@tensorflow-models/deeplab - npm Package Compare versions

Comparing version 0.1.0 to 0.1.1

dist/deeplab.esm.js

@@ -33,3 +33,3 @@ /**
     */
-import{loadGraphModel as e}from"@tensorflow/tfjs-converter";import*as tf from"@tensorflow/tfjs-core";import{tidy as t,Tensor as n,browser as r,image as o,buffer as a,dispose as i,squeeze as s}from"@tensorflow/tfjs-core";
+import{loadGraphModel as e}from"@tensorflow/tfjs-converter";import*as tf from"@tensorflow/tfjs-core";import{tidy as t,Tensor as n,browser as r,image as a,buffer as o,dispose as i,squeeze as s}from"@tensorflow/tfjs-core";
 /*! *****************************************************************************
@@ -48,2 +48,2 @@ Copyright (c) Microsoft Corporation. All rights reserved.
 and limitations under the License.
-***************************************************************************** */function l(e,t,n,r){return new(n||(n=Promise))((function(o,a){function i(e){try{l(r.next(e))}catch(e){a(e)}}function s(e){try{l(r.throw(e))}catch(e){a(e)}}function l(e){e.done?o(e.value):new n((function(t){t(e.value)})).then(i,s)}l((r=r.apply(e,t||[])).next())}))}function c(e,t){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 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table","dog","horse","motorbike","person","potted plant","sheep","sofa","train","TV"],CITYSCAPES:["road","sidewalk","building","wall","fence","pole","traffic light","traffic sign","vegetation","terrain","sky","person","rider","car","truck","bus","train","motorcycle","bicycle"],ADE20K:["background","wall","building","sky","floor","tree","ceiling","road","bed","windowpane","grass","cabinet","sidewalk","person","earth","door","table","mountain","plant","curtain","chair","car","water","painting","sofa","shelf","house","sea","mirror","rug","field","armchair","seat","fence","desk","rock","wardrobe","lamp","bathtub","railing","cushion","base","box","column","signboard","chest","counter","sand","sink","skyscraper","fireplace","refrigerator","grandstand","path","stairs","runway","case","pool","pillow","screen","stairway","river","bridge","bookcase","blind","coffee","toilet","flower","book","hill","bench","countertop","stove","palm","kitchen","computer","swivel","boat","bar","arcade","hovel","bus","towel","light","truck","tower","chandelier","awning","streetlight","booth","television","airplane","dirt","apparel","pole","land","bannister","escalator","ottoman","bottle","buffet","poster","stage","van","ship","fountain","conveyer","canopy","washer","plaything","swimming","stool","barrel","basket","waterfall","tent","bag","minibike","cradle","oven","ball","food","step","tank","trade","microwave","pot","animal","bicycle","lake","dishwasher","screen","blanket","sculpture","hood","sconce","vase","traffic","tray","ashcan","fan","pier","screen","plate","monitor","bulletin","shower","radiator","glass","clock","flag"]}};function f(e,t){return""+u.BASE_PATH+"/"+(4===t?e+"/1/default/1/model.json":e+"/1/quantized/"+t+"/1/model.json")+"?tfjs-format=file"}function d(e){if("pascal"===e)return u.COLORMAPS.PASCAL;if("ade20k"===e)return u.COLORMAPS.ADE20K;if("cityscapes"===e)return u.COLORMAPS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function h(e){if("pascal"===e)return u.LABELS.PASCAL;if("ade20k"===e)return u.LABELS.ADE20K;if("cityscapes"===e)return u.LABELS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function p(e,t,n,o){return l(this,void 0,void 0,(function(){var s,l,u,f,d,h,p,b,m,w,g,y,v,S;return c(this,(function(c){switch(c.label){case 0:if(e.length<t.length)throw new Error("The colormap must be expansive enough to encode each label. Aborting, since the given colormap has length "+e.length+", but there are "+t.length+" labels.");return s=n.shape,l=s[0],u=s[1],f=a([l,u,3],"int32"),[4,n.array()];case 1:for(d=c.sent(),h=new Set,p=0;p<l;++p)for(b=0;b<u;++b)S=d[p][b],h.add(S),f.set(e[S][0],p,b,0),f.set(e[S][1],p,b,1),f.set(e[S][2],p,b,2);return m=f.toTensor(),[4,r.toPixels(m,o)];case 2:for(w=c.sent(),i(m),g={},y=0,v=Array.from(h);y<v.length;y++)S=v[y],g[t[S]]=e[S];return[2,{legend:g,segmentationMap:w}]}}))}))}function b(t){return void 0===t&&(t={base:"pascal",quantizationBytes:2}),l(this,void 0,void 0,(function(){var n;return c(this,(function(r){switch(r.label){case 0:if(null==tf)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(t.base){if(-1===["pascal","cityscapes","ade20k"].indexOf(t.base))throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+t.base+". Try one of 'pascal', 'cityscapes' and 'ade20k'.");if(-1===[1,2,4].indexOf(t.quantizationBytes))throw new Error("Only quantization to 1, 2 or 4 bytes is supported.")}else if(!t.modelUrl)throw new Error("SemanticSegmentation can be constructed either by passing the weights URL or one of the supported base model names from 'pascal', 'cityscapes' and 'ade20k',together with the degree of quantization (either 1, 2 or 4).Aborting, since neither has been provided.");return[4,e(t.modelUrl||f(t.base,t.quantizationBytes))];case 1:return n=r.sent(),[2,new m(n,t.base)]}}))}))}var m=function(){function e(e,t){this.model=e,this.base=t}return e.prototype.predict=function(e){var a=this;return t((function(){var i=function(e){return t((function(){var t=e instanceof n?e:r.fromPixels(e),a=t.shape,i=a[0],s=a[1],l=u.CROP_SIZE/Math.max(s,i),c=Math.round(i*l),f=Math.round(s*l);return o.resizeBilinear(t,[c,f]).expandDims(0)}))}(e);return s(a.model.execute(i))}))},e.prototype.segment=function(e,n){return void 0===n&&(n={}),l(this,void 0,void 0,(function(){var r,o,a,s,l,u,f,b,m,w,g=this;return c(this,(function(c){switch(c.label){case 0:if(!(n.colormap&&n.labels||this.base))throw new Error("Calling the 'segment' method requires either the 'base' attribute to be defined (e.g. 'pascal', 'cityscapes' or'ade20k'), or 'colormap' and 'labels' options to be set. Aborting, since neither has been provided.");return n.colormap&&n.labels||(n.colormap=d(this.base),n.labels=h(this.base)),r=n.colormap,o=n.labels,a=n.canvas,s=t((function(){return g.predict(e)})),l=s.shape,u=l[0],f=l[1],[4,p(r,o,s,a)];case 1:return b=c.sent(),m=b.legend,w=b.segmentationMap,i(s),[2,{legend:m,height:u,width:f,segmentationMap:w}]}}))}))},e.prototype.dispose=function(){return l(this,void 0,void 0,(function(){return c(this,(function(e){return this.model&&this.model.dispose(),[2]}))}))},e}();export{m as SemanticSegmentation,d as getColormap,h as getLabels,f as getURL,b as load,p as toSegmentationImage};
+***************************************************************************** */function l(e,t,n,r){return new(n||(n=Promise))((function(a,o){function i(e){try{l(r.next(e))}catch(e){o(e)}}function s(e){try{l(r.throw(e))}catch(e){o(e)}}function l(e){e.done?a(e.value):new n((function(t){t(e.value)})).then(i,s)}l((r=r.apply(e,t||[])).next())}))}function c(e,t){var n,r,a,o,i={label:0,sent:function(){if(1&a[0])throw a[1];return a[1]},trys:[],ops:[]};return o={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(o[Symbol.iterator]=function(){return this}),o;function s(o){return function(s){return function(o){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(a=2&o[0]?r.return:o[0]?r.throw||((a=r.return)&&a.call(r),0):r.next)&&!(a=a.call(r,o[1])).done)return a;switch(r=0,a&&(o=[2&o[0],a.value]),o[0]){case 0:case 1:a=o;break;case 4:return i.label++,{value:o[1],done:!1};case 5:i.label++,r=o[1],o=[0];continue;case 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u={BASE_PATH:"https://tfhub.dev/tensorflow/tfjs-model/deeplab",CROP_SIZE:513,COLORMAPS:{ADE20K:[[0,0,0],[120,120,120],[180,120,120],[6,230,230],[80,50,50],[4,200,3],[120,120,80],[140,140,140],[204,5,255],[230,230,230],[4,250,7],[224,5,255],[235,255,7],[150,5,61],[120,120,70],[8,255,51],[255,6,82],[143,255,140],[204,255,4],[255,51,7],[204,70,3],[0,102,200],[61,230,250],[255,6,51],[11,102,255],[255,7,71],[255,9,224],[9,7,230],[220,220,220],[255,9,92],[112,9,255],[8,255,214],[7,255,224],[255,184,6],[10,255,71],[255,41,10],[7,255,255],[224,255,8],[102,8,255],[255,61,6],[255,194,7],[255,122,8],[0,255,20],[255,8,41],[255,5,153],[6,51,255],[235,12,255],[160,150,20],[0,163,255],[140,140,140],[250,10,15],[20,255,0],[31,255,0],[255,31,0],[255,224,0],[153,255,0],[0,0,255],[255,71,0],[0,235,255],[0,173,255],[31,0,255],[11,200,200],[255,82,0],[0,255,245],[0,61,255],[0,255,112],[0,255,133],[255,0,0],[255,163,0],[255,102,0],[194,255,0],[0,143,255],[51,255,0],[0,82,255],[0,255,41],[0,255,173],[10,0,255],[173,255,0],[0,255,153],[255,92,0],[255,0,255],[255,0,245],[255,0,102],[255,173,0],[255,0,20],[255,184,184],[0,31,255],[0,255,61],[0,71,255],[255,0,204],[0,255,194],[0,255,82],[0,10,255],[0,112,255],[51,0,255],[0,194,255],[0,122,255],[0,255,163],[255,153,0],[0,255,10],[255,112,0],[143,255,0],[82,0,255],[163,255,0],[255,235,0],[8,184,170],[133,0,255],[0,255,92],[184,0,255],[255,0,31],[0,184,255],[0,214,255],[255,0,112],[92,255,0],[0,224,255],[112,224,255],[70,184,160],[163,0,255],[153,0,255],[71,255,0],[255,0,163],[255,204,0],[255,0,143],[0,255,235],[133,255,0],[255,0,235],[245,0,255],[255,0,122],[255,245,0],[10,190,212],[214,255,0],[0,204,255],[20,0,255],[255,255,0],[0,153,255],[0,41,255],[0,255,204],[41,0,255],[41,255,0],[173,0,255],[0,245,255],[71,0,255],[122,0,255],[0,255,184],[0,92,255],[184,255,0],[0,133,255],[255,214,0],[25,194,194],[102,255,0],[92,0,255]],CITYSCAPES:[[128,64,128],[244,35,232],[70,70,70],[102,102,156],[190,153,153],[153,153,153],[250,170,30],[220,220,0],[107,142,35],[152,251,152],[70,130,180],[220,20,60],[255,0,0],[0,0,142],[0,0,70],[0,60,100],[0,80,100],[0,0,230],[119,11,32]],PASCAL:[[0,0,0],[128,0,0],[0,128,0],[128,128,0],[0,0,128],[128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0],[64,128,0],[192,128,0],[64,0,128],[192,0,128],[64,128,128],[192,128,128],[0,64,0],[128,64,0],[0,192,0],[128,192,0],[0,64,128],[128,64,128],[0,192,128],[128,192,128],[64,64,0],[192,64,0],[64,192,0],[192,192,0],[64,64,128],[192,64,128],[64,192,128],[192,192,128],[0,0,64],[128,0,64],[0,128,64],[128,128,64],[0,0,192],[128,0,192],[0,128,192],[128,128,192],[64,0,64],[192,0,64],[64,128,64],[192,128,64],[64,0,192],[192,0,192],[64,128,192],[192,128,192],[0,64,64],[128,64,64],[0,192,64],[128,192,64],[0,64,192],[128,64,192],[0,192,192],[128,192,192],[64,64,64],[192,64,64],[64,192,64],[192,192,64],[64,64,192],[192,64,192],[64,192,192],[192,192,192],[32,0,0],[160,0,0],[32,128,0],[160,128,0],[32,0,128],[160,0,128],[32,128,128],[160,128,128],[96,0,0],[224,0,0],[96,128,0],[224,128,0],[96,0,128],[224,0,128],[96,128,128],[224,128,128],[32,64,0],[160,64,0],[32,192,0],[160,192,0],[32,64,128],[160,64,128],[32,192,128],[160,192,128],[96,64,0],[224,64,0],[96,192,0],[224,192,0],[96,64,128],[224,64,128],[96,192,128],[224,192,128],[32,0,64],[160,0,64],[32,128,64],[160,128,64],[32,0,192],[160,0,192],[32,128,192],[160,128,192],[96,0,64],[224,0,64],[96,128,64],[224,128,64],[96,0,192],[224,0,192],[96,128,192],[224,128,192],[32,64,64],[160,64,64],[32,192,64],[160,192,64],[32,64,192],[160,64,192],[32,192,192],[160,192,192],[96,64,64],[224,64,64],[96,192,64],[224,192,64],[96,64,192],[224,64,192],[96,192,192],[224,192,192],[0,32,0],[128,32,0],[0,160,0],[128,160,0],[0,32,128],[128,32,128],[0,160,128],[128,160,128],[64,32,0],[192,32,0],[64,160,0],[192,160,0],[64,32,128],[192,32,128],[64,160,128],[192,160,128],[0,96,0],[128,96,0],[0,224,0],[128,224,0],[0,96,128],[128,96,128],[0,224,128],[128,224,128],[64,96,0],[192,96,0],[64,224,0],[192,224,0],[64,96,128],[192,96,128],[64,224,128],[192,224,128],[0,32,64],[128,32,64],[0,160,64],[128,160,64],[0,32,192],[128,32,192],[0,160,192],[128,160,192],[64,32,64],[192,32,64],[64,160,64],[192,160,64],[64,32,192],[192,32,192],[64,160,192],[192,160,192],[0,96,64],[128,96,64],[0,224,64],[128,224,64],[0,96,192],[128,96,192],[0,224,192],[128,224,192],[64,96,64],[192,96,64],[64,224,64],[192,224,64],[64,96,192],[192,96,192],[64,224,192],[192,224,192],[32,32,0],[160,32,0],[32,160,0],[160,160,0],[32,32,128],[160,32,128],[32,160,128],[160,160,128],[96,32,0],[224,32,0],[96,160,0],[224,160,0],[96,32,128],[224,32,128],[96,160,128],[224,160,128],[32,96,0],[160,96,0],[32,224,0],[160,224,0],[32,96,128],[160,96,128],[32,224,128],[160,224,128],[96,96,0],[224,96,0],[96,224,0],[224,224,0],[96,96,128],[224,96,128],[96,224,128],[224,224,128],[32,32,64],[160,32,64],[32,160,64],[160,160,64],[32,32,192],[160,32,192],[32,160,192],[160,160,192],[96,32,64],[224,32,64],[96,160,64],[224,160,64],[96,32,192],[224,32,192],[96,160,192],[224,160,192],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table","dog","horse","motorbike","person","potted plant","sheep","sofa","train","TV"],CITYSCAPES:["road","sidewalk","building","wall","fence","pole","traffic light","traffic sign","vegetation","terrain","sky","person","rider","car","truck","bus","train","motorcycle","bicycle"],ADE20K:["background","wall","building","sky","floor","tree","ceiling","road","bed","windowpane","grass","cabinet","sidewalk","person","earth","door","table","mountain","plant","curtain","chair","car","water","painting","sofa","shelf","house","sea","mirror","rug","field","armchair","seat","fence","desk","rock","wardrobe","lamp","bathtub","railing","cushion","base","box","column","signboard","chest","counter","sand","sink","skyscraper","fireplace","refrigerator","grandstand","path","stairs","runway","case","pool","pillow","screen","stairway","river","bridge","bookcase","blind","coffee","toilet","flower","book","hill","bench","countertop","stove","palm","kitchen","computer","swivel","boat","bar","arcade","hovel","bus","towel","light","truck","tower","chandelier","awning","streetlight","booth","television","airplane","dirt","apparel","pole","land","bannister","escalator","ottoman","bottle","buffet","poster","stage","van","ship","fountain","conveyer","canopy","washer","plaything","swimming","stool","barrel","basket","waterfall","tent","bag","minibike","cradle","oven","ball","food","step","tank","trade","microwave","pot","animal","bicycle","lake","dishwasher","screen","blanket","sculpture","hood","sconce","vase","traffic","tray","ashcan","fan","pier","screen","plate","monitor","bulletin","shower","radiator","glass","clock","flag"]}};function f(e,t){return""+u.BASE_PATH+"/"+(4===t?e+"/1/default/1/model.json":e+"/1/quantized/"+t+"/1/model.json")+"?tfjs-format=file"}function d(e){if("pascal"===e)return u.COLORMAPS.PASCAL;if("ade20k"===e)return u.COLORMAPS.ADE20K;if("cityscapes"===e)return u.COLORMAPS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function h(e){if("pascal"===e)return u.LABELS.PASCAL;if("ade20k"===e)return u.LABELS.ADE20K;if("cityscapes"===e)return u.LABELS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function p(e,t,n,a){return l(this,void 0,void 0,(function(){var s,l,u,f,d,h,p,b,m,w,g,y,v,S;return c(this,(function(c){switch(c.label){case 0:if(e.length<t.length)throw new Error("The colormap must be expansive enough to encode each label. Aborting, since the given colormap has length "+e.length+", but there are "+t.length+" labels.");return s=n.shape,l=s[0],u=s[1],f=o([l,u,3],"int32"),[4,n.array()];case 1:for(d=c.sent(),h=new Set,p=0;p<l;++p)for(b=0;b<u;++b)S=d[p][b],h.add(S),f.set(e[S][0],p,b,0),f.set(e[S][1],p,b,1),f.set(e[S][2],p,b,2);return m=f.toTensor(),[4,r.toPixels(m,a)];case 2:for(w=c.sent(),i(m),g={},y=0,v=Array.from(h);y<v.length;y++)S=v[y],g[t[S]]=e[S];return[2,{legend:g,segmentationMap:w}]}}))}))}var b="0.1.1";function m(t){return void 0===t&&(t={base:"pascal",quantizationBytes:2}),l(this,void 0,void 0,(function(){var n;return c(this,(function(r){switch(r.label){case 0:if(null==tf)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(t.base){if(-1===["pascal","cityscapes","ade20k"].indexOf(t.base))throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+t.base+". Try one of 'pascal', 'cityscapes' and 'ade20k'.");if(-1===[1,2,4].indexOf(t.quantizationBytes))throw new Error("Only quantization to 1, 2 or 4 bytes is supported.")}else if(!t.modelUrl)throw new Error("SemanticSegmentation can be constructed either by passing the weights URL or one of the supported base model names from 'pascal', 'cityscapes' and 'ade20k',together with the degree of quantization (either 1, 2 or 4).Aborting, since neither has been provided.");return[4,e(t.modelUrl||f(t.base,t.quantizationBytes))];case 1:return n=r.sent(),[2,new w(n,t.base)]}}))}))}var w=function(){function e(e,t){this.model=e,this.base=t}return e.prototype.predict=function(e){var o=this;return t((function(){var i=function(e){return t((function(){var t=e instanceof n?e:r.fromPixels(e),o=t.shape,i=o[0],s=o[1],l=u.CROP_SIZE/Math.max(s,i),c=Math.round(i*l),f=Math.round(s*l);return a.resizeBilinear(t,[c,f]).expandDims(0)}))}(e);return s(o.model.execute(i))}))},e.prototype.segment=function(e,n){return void 0===n&&(n={}),l(this,void 0,void 0,(function(){var r,a,o,s,l,u,f,b,m,w,g=this;return c(this,(function(c){switch(c.label){case 0:if(!(n.colormap&&n.labels||this.base))throw new Error("Calling the 'segment' method requires either the 'base' attribute to be defined (e.g. 'pascal', 'cityscapes' or'ade20k'), or 'colormap' and 'labels' options to be set. Aborting, since neither has been provided.");return n.colormap&&n.labels||(n.colormap=d(this.base),n.labels=h(this.base)),r=n.colormap,a=n.labels,o=n.canvas,s=t((function(){return g.predict(e)})),l=s.shape,u=l[0],f=l[1],[4,p(r,a,s,o)];case 1:return b=c.sent(),m=b.legend,w=b.segmentationMap,i(s),[2,{legend:m,height:u,width:f,segmentationMap:w}]}}))}))},e.prototype.dispose=function(){return l(this,void 0,void 0,(function(){return c(this,(function(e){return this.model&&this.model.dispose(),[2]}))}))},e}();export{w as SemanticSegmentation,d as getColormap,h as getLabels,f as getURL,m as load,p as toSegmentationImage,b as version};

dist/deeplab.js

@@ -337,2 +337,4 @@ /**
 
+    var version = '0.1.1';
+
     function load(modelConfig) {
@@ -442,2 +444,3 @@ if (modelConfig === void 0) { modelConfig = {
     exports.toSegmentationImage = toSegmentationImage;
+    exports.version = version;
 
@@ -444,0 +447,0 @@ Object.defineProperty(exports, '__esModule', { value: true });

dist/deeplab.min.js

@@ -47,2 +47,2 @@ /**
     and limitations under the License.
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o={BASE_PATH:"https://tfhub.dev/tensorflow/tfjs-model/deeplab",CROP_SIZE:513,COLORMAPS:{ADE20K:[[0,0,0],[120,120,120],[180,120,120],[6,230,230],[80,50,50],[4,200,3],[120,120,80],[140,140,140],[204,5,255],[230,230,230],[4,250,7],[224,5,255],[235,255,7],[150,5,61],[120,120,70],[8,255,51],[255,6,82],[143,255,140],[204,255,4],[255,51,7],[204,70,3],[0,102,200],[61,230,250],[255,6,51],[11,102,255],[255,7,71],[255,9,224],[9,7,230],[220,220,220],[255,9,92],[112,9,255],[8,255,214],[7,255,224],[255,184,6],[10,255,71],[255,41,10],[7,255,255],[224,255,8],[102,8,255],[255,61,6],[255,194,7],[255,122,8],[0,255,20],[255,8,41],[255,5,153],[6,51,255],[235,12,255],[160,150,20],[0,163,255],[140,140,140],[250,10,15],[20,255,0],[31,255,0],[255,31,0],[255,224,0],[153,255,0],[0,0,255],[255,71,0],[0,235,255],[0,173,255],[31,0,255],[11,200,200],[255,82,0],[0,255,245],[0,61,255],[0,255,112],[0,255,133],[255,0,0],[255,163,0],[255,102,0],[194,255,0],[0,143,255],[51,255,0],[0,82,255],[0,255,41],[0,255,173],[10,0,255],[173,255,0],[0,255,153],[255,92,0],[255,0,255],[255,0,245],[255,0,102],[255,173,0],[255,0,20],[255,184,184],[0,31,255],[0,255,61],[0,71,255],[255,0,204],[0,255,194],[0,255,82],[0,10,255],[0,112,255],[51,0,255],[0,194,255],[0,122,255],[0,255,163],[255,153,0],[0,255,10],[255,112,0],[143,255,0],[82,0,255],[163,255,0],[255,235,0],[8,184,170],[133,0,255],[0,255,92],[184,0,255],[255,0,31],[0,184,255],[0,214,255],[255,0,112],[92,255,0],[0,224,255],[112,224,255],[70,184,160],[163,0,255],[153,0,255],[71,255,0],[255,0,163],[255,204,0],[255,0,143],[0,255,235],[133,255,0],[255,0,235],[245,0,255],[255,0,122],[255,245,0],[10,190,212],[214,255,0],[0,204,255],[20,0,255],[255,255,0],[0,153,255],[0,41,255],[0,255,204],[41,0,255],[41,255,0],[173,0,255],[0,245,255],[71,0,255],[122,0,255],[0,255,184],[0,92,255],[184,255,0],[0,133,255],[255,214,0],[25,194,194],[102,255,0],[92,0,255]],CITYSCAPES:[[128,64,128],[244,35,232],[70,70,70],[102,102,156],[190,153,153],[153,153,153],[250,170,30],[220,220,0],[107,142,35],[152,251,152],[70,130,180],[220,20,60],[255,0,0],[0,0,142],[0,0,70],[0,60,100],[0,80,100],[0,0,230],[119,11,32]],PASCAL:[[0,0,0],[128,0,0],[0,128,0],[128,128,0],[0,0,128],[128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0],[64,128,0],[192,128,0],[64,0,128],[192,0,128],[64,128,128],[192,128,128],[0,64,0],[128,64,0],[0,192,0],[128,192,0],[0,64,128],[128,64,128],[0,192,128],[128,192,128],[64,64,0],[192,64,0],[64,192,0],[192,192,0],[64,64,128],[192,64,128],[64,192,128],[192,192,128],[0,0,64],[128,0,64],[0,128,64],[128,128,64],[0,0,192],[128,0,192],[0,128,192],[128,128,192],[64,0,64],[192,0,64],[64,128,64],[192,128,64],[64,0,192],[192,0,192],[64,128,192],[192,128,192],[0,64,64],[128,64,64],[0,192,64],[128,192,64],[0,64,192],[128,64,192],[0,192,192],[128,192,192],[64,64,64],[192,64,64],[64,192,64],[192,192,64],[64,64,192],[192,64,192],[64,192,192],[192,192,192],[32,0,0],[160,0,0],[32,128,0],[160,128,0],[32,0,128],[160,0,128],[32,128,128],[160,128,128],[96,0,0],[224,0,0],[96,128,0],[224,128,0],[96,0,128],[224,0,128],[96,128,128],[224,128,128],[32,64,0],[160,64,0],[32,192,0],[160,192,0],[32,64,128],[160,64,128],[32,192,128],[160,192,128],[96,64,0],[224,64,0],[96,192,0],[224,192,0],[96,64,128],[224,64,128],[96,192,128],[224,192,128],[32,0,64],[160,0,64],[32,128,64],[160,128,64],[32,0,192],[160,0,192],[32,128,192],[160,128,192],[96,0,64],[224,0,64],[96,128,64],[224,128,64],[96,0,192],[224,0,192],[96,128,192],[224,128,192],[32,64,64],[160,64,64],[32,192,64],[160,192,64],[32,64,192],[160,64,192],[32,192,192],[160,192,192],[96,64,64],[224,64,64],[96,192,64],[224,192,64],[96,64,192],[224,64,192],[96,192,192],[224,192,192],[0,32,0],[128,32,0],[0,160,0],[128,160,0],[0,32,128],[128,32,128],[0,160,128],[128,160,128],[64,32,0],[192,32,0],[64,160,0],[192,160,0],[64,32,128],[192,32,128],[64,160,128],[192,160,128],[0,96,0],[128,96,0],[0,224,0],[128,224,0],[0,96,128],[128,96,128],[0,224,128],[128,224,128],[64,96,0],[192,96,0],[64,224,0],[192,224,0],[64,96,128],[192,96,128],[64,224,128],[192,224,128],[0,32,64],[128,32,64],[0,160,64],[128,160,64],[0,32,192],[128,32,192],[0,160,192],[128,160,192],[64,32,64],[192,32,64],[64,160,64],[192,160,64],[64,32,192],[192,32,192],[64,160,192],[192,160,192],[0,96,64],[128,96,64],[0,224,64],[128,224,64],[0,96,192],[128,96,192],[0,224,192],[128,224,192],[64,96,64],[192,96,64],[64,224,64],[192,224,64],[64,96,192],[192,96,192],[64,224,192],[192,224,192],[32,32,0],[160,32,0],[32,160,0],[160,160,0],[32,32,128],[160,32,128],[32,160,128],[160,160,128],[96,32,0],[224,32,0],[96,160,0],[224,160,0],[96,32,128],[224,32,128],[96,160,128],[224,160,128],[32,96,0],[160,96,0],[32,224,0],[160,224,0],[32,96,128],[160,96,128],[32,224,128],[160,224,128],[96,96,0],[224,96,0],[96,224,0],[224,224,0],[96,96,128],[224,96,128],[96,224,128],[224,224,128],[32,32,64],[160,32,64],[32,160,64],[160,160,64],[32,32,192],[160,32,192],[32,160,192],[160,160,192],[96,32,64],[224,32,64],[96,160,64],[224,160,64],[96,32,192],[224,32,192],[96,160,192],[224,160,192],[32,96,64],[160,96,64],[32,224,64],[160,224,64],[32,96,192],[160,96,192],[32,224,192],[160,224,192],[96,96,64],[224,96,64],[96,224,64],[224,224,64],[96,96,192],[224,96,192],[96,224,192],[224,224,192]]},DATASET_MAX_ENTRIES:{PASCAL:256,CITYSCAPES:256,ADE20K:151},LABELS:{PASCAL:["background","aeroplane","bicycle","bird","boat","bottle","bus","car","cat","chair","cow","dining table","dog","horse","motorbike","person","potted plant","sheep","sofa","train","TV"],CITYSCAPES:["road","sidewalk","building","wall","fence","pole","traffic light","traffic sign","vegetation","terrain","sky","person","rider","car","truck","bus","train","motorcycle","bicycle"],ADE20K:["background","wall","building","sky","floor","tree","ceiling","road","bed","windowpane","grass","cabinet","sidewalk","person","earth","door","table","mountain","plant","curtain","chair","car","water","painting","sofa","shelf","house","sea","mirror","rug","field","armchair","seat","fence","desk","rock","wardrobe","lamp","bathtub","railing","cushion","base","box","column","signboard","chest","counter","sand","sink","skyscraper","fireplace","refrigerator","grandstand","path","stairs","runway","case","pool","pillow","screen","stairway","river","bridge","bookcase","blind","coffee","toilet","flower","book","hill","bench","countertop","stove","palm","kitchen","computer","swivel","boat","bar","arcade","hovel","bus","towel","light","truck","tower","chandelier","awning","streetlight","booth","television","airplane","dirt","apparel","pole","land","bannister","escalator","ottoman","bottle","buffet","poster","stage","van","ship","fountain","conveyer","canopy","washer","plaything","swimming","stool","barrel","basket","waterfall","tent","bag","minibike","cradle","oven","ball","food","step","tank","trade","microwave","pot","animal","bicycle","lake","dishwasher","screen","blanket","sculpture","hood","sconce","vase","traffic","tray","ashcan","fan","pier","screen","plate","monitor","bulletin","shower","radiator","glass","clock","flag"]}};function a(e,t){return""+o.BASE_PATH+"/"+(4===t?e+"/1/default/1/model.json":e+"/1/quantized/"+t+"/1/model.json")+"?tfjs-format=file"}function i(e){if("pascal"===e)return o.COLORMAPS.PASCAL;if("ade20k"===e)return o.COLORMAPS.ADE20K;if("cityscapes"===e)return o.COLORMAPS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function s(e){if("pascal"===e)return o.LABELS.PASCAL;if("ade20k"===e)return o.LABELS.ADE20K;if("cityscapes"===e)return o.LABELS.CITYSCAPES;throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e+". Try one of 'pascal', 'cityscapes' and 'ade20k'.")}function l(e,t,o,a){return n(this,void 0,void 0,(function(){var n,i,s,l,c,u,d,f,h,p,b,g,m,w;return r(this,(function(r){switch(r.label){case 0:if(e.length<t.length)throw new Error("The colormap must be expansive enough to encode each label. Aborting, since the given colormap has length "+e.length+", but there are "+t.length+" labels.");return n=o.shape,i=n[0],s=n[1],l=tf.buffer([i,s,3],"int32"),[4,o.array()];case 1:for(c=r.sent(),u=new Set,d=0;d<i;++d)for(f=0;f<s;++f)w=c[d][f],u.add(w),l.set(e[w][0],d,f,0),l.set(e[w][1],d,f,1),l.set(e[w][2],d,f,2);return h=l.toTensor(),[4,tf.browser.toPixels(h,a)];case 2:for(p=r.sent(),tf.dispose(h),b={},g=0,m=Array.from(u);g<m.length;g++)w=m[g],b[t[w]]=e[w];return[2,{legend:b,segmentationMap:p}]}}))}))}var c=function(){function e(e,t){this.model=e,this.base=t}return e.prototype.predict=function(e){var t=this;return tf.tidy((function(){var n=function(e){return tf.tidy((function(){var t=e instanceof tf.Tensor?e:tf.browser.fromPixels(e),n=t.shape,r=n[0],a=n[1],i=o.CROP_SIZE/Math.max(a,r),s=Math.round(r*i),l=Math.round(a*i);return tf.image.resizeBilinear(t,[s,l]).expandDims(0)}))}(e);return tf.squeeze(t.model.execute(n))}))},e.prototype.segment=function(e,t){return void 0===t&&(t={}),n(this,void 0,void 0,(function(){var n,o,a,c,u,d,f,h,p,b,g=this;return r(this,(function(r){switch(r.label){case 0:if(!(t.colormap&&t.labels||this.base))throw new Error("Calling the 'segment' method requires either the 'base' attribute to be defined (e.g. 'pascal', 'cityscapes' or'ade20k'), or 'colormap' and 'labels' options to be set. Aborting, since neither has been provided.");return t.colormap&&t.labels||(t.colormap=i(this.base),t.labels=s(this.base)),n=t.colormap,o=t.labels,a=t.canvas,c=tf.tidy((function(){return g.predict(e)})),u=c.shape,d=u[0],f=u[1],[4,l(n,o,c,a)];case 1:return h=r.sent(),p=h.legend,b=h.segmentationMap,tf.dispose(c),[2,{legend:p,height:d,width:f,segmentationMap:b}]}}))}))},e.prototype.dispose=function(){return n(this,void 0,void 0,(function(){return r(this,(function(e){return this.model&&this.model.dispose(),[2]}))}))},e}();e.SemanticSegmentation=c,e.getColormap=i,e.getLabels=s,e.getURL=a,e.load=function(e){return void 0===e&&(e={base:"pascal",quantizationBytes:2}),n(this,void 0,void 0,(function(){var n;return r(this,(function(r){switch(r.label){case 0:if(null==tf)throw new Error("Cannot find TensorFlow.js. If you are using a <script> tag, please also include @tensorflow/tfjs on the page before using this model.");if(e.base){if(-1===["pascal","cityscapes","ade20k"].indexOf(e.base))throw new Error("SemanticSegmentation cannot be constructed with an invalid base model "+e.base+". Try one of 'pascal', 'cityscapes' and 'ade20k'.");if(-1===[1,2,4].indexOf(e.quantizationBytes))throw new Error("Only quantization to 1, 2 or 4 bytes is supported.")}else if(!e.modelUrl)throw new Error("SemanticSegmentation can be constructed either by passing the weights URL or one of the supported base model names from 'pascal', 'cityscapes' and 'ade20k',together with the degree of quantization (either 1, 2 or 4).Aborting, since neither has been provided.");return[4,t.loadGraphModel(e.modelUrl||a(e.base,e.quantizationBytes))];case 1:return n=r.sent(),[2,new c(n,e.base)]}}))}))},e.toSegmentationImage=l,e.version="0.1.1",Object.defineProperty(e,"__esModule",{value:!0})}));

dist/index.d.ts

@@ -5,2 +5,3 @@ import * as tfconv from '@tensorflow/tfjs-converter';
 import { getColormap, getLabels, getURL, toSegmentationImage } from './utils';
+export { version } from './version';
 export { getColormap, getLabels, getURL, toSegmentationImage };
@@ -7,0 +8,0 @@ export declare function load(modelConfig?: ModelConfig): Promise<SemanticSegmentation>;

dist/index.js

@@ -45,2 +45,4 @@ "use strict";
 exports.toSegmentationImage = utils_1.toSegmentationImage;
+var version_1 = require("./version");
+exports.version = version_1.version;
 function load(modelConfig) {
@@ -47,0 +49,0 @@ if (modelConfig === void 0) { modelConfig = {

package.json

 {
   "name": "@tensorflow-models/deeplab",
-  "version": "0.1.0",
+  "version": "0.1.1",
   "description": "Semantic Segmentation in the Browser: DeepLab v3 Model",
@@ -41,3 +41,3 @@ "main": "dist/index.js",
     "publish-local": "yarn build && rollup -c && yalc push",
-    "publish-npm": "yarn build && rollup -c && npm publish",
+    "build-npm": "yarn build && rollup -c",
     "lint": "tslint -p . -t verbose"
@@ -44,0 +44,0 @@ },

README.md

@@ -83,5 +83,5 @@ # Semantic Segmentation in the Browser: DeepLab v3 Model
 
-- [PASCAL](./deeplab/src/config.ts#L60)
-- [CityScapes](./deeplab/src/config.ts#L66)
-- [ADE20K](./deeplab/src/config.ts#L72)
+- [PASCAL](./src/config.ts#L142)
+- [CityScapes](./src/config.ts#L149)
+- [ADE20K](./src/config.ts#L155)
 
@@ -88,0 +88,0 @@ #### `model.segment(image, config?)` inputs

New alerts

New author

Supply chain risk

A new npm collaborator published a version of the package for the first time. New collaborators are usually benign additions to a project, but do indicate a change to the security surface area of a package.

Found 1 instance in 1 package

Improved metrics

Number of package files

25

increased by8.7%

Lines of code

1319

increased by0.92%

Worsened metrics

Total package byte prevSize

1740990

decreased by-3.07%

Number of medium supply chain risk alerts

1

increased byInfinity%

No dependency changes