[Other] Refactor js submodule (#415)

* Refactor js submodule

* Remove change-log

* Update ocr module

* Update ocr-detection module

* Update ocr-detection module

* Remove change-log

examples/application/js/package/packages/paddlejs-models/gesture/src/index.ts

@@ -0,0 +1,73 @@
+/**
+ * @file gesture model
+ */
+
+import { Runner } from '@paddlejs/paddlejs-core';
+import '@paddlejs/paddlejs-backend-webgl';
+import WarpAffine from './warpAffine';
+import DetectProcess from './detectProcess';
+import LMProcess from './LMProcess';
+import anchor from './anchor_small.txt';
+
+let box = null;
+let detectRunner = null as Runner;
+let recRunner = null as Runner;
+let anchorResults = null;
+const detFeedShape = 256;
+const canvas = document.createElement('canvas') as HTMLCanvasElement;
+initCanvas();
+
+function initCanvas() {
+    canvas.width = detFeedShape;
+    canvas.height = detFeedShape;
+    canvas.style.position = 'absolute';
+    canvas.style.top = '0';
+    canvas.style.left = '0';
+    canvas.style.zIndex = '-1';
+    canvas.style.opacity = '0';
+    document.body.appendChild(canvas);
+}
+
+export async function load() {
+    anchorResults = anchor.replace(/\s+/g, ',').split(',').map(item => +item);
+
+    detectRunner = new Runner({
+        modelPath: 'https://paddlejs.bj.bcebos.com/models/fuse/gesture/gesture_det_fuse_activation/model.json',
+        webglFeedProcess: true
+    });
+    const detectInit = detectRunner.init();
+
+    recRunner = new Runner({
+        modelPath: 'https://paddlejs.bj.bcebos.com/models/fuse/gesture/gesture_rec_fuse_activation/model.json'
+    });
+    const recInit = recRunner.init();
+
+    WarpAffine.init({
+        width: 224,
+        height: 224
+    });
+
+    return await Promise.all([detectInit, recInit]);
+}
+
+export async function classify(image): Promise<{ box: string | number[][], type: string }> {
+    canvas.getContext('2d')!.drawImage(image, 0, 0, detFeedShape, detFeedShape);
+    const res = await detectRunner.predict(image);
+    const post = new DetectProcess(res, canvas);
+    const result = {
+        box: '',
+        type: ''
+    };
+    box = await post.outputBox(anchorResults);
+    if (box) {
+        // 手势框选位置
+        result.box = box;
+        const feed = await post.outputFeed(recRunner);
+        const res2 = await recRunner.predictWithFeed(feed);
+        const lmProcess = new LMProcess(res2);
+        lmProcess.output();
+        // 识别结果
+        result.type = lmProcess.type || '';
+    }
+    return result;
+}