emoji-net
Version:
EmojiNet is an image to emoji recognizer based on MobileNet / Google Emoji Scavenger Hunt
98 lines • 4.13 kB
JavaScript
;
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Object.defineProperty(exports, "__esModule", { value: true });
/**
* Wechaty Chatbot SDK - https://github.com/wechaty/wechaty
*
* @copyright 2016 Huan LI (李卓桓) <https://github.com/huan>, and
* Wechaty Contributors <https://github.com/wechaty>.
*
* Licensed under the Apache License, Version 2.0 (the "License")
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*/
const path_1 = __importDefault(require("path"));
const tstest_1 = require("tstest");
const tfc = __importStar(require("@tensorflow/tfjs-core"));
const canvas_utils_1 = require("./canvas-utils");
const emoji_net_1 = require("./emoji-net");
const FIXTURE_IMAGE_LABEL_LIST = [
{
file: path_1.default.join(__dirname, '../tests/fixtures/hand.jpg'),
name: 'hand',
},
{
file: path_1.default.join(__dirname, '../tests/fixtures/sofa.jpg'),
name: 'sofa',
},
];
tstest_1.test('EmojiNet smoke testing', async (t) => {
const emojinet = new emoji_net_1.EmojiNet();
await emojinet.load();
const predictItemList = await Promise.all(FIXTURE_IMAGE_LABEL_LIST
.map(x => x.file)
.map(file => emojinet.recognize(file)));
const emojiList = predictItemList.map(x => x[0].name);
const EXPECTED_LIST = FIXTURE_IMAGE_LABEL_LIST.map(x => x.name);
t.same(emojiList, EXPECTED_LIST, 'should get the labels right');
await emojinet.dispose();
});
tstest_1.test('EmojiNet predict() & getTopKClasses()', async (t) => {
class EmojiNetTest extends emoji_net_1.EmojiNet {
predict(input) {
return super.predict(input);
}
getTopKClasses(predictions, topK) {
return super.getTopKClasses(predictions, topK);
}
}
const emojinet = new EmojiNetTest();
await emojinet.load();
for (const { file, name } of FIXTURE_IMAGE_LABEL_LIST) {
const image = await canvas_utils_1.loadImage(file);
const resizedImage = await canvas_utils_1.resizeImage(image, emoji_net_1.MOBILENET_SIZE, emoji_net_1.MOBILENET_SIZE);
const canvas = await canvas_utils_1.createCanvas(emoji_net_1.MOBILENET_SIZE, emoji_net_1.MOBILENET_SIZE);
const ctx = canvas.getContext('2d');
if (!ctx) {
throw new Error('no ctx');
}
ctx.putImageData(resizedImage, 0, 0);
const pixels = tfc.fromPixels(canvas);
const result = await emojinet.predict(pixels);
const top2 = emojinet.getTopKClasses(result, 2);
t.equal(top2[0].name, name, 'should get the right lable: ' + name);
}
await emojinet.dispose();
});
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