Semantic Image Segmentation using Fully Convolutional Networks

Humans have the innate ability to identify the objects that they see in the world around them. The visual cortex present in our brain can distinguish between a cat and a dog effortlessly in almost no time. This is true not only with cats and dogs but with almost all the objects that we see. But a computer is not as smart as a human brain to be able to this on its own. Over the past few decades, Deep Learning researchers have tried to bridge this gap between human brain and computer through a special type of artificial neural networks called Convolutional Neural Networks(CNNs).

What is a Convolutional Neural Network?

After a lot of research to study mammalian brains, researchers found that specific parts of the brain get activated to specific type of stimulus. For example, some parts in the visual cortex get activated when we see vertical edges, some when we see horizontal edges, and some others when we see specific shapes, colors, faces, etc. ML researchers imagined each of these parts as a layer of neural network and considered the idea that a large network of such layers could mimic the human brain.

This intuition gave rise to the advent of CNN, which is a type of neural network whose building blocks are convolutional layers. A convolution layer is nothing but a set of weight matrices called kernels or filters which are used for convolution operation on a feature matrix such as an image.Convolution:

2D convolution is a fairly simple operation, you start with a kernel and ‘stride’ (slide) it over the 2D input data, performing an element-wise multiplication with the part of the input it is currently on, and then summing up the results into a single output cell. The kernel repeats this process for every location it slides over, converting a 2D matrix of features into another 2D matrix of features.

The step size by which the kernel slides on the input feature matrix is called stride. In the below animation, the input matrix has been added with an extra stripe of zeros from all four sides to ensure that the output matrix is of the same size as the input matrix. This is called (zero)padding.

Image for post

2D Convolution: kernel size=3x3, padding=1 or ‘same’, stride=1

Semantic Image Segmentation

Image segmentation is the task of partitioning a digital image into multiple segments (sets of pixels) based on some characteristics. The objective is to simplify or change the image into a representation that is more meaningful and easier to analyze.

Semantic Segmentation refers to assigning a class label to each pixel in the given image. See the below example.

Image for post

Note that segmentation is different from classification. In classification, complete image is assigned a class label whereas in segmentation, each pixel in an image is classified into one of the classes.

1. Business Problem

Having a fair idea about convolutional networks and semantic image segmentation, let’s jump into the problem we need to solve.

Image for post

Severstal is among the top 50 producers of steel in the world and Russia’s biggest player in efficient steel mining and production. One of the key products of Severstal is steel sheets. The production process of flat sheet steel is delicate. From heating and rolling, to drying and cutting, several machines touch flat steel by the time it’s ready to ship. To ensure quality in the production of steel sheets, today, Severstal uses images from high-frequency cameras to power a defect detection algorithm.

Through this competition, Severstal expects the AI community to improve the algorithm by localizing and classifying surface defects on a steel sheet.Business objectives and constraints

A defective sheet must be predicted as defective since there would be serious concerns about quality if we misclassify a defective sheet as non-defective. i.e. high recall value for each of the classes is needed.We need not give the results for a given image in the blink of an eye. (No strict latency concerns)

2. Machine Learning Problem

2.1. Mapping the business problem to an ML problemOur task is to

Detect/localize the defects in a steel sheet using image segmentation andClassify the detected defects into one or more classes from [1, 2, 3, 4]

To put it together, it is a semantic image segmentation problem.2.2. Performance metricThe evaluation metric used is the mean Dice coefficient. The Dice coefficient can be used to compare the pixel-wise agreement between a predicted segmentation and its corresponding ground truth. The formula is given by:

Image for post

where X is the predicted set of pixels and Y is the ground truth.

Read more about Dice Coefficient here.2.3. Data OverviewWe have been given a zip folder of size 2GB which contains the following:

**train_images** —a folder containing 12,568 training images (.jpg files)**test_images** — a folder containing 5506 test images (.jpg files). We need to detect and localize defects in these images**train.csv** — training annotations which provide segments for defects belonging to ClassId = [1, 2, 3, 4]**sample_submission.csv**— a sample submission file in the correct format, with each ImageId repeated 4 times, one for each of the 4 defect classes.

More details about data have been discussed in the next section.

3. Exploratory Data Analysis

The first step in solving any machine learning problem should be a thorough study of the raw data. This gives a fair idea about what our approaches to solving the problem should be. Very often, it also helps us find some latent aspects of the data which might be useful to our models.

Let’s analyze the data and try to draw some meaningful conclusions.3.1. Loading train.csv file

Image for post

**_train.csv _**tells which type of defect is present at what pixel location in an image. It contains the following columns:

**ImageId**: image file name with .jpg extension**ClassId**: type/class of the defect, one of [1, 2, 3, 4]**EncodedPixels**: represents the range of defective pixels in an image in the form of run-length encoded pixels(pixel number where defect starts pixel length of the defect).
e.g. ‘29102 12’ implies the defect is starting at pixel 29102 and running a total of 12 pixels, i.e. pixels 29102, 29103,………, 29113 are defective. The pixels are numbered from top to bottom, then left to right: 1 corresponds to pixel (1,1), 2 corresponds to (2,1), and so on.

#convolutional-network #semantic-segmentation #computer-vision #dilated-convolution #deep-learning #deep learning

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Semantic Image Segmentation using Fully Convolutional Networks

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EasyMDE: Simple, Beautiful and Embeddable JavaScript Markdown Editor

EasyMDE - Markdown Editor

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.

Try the demo

Preview

Quick access

EasyMDE - Markdown Editor

Install EasyMDE

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>

How to use

Loading the editor

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>

Editor functions

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>

Configuration

Options list

autoDownloadFontAwesome: If set to 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.
autofocus: If set to true, focuses the editor automatically. Defaults to false.
autosave: Saves the text that's being written and will load it back in the future. It will forget the text when the form it's contained in is submitted.
- enabled: If set to true, saves the text automatically. Defaults to false.
- delay: Delay between saves, in milliseconds. Defaults to 10000 (10 seconds).
- submit_delay: Delay before assuming that submit of the form failed and saving the text, in milliseconds. Defaults to autosave.delay or 10000 (10 seconds).
- uniqueId: You must set a unique string identifier so that EasyMDE can autosave. Something that separates this from other instances of EasyMDE elsewhere on your website.
- timeFormat: Set DateTimeFormat. More information see DateTimeFormat instances. Default locale: en-US, format: hour:minute.
- text: Set text for autosave.
autoRefresh: Useful, when initializing the editor in a hidden DOM node. If set to { delay: 300 }, it will check every 300 ms if the editor is visible and if positive, call CodeMirror's refresh().
blockStyles: Customize how certain buttons that style blocks of text behave.
- bold: Can be set to ** or __. Defaults to **.
- code: Can be set to ``` or ~~~. Defaults to ```.
- italic: Can be set to * or _. Defaults to *.
unorderedListStyle: can be *, - or +. Defaults to *.
scrollbarStyle: Chooses a scrollbar implementation. The default is "native", showing native scrollbars. The core library also provides the "null" style, which completely hides the scrollbars. Addons can implement additional scrollbar models.
element: The DOM element for the textarea element to use. Defaults to the first textarea element on the page.
forceSync: If set to true, force text changes made in EasyMDE to be immediately stored in original text area. Defaults to false.
hideIcons: An array of icon names to hide. Can be used to hide specific icons shown by default without completely customizing the toolbar.
indentWithTabs: If set to false, indent using spaces instead of tabs. Defaults to true.
initialValue: If set, will customize the initial value of the editor.
previewImagesInEditor: - EasyMDE will show preview of images, false by default, preview for images will appear only for images on separate lines.
imagesPreviewHandler: - A custom function for handling the preview of images. Takes the parsed string between the parantheses of the image markdown ![]( ) 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.
insertTexts: Customize how certain buttons that insert text behave. Takes an array with two elements. The first element will be the text inserted before the cursor or highlight, and the second element will be inserted after. For example, this is the default link value: ["[", "](http://)"].
- horizontalRule
- image
- link
- table
lineNumbers: If set to true, enables line numbers in the editor.
lineWrapping: If set to false, disable line wrapping. Defaults to true.
minHeight: Sets the minimum height for the composition area, before it starts auto-growing. Should be a string containing a valid CSS value like "500px". Defaults to "300px".
maxHeight: Sets fixed height for the composition area. minHeight option will be ignored. Should be a string containing a valid CSS value like "500px". Defaults to undefined.
onToggleFullScreen: A function that gets called when the editor's full screen mode is toggled. The function will be passed a boolean as parameter, true when the editor is currently going into full screen mode, or false.
parsingConfig: Adjust settings for parsing the Markdown during editing (not previewing).
- allowAtxHeaderWithoutSpace: If set to true, will render headers without a space after the #. Defaults to false.
- strikethrough: If set to false, will not process GFM strikethrough syntax. Defaults to true.
- underscoresBreakWords: If set to true, let underscores be a delimiter for separating words. Defaults to false.
overlayMode: Pass a custom codemirror overlay mode to parse and style the Markdown during editing.
- mode: A codemirror mode object.
- combine: If set to 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.
placeholder: If set, displays a custom placeholder message.
previewClass: A string or array of strings that will be applied to the preview screen when activated. Defaults to "editor-preview".
previewRender: Custom function for parsing the plaintext Markdown and returning HTML. Used when user previews.
promptURLs: If set to true, a JS alert window appears asking for the link or image URL. Defaults to false.
promptTexts: Customize the text used to prompt for URLs.
- image: The text to use when prompting for an image's URL. Defaults to URL of the image:.
- link: The text to use when prompting for a link's URL. Defaults to URL for the link:.
uploadImage: If set to 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.
imageMaxSize: Maximum image size in bytes, checked before upload (note: never trust client, always check the image size at server-side). Defaults to 1024 * 1024 * 2 (2 MB).
imageAccept: A comma-separated list of mime-types used to check image type before upload (note: never trust client, always check file types at server-side). Defaults to image/png, image/jpeg.
imageUploadFunction: A custom function for handling the image upload. Using this function will render the options imageMaxSize, imageAccept, imageUploadEndpoint and imageCSRFToken ineffective.
- The function gets a file and onSuccess and onError callback functions as parameters. onSuccess(imageUrl: string) and onError(errorMessage: string)
imageUploadEndpoint: The endpoint where the images data will be sent, via an asynchronous POST request. The server is supposed to save this image, and return a JSON response.
- if the request was successfully processed (HTTP 200 OK): {"data": {"filePath": "<filePath>"}} where filePath is the path of the image (absolute if imagePathAbsolute is set to true, relative if otherwise);
- 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.
imagePathAbsolute: If set to 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: CSRF token to include with AJAX call to upload image. For various instances like Django, Spring and Laravel.
imageCSRFName: CSRF token filed name to include with AJAX call to upload image, applied when imageCSRFToken has value, defaults to csrfmiddlewaretoken.
imageCSRFHeader: If set to true, passing CSRF token via header. Defaults to false, which pass CSRF through request body.
imageTexts: Texts displayed to the user (mainly on the status bar) for the import image feature, where #image_name#, #image_size# and #image_max_size# will replaced by their respective values, that can be used for customization or internationalization:
- sbInit: Status message displayed initially if uploadImage is set to true. Defaults to Attach files by drag and dropping or pasting from clipboard..
- sbOnDragEnter: Status message displayed when the user drags a file to the text area. Defaults to Drop image to upload it..
- sbOnDrop: Status message displayed when the user drops a file in the text area. Defaults to Uploading images #images_names#.
- sbProgress: Status message displayed to show uploading progress. Defaults to Uploading #file_name#: #progress#%.
- sbOnUploaded: Status message displayed when the image has been uploaded. Defaults to Uploaded #image_name#.
- sizeUnits: A comma-separated list of units used to display messages with human-readable file sizes. Defaults to B, KB, MB (example: 218 KB). You can use B,KB,MB instead if you prefer without whitespaces (218KB).
errorMessages: Errors displayed to the user, using the 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:
- noFileGiven: The server did not receive any file from the user. Defaults to You must select a file..
- typeNotAllowed: The user send a file type which doesn't match the imageAccept list, or the server returned this error code. Defaults to This image type is not allowed..
- fileTooLarge: The size of the image being imported is bigger than the 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#..
- importError: An unexpected error occurred when uploading the image. Defaults to Something went wrong when uploading the image #image_name#..
errorCallback: A callback function used to define how to display an error message. Defaults to (errorMessage) => alert(errorMessage).
renderingConfig: Adjust settings for parsing the Markdown during previewing (not editing).
- codeSyntaxHighlighting: If set to 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">
- hljs: An injectible instance of highlight.js. If you don't want to rely on the global namespace (window.hljs), you can provide an instance here. Defaults to undefined.
- markedOptions: Set the internal Markdown renderer's options. Other renderingConfig options will take precedence.
- singleLineBreaks: If set to false, disable parsing GitHub Flavored Markdown (GFM) single line breaks. Defaults to true.
- sanitizerFunction: Custom function for sanitizing the HTML output of Markdown renderer.
shortcuts: Keyboard shortcuts associated with this instance. Defaults to the array of shortcuts.
showIcons: An array of icon names to show. Can be used to show specific icons hidden by default without completely customizing the toolbar.
spellChecker: If set to false, disable the spell checker. Defaults to true. Optionally pass a CodeMirrorSpellChecker-compliant function.
inputStyle: textarea or contenteditable. Defaults to textarea for desktop and contenteditable for mobile. contenteditable option is necessary to enable nativeSpellcheck.
nativeSpellcheck: If set to false, disable native spell checker. Defaults to true.
sideBySideFullscreen: If set to false, allows side-by-side editing without going into fullscreen. Defaults to true.
status: If set to false, hide the status bar. Defaults to the array of built-in status bar items.
- Optionally, you can set an array of status bar items to include, and in what order. You can even define your own custom status bar items.
styleSelectedText: If set to false, remove the CodeMirror-selectedtext class from selected lines. Defaults to true.
syncSideBySidePreviewScroll: If set to false, disable syncing scroll in side by side mode. Defaults to true.
tabSize: If set, customize the tab size. Defaults to 2.
theme: Override the theme. Defaults to easymde.
toolbar: If set to false, hide the toolbar. Defaults to the array of icons.
toolbarTips: If set to false, disable toolbar button tips. Defaults to true.
direction: rtl or ltr. Changes text direction to support right-to-left languages. Defaults to ltr.

Options example

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: ["![](http://", ")"],
        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,
});

Toolbar icons

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

Toolbar customization

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",
                    }
                ]
            },
        // [, ...]
    ]
});

Keyboard shortcuts

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.

Advanced use

Event handling

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());
});

Removing EasyMDE from text area

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().

Useful methods

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

How it works

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).

SimpleMDE fork

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:

FontAwesome 5 compatibility
Guide button works when editor is in preview mode
Links are now https:// by default
Small styling changes
Support for Node 8 and beyond
Lots of refactored code
Links in preview will open in a new tab by default
TypeScript support

My intention is to continue development on this project, improving it and keeping it alive.

Hacking EasyMDE

You may want to edit this library to adapt its behavior to your needs. This can be done in some quick steps:

Follow the prerequisites and installation instructions in the contribution guide;
Do your changes;
Run gulp command, which will generate files: dist/easymde.min.css and dist/easymde.min.js;
Copy-paste those files to your code base, and you are done.

Contributing

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

#react-native #react

Mikayla Bashirian

1595436720

Semantic Image Segmentation using Fully Convolutional Networks

What is a Convolutional Neural Network?

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2D Convolution: kernel size=3x3, padding=1 or ‘same’, stride=1

Semantic Image Segmentation

Semantic Segmentation refers to assigning a class label to each pixel in the given image. See the below example.

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Note that segmentation is different from classification. In classification, complete image is assigned a class label whereas in segmentation, each pixel in an image is classified into one of the classes.

1. Business Problem

Having a fair idea about convolutional networks and semantic image segmentation, let’s jump into the problem we need to solve.

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Through this competition, Severstal expects the AI community to improve the algorithm by localizing and classifying surface defects on a steel sheet.Business objectives and constraints

A defective sheet must be predicted as defective since there would be serious concerns about quality if we misclassify a defective sheet as non-defective. i.e. high recall value for each of the classes is needed.We need not give the results for a given image in the blink of an eye. (No strict latency concerns)

2. Machine Learning Problem

2.1. Mapping the business problem to an ML problemOur task is to

Detect/localize the defects in a steel sheet using image segmentation andClassify the detected defects into one or more classes from [1, 2, 3, 4]

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where X is the predicted set of pixels and Y is the ground truth.

Read more about Dice Coefficient here.2.3. Data OverviewWe have been given a zip folder of size 2GB which contains the following:

**train_images** —a folder containing 12,568 training images (.jpg files)**test_images** — a folder containing 5506 test images (.jpg files). We need to detect and localize defects in these images**train.csv** — training annotations which provide segments for defects belonging to ClassId = [1, 2, 3, 4]**sample_submission.csv**— a sample submission file in the correct format, with each ImageId repeated 4 times, one for each of the 4 defect classes.

More details about data have been discussed in the next section.

3. Exploratory Data Analysis

Let’s analyze the data and try to draw some meaningful conclusions.3.1. Loading train.csv file

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**_train.csv _**tells which type of defect is present at what pixel location in an image. It contains the following columns:

**ImageId**: image file name with .jpg extension**ClassId**: type/class of the defect, one of [1, 2, 3, 4]**EncodedPixels**: represents the range of defective pixels in an image in the form of run-length encoded pixels(pixel number where defect starts pixel length of the defect).
e.g. ‘29102 12’ implies the defect is starting at pixel 29102 and running a total of 12 pixels, i.e. pixels 29102, 29103,………, 29113 are defective. The pixels are numbered from top to bottom, then left to right: 1 corresponds to pixel (1,1), 2 corresponds to (2,1), and so on.

#convolutional-network #semantic-segmentation #computer-vision #dilated-convolution #deep-learning #deep learning

I am Developer

1597469369

Crop and Resize Image Before Upload In Laravel Using with jQuery Copper JS

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 Uploading using Cropper js Tutorial

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:

Step 1: Install New Laravel App
Step 2: Add Database Details
Step 3: Create Migration & Model
Step 4: Add Route
Step 5: Create Controller By Artisan
Step 6: Create Blade View
Step 7: Make Upload Directory
Step 8: Start Development Server

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

Troy Marvin

1592373051

Introduction to Semantic Image Segmentation

The aim of semantic image segmentation is to classify each pixel of an image. Image segmentation is a computer vision task which involves…

#convolutional-neural-net #machine-learning #semantic-segmentation #deep-learning #fully-convolutional-net #programming

Dominic Feeney

1621242214

Semantic Segmentation with TensorFlow Keras - Analytics India Magazine

(https://analyticsindiamag.com/google-arts-culture-uses-ai-to-preserve-endangered-languages/)

Semantic Segmentation laid down the fundamental path to advanced Computer Vision tasks such as object detection, shape recognition, autonomous driving, robotics, and virtual reality. Semantic segmentation can be defined as the process of pixel-level image classification into two or more Object classes. It differs from image classification entirely, as the latter performs image-level classification. For instance, consider an image that consists mainly of a zebra, surrounded by grass fields, a tree and a flying bird. Image classification tells us that the image belongs to the ‘zebra’ class. It can not tell where the zebra is or what its size or pose is. But, semantic segmentation of that image may tell that there is a zebra, grass field, a bird and a tree in the given image (classifies parts of an image into separate classes). And it tells us which pixels in the image belong to which class.

In this article, we discuss semantic segmentation using TensorFlow Keras. Readers are expected to have a fundamental knowledge of deep learning, image classification and transfer learning. Nevertheless, the following articles might fulfil these prerequisites with a quick and clear understanding:

Let’s dive deeper into hands-on learning.

#developers corner #densenet #image classification #keras #object detection #object segmentation #pix2pix #segmentation #semantic segmentation #tensorflow #tensorflow 2.0 #unet