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[update] AMG8833を使ったTensorflowによる転移学習ブロックを追加

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This commit is contained in:
ocogeclub 2021-10-31 13:27:59 +09:00
parent 84077850c0
commit b0b4a953db
7 changed files with 426 additions and 300 deletions
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74
index.html
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@ -512,38 +512,13 @@
<block type="ugj_grideye_init">
<field name="addr">0x69</field>
</block>
<block type="ugj_grideye_thermistor"></block>
<block type="ugj_grideye_read"></block>
<block type="ugj_grideye_stop"></block>
<block type="ugj_create_sub_canvas">
<field name="sub_canvas" id=",Xs[%dbxXS?[s]wV8Ye{">サブキャンバス</field>
<value name="width">
<shadow type="math_number">
<field name="NUM">8</field>
</shadow>
</value>
<value name="height">
<shadow type="math_number">
<field name="NUM">8</field>
</shadow>
</value>
<value name="style_width">
<shadow type="math_number">
<field name="NUM">160</field>
</shadow>
</value>
<value name="style_height">
<shadow type="math_number">
<field name="NUM">160</field>
</shadow>
</value>
</block>
<block type="ugj_grideye_canvas_create"></block>
<block type="ugj_draw_grideyedata">
<field name="color_high">#ff4500</field>
<field name="color_low">#0000ff</field>
<!-- <field name="colorize">TRUE</field> -->
<!-- <value name="amg8833data">
<shadow type="ugj_grideye_read"></shadow>
</value> -->
<field name="color_high">#ff0000</field>
<field name="color_low">#3333ff</field>
<value name="temp_high">
<shadow type="math_number">
<field name="NUM">28</field>
@ -555,6 +530,17 @@
</shadow>
</value>
</block>
<block type="ugj_teachable_machine"></block>
<block type="ugj_grideye_predict_class"></block>
<block type="ugj_grideye_add_example">
<value name="class_id">
<shadow type="math_number">
<field name="NUM">0</field>
</shadow>
</value>
</block>
<block type="ugj_tensorset_stringify"></block>
<block type="ugj_tensorset_parse"></block>
<label text="_" web-line="4.0" web-line-width="200"></label>
</category>
<category name="マルチメディア" css-icon="customIcon fas fa-gamepad" categorystyle="multimedia_category">
@ -834,7 +820,13 @@
</shadow>
</value>
</block>
<block type="ugj_localstorage_keylist"></block>
<block type="ugj_localstorage_remove">
<value name="key">
<shadow type="text">
<field name="TEXT">storage</field>
</shadow>
</value>
</block>
<label text="制御" web-line="4.0" web-line-width="200"></label>
<block type="ugj_sleep">
<value name="sec">
@ -914,28 +906,6 @@
</shadow>
</value>
</block>
<block type="ugj_child_oledtext">
<value name="line1">
<shadow type="text">
<field name="TEXT"></field>
</shadow>
</value>
<value name="line2">
<shadow type="text">
<field name="TEXT"></field>
</shadow>
</value>
<value name="line3">
<shadow type="text">
<field name="TEXT"></field>
</shadow>
</value>
<value name="line4">
<shadow type="text">
<field name="TEXT"></field>
</shadow>
</value>
</block>
<block type="ugj_child_testpy"></block>
<block type="ugj_child_testjs"></block>
<label text="特殊記号" web-line="4.0" web-line-width="200"></label>

24
index.js
View File

@ -36,6 +36,9 @@ var theme = Blockly.Theme.defineTheme('ocoge', {
'multimedia_blocks': {
"colourPrimary": multimedia_color
},
'colour_blocks': {
"colourPrimary": multimedia_color
},
'network_blocks': {
"colourPrimary": network_color
},
@ -87,11 +90,7 @@ Blockly.Msg["UGJ_FOREACH_TITLE"] = "リスト %1 の各 %2 について %3 %4";
Blockly.Msg["UGJ_FOREACH_ITEM"] = "項目";
Blockly.Msg["UGJ_FOREACH_TOOLTIP"] = "リストの各項目について、その項目を変数「項目」としてステートメントを実行します。";
Blockly.Msg["UGJ_CREATE_SUBCANVAS_TITLE"] = "%1 %2 を、幅 %3 px、高さ %4 pxで作成 %5 、幅 %6 px、高さ %7 pxで表示";
Blockly.Msg["UGJ_CREATE_SUBCANVAS_VAR"] = "サブキャンバス";
Blockly.Msg["UGJ_CREATE_SUBCANVAS_TOOLTIP"] = "ディスプレイエリアに、右下寄せでキャンバスを作成します。";
Blockly.Msg["UGJ_DRAW_GRIDEYEDATA_TITLE"] = "赤外線アレイセンサ画像表示 %1 入力 %2 温度範囲上限 %3 %4 温度範囲下限 %5 %6 描画対象キャンバス %7";
Blockly.Msg["UGJ_DRAW_GRIDEYEDATA_TITLE"] = "赤外線アレイセンサ画像表示 %1 温度データ %2 温度範囲上限 %3 %4 温度範囲下限 %5 %6";
Blockly.Msg["UGJ_DRAW_GRIDEYEDATA_TOOLTIP"] = "AMG8833の温度データを、画像としてキャンバスに描画します。「着色」をチェックすると、温度範囲で設定されている色をつけて表示します。";
Blockly.Msg["GPIO_OPEN_TITLE"] = "GPIO を使えるようにする";
@ -154,10 +153,24 @@ Blockly.Msg["UGJ_GESTURE_STOP_TITLE"] = "ジェスチャーセンサーから切
Blockly.Msg["UGJ_GESTURE_STOP_TOOLTIP"] = "センサーとの接続を停止します。";
Blockly.Msg["UGJ_GRIDEYE_INIT_TITLE"] = "赤外線アレイセンサ（アドレス： %1 ）を初期化";
Blockly.Msg["UGJ_GRIDEYE_INIT_TOOLTIP"] = "赤外線アレイセンサ AMG8833 の使用準備をします。";
Blockly.Msg["UGJ_GRIDEYE_THERMISTOR_TITLE"] = "赤外線アレイセンサ本体温度";
Blockly.Msg["UGJ_GRIDEYE_THERMISTOR_TOOLTIP"] = "AMG8833に内蔵されたサーミスタ（温度センサ）の値を取得します。";
Blockly.Msg["UGJ_GRIDEYE_READ_TITLE"] = "赤外線アレイセンサの値";
Blockly.Msg["UGJ_GRIDEYE_READ_TOOLTIP"] = "AMG8833から読み取った温度データを、8x8の配列で取得します。";
Blockly.Msg["UGJ_GRIDEYE_STOP_TITLE"] = "赤外線アレイセンサから切断";
Blockly.Msg["UGJ_GRIDEYE_STOP_TOOLTIP"] = "センサーとの接続を停止します。";
Blockly.Msg["UGJ_GRIDEYE_CANVAS_CREATE_TITLE"] = "赤外線アレイセンサデータ表示キャンバスを作成";
Blockly.Msg["UGJ_GRIDEYE_CANVAS_CREATE_TOOLTIP"] = "ディスプレイエリアにAMG8833データ表示用キャンバスを作成します。";
Blockly.Msg["UGJ_TEACHABLE_MACHINE_TITLE"] = "TensorFlow.jsによる画像分類器の準備";
Blockly.Msg["UGJ_TEACHABLE_MACHINE_TOOLTIP"] = "TensorFlow.jsにMobileNet, KNN Classifierを読み込んで、画像認識（分類）を行う準備をします。";
Blockly.Msg["UGJ_GRIDEYE_PREDICT_CLASS_TITLE"] = "赤外線アレイセンサの画像で推論を行う";
Blockly.Msg["UGJ_GRIDEYE_PREDICT_CLASS_TOOLTIP"] = "キャンバスに表示されたAMG8833の画像を元に画像分類の推論を行います。推論の結果として定義済みのラベルを返します。";
Blockly.Msg["UGJ_GRIDEYE_ADD_EXAMPLE_TITLE"] = "赤外線アレイセンサの画像にラベル %1 をつけてデータセットへ追加";
Blockly.Msg["UGJ_GRIDEYE_ADD_EXAMPLE_TOOLTIP"] = "キャンバスに表示されているAMG8833の画像にラベル（クラス名）をつけてデータセットへ追加します。";
Blockly.Msg["UGJ_TENSORSET_STRINGIFY_TITLE"] = "学習したクラスデータセットを文字列に変換";
Blockly.Msg["UGJ_TENSORSET_STRINGIFY_TOOLTIP"] = "学習したクラスデータセットを文字列に変換して保存します。";
Blockly.Msg["UGJ_TENSORSET_PARSE_TITLE"] = "クラスデータ文字列 %1 を画像分類器にセット";
Blockly.Msg["UGJ_TENSORSET_PARSE_TOOLTIP"] = "JSONテキストをパースして画像分類器に戻します。";
Blockly.Msg["UGJ_CODECHAR_TITLE"] = "コード %1 の文字";
Blockly.Msg["UGJ_CODECHAR_TOOLTIP"] = "文字コードを文字に変換します。";
@ -172,7 +185,6 @@ Blockly.Msg["UGJ_CANVAS_INIT_TITLE"] = "キャンバスを表示";
Blockly.Msg["UGJ_CANVAS_INIT_TOOLTIP"] = "キャンバスを表示し、使用できるようにします。";
Blockly.Msg["UGJ_FACEAPI_TITLE"] = "TensorFlowによる顔検出： %1 ランドマークを検出 %2 %3";
Blockly.Msg["UGJ_FACEAPI_TOOLTIP"] = "TensorFlow とFaceAPI をロードし、顔検出をできるようにします。";
Blockly.Msg["UGJ_SLEEP_TITLE"] = "%1 秒待つ";
Blockly.Msg["UGJ_SLEEP_TOOLTIP"] = "指定した秒数だけ処理を中断します。";

45
index_elutil.js
View File

@ -13,7 +13,7 @@ const ugj_const = {
localStorage_fname: 'ocoge.json',
error_ja_all: 'エラーが発生しました。\n『おこげ倶楽部』までお問い合わせください。',
pig: 'pigpio',
lg: 'lgpio', // lgpioがテストフェーズを終えてハードウェアPWMを実装したら切り替えを実装予定
lg: 'lgpio', // lgpioがハードウェアPWMを実装してRPiOSにプリインストールされるようになったら切り替え予定
i2c_defbus: '6', // 文字列リテラルで指定
dev_hash: '4e9205f9b7e571bec1aa52ab7871f420684fcf96149672a4d550a95863d6b072'
}
@ -325,49 +325,12 @@ if (!is_el) {
case 'fs':
block = 'ファイル';
break;
case 'path':
block = 'キャンバス保存';
break;
default:
throw new Error(ugj_const.error_ja_all);
}
throw `ブロック「${block}」は、Web体験版ではご利用になれません。\n詳しくは https://ocoge.club/ をご覧ください。`;
}
}
// "require" for "BLOCK"s if contextIsolation is true
// ブラウザ動作時にはすべてアラートを表示
// const require = module_name => {
// if (is_el) {
// switch (module_name) {
// case '@ocogeclub/lgpio':
// return window.ocogeapi.lgpio;
// case '@ocogeclub/bme280':
// return window.ocogeapi.bme280;
// case 'fs':
// return window.ocogeapi.fs;
// case 'path':
// return window.ocogeapi.path;
// case '@tensorflow/tfjs-node':
// return window.ocogeapi.tfjs_node;
// case '@vladmandic/face-api/dist/face-api.node.js':
// return window.ocogeapi.face_api;
// default:
// throw new Error(`Unknown module "${module_name}" required.\nStopped.`);
// }
// } else {
// let block;
// switch (module_name) {
// case '@ocogeclub/lgpio':
// block = 'GPIO';
// break;
// case '@ocogeclub/bme280':
// block = 'BME280';
// break;
// case 'fs':
// block = 'ファイル';
// break;
// default:
// throw new Error(ugj_const.error_ja_all);
// }
// throw `ブロック「${block}」は、Web体験版ではご利用になれません。\n詳しくは https://ocoge.club/ をご覧ください。`;
// }
// }

5
local_modules/@ocogeclub/amg8833/AMG8833.js
View File

@ -12,6 +12,11 @@ exports.init = (i2c_bus, i2c_addr) => {
pig._i2c_write_byte_data(pi, i2c_hand, 0x02, 0x00); //10FPS
}
exports.read_thermistor = () => {
let temp = pig._i2c_read_word_data(pi, i2c_hand, 0x0e);
return temp * 0.0625;
}
exports.read_temp_array = () => {
let linedata = [];
for (let i = 0; i < 8; i++) {

7
package.json
View File

@ -27,9 +27,12 @@
"@ocogeclub/bme280": "file:local_modules/@ocogeclub/bme280",
"@ocogeclub/paj7620": "file:local_modules/@ocogeclub/paj7620",
"@ocogeclub/pigpio": "file:local_modules/@ocogeclub/pigpio",
"@tensorflow/tfjs-node": "^3.9.0",
"@tensorflow-models/knn-classifier": "^1.2.2",
"@tensorflow-models/mobilenet": "^2.1.0",
"@tensorflow/tfjs-node": "^3.11.0",
"@vladmandic/face-api": "^1.5.3",
"axios": "^0.21.1",
"nodemailer": "^6.6.0"
"nodemailer": "^6.6.0",
"tensorset": "^1.2.9"
}
}

397
ugj_blocks.js
View File

@ -1264,9 +1264,9 @@ Blockly.Blocks['ugj_grideye_init'] = {
Blockly.JavaScript['ugj_grideye_init'] = function (block) {
var dropdown_addr = block.getFieldValue('addr');
Blockly.JavaScript.provideFunction_(
'require_amg8833', [`const amg8833 = require('@ocogeclub/amg8833');`]
'require_amg8833', [`const _amg8833 = require('@ocogeclub/amg8833');`]
);
var code = `amg8833.init(${elutil.i2c_bus}, ${dropdown_addr});\n`;
var code = `_amg8833.init(${elutil.i2c_bus}, ${dropdown_addr});\n`;
return code;
};
Blockly.Python['ugj_grideye_init'] = function (block) {
@ -1275,6 +1275,33 @@ Blockly.Python['ugj_grideye_init'] = function (block) {
var code = '...\n';
return code;
};
/********************** */
/** Grid-Eye 本体温度 ** */
/********************** */
var ugjGridEyeThermistorDefinition = {
"type": "ugj_grideye_thermistor",
"message0": "%{BKY_UGJ_GRIDEYE_THERMISTOR_TITLE}",
"output": "Number",
"tooltip": "%{BKY_UGJ_GRIDEYE_THERMISTOR_TOOLTIP}",
"helpUrl": "",
"style": "sensor_blocks"
};
Blockly.Blocks['ugj_grideye_thermistor'] = {
init: function () {
this.jsonInit(ugjGridEyeThermistorDefinition);
}
};
Blockly.JavaScript['ugj_grideye_thermistor'] = function (block) {
var code = `_amg8833.read_thermistor()`;
return [code, Blockly.JavaScript.ORDER_NONE];
};
Blockly.Python['ugj_grideye_thermistor'] = function (block) {
// TODO: Assemble Python into code variable.
var code = '...';
// TODO: Change ORDER_NONE to the correct strength.
return [code, Blockly.Python.ORDER_NONE];
};
/**************************** */
/** Read Temperature Array ** */
/**************************** */
@ -1293,7 +1320,7 @@ Blockly.Blocks['ugj_grideye_read'] = {
}
};
Blockly.JavaScript['ugj_grideye_read'] = function (block) {
var code = 'amg8833.read_temp_array()';
var code = '_amg8833.read_temp_array()';
return [code, Blockly.JavaScript.ORDER_ATOMIC];
};
Blockly.Python['ugj_grideye_read'] = function (block) {
@ -1321,7 +1348,7 @@ Blockly.Blocks['ugj_grideye_stop'] = {
}
};
Blockly.JavaScript['ugj_grideye_stop'] = function (block) {
var code = 'amg8833.stop();\n';
var code = '_amg8833.stop();\n';
return code;
};
Blockly.Python['ugj_grideye_stop'] = function (block) {
@ -1798,73 +1825,34 @@ Blockly.JavaScript['ugj_canvas_drawrect'] = function (block) {
return code;
};
/*********************** */
/** Create sub canvas ** */
/*********************** */
var ugjCreateSubCanvasDefinition = {
"type": "ugj_create_sub_canvas",
"message0": "%{BKY_UGJ_CREATE_SUBCANVAS_TITLE}",
"args0": [
{
"type": "field_variable",
"name": "sub_canvas",
"variable": "%{BKY_UGJ_CREATE_SUBCANVAS_VAR}"
},
{
"type": "input_dummy"
},
{
"type": "input_value",
"name": "width",
"check": "Number"
},
{
"type": "input_value",
"name": "height",
"check": "Number"
},
{
"type": "input_dummy"
},
{
"type": "input_value",
"name": "style_width",
"check": "Number"
},
{
"type": "input_value",
"name": "style_height",
"check": "Number"
}
],
/***************************** */
/** GridEye 表示キャンバス作成 ** */
/***************************** */
var ugjGridEyeCanvasCreateDefinition = {
"type": "ugj_grideye_canvas_create",
"message0": "%{BKY_UGJ_GRIDEYE_CANVAS_CREATE_TITLE}",
"inputsInline": true,
"previousStatement": null,
"nextStatement": null,
"tooltip": "%{BKY_UGJ_CREATE_SUBCANVAS_TOOLTIP}",
"tooltip": "%{BKY_UGJ_GRIDEYE_CANVAS_CREATE_TOOLTIP}",
"helpUrl": "",
"style": "multimedia_blocks"
};
Blockly.Blocks['ugj_create_sub_canvas'] = {
Blockly.Blocks['ugj_grideye_canvas_create'] = {
init: function () {
this.jsonInit(ugjCreateSubCanvasDefinition);
this.jsonInit(ugjGridEyeCanvasCreateDefinition);
}
};
Blockly.JavaScript['ugj_create_sub_canvas'] = function (block) {
var variable_sub_canvas = Blockly.JavaScript.nameDB_.getName(block.getFieldValue('sub_canvas'), Blockly.Variables.NAME_TYPE);
var value_width = Blockly.JavaScript.valueToCode(block, 'width', Blockly.JavaScript.ORDER_ATOMIC);
var value_height = Blockly.JavaScript.valueToCode(block, 'height', Blockly.JavaScript.ORDER_ATOMIC);
var value_style_width = Blockly.JavaScript.valueToCode(block, 'style_width', Blockly.JavaScript.ORDER_ATOMIC);
var value_style_height = Blockly.JavaScript.valueToCode(block, 'style_height', Blockly.JavaScript.ORDER_ATOMIC);
var code = `${variable_sub_canvas} = {};
${variable_sub_canvas}.el = document.createElement('canvas');
${variable_sub_canvas}.el.setAttribute('width', ${value_width});
${variable_sub_canvas}.el.setAttribute('height', ${value_height});
${variable_sub_canvas}.el.className = 'subdisplay';
${variable_sub_canvas}.el.style.width = '${value_style_width}px';
${variable_sub_canvas}.el.style.height = '${value_style_height}px';
document.getElementById('display_area').appendChild(${variable_sub_canvas}.el);
${variable_sub_canvas}.ctx = ${variable_sub_canvas}.el.getContext('2d');
${variable_sub_canvas}.imgData = ${variable_sub_canvas}.ctx.createImageData(${variable_sub_canvas}.el.width, ${variable_sub_canvas}.el.height);
Blockly.JavaScript['ugj_grideye_canvas_create'] = function (block) {
var code = `let _grideye_canvas = document.createElement('canvas');
_grideye_canvas.setAttribute('width', 8);
_grideye_canvas.setAttribute('height', 8);
_grideye_canvas.className = 'subdisplay';
_grideye_canvas.style.width = '160px';
_grideye_canvas.style.height = '160px';
document.getElementById('display_area').appendChild(_grideye_canvas);
_grideye_ctx = _grideye_canvas.getContext('2d');
_grideye_imgData = _grideye_ctx.createImageData(8, 8);
`;
return code;
};
@ -1905,12 +1893,6 @@ var ugjDrawGrideyedataDefinition = {
"name": "temp_low",
"check": "Number",
"align": "RIGHT"
},
{
"type": "input_value",
"name": "canvas",
"check": "Canvas",
"align": "RIGHT"
}
],
"inputsInline": false,
@ -1931,7 +1913,6 @@ Blockly.JavaScript['ugj_draw_grideyedata'] = function (block) {
var value_temp_high = Blockly.JavaScript.valueToCode(block, 'temp_high', Blockly.JavaScript.ORDER_ATOMIC);
var colour_color_low = block.getFieldValue('color_low');
var value_temp_low = Blockly.JavaScript.valueToCode(block, 'temp_low', Blockly.JavaScript.ORDER_ATOMIC);
var value_canvas = Blockly.JavaScript.valueToCode(block, 'canvas', Blockly.JavaScript.ORDER_ATOMIC);
var functionName = Blockly.JavaScript.provideFunction_(
'mapVal',
['const ' + Blockly.JavaScript.FUNCTION_NAME_PLACEHOLDER_ + ' = (val, inMin, inMax, outMin, outMax) => {',
@ -1947,27 +1928,185 @@ Blockly.JavaScript['ugj_draw_grideyedata'] = function (block) {
lr = '0x' + colour_color_low.slice(1, 3);
lg = '0x' + colour_color_low.slice(3, 5);
lb = '0x' + colour_color_low.slice(5, 7);
var code = `// colour_color_high = ${colour_color_high}
const color_range = [[${lr}, ${hr}], [${lg}, ${hg}], [${lb}, ${hb}]]; //超簡易的な色付け
// const color_range = [[0, 0xff], [0, 0x3f], [0xff, 0]];
let grideye_data = ${value_amg8833data};//読み取りブロックを入力に直接接続できるようにする
for (let raw = 0; raw < ${value_canvas}.el.height; raw++) {
for (let col = 0; col < ${value_canvas}.el.width; col++) {
var code = ` const _color_range = [[${lr}, ${hr}], [${lg}, ${hg}], [${lb}, ${hb}]];
let _grideye_data = ${value_amg8833data};//読み取りブロックを入力に直接接続できるようにする
for (let raw = 0; raw < _grideye_canvas.height; raw++) {
for (let col = 0; col < _grideye_canvas.width; col++) {
for (let rgb = 0; rgb < 3; rgb++) {
let pixel = ${functionName}(grideye_data[raw][col], ${value_temp_low}, ${value_temp_high}, color_range[rgb][0], color_range[rgb][1]);
${value_canvas}.imgData.data[((raw * ${value_canvas}.el.width * 4) + col * 4) + rgb] = pixel;
let pixel = ${functionName}(_grideye_data[raw][col], ${value_temp_low}, ${value_temp_high}, _color_range[rgb][0], _color_range[rgb][1]);
_grideye_imgData.data[((raw * _grideye_canvas.width * 4) + col * 4) + rgb] = pixel;
}
${value_canvas}.imgData.data[((raw * ${value_canvas}.el.width * 4) + col * 4) + 3] = 0xff;
_grideye_imgData.data[((raw * _grideye_canvas.width * 4) + col * 4) + 3] = 0xff;
}
}
${value_canvas}.ctx.putImageData(${value_canvas}.imgData, 0, 0);
_grideye_ctx.putImageData(_grideye_imgData, 0, 0);
`;
return code;
};
/**************************** */
/** Teachable Machine を開始** */
/**************************** */
var ugjTeachableMachineDefinition = {
"type": "ugj_teachable_machine",
"message0": "%{BKY_UGJ_TEACHABLE_MACHINE_TITLE}",
"inputsInline": true,
"previousStatement": null,
"nextStatement": null,
"tooltip": "%{BKY_UGJ_TEACHABLE_MACHINE_TOOLTIP}",
"helpUrl": "",
"style": "multimedia_blocks"
};
Blockly.Blocks['ugj_teachable_machine'] = {
init: function () {
this.jsonInit(ugjTeachableMachineDefinition);
}
};
Blockly.JavaScript['ugj_teachable_machine'] = function (block) {
Blockly.JavaScript.provideFunction_(
'require_ts', [`const _tf = require('@tensorflow/tfjs-node');`]
);
Blockly.JavaScript.provideFunction_(
'require_mobilenet', [`const _mobilenet = require('@tensorflow-models/mobilenet');`]
);
Blockly.JavaScript.provideFunction_(
'require_knn', [`const _knnClassifier = require('@tensorflow-models/knn-classifier');`]
);
var code = `const _classifier = _knnClassifier.create();
const _net = await _mobilenet.load({ version: 1, alpha: 0.25 }); // 高速・低精度
`;
return code;
};
/************************* */
/** GridEye で推論を行う ** */
/************************* */
var ugjGridEyePredictClassDefinition = {
"type": "ugj_grideye_predict_class",
"message0": "%{BKY_UGJ_GRIDEYE_PREDICT_CLASS_TITLE}",
"inputsInline": true,
"output": "Number",
"tooltip": "%{BKY_UGJ_GRIDEYE_PREDICT_CLASS_TOOLTIP}",
"helpUrl": "",
"style": "multimedia_blocks"
};
Blockly.Blocks['ugj_grideye_predict_class'] = {
init: function () {
this.jsonInit(ugjGridEyePredictClassDefinition);
}
};
Blockly.JavaScript['ugj_grideye_predict_class'] = function (block) {
var functionName = Blockly.JavaScript.provideFunction_(
'_predictClass',
[
`if (_confidence === undefined) var _confidence;`,
`const ${Blockly.JavaScript.FUNCTION_NAME_PLACEHOLDER_} = async (img, clsfr, mblnet) => {`,
`if (clsfr.getNumClasses() > 0) {`,
`const result = await clsfr.predictClass(mblnet.infer(img, 'conv_preds'));`,
`_confidence = result.confidences[result.label];`,
`return result.label;`,
`}`,
`else return 0;`,
`}`
]
);
var code = `await ${functionName}(_grideye_canvas, _classifier, _net)`;
return [code, Blockly.JavaScript.ORDER_NONE];
};
/******************************************** */
/** ラベルをつけて Example をデータセットに追加 ** */
/******************************************** */
var ugjGridEyeAddExampleDefinition = {
"type": "ugj_grideye_add_example",
"message0": "%{BKY_UGJ_GRIDEYE_ADD_EXAMPLE_TITLE}",
"args0": [
{
"type": "input_value",
"name": "class_id",
"check": "Number"
}
],
"inputsInline": true,
"previousStatement": null,
"nextStatement": null,
"tooltip": "%{BKY_UGJ_GRIDEYE_ADD_EXAMPLE_TOOLTIP}",
"helpUrl": "",
"style": "multimedia_blocks"
};
Blockly.Blocks['ugj_grideye_add_example'] = {
init: function () {
this.jsonInit(ugjGridEyeAddExampleDefinition);
}
};
Blockly.JavaScript['ugj_grideye_add_example'] = function (block) {
var value_class_id = Blockly.JavaScript.valueToCode(block, 'class_id', Blockly.JavaScript.ORDER_ATOMIC);
var functionName = Blockly.JavaScript.provideFunction_(
'_addExample',
[
`const ${Blockly.JavaScript.FUNCTION_NAME_PLACEHOLDER_} = async (label, img, clsfr, mblnet) => {`,
`clsfr.addExample(mblnet.infer(img, true), label);`,
`}`
]
);
var code = `await ${functionName}(${value_class_id}, _grideye_canvas, _classifier, _net);`;
return code;
};
/*************************** */
/** 学習したクラスを文字列化 ** */
/*************************** */
var ugjTensorsetStringifyDefinition = {
"type": "ugj_tensorset_stringify",
"message0": "%{BKY_UGJ_TENSORSET_STRINGIFY_TITLE}",
"output": null,
"tooltip": "%{BKY_UGJ_TENSORSET_STRINGIFY_TOOLTIP}",
"helpUrl": "",
"style": "multimedia_blocks"
};
Blockly.Blocks['ugj_tensorset_stringify'] = {
init: function () {
this.jsonInit(ugjTensorsetStringifyDefinition);
}
};
Blockly.JavaScript['ugj_tensorset_stringify'] = function (block) {
Blockly.JavaScript.provideFunction_(
'require_tensorset', [`const _Tensorset = require('tensorset');`]
);
var code = `await _Tensorset.stringify(_classifier.getClassifierDataset())`;
return [code, Blockly.JavaScript.ORDER_NONE];
};
/***************************************** */
/** jsonをデータセットに戻して分類器にセット ** */
/***************************************** */
var ugjTensorsetParseDefinition = {
"type": "ugj_tensorset_parse",
"message0": "%{BKY_UGJ_TENSORSET_PARSE_TITLE}",
"args0": [
{
"type": "input_value",
"name": "class_data_json",
"check": "String"
}
],
"previousStatement": null,
"nextStatement": null,
"tooltip": "%{BKY_UGJ_TENSORSET_PARSE_TOOLTIP}",
"helpUrl": "",
"style": "multimedia_blocks"
};
Blockly.Blocks['ugj_tensorset_parse'] = {
init: function () {
this.jsonInit(ugjTensorsetParseDefinition);
}
};
Blockly.JavaScript['ugj_tensorset_parse'] = function (block) {
Blockly.JavaScript.provideFunction_(
'require_tensorset', [`const _Tensorset = require('tensorset');`]
);
var value_class_data_json = Blockly.JavaScript.valueToCode(block, 'class_data_json', Blockly.JavaScript.ORDER_ATOMIC);
var code = `_classifier.setClassifierDataset(_Tensorset.parse(${value_class_data_json}));\n`;
return code;
};
/****************************** */
@ -2591,7 +2730,7 @@ Blockly.Blocks['ugj_localstorage_load'] = {
this.setInputsInline(true);
this.setOutput(true, "String");
this.setOutputShape(Blockly.OUTPUT_SHAPE_ROUND);
this.setStyle('special_blocks')
this.setStyle('special_blocks');
this.setTooltip("ローカルストレージからテキストデータを読み込みます。");
this.setHelpUrl("");
}
@ -2602,39 +2741,29 @@ Blockly.JavaScript['ugj_localstorage_load'] = function (block) {
return [code, Blockly.JavaScript.ORDER_NONE];
};
/**************************** */
/** Key List in Local Storage */
/**************************** */
Blockly.Blocks['ugj_localstorage_keylist'] = {
/******************************* */
/** Remove Item in Local Storage */
/******************************* */
Blockly.Blocks['ugj_localstorage_remove'] = {
init: function () {
this.appendValueInput("key")
.setCheck("String")
.appendField("ローカルストレージ");
this.appendDummyInput()
.appendField("ローカルストレージに保存されているデータの一覧");
this.setInputsInline(true);
this.setOutput(true, null);
this.setOutputShape(Blockly.OUTPUT_SHAPE_ROUND);
this.setStyle('special_blocks')
this.setTooltip("ローカルストレージに保存されているキーの一覧を取得します。");
.appendField("を削除");
this.setPreviousStatement(true, null);
this.setNextStatement(true, null);
this.setStyle('special_blocks');
this.setTooltip("ローカルストレージに保存されたアイテムを削除します。");
this.setHelpUrl("");
}
};
Blockly.JavaScript['ugj_localstorage_keylist'] = function (block) {
var functionName = Blockly.JavaScript.provideFunction_(
'localStorage_getKeyList',
[
'const ' + Blockly.JavaScript.FUNCTION_NAME_PLACEHOLDER_ + ' = () => {',
'let listArray = [];',
'for (let i=0; i<localStorage.length; i++) {',
`listArray.push(localStorage.key(i));`,
'}',
`return listArray.join('\\n');`,
'}'
]
);
var code = `${functionName}()`;
return [code, Blockly.JavaScript.ORDER_NONE];
Blockly.JavaScript['ugj_localstorage_remove'] = function (block) {
var value_key = Blockly.JavaScript.valueToCode(block, 'key', Blockly.JavaScript.ORDER_ATOMIC);
var code = `localStorage.removeItem(${value_key});\n`;
return code;
};
/********************** */
/** Question and Answer */
/********************** */
@ -2802,37 +2931,6 @@ Blockly.JavaScript['ugj_child_openjtalk'] = function (block) {
return [code, Blockly.JavaScript.ORDER_NONE];
};
// oled.py
Blockly.Blocks['ugj_child_oledtext'] = {
init: function () {
this.appendValueInput("line1")
.setCheck("String")
.appendField("OLEDにテキストを描画：1行目");
this.appendValueInput("line2")
.setCheck("String")
.appendField("2行目");
this.appendValueInput("line3")
.setCheck("String")
.appendField("3行目");
this.appendValueInput("line4")
.setCheck("String")
.appendField("4行目");
this.setInputsInline(true);
this.setOutput(true, "shcmd");
this.setOutputShape(Blockly.OUTPUT_SHAPE_ROUND);
this.setStyle('special_blocks')
this.setTooltip("I2C接続したSSD1306ディスプレイ(128x64)にテキストを描画します。最大4行。");
this.setHelpUrl("");
}
};
Blockly.JavaScript['ugj_child_oledtext'] = function (block) {
var value_line1 = Blockly.JavaScript.valueToCode(block, 'line1', Blockly.JavaScript.ORDER_ATOMIC);
var value_line2 = Blockly.JavaScript.valueToCode(block, 'line2', Blockly.JavaScript.ORDER_ATOMIC);
var value_line3 = Blockly.JavaScript.valueToCode(block, 'line3', Blockly.JavaScript.ORDER_ATOMIC);
var value_line4 = Blockly.JavaScript.valueToCode(block, 'line4', Blockly.JavaScript.ORDER_ATOMIC);
var code = `'python', ['${ugj_const.library_path}oled.py', ${value_line1}, ${value_line2}, ${value_line3}, ${value_line4}]`;
return [code, Blockly.JavaScript.ORDER_NONE];
};
// fswebcam
Blockly.Blocks['ugj_child_fswebcam'] = {
init: function () {
@ -3149,10 +3247,10 @@ Blockly.JavaScript['ugj_sleep'] = function (block) {
var value_sec = Blockly.JavaScript.valueToCode(block, 'sec', Blockly.JavaScript.ORDER_ATOMIC);
var functionName = Blockly.JavaScript.provideFunction_(
'sleep',
['const ' + Blockly.JavaScript.FUNCTION_NAME_PLACEHOLDER_ + ' = milisec =>',
'new Promise(r => setTimeout(r, milisec));']
['const ' + Blockly.JavaScript.FUNCTION_NAME_PLACEHOLDER_ + ' = sec =>',
'new Promise(r => setTimeout(r, sec * 1000));']
);
var code = `await ${functionName}(${value_sec}*1000);\n`;
var code = `await ${functionName}(${value_sec});\n`;
return code;
};
Blockly.Python['ugj_sleep'] = function (block) {
@ -3254,6 +3352,7 @@ Blockly.Blocks['ugj_set_interval'] = {
.setCheck(null);
this.setInputsInline(true);
this.setPreviousStatement(true, null);
this.setNextStatement(true, null);
this.setStyle('special_blocks')
this.setTooltip("非同期で繰り返し処理を行います（停止ボタンまたは停止ブロックで停止）。");
this.setHelpUrl("");
@ -3304,7 +3403,7 @@ Blockly.Blocks['ugj_set_timeout'] = {
// .appendField("この下は待たずに実行");
this.setInputsInline(true);
this.setPreviousStatement(true, null);
this.setNextStatement(false, null);
this.setNextStatement(true, null);
this.setStyle('special_blocks')
this.setTooltip("指定した秒数だけ待ってから実行します。");//内側のブロック部を 外側下に接続したものは待たずに直ちに実行されます（非同期動作）。
this.setHelpUrl("");
@ -3445,17 +3544,3 @@ else console.log('invalid certification');
`;
return code;
};
// Blockly.Blocks['ugj_dev_run_js'] = {
// init: function () {
// this.setPreviousStatement(true, null);
// this.setColour(230);
// this.setTooltip("");
// this.setHelpUrl("");
// }
// };
// Blockly.JavaScript['ugj_dev_run_js'] = function (block) {
// // TODO: Assemble JavaScript into code variable.
// // var code = localStorage.getItem('ocoge_dev_code');
// var code =
// return code;
// };

174
yarn.lock
View File

@ -64,17 +64,17 @@
"@ocogeclub/amg8833@file:local_modules/@ocogeclub/amg8833":
version "0.0.1"
dependencies:
"@ocogeclub/pigpio" "file:../../.cache/yarn/v6/npm-@ocogeclub-amg8833-0.0.1-8dfa7589-6746-40d4-9530-a4546ba16ad3-1634476251665/node_modules/@ocogeclub/pigpio"
"@ocogeclub/pigpio" "file:../../.cache/yarn/v6/npm-@ocogeclub-amg8833-0.0.1-086d238a-3208-4bc5-8baa-2d63d4e9d068-1635603361158/node_modules/@ocogeclub/pigpio"
"@ocogeclub/bme280@file:local_modules/@ocogeclub/bme280":
version "0.0.1"
dependencies:
"@ocogeclub/pigpio" "file:../../.cache/yarn/v6/npm-@ocogeclub-bme280-0.0.1-54967241-c3ef-4a53-ad34-16acb3e76088-1634476250850/node_modules/@ocogeclub/pigpio"
"@ocogeclub/pigpio" "file:../../.cache/yarn/v6/npm-@ocogeclub-bme280-0.0.1-eeffd664-25e9-4ed6-a266-9dcfc579f450-1635603361179/node_modules/@ocogeclub/pigpio"
"@ocogeclub/paj7620@file:local_modules/@ocogeclub/paj7620":
version "0.0.1"
dependencies:
"@ocogeclub/pigpio" "file:../../.cache/yarn/v6/npm-@ocogeclub-paj7620-0.0.1-6dd29fc9-a1f9-43f4-9e96-a79744ba2494-1634476250888/node_modules/@ocogeclub/pigpio"
"@ocogeclub/pigpio" "file:../../.cache/yarn/v6/npm-@ocogeclub-paj7620-0.0.1-102e6c39-d4e3-4b54-886f-60476a90588b-1635603361184/node_modules/@ocogeclub/pigpio"
"@ocogeclub/pigpio@file:local_modules/@ocogeclub/pigpio":
version "0.0.1"
@ -106,35 +106,81 @@
dependencies:
defer-to-connect "^2.0.0"
"@tensorflow/tfjs-backend-cpu@3.9.0":
version "3.9.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-3.9.0.tgz#27ee581a4765039eb0e84d9d473b6d5f2769c813"
integrity sha512-PUv5B3wdQsA8cysk+oUhA0NqMoo/lwP8EazC/axQc8/72Dc6kU8uw/5qZtE5P4xXSqkNSlh2ifFm+8nH/6B+iA==
"@tensorflow-models/knn-classifier@^1.2.2":
version "1.2.2"
resolved "https://registry.yarnpkg.com/@tensorflow-models/knn-classifier/-/knn-classifier-1.2.2.tgz#a5a9045b3d225a06e60f2b1cc2de56bdac6748e8"
integrity sha512-QRnkCf7ErOxSRtvJ6yCwhlLREPcBJGaXRanF46f0iY6ii3Sybjb6Ux0qnNPTrHZChD0izPa3Z4GQEgSAykiHkQ==
"@tensorflow-models/mobilenet@^2.1.0":
version "2.1.0"
resolved "https://registry.yarnpkg.com/@tensorflow-models/mobilenet/-/mobilenet-2.1.0.tgz#58583f0793a7091eda370aa441d09d94b808aeb1"
integrity sha512-JjqT9ijHDFA2FEpUGWg7H2lQ0GrMuE2VmiCRBYmUew6b4JKht8LXDjG5HxZh95YH6c/25sZWTpGeHbquloH+hw==
"@tensorflow/tfjs-backend-cpu@2.8.6":
version "2.8.6"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-2.8.6.tgz#ef60c3294a04c8c600abb4b438263c06d9b7b7bd"
integrity sha512-x9WTTE9p3Pon2D0d6HH1UCIJsU1w3v9sF3vxJcp+YStrjDefWoW5pwxHCckEKTRra7GWg3CwMKK3Si2dat4H1A==
dependencies:
"@types/seedrandom" "2.4.27"
seedrandom "2.4.3"
"@tensorflow/tfjs-backend-webgl@3.9.0":
version "3.9.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-3.9.0.tgz#103630e23d4325492bfbe2ff65b58c24ad852377"
integrity sha512-oUnyQFF9aCnNZpul9AnJwrt8noDJdMmxgq2+e/0DpEMBERcywtVj9qkKCccMaVFsdQV1lQxpV3kjC3vbFMDWKg==
"@tensorflow/tfjs-backend-cpu@3.11.0":
version "3.11.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-cpu/-/tfjs-backend-cpu-3.11.0.tgz#01d5d68b91faf12bee4854adae56bc956b794f1a"
integrity sha512-ShLkrZ4/rmhZwzGKenMFDfQnaEbyZgWA5F8JRa52Iob/vptlZeuOzjq87CZKmZMUmDswR9A2kjzovT/H1bJdWQ==
dependencies:
"@tensorflow/tfjs-backend-cpu" "3.9.0"
"@types/seedrandom" "2.4.27"
seedrandom "2.4.3"
"@tensorflow/tfjs-backend-webgl@2.8.6":
version "2.8.6"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-2.8.6.tgz#b88b4276a2ff4e23b05470c506b5c720bf6eb8c3"
integrity sha512-kPgm3Dim0Li5MleybYKSZVUCu91ipDjZtTA5RrJx/Dli115qwWdiRGOHYwsIEY61hZoE0m3amjWLUBxtwMW1Nw==
dependencies:
"@tensorflow/tfjs-backend-cpu" "2.8.6"
"@types/offscreencanvas" "~2019.3.0"
"@types/seedrandom" "2.4.27"
"@types/webgl-ext" "0.0.30"
"@types/webgl2" "0.0.5"
seedrandom "2.4.3"
"@tensorflow/tfjs-converter@3.9.0":
version "3.9.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-converter/-/tfjs-converter-3.9.0.tgz#e00709002cbe04ff5cc43358d4a5662795513071"
integrity sha512-ftegwQlGkyDCxZGhAVfMyWWXqpNhnyESvNY3oFAUV4eN6i/mmBTCSOQ5AX5VR5lr7PNYPWGO5sJ10Q5HeTPfgw==
"@tensorflow/tfjs-backend-webgl@3.11.0":
version "3.11.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-backend-webgl/-/tfjs-backend-webgl-3.11.0.tgz#fbd7f24c164d17c11d964206b4b075b073b1a3bc"
integrity sha512-rNnc/dZ7LIl9O/Pn9W24I1h8kgpJ+XvG8NrdNSfIoWPCW4fvPSlU7B3yMeZXvRneny+z+T3xRs96nWyU2mZBJw==
dependencies:
"@tensorflow/tfjs-backend-cpu" "3.11.0"
"@types/offscreencanvas" "~2019.3.0"
"@types/seedrandom" "2.4.27"
"@types/webgl-ext" "0.0.30"
"@types/webgl2" "0.0.6"
seedrandom "2.4.3"
"@tensorflow/tfjs-core@3.9.0":
version "3.9.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-3.9.0.tgz#5ca2356a14a58263840a6e3caee2467780db9450"
integrity sha512-wQ+VMsbvCne2OsogiNtRP8Mc01LnRGvAYQ0SGaDa4+1uwY2jsMk5GZjG66JQvf/Ppw8wyvKF170eh0yyCBgfcg==
"@tensorflow/tfjs-converter@2.8.6":
version "2.8.6"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-converter/-/tfjs-converter-2.8.6.tgz#6182d302ae883e0c45f47674a78bdd33e23db3a6"
integrity sha512-Uv4YC66qjVC9UwBxz0IeLZ8KS2CReh63WlGRtHcSwDEYiwsa7cvp9H6lFSSPT7kiJmrK6JtHeJGIVcTuNnSt9w==
"@tensorflow/tfjs-converter@3.11.0":
version "3.11.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-converter/-/tfjs-converter-3.11.0.tgz#0842269a83599b52fd167a8a05372018a9a1ca6a"
integrity sha512-rTRIKvBoqL0qdPYpm8UXauZycOiaBHZB2E2v3OoXoHnjvle/Xn/09uZJdrixgGhR+Kahs3Vz27BEEFz6RI5j2w==
"@tensorflow/tfjs-core@2.8.6":
version "2.8.6"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-2.8.6.tgz#d5e9d5fc1d1a83e3fbf80942f3154300a9f82494"
integrity sha512-jS28M1POUOjnWgx3jp1v5D45DUQE8USsAHHkL/01z75KnYCAAmgqJSH4YKLiYACg3eBLWXH/KTcSc6dHAX7Kfg==
dependencies:
"@types/offscreencanvas" "~2019.3.0"
"@types/seedrandom" "2.4.27"
"@types/webgl-ext" "0.0.30"
node-fetch "~2.6.1"
seedrandom "2.4.3"
"@tensorflow/tfjs-core@3.11.0":
version "3.11.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-core/-/tfjs-core-3.11.0.tgz#1e3986533faaed922bbfc2fe86da506d0e9e5c79"
integrity sha512-JOp+1+LCd0Xg3hu7fu6iQPWZnN8Hc6ssfP7B+625XH5GYY1/OhVASa7Ahe2mJr9gZovY2lw8FUejLh1jMmBb1Q==
dependencies:
"@types/long" "^4.0.1"
"@types/offscreencanvas" "~2019.3.0"
@ -144,26 +190,39 @@
node-fetch "~2.6.1"
seedrandom "2.4.3"
"@tensorflow/tfjs-data@3.9.0":
version "3.9.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-data/-/tfjs-data-3.9.0.tgz#9cb4fd6301c4362a8e7dc03bced563bdf1f0be19"
integrity sha512-1/H9VlYlfEX/LflzobSB5sx3FCavWGmzqRnAyyn5ChjgCzIUa+RtJ7nYgK2+6RC2MIDgKt1jmu36mkKZrwPD3w==
"@tensorflow/tfjs-data@2.8.6":
version "2.8.6"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-data/-/tfjs-data-2.8.6.tgz#5888ad0f7b7f8db2b7a5cf4af38e3c04d65efe32"
integrity sha512-zoDUfd5TfkYdviqu2bObwyJGXJiOvBckOTP9j36PUs6s+4DbTIDttyxdfeEaiiLX9ZUFU58CoW+3LI/dlFVyoQ==
dependencies:
"@types/node-fetch" "^2.1.2"
node-fetch "~2.6.1"
"@tensorflow/tfjs-layers@3.9.0":
version "3.9.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-layers/-/tfjs-layers-3.9.0.tgz#0e05116bcd7f55eb39cff322e9940fa46c9fbda2"
integrity sha512-25I20Oy17YZ3y0x/pabeiN6/vai0vqMQ85/Bp0GLOpcN2kmOLcItdWOAqFW5YPI2nrTqnpNQyk9zhmIh8f6X4w==
"@tensorflow/tfjs-data@3.11.0":
version "3.11.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-data/-/tfjs-data-3.11.0.tgz#90dd23a7181f0a744f2882a12c3442b27047383d"
integrity sha512-+cUHUHzjM/zs0JVOwHQm9wP15Y+BZdRcUpMoYWia8r3kaGSyvoz6WqzacEP1PeXgJVnr2gtU3D+bF32th8fZfQ==
dependencies:
"@types/node-fetch" "^2.1.2"
node-fetch "~2.6.1"
"@tensorflow/tfjs-node@^3.9.0":
version "3.9.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-node/-/tfjs-node-3.9.0.tgz#5b3c04aeaaa120c466c0e746627db239938743cb"
integrity sha512-1DauUOIsuypby7OzuYgVHT3T2NAQZchUiFz3IkIwb1aN0i7lkDR79EjxGNg3mcMyt/FteED74RTafyMrl1EYHg==
"@tensorflow/tfjs-layers@2.8.6":
version "2.8.6"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-layers/-/tfjs-layers-2.8.6.tgz#51dec5422fddde289e7915f318676fedeeb6a226"
integrity sha512-fdZ0i/R2dIKmy8OB5tBAsm5IbAHfJpI6AlbjxpgoU3aWj1HCdDo+pMji928MkDJhP01ISgFTgw/7PseGNaUflw==
"@tensorflow/tfjs-layers@3.11.0":
version "3.11.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-layers/-/tfjs-layers-3.11.0.tgz#456d8dc3fe93937ced329d5d06310da294d3758c"
integrity sha512-BtLgLucJZHv5te1K3yjT3iZdHXgMJArrLuOb/oRPOtTp4R2ad5N0V2m5RtuZJ3sI5/ah0h72xtmTWNyTv3/5dw==
"@tensorflow/tfjs-node@^3.11.0":
version "3.11.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs-node/-/tfjs-node-3.11.0.tgz#6c6f0dc0e2f0eb03658337b53ce3009e97526f8d"
integrity sha512-EFBAkwJDoP4WuiWqjT9ER80dUvilvVFXULlU3WpmVGDluIL/Yq18xEKHvPaYHW5jLq/mvMljLtUBlHqigfvAhw==
dependencies:
"@mapbox/node-pre-gyp" "1.0.4"
"@tensorflow/tfjs" "3.9.0"
"@tensorflow/tfjs" "3.11.0"
adm-zip "^0.5.2"
google-protobuf "^3.9.2"
https-proxy-agent "^2.2.1"
@ -171,17 +230,34 @@
rimraf "^2.6.2"
tar "^4.4.6"
"@tensorflow/tfjs@3.9.0":
version "3.9.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs/-/tfjs-3.9.0.tgz#ff3bcbfcb51800ea6791d7a7a020d354909d2c71"
integrity sha512-TyykXiZ6r9rMoXbQZaAkOKJJUrJHQVAjH/K6XRCPpOG//Hf15ZW97ZODskEByj77yNMw4smFUWCFhprhY2PgDQ==
"@tensorflow/tfjs@3.11.0":
version "3.11.0"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs/-/tfjs-3.11.0.tgz#63d5231f41d57ca11b910664632a8e349eba3967"
integrity sha512-TTYrKdkoh1sHnt4vn6MboLbpi1Es4U1Aw+L3PqwadRvXW4+7ySUtc00McrQ+ooK0q3Qhl3N7cvgchgM7nED3Mg==
dependencies:
"@tensorflow/tfjs-backend-cpu" "3.9.0"
"@tensorflow/tfjs-backend-webgl" "3.9.0"
"@tensorflow/tfjs-converter" "3.9.0"
"@tensorflow/tfjs-core" "3.9.0"
"@tensorflow/tfjs-data" "3.9.0"
"@tensorflow/tfjs-layers" "3.9.0"
"@tensorflow/tfjs-backend-cpu" "3.11.0"
"@tensorflow/tfjs-backend-webgl" "3.11.0"
"@tensorflow/tfjs-converter" "3.11.0"
"@tensorflow/tfjs-core" "3.11.0"
"@tensorflow/tfjs-data" "3.11.0"
"@tensorflow/tfjs-layers" "3.11.0"
argparse "^1.0.10"
chalk "^4.1.0"
core-js "3"
regenerator-runtime "^0.13.5"
yargs "^16.0.3"
"@tensorflow/tfjs@^2.0.1":
version "2.8.6"
resolved "https://registry.yarnpkg.com/@tensorflow/tfjs/-/tfjs-2.8.6.tgz#5115081e7424c33905af9565c0d190e1b4093a5b"
integrity sha512-/Hk3YCAreNicuQJsAIG32UGHaQj8UwX8y8ZrKVb/CrXOhrRyZmxGSZt9KMVe8MDoydenuGhZCqJUIaWdIKIA5g==
dependencies:
"@tensorflow/tfjs-backend-cpu" "2.8.6"
"@tensorflow/tfjs-backend-webgl" "2.8.6"
"@tensorflow/tfjs-converter" "2.8.6"
"@tensorflow/tfjs-core" "2.8.6"
"@tensorflow/tfjs-data" "2.8.6"
"@tensorflow/tfjs-layers" "2.8.6"
argparse "^1.0.10"
chalk "^4.1.0"
core-js "3"
@ -265,6 +341,11 @@
resolved "https://registry.yarnpkg.com/@types/webgl2/-/webgl2-0.0.5.tgz#dd925e20ab8ace80eb4b1e46fda5b109c508fb0d"
integrity sha512-oGaKsBbxQOY5+aJFV3KECDhGaXt+yZJt2y/OZsnQGLRkH6Fvr7rv4pCt3SRH1somIHfej/c4u7NSpCyd9x+1Ow==
"@types/webgl2@0.0.6":
version "0.0.6"
resolved "https://registry.yarnpkg.com/@types/webgl2/-/webgl2-0.0.6.tgz#1ea2db791362bd8521548d664dbd3c5311cdf4b6"
integrity sha512-50GQhDVTq/herLMiqSQkdtRu+d5q/cWHn4VvKJtrj4DJAjo1MNkWYa2MA41BaBO1q1HgsUjuQvEOk0QHvlnAaQ==
"@vladmandic/face-api@^1.5.3":
version "1.5.4"
resolved "https://registry.yarnpkg.com/@vladmandic/face-api/-/face-api-1.5.4.tgz#1b3e6f0f5e4e79f7e876d5ae32703ec811952e76"
@ -1904,6 +1985,13 @@ tar@^6.0.2, tar@^6.0.5, tar@^6.1.0, tar@^6.1.2:
mkdirp "^1.0.3"
yallist "^4.0.0"
tensorset@^1.2.9:
version "1.2.9"
resolved "https://registry.yarnpkg.com/tensorset/-/tensorset-1.2.9.tgz#0506cdd7ef0658d8b5d753bf29d0aa2e1275f08f"
integrity sha512-xlMAotSaXkGf3U8/4n/n+svHgvT1HvwrssMTaRgSo2PQcYFVseYWPg9qnppi+/fnf4EdQpQdtqIYXyu8jSK73A==
dependencies:
"@tensorflow/tfjs" "^2.0.1"
to-readable-stream@^1.0.0:
version "1.0.0"
resolved "https://registry.yarnpkg.com/to-readable-stream/-/to-readable-stream-1.0.0.tgz#ce0aa0c2f3df6adf852efb404a783e77c0475771"