Inset-shadow on HTML5 canvas image

Question

I've seen this question before but the answers given are for canvas images that have been drawn on via path however, i'm drawing an image.

Is it possible to create an inset-shadow?

context.shadowOffsetX = 0;
context.shadowOffsetY = 0;
context.shadowBlur = 10;
context.shadowColor = 'rgba(30,30,30, 0.4)';

var imgOne = new Image();
imgOne.onload = function() {
    context.drawImage(imgOne, 0, 0);
};
imgOne.src = "./public/circle.png";

So I draw the circle picture on. I've now at the moment got a slight shadow on the outside of the circle, how can I get this inset instead of offset?

Answer 1

const width = 100 * devicePixelRatio;
const height = 100 * devicePixelRatio;

// original canvas
const c = document.getElementById('canvas');
c.width = 300 * devicePixelRatio;
c.height = 300 * devicePixelRatio;
c.style.width = '300px';
c.style.height = '300px';
const cctx = c.getContext('2d');
cctx.fillStyle = 'rgb(20,205,75)';
cctx.arc(150 * devicePixelRatio, 150 * devicePixelRatio, 50 * devicePixelRatio, 0, Math.PI * 2);
cctx.fill();

// temporary canvas
const canvas = document.createElement('canvas');
canvas.width = width;
canvas.height = height;
canvas.style.width = `${width / devicePixelRatio}px`;
canvas.style.height = `${height / devicePixelRatio}px`;
document.body.appendChild(canvas);
var ctx = canvas.getContext('2d');

// original object on temporary canvas
ctx.arc(50 * devicePixelRatio, 50 * devicePixelRatio, 50 * devicePixelRatio, 0, Math.PI * 2);
ctx.fill();

// shadow cutting
ctx.globalCompositeOperation = 'xor';
ctx.arc(50 * devicePixelRatio, 50 * devicePixelRatio, 50 * devicePixelRatio, 0, Math.PI * 2);
ctx.fill();

// shadow props
ctx.shadowBlur = 50;
ctx.shadowOffsetX = 0;
ctx.shadowOffsetY = -25;
ctx.shadowColor = '#000';
ctx.arc(50 * devicePixelRatio, 50 * devicePixelRatio, 50 * devicePixelRatio, 0, Math.PI * 2);
ctx.fill();

// shadow color
ctx.globalCompositeOperation = 'source-in';
ctx.fillStyle = 'blue';
ctx.fillRect(0, 0, canvas.width, canvas.height);

// object cutting
ctx.globalCompositeOperation = 'destination-in';
ctx.arc(50 * devicePixelRatio, 50 * devicePixelRatio, 50 * devicePixelRatio, 0, Math.PI * 2);
ctx.fill();

// shadow opacity
cctx.globalAlpha = .4

// inserting shadow into original canvas
cctx.drawImage(canvas, 200, 200);

Colored shadow /w opacity

Answer 2

Inspired by markE's answer , I made my own version based on a png instead of vector-graphics.

Additionnaly, I made possible to choose the true alpha of the shadow (because the default shadow strength is a way too soft in my opinion)

var img = document.getElementById("myImage");
img.onload = function(){
   createInnerShadow(this,5,1);
}

function createInnerShadow(img,distance,alpha){
  
   //the size of the shadow depends on the size of the target,
   //then I will create extra "walls" around the picture to be sure
   //tbat the shadow will be correctly filled (with the same intensity everywhere)
   //(it's not obvious with this image, but it is when there is no space at all between the image and its border)
   var offset = 50 + distance;
   var hole = document.createElement("canvas");
   var holeContext = hole.getContext("2d");
   hole.width = img.width + offset*2;
   hole.height = img.height + offset*2;
   
    //first, I draw a big black rect
   holeContext.fillStyle = "#000000";
   holeContext.fillRect(0,0,hole.width,hole.height);
   
   //then I use the image to make an hole in it
   holeContext.globalCompositeOperation = "destination-out";
   holeContext.drawImage(img,offset,offset);
   
   //I create a new canvas that will contains the shadow of the hole only
   var shadow = document.createElement("canvas");
   var shadowContext = shadow.getContext("2d");
   shadow.width = img.width;
   shadow.height = img.height;
   shadowContext.filter = "drop-shadow(0px 0px "+distance+"px #000000 ) ";
   shadowContext.drawImage(hole,-offset,-offset);
   shadowContext.globalCompositeOperation = "destination-out";
   shadowContext.drawImage(hole,-offset,-offset);
  
   //now, because the default-shadow filter is really to soft, I normalize the shadow 
   //then I will be sure that the alpha-gradient of the shadow will start at "alpha" and end at 0
   normalizeAlphaShadow(shadow,alpha);
   
   
   //Finally, I create another canvas that will contain the image and the shadow over it
   var result = document.createElement("canvas");
   result.width = img.width;
   result.height = img.height;
   var context = result.getContext("2d");
   context.drawImage(img,0,0)
   context.drawImage(shadow,0,0);
   
   //and that's it !
   document.body.appendChild(result);
   
}

function normalizeAlphaShadow(canvas,alpha){
   var imageData = canvas.getContext("2d").getImageData(0,0,canvas.width,canvas.height);
   var pixelData = imageData.data;
   var i,len = pixelData.length;
   var max = 0;
   for(i=3;i<len;i+=4) if(pixelData[i]>max) max = pixelData[i];
   
   max = (255/max) * alpha;
   for(i=3;i<len;i+=4) pixelData[i] *= max;
   
   canvas.getContext("2d").putImageData(imageData,0,0)
   
}

<html>
<body>
<img id="myImage" src="data:image/png;base64,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" />
</body>
</html>

the jsfiddle is here : https://jsfiddle.net/jrekw5og/141/

Answer 3

Inspired by K3N's answer, I've created Inset.js for this exact situation!

Inset.js

Only requires setting ctx.shadowInset = true;

For example: http://codepen.io/patlillis/pen/ryoWey

var ctx = canvas.getContext('2d');
var img = new Image;
img.onload = function() {
  ctx.shadowInset = true;
  ctx.shadowBlur = 25;
  ctx.shadowColor = "#000";
  ctx.drawImage(this, 0, 0);
}
img.src = "http://i.imgur.com/Qrfga2b.png";

Answer 4

Canvas will shadow where an image changes from opaque to transparent so, as K3N shows in his correct answer, you can turn the image inside out (opaque becomes transparent & visa-versa) so the shadows are drawn inside the circle.

If you know your circle's centerpoint and radius, you can use a stroked-path to create an inset circle shadow. Here's an example:

var canvas=document.getElementById("canvas");
var context=canvas.getContext("2d");
var cw=canvas.width;
var ch=canvas.height;

context.beginPath();
context.arc(cw/2,ch/2,75,0,Math.PI*2);
context.fillStyle='lightcyan';
context.fill();

context.globalCompositeOperation='source-atop';

context.shadowOffsetX = 500;
context.shadowOffsetY = 0;
context.shadowBlur = 15;
context.shadowColor = 'rgba(30,30,30,1)';

context.beginPath();
context.arc(cw/2-500,ch/2,75,0,Math.PI*2);
context.stroke();
context.stroke();
context.stroke();

context.globalCompositeOperation='source-over';

<canvas id="canvas" width=300 height=300></canvas>

If your path is irregular or hard to define mathematically, you can also use edge-path detection algorithms. One common edge-path algorithm is Marching Squares. Stackoverflow's K3N has coded a nice Marching Squares algorithm.

Answer 5

Composition chain

Use a series of composite + draw operation to obtain inset shadow.

Note: the solution require exclusive access to the canvas element when created so either do this on an off-screen canvas and draw back to main, or if possible, plan secondary graphics to be drawn after this has been generated.

The needed steps:

• Draw in original image
• Invert alpha channel filling the canvas with a solid using xor composition
• Define shadow and draw itself back in
• Deactivate shadow and draw in original image (destination-atop)

var ctx = c.getContext("2d"), img = new Image;
img.onload = function() {

  // draw in image to main canvas
  ctx.drawImage(this, 0, 0);

  // invert alpha channel
  ctx.globalCompositeOperation = "xor";
  ctx.fillRect(0, 0, c.width, c.height);

  // draw itself again using drop-shadow filter
  ctx.shadowBlur = 7*2;  // use double of what is in CSS filter (Chrome x4)
  ctx.shadowOffsetX = ctx.shadowOffsetY = 5;
  ctx.shadowColor = "#000";
  ctx.drawImage(c, 0, 0);

  // draw original image with background mixed on top
  ctx.globalCompositeOperation = "destination-atop";
  ctx.shadowColor = "transparent";                  // remove shadow !
  ctx.drawImage(this, 0, 0);
}
img.src = "http://i.imgur.com/Qrfga2b.png";

<canvas id=c height=300></canvas>