Bug 634139 - Add a service for finding the representative color in an image. r=MattN

--HG--
extra : rebase_source : 111c2a3e6b0abfd8b75b90afbe5e736f80ff2939
This commit is contained in:
Andrew Hurle 2012-08-03 14:18:00 -07:00
Родитель 7190d27706
Коммит 3bb0d3dde1
14 изменённых файлов: 1204 добавлений и 0 удалений

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@BINPATH@/components/nsPlacesExpiration.js
@BINPATH@/components/PlacesProtocolHandler.js
@BINPATH@/components/PlacesCategoriesStarter.js
@BINPATH@/components/ColorAnalyzer.js
@BINPATH@/components/PageThumbsProtocol.js
@BINPATH@/components/nsDefaultCLH.manifest
@BINPATH@/components/nsDefaultCLH.js

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/* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/. */
/**
* Class that can run the hierarchical clustering algorithm with the given
* parameters.
*
* @param distance
* Function that should return the distance between two items.
* Defaults to clusterlib.euclidean_distance.
* @param merge
* Function that should take in two items and return a merged one.
* Defaults to clusterlib.average_linkage.
* @param threshold
* The maximum distance between two items for which their clusters
* can be merged.
*/
function HierarchicalClustering(distance, merge, threshold) {
this.distance = distance || clusterlib.euclidean_distance;
this.merge = merge || clusterlib.average_linkage;
this.threshold = threshold == undefined ? Infinity : threshold;
}
HierarchicalClustering.prototype = {
/**
* Run the hierarchical clustering algorithm on the given items to produce
* a final set of clusters. Uses the parameters set in the constructor.
*
* @param items
* An array of "things" to cluster - this is the domain-specific
* collection you're trying to cluster (colors, points, etc.)
* @param snapshotGap
* How many iterations of the clustering algorithm to wait between
* calling the snapshotCallback
* @param snapshotCallback
* If provided, will be called as clusters are merged to let you view
* the progress of the algorithm. Passed the current array of
* clusters, cached distances, and cached closest clusters.
*
* @return An array of merged clusters. The represented item can be
* found in the "item" property of the cluster.
*/
cluster: function HC_cluster(items, snapshotGap, snapshotCallback) {
// array of all remaining clusters
let clusters = [];
// 2D matrix of distances between each pair of clusters, indexed by key
let distances = [];
// closest cluster key for each cluster, indexed by key
let neighbors = [];
// an array of all clusters, but indexed by key
let clustersByKey = [];
// set up clusters from the initial items array
for (let index = 0; index < items.length; index++) {
let cluster = {
// the item this cluster represents
item: items[index],
// a unique key for this cluster, stays constant unless merged itself
key: index,
// index of cluster in clusters array, can change during any merge
index: index,
// how many clusters have been merged into this one
size: 1
};
clusters[index] = cluster;
clustersByKey[index] = cluster;
distances[index] = [];
neighbors[index] = 0;
}
// initialize distance matrix and cached neighbors
for (let i = 0; i < clusters.length; i++) {
for (let j = 0; j <= i; j++) {
var dist = (i == j) ? Infinity :
this.distance(clusters[i].item, clusters[j].item);
distances[i][j] = dist;
distances[j][i] = dist;
if (dist < distances[i][neighbors[i]]) {
neighbors[i] = j;
}
}
}
// merge the next two closest clusters until none of them are close enough
let next = null, i = 0;
for (; next = this.closestClusters(clusters, distances, neighbors); i++) {
if (snapshotCallback && (i % snapshotGap) == 0) {
snapshotCallback(clusters);
}
this.mergeClusters(clusters, distances, neighbors, clustersByKey,
clustersByKey[next[0]], clustersByKey[next[1]]);
}
return clusters;
},
/**
* Once we decide to merge two clusters in the cluster method, actually
* merge them. Alters the given state of the algorithm.
*
* @param clusters
* The array of all remaining clusters
* @param distances
* Cached distances between pairs of clusters
* @param neighbors
* Cached closest clusters
* @param clustersByKey
* Array of all clusters, indexed by key
* @param cluster1
* First cluster to merge
* @param cluster2
* Second cluster to merge
*/
mergeClusters: function HC_mergeClus(clusters, distances, neighbors,
clustersByKey, cluster1, cluster2) {
let merged = { item: this.merge(cluster1.item, cluster2.item),
left: cluster1,
right: cluster2,
key: cluster1.key,
size: cluster1.size + cluster2.size };
clusters[cluster1.index] = merged;
clusters.splice(cluster2.index, 1);
clustersByKey[cluster1.key] = merged;
// update distances with new merged cluster
for (let i = 0; i < clusters.length; i++) {
var ci = clusters[i];
var dist;
if (cluster1.key == ci.key) {
dist = Infinity;
} else if (this.merge == clusterlib.single_linkage) {
dist = distances[cluster1.key][ci.key];
if (distances[cluster1.key][ci.key] >
distances[cluster2.key][ci.key]) {
dist = distances[cluster2.key][ci.key];
}
} else if (this.merge == clusterlib.complete_linkage) {
dist = distances[cluster1.key][ci.key];
if (distances[cluster1.key][ci.key] <
distances[cluster2.key][ci.key]) {
dist = distances[cluster2.key][ci.key];
}
} else if (this.merge == clusterlib.average_linkage) {
dist = (distances[cluster1.key][ci.key] * cluster1.size
+ distances[cluster2.key][ci.key] * cluster2.size)
/ (cluster1.size + cluster2.size);
} else {
dist = this.distance(ci.item, cluster1.item);
}
distances[cluster1.key][ci.key] = distances[ci.key][cluster1.key]
= dist;
}
// update cached neighbors
for (let i = 0; i < clusters.length; i++) {
var key1 = clusters[i].key;
if (neighbors[key1] == cluster1.key ||
neighbors[key1] == cluster2.key) {
let minKey = key1;
for (let j = 0; j < clusters.length; j++) {
var key2 = clusters[j].key;
if (distances[key1][key2] < distances[key1][minKey]) {
minKey = key2;
}
}
neighbors[key1] = minKey;
}
clusters[i].index = i;
}
},
/**
* Given the current state of the algorithm, return the keys of the two
* clusters that are closest to each other so we know which ones to merge
* next.
*
* @param clusters
* The array of all remaining clusters
* @param distances
* Cached distances between pairs of clusters
* @param neighbors
* Cached closest clusters
*
* @return An array of two keys of clusters to merge, or null if there are
* no more clusters close enough to merge
*/
closestClusters: function HC_closestClus(clusters, distances, neighbors) {
let minKey = 0, minDist = Infinity;
for (let i = 0; i < clusters.length; i++) {
var key = clusters[i].key;
if (distances[key][neighbors[key]] < minDist) {
minKey = key;
minDist = distances[key][neighbors[key]];
}
}
if (minDist < this.threshold) {
return [minKey, neighbors[minKey]];
}
return null;
}
};
let clusterlib = {
hcluster: function hcluster(items, distance, merge, threshold, snapshotGap,
snapshotCallback) {
return (new HierarchicalClustering(distance, merge, threshold))
.cluster(items, snapshotGap, snapshotCallback);
},
single_linkage: function single_linkage(cluster1, cluster2) {
return cluster1;
},
complete_linkage: function complete_linkage(cluster1, cluster2) {
return cluster1;
},
average_linkage: function average_linkage(cluster1, cluster2) {
return cluster1;
},
euclidean_distance: function euclidean_distance(v1, v2) {
let total = 0;
for (let i = 0; i < v1.length; i++) {
total += Math.pow(v2[i] - v1[i], 2);
}
return Math.sqrt(total);
},
manhattan_distance: function manhattan_distance(v1, v2) {
let total = 0;
for (let i = 0; i < v1.length; i++) {
total += Math.abs(v2[i] - v1[i]);
}
return total;
},
max_distance: function max_distance(v1, v2) {
let max = 0;
for (let i = 0; i < v1.length; i++) {
max = Math.max(max, Math.abs(v2[i] - v1[i]));
}
return max;
}
};

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/* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/. */
"use strict";
const Ci = Components.interfaces;
const Cc = Components.classes;
const Cu = Components.utils;
Cu.import("resource://gre/modules/XPCOMUtils.jsm");
const XHTML_NS = "http://www.w3.org/1999/xhtml";
const MAXIMUM_PIXELS = Math.pow(128, 2);
function ColorAnalyzer() {
// a queue of callbacks for each job we give to the worker
this.callbacks = [];
this.hiddenWindowDoc = Cc["@mozilla.org/appshell/appShellService;1"].
getService(Ci.nsIAppShellService).
hiddenDOMWindow.document;
this.worker = new ChromeWorker("resource://gre/modules/ColorAnalyzer_worker.js");
this.worker.onmessage = this.onWorkerMessage.bind(this);
this.worker.onerror = this.onWorkerError.bind(this);
}
ColorAnalyzer.prototype = {
findRepresentativeColor: function ColorAnalyzer_frc(imageURI, callback) {
function cleanup() {
image.removeEventListener("load", loadListener);
image.removeEventListener("error", errorListener);
}
let image = this.hiddenWindowDoc.createElementNS(XHTML_NS, "img");
let loadListener = this.onImageLoad.bind(this, image, callback, cleanup);
let errorListener = this.onImageError.bind(this, image, callback, cleanup);
image.addEventListener("load", loadListener);
image.addEventListener("error", errorListener);
image.src = imageURI.spec;
},
onImageLoad: function ColorAnalyzer_onImageLoad(image, callback, cleanup) {
if (image.naturalWidth * image.naturalHeight > MAXIMUM_PIXELS) {
// this will probably take too long to process - fail
callback.onComplete(false);
} else {
let canvas = this.hiddenWindowDoc.createElementNS(XHTML_NS, "canvas");
canvas.width = image.naturalWidth;
canvas.height = image.naturalHeight;
let ctx = canvas.getContext("2d");
ctx.drawImage(image, 0, 0);
this.startJob(ctx.getImageData(0, 0, canvas.width, canvas.height),
callback);
}
cleanup();
},
onImageError: function ColorAnalyzer_onImageError(image, callback, cleanup) {
Cu.reportError("ColorAnalyzer: image at " + image.src + " didn't load");
callback.onComplete(false);
cleanup();
},
startJob: function ColorAnalyzer_startJob(imageData, callback) {
this.callbacks.push(callback);
this.worker.postMessage({ imageData: imageData, maxColors: 1 });
},
onWorkerMessage: function ColorAnalyzer_onWorkerMessage(event) {
// colors can be empty on failure
if (event.data.colors.length < 1) {
this.callbacks.shift().onComplete(false);
} else {
this.callbacks.shift().onComplete(true, event.data.colors[0]);
}
},
onWorkerError: function ColorAnalyzer_onWorkerError(error) {
// this shouldn't happen, but just in case
error.preventDefault();
Cu.reportError("ColorAnalyzer worker: " + error.message);
this.callbacks.shift().onComplete(false);
},
classID: Components.ID("{d056186c-28a0-494e-aacc-9e433772b143}"),
QueryInterface: XPCOMUtils.generateQI([Ci.mozIColorAnalyzer])
};
let NSGetFactory = XPCOMUtils.generateNSGetFactory([ColorAnalyzer]);

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/* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/. */
"use strict";
importScripts("ClusterLib.js", "ColorConversion.js");
// Offsets in the ImageData pixel array to reach pixel colors
const PIXEL_RED = 0;
const PIXEL_GREEN = 1;
const PIXEL_BLUE = 2;
const PIXEL_ALPHA = 3;
// Number of components in one ImageData pixel (RGBA)
const NUM_COMPONENTS = 4;
// Shift a color represented as a 24 bit integer by N bits to get a component
const RED_SHIFT = 16;
const GREEN_SHIFT = 8;
// Only run the N most frequent unique colors through the clustering algorithm
// Images with more than this many unique colors will have reduced accuracy.
const MAX_COLORS_TO_MERGE = 500;
// Each cluster of colors has a mean color in the Lab color space.
// If the euclidean distance between the means of two clusters is greater
// than or equal to this threshold, they won't be merged.
const MERGE_THRESHOLD = 12;
// The highest the distance handicap can be for large clusters
const MAX_SIZE_HANDICAP = 5;
// If the handicap is below this number, it is cut off to zero
const SIZE_HANDICAP_CUTOFF = 2;
// If potential background colors deviate from the mean background color by
// this threshold or greater, finding a background color will fail
const BACKGROUND_THRESHOLD = 10;
// Alpha component of colors must be larger than this in order to make it into
// the clustering algorithm or be considered a background color (0 - 255).
const MIN_ALPHA = 25;
// The euclidean distance in the Lab color space under which merged colors
// are weighted lower for being similar to the background color
const BACKGROUND_WEIGHT_THRESHOLD = 15;
// The range in which color chroma differences will affect desirability.
// Colors with chroma outside of the range take on the desirability of
// their nearest extremes. Should be roughly 0 - 150.
const CHROMA_WEIGHT_UPPER = 90;
const CHROMA_WEIGHT_LOWER = 1;
const CHROMA_WEIGHT_MIDDLE = (CHROMA_WEIGHT_UPPER + CHROMA_WEIGHT_LOWER) / 2;
/**
* When we receive a message from the outside world, find the representative
* colors of the given image. The colors will be posted back to the caller
* through the "colors" property on the event data object as an array of
* integers. Colors of lower indices are more representative.
* This array can be empty if this worker can't find a color.
*
* @param event
* A MessageEvent whose data should have the following properties:
* imageData - A DOM ImageData instance to analyze
* maxColors - The maximum number of representative colors to find,
* defaults to 1 if not provided
*/
onmessage = function(event) {
let imageData = event.data.imageData;
let pixels = imageData.data;
let width = imageData.width;
let height = imageData.height;
let maxColors = event.data.maxColors;
if (typeof(maxColors) != "number") {
maxColors = 1;
}
let allColors = getColors(pixels, width, height);
// Only merge top colors by frequency for speed.
let mergedColors = mergeColors(allColors.slice(0, MAX_COLORS_TO_MERGE),
width * height, MERGE_THRESHOLD);
let backgroundColor = getBackgroundColor(pixels, width, height);
mergedColors = mergedColors.map(function(cluster) {
// metadata holds a bunch of information about the color represented by
// this cluster
let metadata = cluster.item;
// the basis of color desirability is how much of the image the color is
// responsible for, but we'll need to weigh this number differently
// depending on other factors
metadata.desirability = metadata.ratio;
let weight = 1;
// if the color is close to the background color, we don't want it
if (backgroundColor != null) {
let backgroundDistance = labEuclidean(metadata.mean, backgroundColor);
if (backgroundDistance < BACKGROUND_WEIGHT_THRESHOLD) {
weight = backgroundDistance / BACKGROUND_WEIGHT_THRESHOLD;
}
}
// prefer more interesting colors, but don't knock low chroma colors
// completely out of the running (lower bound), and we don't really care
// if a color is slightly more intense than another on the higher end
let chroma = labChroma(metadata.mean);
if (chroma < CHROMA_WEIGHT_LOWER) {
chroma = CHROMA_WEIGHT_LOWER;
} else if (chroma > CHROMA_WEIGHT_UPPER) {
chroma = CHROMA_WEIGHT_UPPER;
}
weight *= chroma / CHROMA_WEIGHT_MIDDLE;
metadata.desirability *= weight;
return metadata;
});
// only send back the most desirable colors
mergedColors.sort(function(a, b) {
return b.desirability - a.desirability;
});
mergedColors = mergedColors.map(function(metadata) {
return metadata.color;
}).slice(0, maxColors);
postMessage({ colors: mergedColors });
};
/**
* Given the pixel data and dimensions of an image, return an array of objects
* associating each unique color and its frequency in the image, sorted
* descending by frequency. Sufficiently transparent colors are ignored.
*
* @param pixels
* Pixel data array for the image to get colors from (ImageData.data).
* @param width
* Width of the image, in # of pixels.
* @param height
* Height of the image, in # of pixels.
*
* @return An array of objects with color and freq properties, sorted
* descending by freq
*/
function getColors(pixels, width, height) {
let colorFrequency = {};
for (let x = 0; x < width; x++) {
for (let y = 0; y < height; y++) {
let offset = (x * NUM_COMPONENTS) + (y * NUM_COMPONENTS * width);
if (pixels[offset + PIXEL_ALPHA] < MIN_ALPHA) {
continue;
}
let color = pixels[offset + PIXEL_RED] << RED_SHIFT
| pixels[offset + PIXEL_GREEN] << GREEN_SHIFT
| pixels[offset + PIXEL_BLUE];
if (color in colorFrequency) {
colorFrequency[color]++;
} else {
colorFrequency[color] = 1;
}
}
}
let colors = [];
for (var color in colorFrequency) {
colors.push({ color: +color, freq: colorFrequency[+color] });
}
colors.sort(descendingFreqSort);
return colors;
}
/**
* Given an array of objects from getColors, the number of pixels in the
* image, and a merge threshold, run the clustering algorithm on the colors
* and return the set of merged clusters.
*
* @param colorFrequencies
* An array of objects from getColors to cluster
* @param numPixels
* The number of pixels in the image
* @param threshold
* The maximum distance between two clusters for which those clusters
* can be merged.
*
* @return An array of merged clusters
*
* @see clusterlib.hcluster
* @see getColors
*/
function mergeColors(colorFrequencies, numPixels, threshold) {
let items = colorFrequencies.map(function(colorFrequency) {
let color = colorFrequency.color;
let freq = colorFrequency.freq;
return {
mean: rgb2lab(color >> RED_SHIFT, color >> GREEN_SHIFT & 0xff,
color & 0xff),
// the canonical color of the cluster
// (one w/ highest freq or closest to mean)
color: color,
colors: [color],
highFreq: freq,
highRatio: freq / numPixels,
// the individual color w/ the highest frequency in this cluster
highColor: color,
// ratio of image taken up by colors in this cluster
ratio: freq / numPixels,
freq: freq,
};
});
let merged = clusterlib.hcluster(items, distance, merge, threshold);
return merged;
}
function descendingFreqSort(a, b) {
return b.freq - a.freq;
}
/**
* Given two items for a pair of clusters (as created in mergeColors above),
* determine the distance between them so we know if we should merge or not.
* Uses the euclidean distance between their mean colors in the lab color
* space, weighted so larger items are harder to merge.
*
* @param item1
* The first item to compare
* @param item2
* The second item to compare
*
* @return The distance between the two items
*/
function distance(item1, item2) {
// don't cluster large blocks of color unless they're really similar
let minRatio = Math.min(item1.ratio, item2.ratio);
let dist = labEuclidean(item1.mean, item2.mean);
let handicap = Math.min(MAX_SIZE_HANDICAP, dist * minRatio);
if (handicap <= SIZE_HANDICAP_CUTOFF) {
handicap = 0;
}
return dist + handicap;
}
/**
* Find the euclidean distance between two colors in the Lab color space.
*
* @param color1
* The first color to compare
* @param color2
* The second color to compare
*
* @return The euclidean distance between the two colors
*/
function labEuclidean(color1, color2) {
return Math.sqrt(
Math.pow(color2.lightness - color1.lightness, 2)
+ Math.pow(color2.a - color1.a, 2)
+ Math.pow(color2.b - color1.b, 2));
}
/**
* Given items from two clusters we know are appropriate for merging,
* merge them together into a third item such that its metadata describes both
* input items. The "color" property is set to the color in the new item that
* is closest to its mean color.
*
* @param item1
* The first item to merge
* @param item2
* The second item to merge
*
* @return An item that represents the merging of the given items
*/
function merge(item1, item2) {
let lab1 = item1.mean;
let lab2 = item2.mean;
/* algorithm tweak point - weighting the mean of the cluster */
let num1 = item1.freq;
let num2 = item2.freq;
let total = num1 + num2;
let mean = {
lightness: (lab1.lightness * num1 + lab2.lightness * num2) / total,
a: (lab1.a * num1 + lab2.a * num2) / total,
b: (lab1.b * num1 + lab2.b * num2) / total
};
let colors = item1.colors.concat(item2.colors);
// get the canonical color of the new cluster
let color;
let avgFreq = colors.length / (item1.freq + item2.freq);
if ((item1.highFreq > item2.highFreq) && (item1.highFreq > avgFreq * 2)) {
color = item1.highColor;
} else if (item2.highFreq > avgFreq * 2) {
color = item2.highColor;
} else {
// if there's no stand-out color
let minDist = Infinity, closest = 0;
for (let i = 0; i < colors.length; i++) {
let color = colors[i];
let lab = rgb2lab(color >> RED_SHIFT, color >> GREEN_SHIFT & 0xff,
color & 0xff);
let dist = labEuclidean(lab, mean);
if (dist < minDist) {
minDist = dist;
closest = i;
}
}
color = colors[closest];
}
const higherItem = item1.highFreq > item2.highFreq ? item1 : item2;
return {
mean: mean,
color: color,
highFreq: higherItem.highFreq,
highColor: higherItem.highColor,
highRatio: higherItem.highRatio,
ratio: item1.ratio + item2.ratio,
freq: item1.freq + item2.freq,
colors: colors,
};
}
/**
* Find the background color of the given image.
*
* @param pixels
* The pixel data for the image (an array of component integers)
* @param width
* The width of the image
* @param height
* The height of the image
*
* @return The background color of the image as a Lab object, or null if we
* can't determine the background color
*/
function getBackgroundColor(pixels, width, height) {
// we'll assume that if the four corners are roughly the same color,
// then that's the background color
let coordinates = [[0, 0], [width - 1, 0], [width - 1, height - 1],
[0, height - 1]];
// find the corner colors in LAB
let cornerColors = [];
for (let i = 0; i < coordinates.length; i++) {
let offset = (coordinates[i][0] * NUM_COMPONENTS)
+ (coordinates[i][1] * NUM_COMPONENTS * width);
if (pixels[offset + PIXEL_ALPHA] < MIN_ALPHA) {
// we can't make very accurate judgements below this opacity
continue;
}
cornerColors.push(rgb2lab(pixels[offset + PIXEL_RED],
pixels[offset + PIXEL_GREEN],
pixels[offset + PIXEL_BLUE]));
}
// we want at least two points at acceptable alpha levels
if (cornerColors.length <= 1) {
return null;
}
// find the average color among the corners
let averageColor = { lightness: 0, a: 0, b: 0 };
cornerColors.forEach(function(color) {
for (let i in color) {
averageColor[i] += color[i];
}
});
for (let i in averageColor) {
averageColor[i] /= cornerColors.length;
}
// if we have fewer points due to low alpha, they need to be closer together
let threshold = BACKGROUND_THRESHOLD
* (cornerColors.length / coordinates.length);
// if any of the corner colors deviate enough from the average, they aren't
// similar enough to be considered the background color
for (let cornerColor of cornerColors) {
if (labEuclidean(cornerColor, averageColor) > threshold) {
return null;
}
}
return averageColor;
}

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/* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/. */
/**
* Given a color in the Lab space, return its chroma (colorfulness,
* saturation).
*
* @param lab
* The lab color to get the chroma from
*
* @return A number greater than zero that measures chroma in the image
*/
function labChroma(lab) {
return Math.sqrt(Math.pow(lab.a, 2) + Math.pow(lab.b, 2));
}
/**
* Given the RGB components of a color as integers from 0-255, return the
* color in the XYZ color space.
*
* @return An object with x, y, z properties holding those components of the
* color in the XYZ color space.
*/
function rgb2xyz(r, g, b) {
r /= 255;
g /= 255;
b /= 255;
// assume sRGB
r = r > 0.04045 ? Math.pow(((r + 0.055) / 1.055), 2.4) : (r / 12.92);
g = g > 0.04045 ? Math.pow(((g + 0.055) / 1.055), 2.4) : (g / 12.92);
b = b > 0.04045 ? Math.pow(((b + 0.055) / 1.055), 2.4) : (b / 12.92);
return {
x: ((r * 0.4124) + (g * 0.3576) + (b * 0.1805)) * 100,
y: ((r * 0.2126) + (g * 0.7152) + (b * 0.0722)) * 100,
z: ((r * 0.0193) + (g * 0.1192) + (b * 0.9505)) * 100
};
}
/**
* Given the RGB components of a color as integers from 0-255, return the
* color in the Lab color space.
*
* @return An object with lightness, a, b properties holding those components
* of the color in the Lab color space.
*/
function rgb2lab(r, g, b) {
let xyz = rgb2xyz(r, g, b),
x = xyz.x / 95.047,
y = xyz.y / 100,
z = xyz.z / 108.883;
x = x > 0.008856 ? Math.pow(x, 1/3) : (7.787 * x) + (16 / 116);
y = y > 0.008856 ? Math.pow(y, 1/3) : (7.787 * y) + (16 / 116);
z = z > 0.008856 ? Math.pow(z, 1/3) : (7.787 * z) + (16 / 116);
return {
lightness: (116 * y) - 16,
a: 500 * (x - y),
b: 200 * (y - z)
};
}

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@ -28,6 +28,7 @@ XPIDLSRCS += \
mozIAsyncFavicons.idl \
mozIAsyncLivemarks.idl \
mozIPlacesAutoComplete.idl \
mozIColorAnalyzer.idl \
nsIAnnotationService.idl \
nsIBrowserHistory.idl \
nsIFaviconService.idl \
@ -83,6 +84,7 @@ EXTRA_COMPONENTS = \
nsTaggingService.js \
nsPlacesExpiration.js \
PlacesCategoriesStarter.js \
ColorAnalyzer.js \
$(NULL)
ifdef MOZ_XUL
@ -92,6 +94,9 @@ endif
EXTRA_JS_MODULES = \
PlacesDBUtils.jsm \
BookmarkHTMLUtils.jsm \
ColorAnalyzer_worker.js \
ColorConversion.js \
ClusterLib.js \
$(NULL)
EXTRA_PP_JS_MODULES = \

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@ -0,0 +1,52 @@
/* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/. */
#include "nsISupports.idl"
interface nsIURI;
[function, scriptable, uuid(e4089e21-71b6-40af-b546-33c21b90e874)]
interface mozIRepresentativeColorCallback : nsISupports
{
/**
* Will be called when color analysis finishes.
*
* @param success
* True if analysis was successful, false otherwise.
* Analysis can fail if the image is transparent, imageURI doesn't
* resolve to a valid image, or the image is too big.
*
* @param color
* The representative color as an integer in RGB form.
* e.g. 0xFF0102 == rgb(255,1,2)
* If success is false, color is not provided.
*/
void onComplete(in boolean success, [optional] in unsigned long color);
};
[scriptable, uuid(d056186c-28a0-494e-aacc-9e433772b143)]
interface mozIColorAnalyzer : nsISupports
{
/**
* Given an image URI, find the most representative color for that image
* based on the frequency of each color. Preference is given to colors that
* are more interesting. Avoids the background color if it can be
* discerned. Ignores sufficiently transparent colors.
*
* This is intended to be used on favicon images. Larger images take longer
* to process, especially those with a larger number of unique colors. If
* imageURI points to an image that has more than 128^2 pixels, this method
* will fail before analyzing it for performance reasons.
*
* @param imageURI
* A URI pointing to the image - ideally a data: URI, but any scheme
* that will load when setting the src attribute of a DOM img element
* should work.
* @param callback
* Function to call when the representative color is found or an
* error occurs.
*/
void findRepresentativeColor(in nsIURI imageURI,
in mozIRepresentativeColorCallback callback);
};

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@ -16,12 +16,17 @@ MOCHITEST_BROWSER_FILES = \
browser_bug399606.js \
browser_bug646422.js \
browser_bug680727.js \
browser_colorAnalyzer.js \
browser_notfound.js \
browser_redirect.js \
browser_visituri.js \
browser_visituri_nohistory.js \
browser_visituri_privatebrowsing.js \
browser_settitle.js \
colorAnalyzer/category-discover.png \
colorAnalyzer/dictionaryGeneric-16.png \
colorAnalyzer/extensionGeneric-16.png \
colorAnalyzer/localeGeneric.png \
$(NULL)
# These are files that need to be loaded via the HTTP proxy server

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@ -0,0 +1,343 @@
/* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/. */
"use strict";
Cu.import("resource://gre/modules/Services.jsm");
const CA = Cc["@mozilla.org/places/colorAnalyzer;1"].
getService(Ci.mozIColorAnalyzer);
const hiddenWindowDoc = Cc["@mozilla.org/appshell/appShellService;1"].
getService(Ci.nsIAppShellService).
hiddenDOMWindow.document;
const XHTML_NS = "http://www.w3.org/1999/xhtml";
// to make async tests easier, push them into here and call nextStep
// when a test finishes
let tests = [];
function generatorTest() {
while (tests.length > 0) {
tests.shift()();
yield;
}
}
/**
* Passes the given uri to findRepresentativeColor.
* If expected is null, you expect it to fail.
* If expected is a function, it will call that function.
* If expected is a color, you expect that color to be returned.
* Message is used in the calls to is().
*/
function frcTest(uri, expected, message, skipNextStep) {
CA.findRepresentativeColor(Services.io.newURI(uri, "", null),
function(success, color) {
if (expected == null) {
ok(!success, message);
} else if (typeof expected == "function") {
expected(color, message);
} else {
ok(success, "success: " + message);
is(color, expected, message);
}
if (!skipNextStep) {
nextStep();
}
}
);
}
/**
* Handy function for getting an image into findRepresentativeColor and testing it.
* Makes a canvas with the given dimensions, calls paintCanvasFunc with the 2d
* context of the canvas, sticks the generated canvas into findRepresentativeColor.
* See frcTest.
*/
function canvasTest(width, height, paintCanvasFunc, expected, message, skipNextStep) {
let canvas = hiddenWindowDoc.createElementNS(XHTML_NS, "canvas");
canvas.width = width;
canvas.height = height;
paintCanvasFunc(canvas.getContext("2d"));
let uri = canvas.toDataURL();
frcTest(uri, expected, message, skipNextStep);
}
// simple test - draw a red box in the center, make sure we get red back
tests.push(function test_redSquare() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "red";
ctx.fillRect(2, 2, 12, 12);
}, 0xFF0000, "redSquare analysis returns red");
});
// draw a blue square in one corner, red in the other, such that blue overlaps
// red by one pixel, making it the dominant color
tests.push(function test_blueOverlappingRed() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "red";
ctx.fillRect(0, 0, 8, 8);
ctx.fillStyle = "blue";
ctx.fillRect(7, 7, 8, 8);
}, 0x0000FF, "blueOverlappingRed analysis returns blue");
});
// draw a red gradient next to a solid blue rectangle to ensure that a large
// block of similar colors beats out a smaller block of one color
tests.push(function test_redGradientBlueSolid() {
canvasTest(16, 16, function(ctx) {
let gradient = ctx.createLinearGradient(0, 0, 1, 15);
gradient.addColorStop(0, "#FF0000");
gradient.addColorStop(1, "#FF0808");
ctx.fillStyle = gradient;
ctx.fillRect(0, 0, 16, 16);
ctx.fillStyle = "blue";
ctx.fillRect(9, 0, 7, 16);
}, function(actual, message) {
ok(actual > 0xFF0000 && actual < 0xFF0808, message);
}, "redGradientBlueSolid analysis returns redish");
});
// try a transparent image, should fail
tests.push(function test_transparent() {
canvasTest(16, 16, function(ctx) {
//do nothing!
}, null, "transparent analysis fails");
});
tests.push(function test_invalidURI() {
frcTest("data:blah,Imnotavaliddatauri", null, "invalid URI analysis fails");
});
tests.push(function test_malformedPNGURI() {
frcTest("data:image/png;base64,iVBORblahblahblah", null,
"malformed PNG URI analysis fails");
});
tests.push(function test_unresolvableURI() {
frcTest("http://www.example.com/blah/idontexist.png", null,
"unresolvable URI analysis fails");
});
// draw a small blue box on a red background to make sure the algorithm avoids
// using the background color
tests.push(function test_blueOnRedBackground() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "red";
ctx.fillRect(0, 0, 16, 16);
ctx.fillStyle = "blue";
ctx.fillRect(4, 4, 8, 8);
}, 0x0000FF, "blueOnRedBackground analysis returns blue");
});
// draw a slightly different color in the corners to make sure the corner colors
// don't have to be exactly equal to be considered the background color
tests.push(function test_variableBackground() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "white";
ctx.fillRect(0, 0, 16, 16);
ctx.fillStyle = "#FEFEFE";
ctx.fillRect(15, 0, 1, 1);
ctx.fillStyle = "#FDFDFD";
ctx.fillRect(15, 15, 1, 1);
ctx.fillStyle = "#FCFCFC";
ctx.fillRect(0, 15, 1, 1);
ctx.fillStyle = "black";
ctx.fillRect(4, 4, 8, 8);
}, 0x000000, "variableBackground analysis returns black");
});
// like the above test, but make the colors different enough that they aren't
// considered the background color
tests.push(function test_tooVariableBackground() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "white";
ctx.fillRect(0, 0, 16, 16);
ctx.fillStyle = "#EEDDCC";
ctx.fillRect(15, 0, 1, 1);
ctx.fillStyle = "#DDDDDD";
ctx.fillRect(15, 15, 1, 1);
ctx.fillStyle = "#CCCCCC";
ctx.fillRect(0, 15, 1, 1);
ctx.fillStyle = "black";
ctx.fillRect(4, 4, 8, 8);
}, function(actual, message) {
isnot(actual, 0x000000, message);
}, "tooVariableBackground analysis doesn't return black");
});
// draw a small black/white box over transparent background to make sure the
// algorithm doesn't think rgb(0,0,0) == rgba(0,0,0,0)
tests.push(function test_transparentBackgroundConflation() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "black";
ctx.fillRect(2, 2, 12, 12);
ctx.fillStyle = "white";
ctx.fillRect(5, 5, 6, 6);
}, 0x000000, "transparentBackgroundConflation analysis returns black");
});
// make sure we fall back to the background color if we have no other choice
// (instead of failing as if there were no colors)
tests.push(function test_backgroundFallback() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "black";
ctx.fillRect(0, 0, 16, 16);
}, 0x000000, "backgroundFallback analysis returns black");
});
// draw red rectangle next to a pink one to make sure the algorithm picks the
// more interesting color
tests.push(function test_interestingColorPreference() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "#FFDDDD";
ctx.fillRect(0, 0, 16, 16);
ctx.fillStyle = "red";
ctx.fillRect(0, 0, 3, 16);
}, 0xFF0000, "interestingColorPreference analysis returns red");
});
// draw high saturation but dark red next to slightly less saturated color but
// much lighter, to make sure the algorithm doesn't pick colors that are
// nearly black just because of high saturation (in HSL terms)
tests.push(function test_saturationDependence() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "hsl(0, 100%, 5%)";
ctx.fillRect(0, 0, 16, 16);
ctx.fillStyle = "hsl(0, 90%, 35%)";
ctx.fillRect(0, 0, 8, 16);
}, 0xA90808, "saturationDependence analysis returns lighter red");
});
// make sure the preference for interesting colors won't stupidly pick 1 pixel
// of red over 169 black pixels
tests.push(function test_interestingColorPreferenceLenient() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "black";
ctx.fillRect(1, 1, 13, 13);
ctx.fillStyle = "red";
ctx.fillRect(3, 3, 1, 1);
}, 0x000000, "interestingColorPreferenceLenient analysis returns black");
});
// ...but 6 pixels of red is more reasonable
tests.push(function test_interestingColorPreferenceNotTooLenient() {
canvasTest(16, 16, function(ctx) {
ctx.fillStyle = "black";
ctx.fillRect(1, 1, 13, 13);
ctx.fillStyle = "red";
ctx.fillRect(3, 3, 3, 2);
}, 0xFF0000, "interestingColorPreferenceNotTooLenient analysis returns red");
});
// make sure that images larger than 128x128 fail
tests.push(function test_imageTooLarge() {
canvasTest(129, 129, function(ctx) {
ctx.fillStyle = "red";
ctx.fillRect(0, 0, 129, 129);
}, null, "imageTooLarge analysis fails");
});
// these next tests are for performance (and also to make sure concurrency
// doesn't break anything)
let maxColor = Math.pow(2, 24) - 1;
function getRandomColor() {
let randomColor = (Math.ceil(Math.random() * maxColor)).toString(16);
return "000000".slice(0, 6 - randomColor.length) + randomColor;
}
function testFiller(color, ctx) {
ctx.fillStyle = "#" + color;
ctx.fillRect(2, 2, 12, 12);
}
tests.push(function test_perfInSeries() {
let t1 = new Date();
let numTests = 20;
let allCorrect = true;
function nextPerfTest() {
let color = getRandomColor();
canvasTest(16, 16, testFiller.bind(this, color), function(actual) {
if (actual != parseInt(color, 16)) {
allCorrect = false;
}
if (--numTests > 0) {
nextPerfTest();
} else {
is(allCorrect, true, "perfInSeries colors are all correct");
info("perfInSeries: " + ((new Date()) - t1) + "ms");
nextStep();
}
}, "", true);
}
nextPerfTest();
});
tests.push(function test_perfInParallel() {
let t1 = new Date();
let numTests = 20;
let testsDone = 0;
let allCorrect = true;
for (let i = 0; i < numTests; i++) {
let color = getRandomColor();
canvasTest(16, 16, testFiller.bind(this, color), function(actual) {
if (actual != parseInt(color, 16)) {
allCorrect = false;
}
if (++testsDone >= 20) {
is(allCorrect, true, "perfInParallel colors are all correct");
info("perfInParallel: " + ((new Date()) - t1) + "ms");
nextStep();
}
}, "", true);
}
});
tests.push(function test_perfBigImage() {
let t1 = 0;
canvasTest(128, 128, function(ctx) {
// make sure to use a bunch of unique colors so the clustering algorithm
// actually has to do work
for (let y = 0; y < 128; y++) {
for (let x = 0; x < 128; x++) {
ctx.fillStyle = "#" + getRandomColor();
ctx.fillRect(x, y, 1, 1);
}
}
t1 = new Date();
}, function(actual) {
info("perfBigImage: " + ((new Date()) - t1) + "ms");
nextStep();
}, "", true);
});
// the rest of the tests are for coverage of "real" favicons
// exact color isn't terribly important, just make sure it's reasonable
const filePrefix = getRootDirectory(gTestPath);
tests.push(function test_categoryDiscover() {
frcTest(filePrefix + "category-discover.png", 0xB28D3A,
"category-discover analysis returns red");
});
tests.push(function test_localeGeneric() {
frcTest(filePrefix + "localeGeneric.png", 0x00A400,
"localeGeneric analysis returns orange");
});
tests.push(function test_dictionaryGeneric() {
frcTest(filePrefix + "dictionaryGeneric-16.png", 0x502E1E,
"dictionaryGeneric-16 analysis returns blue");
});
tests.push(function test_extensionGeneric() {
frcTest(filePrefix + "extensionGeneric-16.png", 0x53BA3F,
"extensionGeneric-16 analysis returns green");
});

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@ -18,3 +18,7 @@ component {803938d5-e26d-4453-bf46-ad4b26e41114} PlacesCategoriesStarter.js
contract @mozilla.org/places/categoriesStarter;1 {803938d5-e26d-4453-bf46-ad4b26e41114}
category idle-daily PlacesCategoriesStarter @mozilla.org/places/categoriesStarter;1
category bookmark-observers PlacesCategoriesStarter @mozilla.org/places/categoriesStarter;1
# ColorAnalyzer.js
component {d056186c-28a0-494e-aacc-9e433772b143} ColorAnalyzer.js
contract @mozilla.org/places/colorAnalyzer;1 {d056186c-28a0-494e-aacc-9e433772b143}