OpenCV.js: JavaScript Binding That Exposes OpenCV Library to The Web

OpenCV.js

This is a JavaScript binding that exposes OpenCV library to the web. This project is made possible by support of Intel corporation. Currently, this is based on OpenCV 3.1.0.

How to Build

  1. Get the source code
git clone https://github.com/ucisysarch/opencvjs.git
cd opencvjs
git clone https://github.com/opencv/opencv
cd opencv
git checkout 3.1.0

2.   Install emscripten. You can obtain emscripten by using Emscripten SDK.

./emsdk update
./emsdk install sdk-master-64bit --shallow
./emsdk activate sdk-master-64bit
source ./emsdk_env.sh

3.   Patch Emscripten & Rebuild.

patch -p1 < PATH/TO/patch_emscripten_master.diff

4.   Rebuild emscripten

./emsdk install sdk-master-64bit --shallow

5.   Compile OpenCV and generate bindings by executing make.py script.

  python make.py

Tests

Test suite contains several tests and examples demonstrating how the API can be used. Run the tests by launching test/tests.html file usig a browser.

Exported OpenCV Subset

Classes and functions that are intended for binding generators (i.e. come with wrapping macros such as CV_EXPORTS_W and CV_WRAP) are exposed. Hence, supported OpenCV subset is comparable to OpenCV for Python. Also, enums with exception of anonymous enums are also exported.

Currently, the following modules are supported. You can modify the make script to exclude certain modules.

  1. Core
  2. Image processing
  3. Photo
  4. Shape
  5. Video
  6. Object detection
  7. Features framework
  8. Image codecs

At a glance

The following example demonstrates how to apply a gaussian blur filter on an image. Note that everything is wrapped in a JavaScript module ('cv').

  // Gaussian Blur
  var mat1 = cv.Mat.ones(7, 7, cv.CV_8UC1),
      mat2 = new cv.Mat();

  cv.GaussianBlur(mat1, mat2, [3, 3], 0, 0, cv.BORDER_DEFAULT);

  mat1.delete();
  mat2.delete();

Next example shows how to calculate image keypoints and their descriptors using ORB (Oriented Brief) method.

  var numFeatures = 900,
        scaleFactor = 1.2,
        numLevels = 8,
        edgeThreshold = 31,
          firstLevel =0,
          WTA_K= 2,
          scoreType = 0, //ORB::HARRIS_SCORE
          patchSize = 31,
          fastThreshold=20,
          keyPoints = new cv.KeyPointVector(),
          descriptors = new cv.Mat();

    var orb = new cv.ORB(numFeatures, scaleFactor, numLevels, edgeThreshold, firstLevel,
                                         WTA_K, scoreType, patchSize, fastThreshold);

  // image and mask are of type cv.Mat
    orb.detect(image, keyPoints, mask);
    orb.compute(image, keyPoints, descriptors);

    keyPoints.delete();
    descriptors.delete();
    orb.delete();

Functions work on cv::Mat and various vectors. The following vectors are registered and can be used.

  register_vector<int>("IntVector");
  register_vector<unsigned char>("UCharVector"););
  register_vector<float>("FloatVector");
  register_vector<std::vector<Point>>("PointVectorVector");
  register_vector<cv::Point>("PointVector");
  register_vector<cv::Mat>("MatVector");
  register_vector<cv::KeyPoint>("KeyPointVector");
  register_vector<cv::Rect>("RectVector");
  register_vector<cv::Point2f>("Point2fVector");

Memory management

All the allocated objects should be freed manually by calling delete() method. To avoid manual memory management for basic types, the following data types are exported as JavaScript value arrays.

cv.Size
cv.Point

File System Access

If your OpenCV application needs to access a file, for instance a dataset or a previoulsy trained classifier, you can modify the make script and attach the files by using emscripten "--preload-file" flag.

Limitations

  1. MatExpr is not exported.
  2. No support for default parameters yet.
  3. Constructor overloading are implemented by number of paramteres and not their types. Hence, only following Mat constructors are exported.
  cv::Mat()
  cv::Mat(const std::vector<unsigned char>& data)
  cv::Mat(Size size, int type)
  cv::Mat(int rows, int cols, int type)
  cv::Mat(Size size, int type, void* data, size_t step)

Download Details:
Author: ucisysarch
Source Code: https://github.com/ucisysarch/opencvjs
License: View license

#opencv #python #javascript 

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OpenCV.js: JavaScript Binding That Exposes OpenCV Library to The Web

NBB: Ad-hoc CLJS Scripting on Node.js

Nbb

Not babashka. Node.js babashka!?

Ad-hoc CLJS scripting on Node.js.

Status

Experimental. Please report issues here.

Goals and features

Nbb's main goal is to make it easy to get started with ad hoc CLJS scripting on Node.js.

Additional goals and features are:

  • Fast startup without relying on a custom version of Node.js.
  • Small artifact (current size is around 1.2MB).
  • First class macros.
  • Support building small TUI apps using Reagent.
  • Complement babashka with libraries from the Node.js ecosystem.

Requirements

Nbb requires Node.js v12 or newer.

How does this tool work?

CLJS code is evaluated through SCI, the same interpreter that powers babashka. Because SCI works with advanced compilation, the bundle size, especially when combined with other dependencies, is smaller than what you get with self-hosted CLJS. That makes startup faster. The trade-off is that execution is less performant and that only a subset of CLJS is available (e.g. no deftype, yet).

Usage

Install nbb from NPM:

$ npm install nbb -g

Omit -g for a local install.

Try out an expression:

$ nbb -e '(+ 1 2 3)'
6

And then install some other NPM libraries to use in the script. E.g.:

$ npm install csv-parse shelljs zx

Create a script which uses the NPM libraries:

(ns script
  (:require ["csv-parse/lib/sync$default" :as csv-parse]
            ["fs" :as fs]
            ["path" :as path]
            ["shelljs$default" :as sh]
            ["term-size$default" :as term-size]
            ["zx$default" :as zx]
            ["zx$fs" :as zxfs]
            [nbb.core :refer [*file*]]))

(prn (path/resolve "."))

(prn (term-size))

(println (count (str (fs/readFileSync *file*))))

(prn (sh/ls "."))

(prn (csv-parse "foo,bar"))

(prn (zxfs/existsSync *file*))

(zx/$ #js ["ls"])

Call the script:

$ nbb script.cljs
"/private/tmp/test-script"
#js {:columns 216, :rows 47}
510
#js ["node_modules" "package-lock.json" "package.json" "script.cljs"]
#js [#js ["foo" "bar"]]
true
$ ls
node_modules
package-lock.json
package.json
script.cljs

Macros

Nbb has first class support for macros: you can define them right inside your .cljs file, like you are used to from JVM Clojure. Consider the plet macro to make working with promises more palatable:

(defmacro plet
  [bindings & body]
  (let [binding-pairs (reverse (partition 2 bindings))
        body (cons 'do body)]
    (reduce (fn [body [sym expr]]
              (let [expr (list '.resolve 'js/Promise expr)]
                (list '.then expr (list 'clojure.core/fn (vector sym)
                                        body))))
            body
            binding-pairs)))

Using this macro we can look async code more like sync code. Consider this puppeteer example:

(-> (.launch puppeteer)
      (.then (fn [browser]
               (-> (.newPage browser)
                   (.then (fn [page]
                            (-> (.goto page "https://clojure.org")
                                (.then #(.screenshot page #js{:path "screenshot.png"}))
                                (.catch #(js/console.log %))
                                (.then #(.close browser)))))))))

Using plet this becomes:

(plet [browser (.launch puppeteer)
       page (.newPage browser)
       _ (.goto page "https://clojure.org")
       _ (-> (.screenshot page #js{:path "screenshot.png"})
             (.catch #(js/console.log %)))]
      (.close browser))

See the puppeteer example for the full code.

Since v0.0.36, nbb includes promesa which is a library to deal with promises. The above plet macro is similar to promesa.core/let.

Startup time

$ time nbb -e '(+ 1 2 3)'
6
nbb -e '(+ 1 2 3)'   0.17s  user 0.02s system 109% cpu 0.168 total

The baseline startup time for a script is about 170ms seconds on my laptop. When invoked via npx this adds another 300ms or so, so for faster startup, either use a globally installed nbb or use $(npm bin)/nbb script.cljs to bypass npx.

Dependencies

NPM dependencies

Nbb does not depend on any NPM dependencies. All NPM libraries loaded by a script are resolved relative to that script. When using the Reagent module, React is resolved in the same way as any other NPM library.

Classpath

To load .cljs files from local paths or dependencies, you can use the --classpath argument. The current dir is added to the classpath automatically. So if there is a file foo/bar.cljs relative to your current dir, then you can load it via (:require [foo.bar :as fb]). Note that nbb uses the same naming conventions for namespaces and directories as other Clojure tools: foo-bar in the namespace name becomes foo_bar in the directory name.

To load dependencies from the Clojure ecosystem, you can use the Clojure CLI or babashka to download them and produce a classpath:

$ classpath="$(clojure -A:nbb -Spath -Sdeps '{:aliases {:nbb {:replace-deps {com.github.seancorfield/honeysql {:git/tag "v2.0.0-rc5" :git/sha "01c3a55"}}}}}')"

and then feed it to the --classpath argument:

$ nbb --classpath "$classpath" -e "(require '[honey.sql :as sql]) (sql/format {:select :foo :from :bar :where [:= :baz 2]})"
["SELECT foo FROM bar WHERE baz = ?" 2]

Currently nbb only reads from directories, not jar files, so you are encouraged to use git libs. Support for .jar files will be added later.

Current file

The name of the file that is currently being executed is available via nbb.core/*file* or on the metadata of vars:

(ns foo
  (:require [nbb.core :refer [*file*]]))

(prn *file*) ;; "/private/tmp/foo.cljs"

(defn f [])
(prn (:file (meta #'f))) ;; "/private/tmp/foo.cljs"

Reagent

Nbb includes reagent.core which will be lazily loaded when required. You can use this together with ink to create a TUI application:

$ npm install ink

ink-demo.cljs:

(ns ink-demo
  (:require ["ink" :refer [render Text]]
            [reagent.core :as r]))

(defonce state (r/atom 0))

(doseq [n (range 1 11)]
  (js/setTimeout #(swap! state inc) (* n 500)))

(defn hello []
  [:> Text {:color "green"} "Hello, world! " @state])

(render (r/as-element [hello]))

Promesa

Working with callbacks and promises can become tedious. Since nbb v0.0.36 the promesa.core namespace is included with the let and do! macros. An example:

(ns prom
  (:require [promesa.core :as p]))

(defn sleep [ms]
  (js/Promise.
   (fn [resolve _]
     (js/setTimeout resolve ms))))

(defn do-stuff
  []
  (p/do!
   (println "Doing stuff which takes a while")
   (sleep 1000)
   1))

(p/let [a (do-stuff)
        b (inc a)
        c (do-stuff)
        d (+ b c)]
  (prn d))
$ nbb prom.cljs
Doing stuff which takes a while
Doing stuff which takes a while
3

Also see API docs.

Js-interop

Since nbb v0.0.75 applied-science/js-interop is available:

(ns example
  (:require [applied-science.js-interop :as j]))

(def o (j/lit {:a 1 :b 2 :c {:d 1}}))

(prn (j/select-keys o [:a :b])) ;; #js {:a 1, :b 2}
(prn (j/get-in o [:c :d])) ;; 1

Most of this library is supported in nbb, except the following:

  • destructuring using :syms
  • property access using .-x notation. In nbb, you must use keywords.

See the example of what is currently supported.

Examples

See the examples directory for small examples.

Also check out these projects built with nbb:

API

See API documentation.

Migrating to shadow-cljs

See this gist on how to convert an nbb script or project to shadow-cljs.

Build

Prequisites:

  • babashka >= 0.4.0
  • Clojure CLI >= 1.10.3.933
  • Node.js 16.5.0 (lower version may work, but this is the one I used to build)

To build:

  • Clone and cd into this repo
  • bb release

Run bb tasks for more project-related tasks.

Download Details:
Author: borkdude
Download Link: Download The Source Code
Official Website: https://github.com/borkdude/nbb 
License: EPL-1.0

#node #javascript

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OpenCV.js: JavaScript Binding That Exposes OpenCV Library to The Web

OpenCV.js

This is a JavaScript binding that exposes OpenCV library to the web. This project is made possible by support of Intel corporation. Currently, this is based on OpenCV 3.1.0.

How to Build

  1. Get the source code
git clone https://github.com/ucisysarch/opencvjs.git
cd opencvjs
git clone https://github.com/opencv/opencv
cd opencv
git checkout 3.1.0

2.   Install emscripten. You can obtain emscripten by using Emscripten SDK.

./emsdk update
./emsdk install sdk-master-64bit --shallow
./emsdk activate sdk-master-64bit
source ./emsdk_env.sh

3.   Patch Emscripten & Rebuild.

patch -p1 < PATH/TO/patch_emscripten_master.diff

4.   Rebuild emscripten

./emsdk install sdk-master-64bit --shallow

5.   Compile OpenCV and generate bindings by executing make.py script.

  python make.py

Tests

Test suite contains several tests and examples demonstrating how the API can be used. Run the tests by launching test/tests.html file usig a browser.

Exported OpenCV Subset

Classes and functions that are intended for binding generators (i.e. come with wrapping macros such as CV_EXPORTS_W and CV_WRAP) are exposed. Hence, supported OpenCV subset is comparable to OpenCV for Python. Also, enums with exception of anonymous enums are also exported.

Currently, the following modules are supported. You can modify the make script to exclude certain modules.

  1. Core
  2. Image processing
  3. Photo
  4. Shape
  5. Video
  6. Object detection
  7. Features framework
  8. Image codecs

At a glance

The following example demonstrates how to apply a gaussian blur filter on an image. Note that everything is wrapped in a JavaScript module ('cv').

  // Gaussian Blur
  var mat1 = cv.Mat.ones(7, 7, cv.CV_8UC1),
      mat2 = new cv.Mat();

  cv.GaussianBlur(mat1, mat2, [3, 3], 0, 0, cv.BORDER_DEFAULT);

  mat1.delete();
  mat2.delete();

Next example shows how to calculate image keypoints and their descriptors using ORB (Oriented Brief) method.

  var numFeatures = 900,
        scaleFactor = 1.2,
        numLevels = 8,
        edgeThreshold = 31,
          firstLevel =0,
          WTA_K= 2,
          scoreType = 0, //ORB::HARRIS_SCORE
          patchSize = 31,
          fastThreshold=20,
          keyPoints = new cv.KeyPointVector(),
          descriptors = new cv.Mat();

    var orb = new cv.ORB(numFeatures, scaleFactor, numLevels, edgeThreshold, firstLevel,
                                         WTA_K, scoreType, patchSize, fastThreshold);

  // image and mask are of type cv.Mat
    orb.detect(image, keyPoints, mask);
    orb.compute(image, keyPoints, descriptors);

    keyPoints.delete();
    descriptors.delete();
    orb.delete();

Functions work on cv::Mat and various vectors. The following vectors are registered and can be used.

  register_vector<int>("IntVector");
  register_vector<unsigned char>("UCharVector"););
  register_vector<float>("FloatVector");
  register_vector<std::vector<Point>>("PointVectorVector");
  register_vector<cv::Point>("PointVector");
  register_vector<cv::Mat>("MatVector");
  register_vector<cv::KeyPoint>("KeyPointVector");
  register_vector<cv::Rect>("RectVector");
  register_vector<cv::Point2f>("Point2fVector");

Memory management

All the allocated objects should be freed manually by calling delete() method. To avoid manual memory management for basic types, the following data types are exported as JavaScript value arrays.

cv.Size
cv.Point

File System Access

If your OpenCV application needs to access a file, for instance a dataset or a previoulsy trained classifier, you can modify the make script and attach the files by using emscripten "--preload-file" flag.

Limitations

  1. MatExpr is not exported.
  2. No support for default parameters yet.
  3. Constructor overloading are implemented by number of paramteres and not their types. Hence, only following Mat constructors are exported.
  cv::Mat()
  cv::Mat(const std::vector<unsigned char>& data)
  cv::Mat(Size size, int type)
  cv::Mat(int rows, int cols, int type)
  cv::Mat(Size size, int type, void* data, size_t step)

Download Details:
Author: ucisysarch
Source Code: https://github.com/ucisysarch/opencvjs
License: View license

#opencv #python #javascript 

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bruce gibson

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Top 5 JavaScript Libraries to Create an Organizational Chart

In this article, we’ll review five JavaScript libraries that allow you to create online organizational charts. To make this info useful for different categories of readers, we’ve gathered together libraries with different functionality and pricing policy. To help you decide whether one of them is worthy of your attention or not, we’ll take a look at the main features and check if the documentation is user-friendly.

DHTMLX Diagram Library

The DHTMLX diagram library allows creating easily configurable graphs for visualization of hierarchical data. Besides org charts, you can create almost any type of hierarchical diagrams. You can choose from organizational charts, flowcharts, block and network diagrams, decision trees, mind maps, UML Class diagrams, mixed diagrams, and any other types of diagrams. This variety of diagrams can be generated using a built-in set of shapes or with the help of custom shapes.

You can set up any diagram shape you need with text, icons, images, and any other custom content via templates in a few lines of code. All these parameters can be later changed from the UI via the sidebar options in the editor.

Top 9 JavaScript Charting Libraries

The edit mode gives an opportunity to make changes on-the-fly without messing with the source code. An interactive interface of the editor supports drag-and-drop and permits you to change each item of your diagram. You can drag diagram items with your mouse and set the size and position property of an item via the editor. The multiselection feature can help to speed up your work in the editor, as it enables you to manipulate several shapes.

The library has an exporting feature. You can export your diagram to a PDF, PNG, or JSON format. Zooming and scrolling options will be useful in case you work with diagrams containing a big number of items. There is also a search feature that helps you to quickly find the necessary shape and make your work with complex diagrams even more convenient by expanding and collapsing shapes when necessary. To show the structure of an organization compactly, you can use the vertical mode.

The documentation page will appeal both to beginners and experienced developers. A well-written beginner’s guide contains the source code with explanations. A bunch of guides will help with further configuration, so you’ll be able to create a diagram that better suits your needs. At the moment, there are three types of licenses available. The commercial license for the team of five or fewer developers costs $599, the enterprise license goes for $1299 per company, and the ultimate license has a price tag of $2899.

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