = mask_frame(frame,detections_temp)   
                    frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)   
                    diff = get_frame_difference(frame,frame_temp)
                    diff = diff/255
                    images_frame_batches.append(diff)
                    
                    frame_temp = frame
                    detections_temp=detections

                if counter < batch_size:
                    images_batches.append(images_frame_batches)
                    counter = counter + 1
                    labelss.append(labels[i])
                    

                else:
                    yield np.array(images_batches).reshape((batch_size,FRAME_BATCH_SIZE-1,IMG_SIZE,1)),np.array(labelss)
                    labelss=[]
                    images_batches=[]
                    counter = 0
        break
  
gen = my_generater(2,train_dir,800)
videos,next_frame = next(gen)

print(np.array(videos).shape)
np.array(next_frame).shape
(2,3,128,1)
(2,2)

#gets total frames combined of all videos
total_number_frames_train = get_total_frames(train_dir,800)
total_number_frames_valid = get_total_frames(val_dir,100)

print(total_number_frames_train)
print(total_number_frames_valid)
224108
18283
steps_per_epoch = total_number_frames_train // (batch_size * FRAME_BATCH_SIZE)
validation_steps = total_number_frames_valid // (batch_size * FRAME_BATCH_SIZE)

print(steps_per_epoch)
print(validation_steps)
7003
571
inp = Input((FRAME_BATCH_SIZE - 1,1))
permuted = Permute((2,4,1))(inp)
noise = GaussianNoise(0.1)(permuted)
c=4
x = Permute((4,1,2,3))(noise)
x =(ConvLSTM2D(filters=c,kernel_size=(3,3),padding=''same'',name=''conv_lstm1'',return_sequences=True))(x)

c1=(Batchnormalization())(x)
x = Dropout(0.2)(x)
x =(Timedistributed(MaxPooling2D(pool_size=(2,2))))(c1)

x =(ConvLSTM2D(filters=2*c,name=''conv_lstm2'',return_sequences=True))(x)
c2=(Batchnormalization())(x)
x = Dropout(0.2)(x)

x =(Timedistributed(MaxPooling2D(pool_size=(2,2))))(c2)
x =(ConvLSTM2D(filters=4*c,name=''conv_lstm3'',return_sequences=True))(x)

x =(Timedistributed(UpSampling2D(size=(2,2))))(x)
x =(ConvLSTM2D(filters=4*c,name=''conv_lstm4'',return_sequences=True))(x)
x =(Batchnormalization())(x)

x =(ConvLSTM2D(filters=2*c,name=''conv_lstm5'',return_sequences=True))(x)
x =(Batchnormalization())(x)
x = Add()([c2,x])
x = Dropout(0.2)(x)

x =(Timedistributed(UpSampling2D(size=(2,2))))(x)
x =(ConvLSTM2D(filters=c,name=''conv_lstm6'',return_sequences=False))(x)
x =(Batchnormalization())(x)

x = (Flatten())(x)
x = (Dense(units=50,activation=''relu''))(x)

x = (Dense(units=2,activation=''relu''))(x)

model=Model(inputs=[inp],outputs=[x])

model.summary()  #works,but i have deleted it due to character limit

   

Total params: 3,322,408
Trainable params: 3,328
Non-trainable params: 80
__________________________________________________________________________________________________
model.compile(optimizer=''adam'',loss=''mse'',metrics=[''accuracy''])
!pip install tensorflow-gpu==1.14.0
import tensorflow as tf
Collecting tensorflow-gpu==1.14.0
  Downloading https://files.pythonhosted.org/packages/76/04/43153bfdfcf6c9a4c38ecdb971ca9a75b9a791bb69a764d652c359aca504/tensorflow_gpu-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (377.0MB)
    100% |████████████████████████████████| 377.0MB 113kB/s 
Requirement already satisfied: six>=1.10.0 in /opt/conda/lib/python3.6/site-packages (from tensorflow-gpu==1.14.0) (1.12.0)
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  Downloading https://files.pythonhosted.org/packages/a3/de/c648ef6835192e6e2cc03f40b19eeda4382c49b5bafb43d88b931c4c74ac/google_pasta-0.2.0-py3-none-any.whl (57kB)
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Collecting tensorboard<1.15.0,>=1.14.0 (from tensorflow-gpu==1.14.0)
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Requirement already satisfied: keras-applications>=1.0.6 in /opt/conda/lib/python3.6/site-packages (from tensorflow-gpu==1.14.0) (1.0.7)
Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 (from tensorflow-gpu==1.14.0)
  Downloading https://files.pythonhosted.org/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488kB)
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Collecting wrapt>=1.11.1 (from tensorflow-gpu==1.14.0)
  Downloading https://files.pythonhosted.org/packages/82/f7/e43cefbe88c5fd371f4cf0cf5eb3feccd07515af9fd6cf7dbf1d1793a797/wrapt-1.12.1.tar.gz
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Collecting setuptools>=41.0.0 (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0)
  Downloading https://files.pythonhosted.org/packages/44/a6/7fb6e8b3f4a6051e72e4e2218889351f0ee484b9ee17e995f5ccff780300/setuptools-50.3.0-py3-none-any.whl (785kB)
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Requirement already satisfied: h5py in /opt/conda/lib/python3.6/site-packages (from keras-applications>=1.0.6->tensorflow-gpu==1.14.0) (2.9.0)
Building wheels for collected packages: wrapt
  Running setup.py bdist_wheel for wrapt ... - \\ | / done
  Stored in directory: /root/.cache/pip/wheels/b1/c2/ed/d62208260edbd3fa7156545c00ef966f45f2063d0a84f8208a
Successfully built wrapt
thinc 6.12.1 has requirement wrapt<1.11.0,>=1.10.0,but you''ll have wrapt 1.12.1 which is incompatible.
tensorflow 1.12.0 has requirement tensorboard<1.13.0,>=1.12.0,but you''ll have tensorboard 1.14.0 which is incompatible.
pytest-cov 2.6.1 has requirement pytest>=3.6,but you''ll have pytest 3.5.1 which is incompatible.
anaconda-client 1.6.14 has requirement python-dateutil>=2.6.1,but you''ll have python-dateutil 2.6.0 which is incompatible.
Installing collected packages: google-pasta,setuptools,tensorboard,tensorflow-estimator,wrapt,tensorflow-gpu
  Found existing installation: setuptools 39.1.0
    Uninstalling setuptools-39.1.0:
      Successfully uninstalled setuptools-39.1.0
  Found existing installation: tensorboard 1.12.2
    Uninstalling tensorboard-1.12.2:
      Successfully uninstalled tensorboard-1.12.2
  Found existing installation: wrapt 1.10.11
Cannot uninstall ''wrapt''. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
You are using pip version 18.1,however version 20.2.3 is available.
You should consider upgrading via the ''pip install --upgrade pip'' command.
# device_name = tf.test.gpu_device_name()
if tf.test.gpu_device_name():
    print(''Default GPU Device: {}''.format(tf.test.gpu_device_name()))
else:
    print("Please install GPU version of TF")
Default GPU Device: /device:GPU:0
filepath = "/kaggle/working/saved-model-{epoch:02d}-{val_acc:.2f}.hdf5"

checkpoint = ModelCheckpoint(filepath,monitor=''val_acc'',verbose=1,save_best_only=False,mode=''max'')
config = tf.ConfigProto( device_count = {''GPU'': 1,''cpu'': 56} ) 
sess = tf.Session(config=config) 
tf.keras.backend.set_session(sess)
model.fit_generator(my_generater(batch_size,800),steps_per_epoch=steps_per_epoch//4,epochs=EPOCHS,validation_steps=validation_steps//4,validation_data=my_generater(batch_size,val_dir,100),callbacks = [checkpoint])
Epoch 1/5
1750/1750 [==============================] - 4570s 3s/step - loss: 0.5375 - acc: 0.5976 - val_loss: 0.5000 - val_acc: 0.5599

Epoch 00001: saving model to /kaggle/working/saved-model-01-0.56.hdf5
Epoch 2/5
   5/1750 [..............................] - ETA: 3:58 - loss: 0.5000 - acc: 0.3000
---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
/kaggle/working/imageai/Detection/__init__.py in detectCustomObjectsFromImage(self,custom_objects,input_image,output_image_path,input_type,output_type,extract_detected_objects,minimum_percentage_probability,display_percentage_probability,display_object_name)
    694                     model = self.__model_collection[0]
--> 695                     _,_,detections = model.predict_on_batch(np.expand_dims(image,axis=0))
    696                     predicted_numbers = np.argmax(detections[0,:,4:],axis=1)

/opt/conda/lib/python3.6/site-packages/keras/engine/training.py in predict_on_batch(self,x)
   1273         self._make_predict_function()
-> 1274         outputs = self.predict_function(ins)
   1275         return unpack_singleton(outputs)

/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in __call__(self,inputs)
   2714 
-> 2715             return self._call(inputs)
   2716         else:

/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in _call(self,inputs)
   2674         else:
-> 2675             fetched = self._callable_fn(*array_vals)
   2676         return fetched[:len(self.outputs)]

/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in __call__(self,*args,**kwargs)
   1438               self._session._session,self._handle,args,status,-> 1439               run_Metadata_ptr)
   1440         if run_Metadata:

/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self,type_arg,value_arg,traceback_arg)
    527             compat.as_text(c_api.TF_Message(self.status.status)),--> 528             c_api.TF_GetCode(self.status.status))
    529     # Delete the underlying status object from memory otherwise it stays alive

InvalidArgumentError: indices[0] = 78 is not in [0,0)
     [[{{node nms/embedding_lookup_52}} = GatherV2[Taxis=DT_INT32,Tindices=DT_INT32,Tparams=DT_INT32,_device="/job:localhost/replica:0/task:0/device:cpu:0"](nms/Cast_17,nms/non_max_suppression_17/NonMaxSuppressionV3,nms/embedding_lookup_52/axis)]]

During handling of the above exception,another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-19-37687fd66644> in <module>()
      4                     validation_steps=validation_steps//4,5                     validation_data=my_generater(batch_size,----> 6                     callbacks = [checkpoint])

/opt/conda/lib/python3.6/site-packages/keras/legacy/interfaces.py in wrapper(*args,**kwargs)
     89                 warnings.warn(''Update your `'' + object_name + ''` call to the '' +
     90                               ''Keras 2 API: '' + signature,stacklevel=2)
---> 91             return func(*args,**kwargs)
     92         wrapper._original_function = func
     93         return wrapper

/opt/conda/lib/python3.6/site-packages/keras/engine/training.py in fit_generator(self,generator,steps_per_epoch,epochs,verbose,callbacks,validation_data,validation_steps,class_weight,max_queue_size,workers,use_multiprocessing,shuffle,initial_epoch)
   1416             use_multiprocessing=use_multiprocessing,1417             shuffle=shuffle,-> 1418             initial_epoch=initial_epoch)
   1419 
   1420     @interfaces.legacy_generator_methods_support

/opt/conda/lib/python3.6/site-packages/keras/engine/training_generator.py in fit_generator(model,initial_epoch)
    179             batch_index = 0
    180             while steps_done < steps_per_epoch:
--> 181                 generator_output = next(output_generator)
    182 
    183                 if not hasattr(generator_output,''__len__''):

/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
    707                     "`use_multiprocessing=False,workers > 1`."
    708                     "For more information see issue #1638.")
--> 709             six.reraise(*sys.exc_info())

/opt/conda/lib/python3.6/site-packages/six.py in reraise(tp,value,tb)
    691             if value.__traceback__ is not tb:
    692                 raise value.with_traceback(tb)
--> 693             raise value
    694         finally:
    695             value = None

/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py in get(self)
    683         try:
    684             while self.is_running():
--> 685                 inputs = self.queue.get(block=True).get()
    686                 self.queue.task_done()
    687                 if inputs is not None:

/opt/conda/lib/python3.6/multiprocessing/pool.py in get(self,timeout)
    642             return self._value
    643         else:
--> 644             raise self._value
    645 
    646     def _set(self,i,obj):

/opt/conda/lib/python3.6/multiprocessing/pool.py in worker(inqueue,outqueue,initializer,initargs,maxtasks,wrap_exception)
    117         job,func,kwds = task
    118         try:
--> 119             result = (True,func(*args,**kwds))
    120         except Exception as e:
    121             if wrap_exception and func is not _helper_reraises_exception:

/opt/conda/lib/python3.6/site-packages/keras/utils/data_utils.py in next_sample(uid)
    624         The next value of generator `uid`.
    625     """
--> 626     return six.next(_SHARED_SEQUENCES[uid])
    627 
    628 

<ipython-input-8-1e63801f9903> in my_generater(batch_size,videos_per_category)
     65                     success,frame = vidcap.read()
     66                     frame = cv2.resize(frame,interpolation=cv2.INTER_CUBIC)
---> 67                     detections = get_Boxes(frame)
     68                     frame = mask_frame(frame,detections_temp)
     69                     frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)

<ipython-input-6-a5006495da23> in get_Boxes(frame)
      4         input_type="array",5         input_image= frame,----> 6         output_type="array"
      7         )
      8     return detections

/kaggle/working/imageai/Detection/__init__.py in detectCustomObjectsFromImage(self,display_object_name)
    884             except:
    885                 raise ValueError(
--> 886                     "Ensure you specified correct input image,input type,output type and/or output image path ")
    887 
    888 

ValueError: Ensure you specified correct input image,output type and/or output image path

''''''

android – 输入类型=数字键盘在Galaxy Tab中禁用了点

我知道有很多问题已经回复了,但是没有一个解决方案对我有用.

我正在为使用三星galaxy Tab的企业开发一个网站.有很多输入,其内容是十进制数.所以我尝试使用< input type =“number”/>.但导航器会显示一个带点和逗号的键盘.不仅如此,还会从值中删除onfocus,点或逗号分隔符.

我已经尝试设置一个像“0,1”,“0,01”,“0.1”,“0.01”,“任何”等步骤,尝试使用匹配十进制数的正则表达式的min,max,pattern …在stackoverflow中没有任何建议可以尝试,这是可怕的哈哈.

我使用三星galaxy Tab 10.1和默认浏览器,但我没有找到任何文件谈论它的html5实现的任何限制.所以现在我不知道是否有我遗漏的东西,如果有什么我可以尝试,或者如果有什么东西知道这是galaxy Tab的错,所以我无能为力.

非常感谢提前.

解决方法

在 android命名空间中,您可以使用：

android:inputType="numberDecimal"

这使您可以输入“.”

Android 之设置 EditText 输入类型

EditText editText;

// 输入类型为没有指定明确的类型的特殊内容类型
editText.setInputType(InputType.TYPE_NULL);

// 输入类型为普通文本
editText.setInputType(InputType.TYPE_CLASS_TEXT);

// 输入类型为数字文本
editText.setInputType(InputType.TYPE_CLASS_NUMBER);

// 输入类型为电话号码
editText.setInputType(InputType.TYPE_CLASS_PHONE);

// 输入类型为日期和时间
editText.setInputType(InputType.TYPE_CLASS_DATETIME);

// 输入类型为 {@link#TYPE_CLASS_DATETIME} 的缺省变化值，允许输入日期和时间。
editText.setInputType(InputType.TYPE_DATETIME_VARIATION_NORMAL);

// 输入类型为 {@link#TYPE_CLASS_DATETIME} 的缺省变化值，只允许输入一个日期。
editText.setInputType(InputType.TYPE_DATETIME_VARIATION_DATE);

// 输入类型为 {@link#TYPE_CLASS_DATETIME} 的缺省变化值，只允许输入一个时间。
editText.setInputType(InputType.TYPE_DATETIME_VARIATION_TIME);

// 输入类型为决定所给文本整体类的位掩码
editText.setInputType(InputType.TYPE_MASK_CLASS);

// 输入类型为提供附加标志位选项的位掩码
editText.setInputType(InputType.TYPE_MASK_FLAGS);

// 输入类型为决定基类内容变化的位掩码
editText.setInputType(InputType.TYPE_MASK_VARIATION);

// 输入类型为小数数字，允许十进制小数点提供分数值。
editText.setInputType(InputType.TYPE_NUMBER_FLAG_DECIMAL);
// 输入类型为数字是带符号的，允许在开头带正号或者负号
editText.setInputType(InputType.TYPE_NUMBER_FLAG_SIGNED);

// 输入类型为 {@link#TYPE_CLASS_NUMBER} 的缺省变化值：为纯普通数字文本
editText.setInputType(InputType.TYPE_NUMBER_VARIATION_NORMAL);

// 输入类型为 {@link#TYPE_CLASS_NUMBER} 的缺省变化值：为数字密码
editText.setInputType(InputType.TYPE_NUMBER_VARIATION_PASSWORD);

// 输入类型为自动完成文本类型
editText.setInputType(InputType.TYPE_TEXT_FLAG_AUTO_COMPLETE);

// 输入类型为自动纠正文本类型
editText.setInputType(InputType.TYPE_TEXT_FLAG_AUTO_CORRECT);

// 输入类型为所有字符大写
editText.setInputType(InputType.TYPE_TEXT_FLAG_CAP_CHARACTERS);

// 输入类型为每句的第一个字符大写
editText.setInputType(InputType.TYPE_TEXT_FLAG_CAP_SENTENCES);

// 输入类型为每个单词的第一个字母大写
editText.setInputType(InputType.TYPE_TEXT_FLAG_CAP_WORDS);

// 输入多行文本
editText.setInputType(InputType.TYPE_TEXT_FLAG_IME_MULTI_LINE);

// 进行输入时，输入法无提示
editText.setInputType(InputType.TYPE_TEXT_FLAG_NO_SUGGESTIONS);

// 输入一个短的，可能是非正式的消息，如即时消息或短信。
editText.setInputType(InputType.TYPE_TEXT_VARIATION_SHORT_MESSAGE);

// 输入长内容，可能是正式的消息内容，比如电子邮件的主体
editText.setInputType(InputType.TYPE_TEXT_VARIATION_LONG_MESSAGE);

// 输入文本以过滤列表等内容
editText.setInputType(InputType.TYPE_TEXT_VARIATION_FILTER);

// 输入一个电子邮件地址
editText.setInputType(InputType.TYPE_TEXT_VARIATION_EMAIL_ADDRESS);

// 输入电子邮件主题行
editText.setInputType(InputType.TYPE_TEXT_VARIATION_EMAIL_SUBJECT);

// 输入一个密码
editText.setInputType(InputType.TYPE_TEXT_VARIATION_PASSWORD);

// 输入老式的普通文本
editText.setInputType(InputType.TYPE_TEXT_VARIATION_NORMAL);

// 输入人名
editText.setInputType(InputType.TYPE_TEXT_VARIATION_PERSON_NAME);

// 输入邮寄地址
editText.setInputType(InputType.TYPE_TEXT_VARIATION_POSTAL_ADDRESS);

// 输入语音发音输入文本，如联系人拼音名称字段
editText.setInputType(InputType.TYPE_TEXT_VARIATION_PHONETIC);

// 输入 URI
editText.setInputType(InputType.TYPE_TEXT_VARIATION_URI);

// 输入对用户可见的密码
editText.setInputType(InputType.TYPE_TEXT_VARIATION_VISIBLE_PASSWORD);

// 输入网页表单中的文本
editText.setInputType(InputType.TYPE_TEXT_VARIATION_WEB_EDIT_TEXT);

// 输入网页表单中的邮件地址
editText.setInputType(InputType.TYPE_TEXT_VARIATION_WEB_EMAIL_ADDRESS);

// 输入网页表单中的密码
editText.setInputType(InputType.TYPE_TEXT_VARIATION_WEB_PASSWORD);

asp.net-mvc – 输入类型=“隐藏”vs htmlhelper奇怪的问题

我花了一个小时试图弄清楚为什么隐藏字段中的值与我期望的值不同.作为最后的努力,我把它切换到一个隐藏的领域,它开始渲染为我期待的那个.为什么会这样？

某些上下文是使用htmlhelper的ID正在使用的ID与querystring ID参数中的ID相同.

// renders 123
  @using (Html.BeginForm()){
         <input type="hidden" name="id" value="@Model.ID" />
  }

// renders 456
@using (Html.BeginForm()){
         @Html.Hidden("id",Model.ID)
}

解决方法

我相信这是MVC命名约定的一部分.这发生在我身上,因为我的一个模型属性是“标题”,它与ViewBag.Title冲突.它开始显示ViewBag.Title而不是实际的Model’s Title.

我相信只有在HtmlHelper中明确声明“Model.ID”时才会发生这种情况.它查看属性名称“ID”,然后查看ViewContext并查找查询字符串“ID”并使用它.

对于不使用HtmlHelper的查询字符串,它不使用查询字符串“ID”,因为它不会通过ViewContext查找该名称;它只是放置模型中的任何值. HtmlHelpers通常会查看ViewContext,并根据属性名称确定要使用的值,而不是它的确切来源.

如果你想使用HtmlHelper,试试看它的作用：

@Html.HiddenFor(model => model.ID)

今天的关于使用 google 外部 css 时，输入类型=复选框不确定状态未可视化和谷歌css样式加载不了的分享已经结束，谢谢您的关注，如果想了解更多关于2个纪元后出现imageai错误：ValueError：确保您指定了正确的输入图像，输入类型，输出类型和/或输出图像路径、android – 输入类型=数字键盘在Galaxy Tab中禁用了点、Android 之设置 EditText 输入类型、asp.net-mvc – 输入类型=“隐藏”vs htmlhelper奇怪的问题的相关知识，请在本站进行查询。

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