我有一个切换Bootstrap模式的按钮。按钮本身包含在div中,因此工具提示会在悬停时显示。
当我关闭模态时,按钮会聚焦,工具提示会显示而不会悬停元素。
<span data-toggle="tooltip" data-placement="top" data-title="Tooltip">
<button data-toggle="modal" data-target="#modal">Toggle</button>
</span>
<div class="modal fade" id="modal" tabindex="-1" role="dialog" aria-hidden="true">
<div class="modal-dialog">
<div class="modal-content">
<div class="modal-header">
<button type="button" class="close" data-dismiss="modal">×</button>
</div>
<div class="modal-body">
<p>lorem ispum dolor sit amet</p>
</div>
<div class="modal-footer">
<button type="button" class="btn btn-primary">Submit</button>
<button type="button" class="btn btn-default" data-dismiss="modal">Cancel</button>
</div>
</div>
</div>
</div>
在这里看到发生了什么:http://jsfiddle.net/6t3kxhLb/
到目前为止,我唯一可以解决的方法是在hidden.bs.modal事件触发时模糊按钮。但我对结果并不满意。
$('#modal').on('hidden.bs.modal', function(event){
setTimeout(function(){
$('[data-toggle="modal"]').blur();
});
});
当模态关闭时,你们知道有什么方法可以防止对切换按钮的关注吗?
答案 0 :(得分:3)
根据Bootstrap documentation,您需要指定触发工具提示的内容。选项包括click
,hover
,focus
和manual
,而默认选项包含hover
和focus
。所以只需将data-trigger="hover"
添加到您的元素:
<span data-toggle="tooltip" data-placement="top" data-title="Tooltip" data-trigger="hover">
<button data-toggle="modal" data-target="#modal">Toggle</button>
</span>
答案 1 :(得分:0)
我会在一段时间后讨论这个话题。我避免使用Bootstraps数据属性并使用Jquery。我通过以下方式实现了这一目标......
import tensorflow as tf
from tensorflow.python.framework import ops
shuffle = True
batch_size = 128
num_threads = 8
def get_data():
"""
Return image_paths, labels such that label[i] corresponds to image_paths[i].
image_paths: list of strings
labels: list/np array of labels
"""
raise NotImplementedError()
def preprocess_image_tensor(image_tf):
"""Preprocess a single image."""
image = tf.image.convert_image_dtype(image_tf, dtype=tf.float32)
image = tf.image.resize_image_with_crop_or_pad(image, 300, 300)
image = tf.image.per_image_standardization(image)
return image
image_paths, labels = get_data()
image_paths_tf = ops.convert_to_tensor(image_paths, dtype=tf.string, name='image_paths')
labels_tf = ops.convert_to_tensor(image_paths, dtype=tf.int32, name='labels')
image_path_tf, label_tf = tf.train.slice_input_producer([image_paths_tf, labels_tf], shuffle=shuffle)
# preprocess single image paths
image_buffer_tf = tf.read_file(image_path_tf, name='image_buffer')
image_tf = tf.image.decode_jpeg(image_buffer_tf, channels=3, name='image')
image_tf = preprocess_image_tensor(image_tf)
# batch the results
image_batch_tf, labels_batch_tf = tf.train.batch([image_tf, label_tf], batch_size=batch_size, num_threads=num_threads)
答案 2 :(得分:0)
这是我使用的解决方案,就像一个魅力:
$('.modal').on('hidden.bs.modal',function(event){
event.stopImmediatePropagation();
});