1595572800
In the previous article, we discussed about object detection using GluonCV. In this article, we will discuss how to implement a binary image classifier that classify whether a given image is a tennis ball or not using a pre-trained image classification network from GluonCV. We implement the machine learning pipeline step by step, from loading and transforming an input image, to loading and using a pre-trained model.
To start with some initial setup we will import packages and set the path to the data.
import mxnet as mx
import gluoncv as gcv
import matplotlib.pyplot as plt
import numpy as np
import os
from pathlib import Path
2. Load Image
To load the image, let us implement a function that loads an image from disk given a filepath. The function should return an 8-bit image array, that’s in MXNet’s NDArray format and in HWC layout (i.e. height, width then channel).
def load_image(filepath):
image = mx.image.imread(filepath)
return image
3. Transform the Image
After loading the image, we should transform the image so it can be used as input to the pre-trained network. We plan to use a pre-trained network on **ImageNet. **Therefore, the image transformation should follow the same steps used for ImageNet pre-training. The image should be transformed by:
This can be achived using the following function.
def transform_image(array):
image = gcv.data.transforms.presets.imagenet.transform_eval(array)
return image
4. Load a Model
We will use a MobileNet 1.0 image classification model that’s been pre-trained on ImageNet. The model can be loaded from the GluonCV model zoo as follows:
def load_pretrained_classification_network():
model = gcv.model_zoo.get_model('MobileNet1.0', pretrained=True, root = M3_MODELS)
return model
5. Use a Model
After loading an image, next task is to pass your transformed image through the pretrained network to obtain predicted probabilities for all ImageNet classes (ignore the tennis ball class for now).
Hint #1: Don’t forget that you’re typically working with a batch of images, even when you only have one image.
Hint #2: Remember that the direct outputs of our network aren’t probabilities.
#gluoncv #deep-learning #image-processing #mxnet #image-classification #deep learning
1653123600
This repository is a fork of SimpleMDE, made by Sparksuite. Go to the dedicated section for more information.
A drop-in JavaScript text area replacement for writing beautiful and understandable Markdown. EasyMDE allows users who may be less experienced with Markdown to use familiar toolbar buttons and shortcuts.
In addition, the syntax is rendered while editing to clearly show the expected result. Headings are larger, emphasized words are italicized, links are underlined, etc.
EasyMDE also features both built-in auto saving and spell checking. The editor is entirely customizable, from theming to toolbar buttons and javascript hooks.
Via npm:
npm install easymde
Via the UNPKG CDN:
<link rel="stylesheet" href="https://unpkg.com/easymde/dist/easymde.min.css">
<script src="https://unpkg.com/easymde/dist/easymde.min.js"></script>
Or jsDelivr:
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/easymde/dist/easymde.min.css">
<script src="https://cdn.jsdelivr.net/npm/easymde/dist/easymde.min.js"></script>
After installing and/or importing the module, you can load EasyMDE onto the first textarea
element on the web page:
<textarea></textarea>
<script>
const easyMDE = new EasyMDE();
</script>
Alternatively you can select a specific textarea
, via JavaScript:
<textarea id="my-text-area"></textarea>
<script>
const easyMDE = new EasyMDE({element: document.getElementById('my-text-area')});
</script>
Use easyMDE.value()
to get the content of the editor:
<script>
easyMDE.value();
</script>
Use easyMDE.value(val)
to set the content of the editor:
<script>
easyMDE.value('New input for **EasyMDE**');
</script>
true
, force downloads Font Awesome (used for icons). If set to false
, prevents downloading. Defaults to undefined
, which will intelligently check whether Font Awesome has already been included, then download accordingly.true
, focuses the editor automatically. Defaults to false
.true
, saves the text automatically. Defaults to false
.10000
(10 seconds).autosave.delay
or 10000
(10 seconds).locale: en-US, format: hour:minute
.{ delay: 300 }
, it will check every 300 ms if the editor is visible and if positive, call CodeMirror's refresh()
.**
or __
. Defaults to **
.```
or ~~~
. Defaults to ```
.*
or _
. Defaults to *
.*
, -
or +
. Defaults to *
.textarea
element to use. Defaults to the first textarea
element on the page.true
, force text changes made in EasyMDE to be immediately stored in original text area. Defaults to false
.false
, indent using spaces instead of tabs. Defaults to true
.false
by default, preview for images will appear only for images on separate lines.
as argument and returns a string that serves as the src
attribute of the <img>
tag in the preview. Enables dynamic previewing of images in the frontend without having to upload them to a server, allows copy-pasting of images to the editor with preview.["[", "](http://)"]
.true
, enables line numbers in the editor.false
, disable line wrapping. Defaults to true
."500px"
. Defaults to "300px"
.minHeight
option will be ignored. Should be a string containing a valid CSS value like "500px"
. Defaults to undefined
.true
when the editor is currently going into full screen mode, or false
.true
, will render headers without a space after the #
. Defaults to false
.false
, will not process GFM strikethrough syntax. Defaults to true
.true
, let underscores be a delimiter for separating words. Defaults to false
.false
, will replace CSS classes returned by the default Markdown mode. Otherwise the classes returned by the custom mode will be combined with the classes returned by the default mode. Defaults to true
."editor-preview"
.true
, a JS alert window appears asking for the link or image URL. Defaults to false
.URL of the image:
.URL for the link:
.true
, enables the image upload functionality, which can be triggered by drag and drop, copy-paste and through the browse-file window (opened when the user click on the upload-image icon). Defaults to false
.1024 * 1024 * 2
(2 MB).image/png, image/jpeg
.imageMaxSize
, imageAccept
, imageUploadEndpoint
and imageCSRFToken
ineffective.onSuccess
and onError
callback functions as parameters. onSuccess(imageUrl: string)
and onError(errorMessage: string)
{"data": {"filePath": "<filePath>"}}
where filePath is the path of the image (absolute if imagePathAbsolute
is set to true, relative if otherwise);{"error": "<errorCode>"}
, where errorCode can be noFileGiven
(HTTP 400 Bad Request), typeNotAllowed
(HTTP 415 Unsupported Media Type), fileTooLarge
(HTTP 413 Payload Too Large) or importError
(see errorMessages below). If errorCode is not one of the errorMessages, it is alerted unchanged to the user. This allows for server-side error messages. No default value.true
, will treat imageUrl
from imageUploadFunction
and filePath returned from imageUploadEndpoint
as an absolute rather than relative path, i.e. not prepend window.location.origin
to it.imageCSRFToken
has value, defaults to csrfmiddlewaretoken
.true
, passing CSRF token via header. Defaults to false
, which pass CSRF through request body.#image_name#
, #image_size#
and #image_max_size#
will replaced by their respective values, that can be used for customization or internationalization:uploadImage
is set to true
. Defaults to Attach files by drag and dropping or pasting from clipboard.
.Drop image to upload it.
.Uploading images #images_names#
.Uploading #file_name#: #progress#%
.Uploaded #image_name#
.B, KB, MB
(example: 218 KB
). You can use B,KB,MB
instead if you prefer without whitespaces (218KB
).errorCallback
option, where #image_name#
, #image_size#
and #image_max_size#
will replaced by their respective values, that can be used for customization or internationalization:You must select a file.
.imageAccept
list, or the server returned this error code. Defaults to This image type is not allowed.
.imageMaxSize
, or if the server returned this error code. Defaults to Image #image_name# is too big (#image_size#).\nMaximum file size is #image_max_size#.
.Something went wrong when uploading the image #image_name#.
.(errorMessage) => alert(errorMessage)
.true
, will highlight using highlight.js. Defaults to false
. To use this feature you must include highlight.js on your page or pass in using the hljs
option. For example, include the script and the CSS files like:<script src="https://cdn.jsdelivr.net/highlight.js/latest/highlight.min.js"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/highlight.js/latest/styles/github.min.css">
window.hljs
), you can provide an instance here. Defaults to undefined
.renderingConfig
options will take precedence.false
, disable parsing GitHub Flavored Markdown (GFM) single line breaks. Defaults to true
.false
, disable the spell checker. Defaults to true
. Optionally pass a CodeMirrorSpellChecker-compliant function.textarea
or contenteditable
. Defaults to textarea
for desktop and contenteditable
for mobile. contenteditable
option is necessary to enable nativeSpellcheck.false
, disable native spell checker. Defaults to true
.false
, allows side-by-side editing without going into fullscreen. Defaults to true
.false
, hide the status bar. Defaults to the array of built-in status bar items.false
, remove the CodeMirror-selectedtext
class from selected lines. Defaults to true
.false
, disable syncing scroll in side by side mode. Defaults to true
.2
.easymde
.false
, hide the toolbar. Defaults to the array of icons.false
, disable toolbar button tips. Defaults to true
.rtl
or ltr
. Changes text direction to support right-to-left languages. Defaults to ltr
.Most options demonstrate the non-default behavior:
const editor = new EasyMDE({
autofocus: true,
autosave: {
enabled: true,
uniqueId: "MyUniqueID",
delay: 1000,
submit_delay: 5000,
timeFormat: {
locale: 'en-US',
format: {
year: 'numeric',
month: 'long',
day: '2-digit',
hour: '2-digit',
minute: '2-digit',
},
},
text: "Autosaved: "
},
blockStyles: {
bold: "__",
italic: "_",
},
unorderedListStyle: "-",
element: document.getElementById("MyID"),
forceSync: true,
hideIcons: ["guide", "heading"],
indentWithTabs: false,
initialValue: "Hello world!",
insertTexts: {
horizontalRule: ["", "\n\n-----\n\n"],
image: [""],
link: ["[", "](https://)"],
table: ["", "\n\n| Column 1 | Column 2 | Column 3 |\n| -------- | -------- | -------- |\n| Text | Text | Text |\n\n"],
},
lineWrapping: false,
minHeight: "500px",
parsingConfig: {
allowAtxHeaderWithoutSpace: true,
strikethrough: false,
underscoresBreakWords: true,
},
placeholder: "Type here...",
previewClass: "my-custom-styling",
previewClass: ["my-custom-styling", "more-custom-styling"],
previewRender: (plainText) => customMarkdownParser(plainText), // Returns HTML from a custom parser
previewRender: (plainText, preview) => { // Async method
setTimeout(() => {
preview.innerHTML = customMarkdownParser(plainText);
}, 250);
return "Loading...";
},
promptURLs: true,
promptTexts: {
image: "Custom prompt for URL:",
link: "Custom prompt for URL:",
},
renderingConfig: {
singleLineBreaks: false,
codeSyntaxHighlighting: true,
sanitizerFunction: (renderedHTML) => {
// Using DOMPurify and only allowing <b> tags
return DOMPurify.sanitize(renderedHTML, {ALLOWED_TAGS: ['b']})
},
},
shortcuts: {
drawTable: "Cmd-Alt-T"
},
showIcons: ["code", "table"],
spellChecker: false,
status: false,
status: ["autosave", "lines", "words", "cursor"], // Optional usage
status: ["autosave", "lines", "words", "cursor", {
className: "keystrokes",
defaultValue: (el) => {
el.setAttribute('data-keystrokes', 0);
},
onUpdate: (el) => {
const keystrokes = Number(el.getAttribute('data-keystrokes')) + 1;
el.innerHTML = `${keystrokes} Keystrokes`;
el.setAttribute('data-keystrokes', keystrokes);
},
}], // Another optional usage, with a custom status bar item that counts keystrokes
styleSelectedText: false,
sideBySideFullscreen: false,
syncSideBySidePreviewScroll: false,
tabSize: 4,
toolbar: false,
toolbarTips: false,
});
Below are the built-in toolbar icons (only some of which are enabled by default), which can be reorganized however you like. "Name" is the name of the icon, referenced in the JavaScript. "Action" is either a function or a URL to open. "Class" is the class given to the icon. "Tooltip" is the small tooltip that appears via the title=""
attribute. Note that shortcut hints are added automatically and reflect the specified action if it has a key bind assigned to it (i.e. with the value of action
set to bold
and that of tooltip
set to Bold
, the final text the user will see would be "Bold (Ctrl-B)").
Additionally, you can add a separator between any icons by adding "|"
to the toolbar array.
Name | Action | Tooltip Class |
---|---|---|
bold | toggleBold | Bold fa fa-bold |
italic | toggleItalic | Italic fa fa-italic |
strikethrough | toggleStrikethrough | Strikethrough fa fa-strikethrough |
heading | toggleHeadingSmaller | Heading fa fa-header |
heading-smaller | toggleHeadingSmaller | Smaller Heading fa fa-header |
heading-bigger | toggleHeadingBigger | Bigger Heading fa fa-lg fa-header |
heading-1 | toggleHeading1 | Big Heading fa fa-header header-1 |
heading-2 | toggleHeading2 | Medium Heading fa fa-header header-2 |
heading-3 | toggleHeading3 | Small Heading fa fa-header header-3 |
code | toggleCodeBlock | Code fa fa-code |
quote | toggleBlockquote | Quote fa fa-quote-left |
unordered-list | toggleUnorderedList | Generic List fa fa-list-ul |
ordered-list | toggleOrderedList | Numbered List fa fa-list-ol |
clean-block | cleanBlock | Clean block fa fa-eraser |
link | drawLink | Create Link fa fa-link |
image | drawImage | Insert Image fa fa-picture-o |
table | drawTable | Insert Table fa fa-table |
horizontal-rule | drawHorizontalRule | Insert Horizontal Line fa fa-minus |
preview | togglePreview | Toggle Preview fa fa-eye no-disable |
side-by-side | toggleSideBySide | Toggle Side by Side fa fa-columns no-disable no-mobile |
fullscreen | toggleFullScreen | Toggle Fullscreen fa fa-arrows-alt no-disable no-mobile |
guide | This link | Markdown Guide fa fa-question-circle |
undo | undo | Undo fa fa-undo |
redo | redo | Redo fa fa-redo |
Customize the toolbar using the toolbar
option.
Only the order of existing buttons:
const easyMDE = new EasyMDE({
toolbar: ["bold", "italic", "heading", "|", "quote"]
});
All information and/or add your own icons
const easyMDE = new EasyMDE({
toolbar: [
{
name: "bold",
action: EasyMDE.toggleBold,
className: "fa fa-bold",
title: "Bold",
},
"italics", // shortcut to pre-made button
{
name: "custom",
action: (editor) => {
// Add your own code
},
className: "fa fa-star",
title: "Custom Button",
attributes: { // for custom attributes
id: "custom-id",
"data-value": "custom value" // HTML5 data-* attributes need to be enclosed in quotation marks ("") because of the dash (-) in its name.
}
},
"|" // Separator
// [, ...]
]
});
Put some buttons on dropdown menu
const easyMDE = new EasyMDE({
toolbar: [{
name: "heading",
action: EasyMDE.toggleHeadingSmaller,
className: "fa fa-header",
title: "Headers",
},
"|",
{
name: "others",
className: "fa fa-blind",
title: "others buttons",
children: [
{
name: "image",
action: EasyMDE.drawImage,
className: "fa fa-picture-o",
title: "Image",
},
{
name: "quote",
action: EasyMDE.toggleBlockquote,
className: "fa fa-percent",
title: "Quote",
},
{
name: "link",
action: EasyMDE.drawLink,
className: "fa fa-link",
title: "Link",
}
]
},
// [, ...]
]
});
EasyMDE comes with an array of predefined keyboard shortcuts, but they can be altered with a configuration option. The list of default ones is as follows:
Shortcut (Windows / Linux) | Shortcut (macOS) | Action |
---|---|---|
Ctrl-' | Cmd-' | "toggleBlockquote" |
Ctrl-B | Cmd-B | "toggleBold" |
Ctrl-E | Cmd-E | "cleanBlock" |
Ctrl-H | Cmd-H | "toggleHeadingSmaller" |
Ctrl-I | Cmd-I | "toggleItalic" |
Ctrl-K | Cmd-K | "drawLink" |
Ctrl-L | Cmd-L | "toggleUnorderedList" |
Ctrl-P | Cmd-P | "togglePreview" |
Ctrl-Alt-C | Cmd-Alt-C | "toggleCodeBlock" |
Ctrl-Alt-I | Cmd-Alt-I | "drawImage" |
Ctrl-Alt-L | Cmd-Alt-L | "toggleOrderedList" |
Shift-Ctrl-H | Shift-Cmd-H | "toggleHeadingBigger" |
F9 | F9 | "toggleSideBySide" |
F11 | F11 | "toggleFullScreen" |
Here is how you can change a few, while leaving others untouched:
const editor = new EasyMDE({
shortcuts: {
"toggleOrderedList": "Ctrl-Alt-K", // alter the shortcut for toggleOrderedList
"toggleCodeBlock": null, // unbind Ctrl-Alt-C
"drawTable": "Cmd-Alt-T", // bind Cmd-Alt-T to drawTable action, which doesn't come with a default shortcut
}
});
Shortcuts are automatically converted between platforms. If you define a shortcut as "Cmd-B", on PC that shortcut will be changed to "Ctrl-B". Conversely, a shortcut defined as "Ctrl-B" will become "Cmd-B" for Mac users.
The list of actions that can be bound is the same as the list of built-in actions available for toolbar buttons.
You can catch the following list of events: https://codemirror.net/doc/manual.html#events
const easyMDE = new EasyMDE();
easyMDE.codemirror.on("change", () => {
console.log(easyMDE.value());
});
You can revert to the initial text area by calling the toTextArea
method. Note that this clears up the autosave (if enabled) associated with it. The text area will retain any text from the destroyed EasyMDE instance.
const easyMDE = new EasyMDE();
// ...
easyMDE.toTextArea();
easyMDE = null;
If you need to remove registered event listeners (when the editor is not needed anymore), call easyMDE.cleanup()
.
The following self-explanatory methods may be of use while developing with EasyMDE.
const easyMDE = new EasyMDE();
easyMDE.isPreviewActive(); // returns boolean
easyMDE.isSideBySideActive(); // returns boolean
easyMDE.isFullscreenActive(); // returns boolean
easyMDE.clearAutosavedValue(); // no returned value
EasyMDE is a continuation of SimpleMDE.
SimpleMDE began as an improvement of lepture's Editor project, but has now taken on an identity of its own. It is bundled with CodeMirror and depends on Font Awesome.
CodeMirror is the backbone of the project and parses much of the Markdown syntax as it's being written. This allows us to add styles to the Markdown that's being written. Additionally, a toolbar and status bar have been added to the top and bottom, respectively. Previews are rendered by Marked using GitHub Flavored Markdown (GFM).
I originally made this fork to implement FontAwesome 5 compatibility into SimpleMDE. When that was done I submitted a pull request, which has not been accepted yet. This, and the project being inactive since May 2017, triggered me to make more changes and try to put new life into the project.
Changes include:
https://
by defaultMy intention is to continue development on this project, improving it and keeping it alive.
You may want to edit this library to adapt its behavior to your needs. This can be done in some quick steps:
gulp
command, which will generate files: dist/easymde.min.css
and dist/easymde.min.js
;Want to contribute to EasyMDE? Thank you! We have a contribution guide just for you!
Author: Ionaru
Source Code: https://github.com/Ionaru/easy-markdown-editor
License: MIT license
1595572800
In the previous article, we discussed about object detection using GluonCV. In this article, we will discuss how to implement a binary image classifier that classify whether a given image is a tennis ball or not using a pre-trained image classification network from GluonCV. We implement the machine learning pipeline step by step, from loading and transforming an input image, to loading and using a pre-trained model.
To start with some initial setup we will import packages and set the path to the data.
import mxnet as mx
import gluoncv as gcv
import matplotlib.pyplot as plt
import numpy as np
import os
from pathlib import Path
2. Load Image
To load the image, let us implement a function that loads an image from disk given a filepath. The function should return an 8-bit image array, that’s in MXNet’s NDArray format and in HWC layout (i.e. height, width then channel).
def load_image(filepath):
image = mx.image.imread(filepath)
return image
3. Transform the Image
After loading the image, we should transform the image so it can be used as input to the pre-trained network. We plan to use a pre-trained network on **ImageNet. **Therefore, the image transformation should follow the same steps used for ImageNet pre-training. The image should be transformed by:
This can be achived using the following function.
def transform_image(array):
image = gcv.data.transforms.presets.imagenet.transform_eval(array)
return image
4. Load a Model
We will use a MobileNet 1.0 image classification model that’s been pre-trained on ImageNet. The model can be loaded from the GluonCV model zoo as follows:
def load_pretrained_classification_network():
model = gcv.model_zoo.get_model('MobileNet1.0', pretrained=True, root = M3_MODELS)
return model
5. Use a Model
After loading an image, next task is to pass your transformed image through the pretrained network to obtain predicted probabilities for all ImageNet classes (ignore the tennis ball class for now).
Hint #1: Don’t forget that you’re typically working with a batch of images, even when you only have one image.
Hint #2: Remember that the direct outputs of our network aren’t probabilities.
#gluoncv #deep-learning #image-processing #mxnet #image-classification #deep learning
1597469369
Crop and resize image size before upload in laravel using jquery copper js. In this post, i will show you how to crop and resize image size in laravel using jQuery copper js in laravel.
This laravel crop image before upload using cropper js looks like:
Laravel crop image before upload tutorial, follow the following steps and learn how to use cropper js to crop image before uploading in laravel app:
Read More => https://www.tutsmake.com/laravel-crop-image-before-upload-using-jquery-copper-js/
Live Demo Laravel Crop image Before Upload.
#laravel crop image before upload, #laravel crop and resize image using cropper.js #ajax image upload and crop with jquery and laravel #crop and upload image ajax jquery laravel #crop image while uploading with jquery laravel #image crop and upload using jquery with laravel ajax
1597637160
In the previous article, we discussed about image classification using GluonCV on a pretrained network. In this article, we will discuss how to implement a binary image classifier by training LeNet by bringing different components of gluoncv together such as autograd, trainer, dataset, and dataloader, to train a LeNet network. We can accomplish this is by writing a training loop.
We first import the libraries. We initialize mxnet.init for more parameter initialization methods, matplotlib for drawing, time for benchmarking as well as other gluon packages.
from mxnet import nd, gluon, init, autograd, metric
from mxnet.gluon import nn
from mxnet.gluon.data.vision import datasets, transforms
import matplotlib.pyplot as plt
from time import time
2. Data
We will use **_fashion m-nest _**dataset for training.
2.1 Load Data:
Fashion m-nest dataset is automatically downloaded through gluonsdata.vision.datasets module. The dataset can be downloaded using the following code. It also shows the properties of the first example.
mnist_train = datasets.FashionMNIST(train=True)
x, y = mnist_train[0]
print('X shape: %s dtype : %s' % (x.shape, x.dtype))
print('Number of images: %d'%len(mnist_train))
Each example in this dataset is a 28 by 28 sides gray image which is presented as an NDRA with the shape format of height X width X channel. The label is a scalar.
#neural-networks #lenet-5 #deeplearing #gluoncv #image-classification #neural networks
1591903440
During my studies at JKU there was a task for preprocessing images for a machine learning project. It is necessary to clean the raw images before using them in a learning algorithm, so thats why we create a pre-processing function. I think it can be quite useful for others as well so I want to share a bit of my approach. The file is structured in a way that it is easy to understand and also should have a tutorial-like effect.
#image-recognition #image #image-classification #machine-learning #image-processing