Question

有没有人知道如何优化我的Javascript桶填充的性能？我首先尝试使用递归函数，但它遇到了堆栈溢出。目前的代码在Chrome中运行良好，但Firefox将停止执行。： - （

var canvas = {
  height: 300,
  width: 300
};

var color;
var imgArray = [];
var imgdata;

$(document).ready(function() {
  canvas.obj = document.createElement('canvas');

  $('body').append(canvas.obj);

  $(canvas.obj).attr('height', canvas.height);
  $(canvas.obj).attr('width', canvas.width);

  var context = canvas.obj.getContext('2d');
  var imageObj = new Image();

  imageObj.onload = function() {
    context.drawImage(imageObj, 0, 0, canvas.height, canvas.width);
    imgdata = context.getImageData(0, 0, canvas.width, canvas.height).data;
  };

  imageObj.src = 'data:image/png;base64,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';


  $('canvas').click(function(e) {
    color = getRGBA(getPos(e.pageX, e.pageY));
    addToImageArray(e.pageX, e.pageY, true);
    checkImage();

    $('#currentcolor').css('background-color', 'rgba('+color.r+','+color.g+','+color.b+','+color.a+')');

    for (var y = 0; y < canvas.height; y++) {
      for (var x = 0; x < canvas.width; x++) {
        var posarray = searchArray(x, y);

        if (posarray.inarray && posarray.inrange) {
          var poscolor = getRGBA(getPos(x, y));

          $('#imgholder').append('<div style="height: 1px; width: 1px; background-color: rgba('+poscolor.r+','+poscolor.g+','+poscolor.b+','+poscolor.a+'); display:inline-block; position:absolute; top: '+y+'px; left: '+x+'px"></div>');
        }
      }
    }

  });
});

function checkImage() {
  var changed = true;
  var i = 0;

  console.log('start');

  while (changed) {
    changed = false;

    for (var y = 0; y < canvas.height; y++) {
      for (var x = 0; x < canvas.width; x++) {
        var posarray = searchArray(x, y);

        if (posarray.inarray && posarray.inrange) {
          var pos = {
            top: {
              x: x,
              y: y-1
            },
            bottom: {
              x: x,
              y: y+1
            },
            left: {
              x: x-1,
              y: y
            },
            right: {
              x: x+1,
              y: y
            }
          };

          i++;

          //top
          var topposarray = searchArray(pos.top.x, pos.top.y);

          if (pos.top.y >= 0 && !topposarray.inarray) {
            if (inrange(color, getRGBA(getPos(pos.top.x, pos.top.y)))) {
              addToImageArray(pos.top.x, pos.top.y, true);
              changed = true;
            }
            else {
              addToImageArray(pos.top.x, pos.top.y, false);
            }
          }

          //bottom
          var bottomposarray = searchArray(pos.bottom.x, pos.bottom.y);

          if (pos.top.y <= canvas.height && !bottomposarray.inarray) {
            if (inrange(color, getRGBA(getPos(pos.bottom.x, pos.bottom.y)))) {
              addToImageArray(pos.bottom.x, pos.bottom.y, true);
              changed = true;
            }
            else {
              addToImageArray(pos.bottom.x, pos.bottom.y, false);
            }
          }

          //left
          var leftposarray = searchArray(pos.left.x, pos.left.y);

          if (pos.top.x >= 0 && !leftposarray.inarray) {
            if (inrange(color, getRGBA(getPos(pos.left.x, pos.left.y)))) {
              addToImageArray(pos.left.x, pos.left.y, true);
              changed = true;
            }
            else {
              addToImageArray(pos.left.x, pos.left.y, false);
            }
          }

          //right
          var rightposarray = searchArray(pos.right.x, pos.right.y);

          if (pos.top.x <= canvas.width && !rightposarray.inarray) {
            if (inrange(color, getRGBA(getPos(pos.right.x, pos.right.y)))) {
              addToImageArray(pos.right.x, pos.right.y, true);
              changed = true;
            }
            else {
              addToImageArray(pos.right.x, pos.right.y, false);
            }
          }
        }
      }
    }
  }

  console.log('done');
  $('#currentcolor').html('loops: '+ i);
}

function crange(color1, color2) {
  return (color1 > color2-10 && color1 < color2+10);
}

function inrange(color1, color2) {
  if (crange(color1.r,color2.r) && crange(color1.g,color2.g) && crange(color1.b,color2.b)) {
    return true;
  }
  else {
    return false;
  }
}

function searchArray(x, y) {
  if (imgArray[y] !== undefined && imgArray[y][x] !== undefined) {
    return {
      inarray: true,
      inrange: imgArray[y][x]
    };
  }
  else {
    return {
      inarray: false
    };
  }
}

function addToImageArray(x, y, inrange) {
  if (imgArray[y] === undefined) {
    imgArray[y] = [];
  }

  imgArray[y][x] = inrange;
}

function getRGBA(pos) {
  var color = {
    r: imgdata[pos],
    g: imgdata[pos+1],
    b: imgdata[pos+2],
    a: imgdata[pos+3]
  };

  return color;
}

function getPos(x, y) {
  var pos = (y * 4)* canvas.width;
  pos += (x*4);

  return pos;
}

Test code http://jsfiddle.net/XzMxx/

提前致谢！

如何提升Javascript Canvas桶填充

0 个答案: