initial commit

LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2017 Houston Engineering, Inc., http://www.houstoneng.com
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

package.json

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+{
+  "name": "cordova-plugin-tensorflow",
+  "version": "0.0.1",
+  "description": "TensorFlow for Cordova",
+  "cordova": {
+    "id": "cordova-plugin-tensorflow",
+    "platforms": [
+      "android"
+    ]
+  },
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/heigeo/cordova-plugin-tensorflow.git"
+  },
+  "keywords": [
+    "ai",
+    "inference",
+    "classification",
+    "imagerecognition",
+    "neuralnetworks",
+    "machinelearning",
+    "tensorflow",
+    "inception",
+    "ecosystem:cordova",
+    "cordova-android"
+  ],
+  "engines": [
+    {
+      "name": "cordova-android",
+      "version": ">=5.1.0"
+    }
+  ],
+  "author": "Houston Engineering, Inc.",
+  "license": "MIT",
+  "bugs": {
+    "url": "https://github.com/heigeo/cordova-plugin-tensorflow/issues"
+  },
+  "homepage": "https://github.com/heigeo/cordova-plugin-tensorflow#readme"
+}

plugin.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<plugin xmlns="http://apache.org/cordova/ns/plugins/1.0"
+        id="cordova-plugin-tensorflow" version="0.0.1">
+    <name>TensorFlow for Cordova</name>
+    <description>Cordova/PhoneGap wrapper for TensorFlow's image recognition binary library.</description>
+    <license>MIT</license>
+    <keywords>cordova,device</keywords>
+
+    <dependency id="cordova-plugin-file-transfer" />
+    <dependency id="cordova-plugin-zip" />
+
+    <js-module src="www/tensorflow.js" name="tensorflow">
+        <clobbers target="TensorFlow" />
+    </js-module>
+
+    <platform name="android">
+	<config-file target="config.xml" parent="/*">
+            <feature name="TensorFlow">
+              <param name="android-package" value="io.wq.tensorflow.TensorFlow" />
+            </feature>
+	</config-file>
+
+	<source-file src="src/android/TensorFlow.java" target-dir="src/io/wq/tensorflow" />
+	<source-file src="src/android/tf_libs/Classifier.java" target-dir="src/org/tensorflow/demo" />
+	<source-file src="src/android/tf_libs/TensorFlowImageClassifier.java" target-dir="src/org/tensorflow/demo" />
+	<source-file src="src/android/tf_libs/libandroid_tensorflow_inference_java.jar" target-dir="libs" />
+        <source-file src="src/android/tf_libs/armeabi-v7a/libtensorflow_inference.so" target-dir="libs/armeabi" />
+
+    </platform>
+
+</plugin>
+

src/android/TensorFlow.java

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+package io.wq.tensorflow;
+
+import org.apache.cordova.CordovaPlugin;
+import org.apache.cordova.CallbackContext;
+import org.json.JSONArray;
+import org.json.JSONObject;
+import org.json.JSONException;
+
+import android.util.Base64;
+import android.graphics.Bitmap;
+import android.graphics.Bitmap.Config;
+import android.graphics.BitmapFactory;
+
+import org.tensorflow.demo.TensorFlowImageClassifier;
+import org.tensorflow.demo.Classifier.Recognition;
+import org.tensorflow.demo.Classifier;
+import java.util.List;
+import java.util.Map;
+import java.util.HashMap;
+
+import android.media.ThumbnailUtils;
+
+public class TensorFlow extends CordovaPlugin {
+
+@Override
+public boolean execute(String action, JSONArray args, CallbackContext callbackContext) throws JSONException {
+    if (action.equals("loadModel")) {
+        this.loadModel(
+            args.getString(0),
+            args.getString(1),
+            args.getString(2),
+            args.getInt(3),
+            args.getInt(4),
+            (float) args.getDouble(5),
+            args.getString(6),
+            args.getString(7),
+            callbackContext
+        );
+        return true;
+    } else if (action.equals("classify")) {
+        this.classify(args.getString(0), args.getString(1), callbackContext);
+        return true;
+    } else {
+        return false;
+    }
+}
+
+
+private Map<String,Classifier> classifiers = new HashMap();
+private Map<String,Integer> sizes = new HashMap();
+
+private void loadModel(String modelName, String modelFile, String labelFile,
+                        int inputSize, int imageMean, float imageStd,
+                        String inputName, String outputName,
+                        CallbackContext callbackContext) {
+    classifiers.put(modelName, TensorFlowImageClassifier.create(
+        cordova.getActivity().getAssets(),
+        modelFile,
+        labelFile,
+        inputSize,
+        imageMean,
+        imageStd,
+        inputName,
+        outputName
+    ));
+    sizes.put(modelName, inputSize);
+    callbackContext.success();
+}
+
+private void classify(String modelName, String image, CallbackContext callbackContext) {
+    byte[] imageData = Base64.decode(image, Base64.DEFAULT);
+    Classifier classifier = classifiers.get(modelName);
+    int size = sizes.get(modelName);
+    Bitmap bitmap = BitmapFactory.decodeByteArray(imageData, 0, imageData.length);
+    Bitmap cropped = ThumbnailUtils.extractThumbnail(bitmap, size, size);
+    List<Recognition> results = classifier.recognizeImage(cropped);
+    JSONArray output = new JSONArray();
+    try {
+        JSONObject info = new JSONObject();
+        info.put("title", "Total");
+        info.put("confidence", results.size());
+        output.put(info);
+        for (Recognition result : results) {
+            JSONObject record = new JSONObject();
+            record.put("title", result.getTitle());
+            record.put("confidence", result.getConfidence());
+            output.put(record);
+        }
+    } catch (JSONException e) {
+    }
+    callbackContext.success(output);
+}
+
+
+}