v.1.0.0 (final)

* backport correct selection handling of detection from HaarDetector of FILTER.js
* release final version, further development is in FILTER.js

Readme.md

@@ -1,8 +1,6 @@
 # HAAR.js 
 
-### Further development on this project has stopped!!
-
-This project has stopped, no new features are added or updated. However the functionality works as stated in this document and the API reference. Further development has moved to the [FILTER.js](https://github.com/foo123/FILTER.js) project, for Image Processing and Computer Vision, which includes a new [**HaarDetector plugin**](https://github.com/foo123/FILTER.js/blob/master/src/plugins/HaarDetector.js) which can be seen as the continuation of this project.
+**Note:** Further development has moved to the [FILTER.js](https://github.com/foo123/FILTER.js) project, for Image Processing and Computer Vision, which includes a new [**HaarDetector plugin**](https://github.com/foo123/FILTER.js/blob/master/src/plugins/HaarDetector.js) which can be seen as the continuation of this project.
 
 
 __Feature Detection Library for JavaScript__    (uses HTML5 canvas on browser and Canvas package on Node)
@@ -145,7 +143,7 @@ The configuration would be the same inside a browser
 - [ ] add some real performance tests (anyone interested??)
 
 <!--
-*URL* [Nikos Web Development](http://nikos-web-development.netai.net/ "Nikos Web Development")  
+*URL* [Nikos Web Development](http://nikos-web-dev.000webhostapp.com/ "Nikos Web Development")  
 *URL* [Haar.js blog post](http://nikos-web-development.netai.net/blog/haar-js-feature-detection-in-javascript-and-html5-canvas/ "Haar.js blog post")  
 *URL* [WorkingClassCode](http://workingclasscode.uphero.com/ "Working Class Code")  
 -->

api-reference.md

@@ -2,7 +2,7 @@
 
 #### Detector Methods
 
-    
+
 
 
 __constructor(haardata, Parallel)__
@@ -14,7 +14,7 @@ __Explanation of parameters__
 
 * _haardata_ : The actual haardata (as generated by haartojs tool), this is specific per feature, openCV haar data can be used.
 * _Parallel_ : Optional, this is the _Parallel_ object, as returned by the _parallel.js_ script (included). It enables HAAR.js to run parallel computations both in browser and node (can be much faster)
-    
+
 
 
 __dispose()__
@@ -23,7 +23,7 @@ detector.dispose();
 ```
 
 Disposes the detector and clears space of data cached
-        
+
 
 
 __clearCache()__
@@ -32,7 +32,7 @@ detector.clearCache();
 ```
 
 Clear any cached image data and haardata in case space is an issue. Use image method and cascade method (see below) to re-set image and haar data
-        
+
 
 
 __cascade(haardata)__
@@ -45,7 +45,7 @@ Allow to use same detector (with its cached image data), to detect different fea
 __Explanation of parameters__
 
 * _haardata_ : The actual haardata (as generated by haartojs tool), this is specific per feature, openCV haar data can be used.
-        
+
 
 
 __parallel(Parallel)__
@@ -58,7 +58,7 @@ Enable/disable parallel processing (passing the Parallel Object or null/false)
 __Explanation of parameters__
 
 * _Parallel_ : The actual Parallel object used in parallel.js (included)
-        
+
 
 
 __image(ImageOrVideoOrCanvas, scale, CanvasClass)__
@@ -71,7 +71,7 @@ __Explanation of parameters__
 * _ImageOrVideoOrCanvas_ : an actual Image or Video element or Canvas Object (in this case they are equivalent).
 * _scale_ : The percent of scaling from the original image, so detection proceeds faster on a smaller image (default __0.5__ ). __NOTE__ scaling might alter the detection results sometimes, if having problems opt towards 1 (slower)
 * _CanvasClass_ : This is optional and used only when running in node (passing the node-canvas object).
-        
+
 
 
 __interval(detectionInterval)__
@@ -82,7 +82,7 @@ detector.interval(detectionInterval);
 __Explanation of parameters__
 
 * _detectionInterval_ : interval to run the detection asynchronously (if not parallel) in  microseconds (default __30__).
-        
+
 
 
 __cannyThreshold({low: lowThreshold, high: highThreshold})__
@@ -98,7 +98,7 @@ __Explanation of parameters__
 
 * _low_ : (Optional) The low threshold (default __20__ ).
 * _high_ : (Optional) The high threshold (default __100__ ).
-        
+
 
 
 __select|selection('auto'|object|feature|x [,y, width, height])__
@@ -116,7 +116,7 @@ __Explanation of parameters__
 * _height_ : (Optional) the selection height, can be an actual value or 'auto' (height=image.height)
 
 The actual selection rectangle/feature is available as this.Selection or detector.Selection
-        
+
 
 
 __complete(callback)__
@@ -129,7 +129,7 @@ Set the callback handler when detection completes (for parallel and asynchronous
 __Explanation of parameters__
 
 * _callback_ : The user-defined callback function (will be called within the detectors scope, the value of 'this' will be the detector instance).
-        
+
 
 
 __detect(baseScale, scale_inc, increment, min_neighbors, doCannyPruning)__
@@ -144,7 +144,7 @@ __Explanation of parameters__ ([JViolaJones Parameters](http://code.google.com/p
 * *increment* : The shift of the window at each sub-step, in terms of percentage of the window size (default __0.5__ ).
 * *min_neighbors* : The minimum numbers of similar rectangles needed for the region to be considered as a feature (avoid noise) (default __1__ )
 * *doCannyPruning* : enable Canny Pruning to pre-detect regions unlikely to contain features, in order to speed up the execution (optional default __true__ ). 
-        
+
 
 
 __detectSync(baseScale, scale_inc, increment, min_neighbors, doCannyPruning)__
@@ -156,4 +156,4 @@ Run detector synchronously in one step, instead of parallel or asynchronously. C
 
 __Explanation of parameters__ (similar to *detect* method)
 
-        
+

beeld.config

@@ -26,7 +26,7 @@ tasks =[{}]
             # to provide canvas support for browsers that lack support for canvas element :P
             #./src/externals/excanvas.js
 
-            !tpl:umd-header-single.tpl.js  # include a umd-header template
+            !tpl:umd-header.tpl.js  # include a umd-header template
 
             ./src/haar-detector.js
 
@@ -40,9 +40,9 @@ tasks =[{}]
         # do any replacements in the source (orderedmap)
         replace =[{}]
 
-            "@@ROOT@@" = "this"
+            "@@ROOT@@" = "'undefined' !== typeof self ? self : this"
             "@@MODULE@@" = "HAAR"
-            "@@VERSION@@" = "0.4.8"
+            "@@VERSION@@" = "1.0.0"
 
         @
 

changelog.md

@@ -1,5 +1,9 @@
 ### ChangeLog
 
+__1.0.0__
+* backport from FILTER.js HAARDetector plugin correct handling of detecting with selection
+* release final version 1.0.0
+
 __0.4.8__
 * fix undeclared variable issue in *detectSingleStep* method
 * add *detectSync* method

package.json

@@ -1,6 +1,6 @@
 {
   "name": "HAAR.js",
-  "version": "0.4.8",
+  "version": "1.0.0",
   "description": "HAAR.js Feature Detection Library for JavaScript and Node based on Viola-Jones algorithm",
   "main": "build/haar-detector.min.js",
   "directories": {

src/haar-detector.js

@@ -368,7 +368,8 @@ function detectSingleStep(self)
 {
     var Sqrt = Sqrt || Math.sqrt, ret = [],
         haar = self.haardata, haar_stages = haar.stages, scaledSelection = self.scaledSelection,
-        w = scaledSelection.width, h = scaledSelection.height, imArea=w*h, imArea1=imArea-1,
+        w = self.width, h = self.height,
+        selw = scaledSelection.width, selh = scaledSelection.height, imArea=w*h, imArea1=imArea-1,
         sizex = haar.size1, sizey = haar.size2, xstep, ystep, xsize, ysize,
         startx = scaledSelection.x, starty = scaledSelection.y, startty,
         x, y, ty, tyw, tys, sl = haar_stages.length,
@@ -395,8 +396,8 @@ function detectSingleStep(self)
     tyw = ysize*w; 
     tys = ystep*w; 
     startty = starty*tys; 
-    xl = w-xsize; 
-    yl = h-ysize;
+    xl = selw-xsize; 
+    yl = selh-ysize;
     swh = xsize*ysize; 
     inv_area = 1.0/swh;
 
@@ -565,7 +566,7 @@ function detectEnd(self, rects)
 
 /**[DOC_MARKDOWN]
 *
-* ####Detector Methods
+* #### Detector Methods
 *
 [/DOC_MARKDOWN]**/
 
@@ -903,7 +904,7 @@ Detector[proto] = {
         min_neighbors = (undef === min_neighbors) ? 1 : min_neighbors;
         doCannyPruning = self.doCannyPruning = (undef === doCannyPruning) ? true : doCannyPruning;
 
-        maxScale = self.maxScale = Min(width/sizex, height/sizey); 
+        maxScale = self.maxScale = Min(scaledSelection.width/sizex, scaledSelection.height/sizey); 
         scale = self.scale = baseScale; 
         self.min_neighbors = min_neighbors; 
         self.scale_inc = scale_inc; 
@@ -1005,7 +1006,7 @@ Detector[proto] = {
         min_neighbors = (typeof min_neighbors == 'undefined') ? 1 : min_neighbors;
         self.doCannyPruning = (typeof doCannyPruning == 'undefined') ? true : doCannyPruning;
 
-        maxScale = self.maxScale = Min(self.width/sizex, self.height/sizey); 
+        maxScale = self.maxScale = Min(self.scaledSelection.width/sizex, self.scaledSelection.height/sizey); 
         scale = self.scale = baseScale; 
         self.min_neighbors = min_neighbors; 
         self.scale_inc = scale_inc;