jupyter book cleaned, started the upload of the images in the smartphone app

7 years ago · dc56f58d90
7 changed files with 188 additions and 469 deletions
--- a/README.md
+++ b/README.md
@ -1,18 +1,7 @@
 # objectImageIdentifierAI
 - imagesToDataset
    - From two directories ('object' and 'noobject'), gets all the images inside the directories and generates the dataset
 - nnTrain
    - From the dataset file generated in the previous step, train the Neural Network
 - serverPredictor
    - Runs a server API, that with the Neural Network classifies the incoming images
 - smartphoneApp
    - Take photo and upload to the server, to get the response (object or no object)
 ![hotdognohotdog](https://raw.githubusercontent.com/arnaucode/objectImageIdentifierAI/master/hotdognohotdog.png "hotdognohotdog")
 ## Real steps
 ## Run
 - download images
    - for example, can be done with https://github.com/arnaucode/imgDownloader.git
 - In /serverPredictor directory
@ -23,3 +12,15 @@ This will generate the model.pkl. Then, run the serverPredictor.py
 ```
 python serverPredictor.py
 ```
 - Can run the tests:
 ```
 bash test.sh
 ```
 - The deploy.sh will execute the classifierChooser.py, and then the serverPredictor.py
 ```
 bash deploy.sh
 ```
 ![hotdognohotdog](https://raw.githubusercontent.com/arnaucode/objectImageIdentifierAI/master/hotdognohotdog.png "hotdognohotdog")
--- a/serverPredictor/HotdogClassifierChooser.ipynb
+++ b/serverPredictor/HotdogClassifierChooser.ipynb
@ -17,7 +17,6 @@
   "source": [
    "from PIL import Image, ImageOps\n",
    "import numpy, os\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "import numpy as np\n",
    "import pandas as pd"
@ -46,182 +45,15 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset/object/27.png\n",
      "dataset/object/82.png\n",
      "dataset/object/83.png\n",
      "dataset/object/100.png\n",
      "dataset/object/0.png\n",
      "dataset/object/13.png\n",
      "dataset/object/45.png\n",
      "dataset/object/64.png\n",
      "dataset/object/19.png\n",
      "dataset/object/101.png\n",
      "dataset/object/40.png\n",
      "dataset/object/97.png\n",
      "dataset/object/41.png\n",
      "dataset/object/7.png\n",
      "dataset/object/66.png\n",
      "dataset/object/55.png\n",
      "dataset/object/56.png\n",
      "dataset/object/65.png\n",
      "dataset/object/18.png\n",
      "dataset/object/24.png\n",
      "dataset/object/105.png\n",
      "dataset/object/116.png\n",
      "dataset/object/117.png\n",
      "dataset/object/104.png\n",
      "dataset/object/63.png\n",
      "dataset/object/38.png\n",
      "dataset/object/58.png\n",
      "dataset/object/103.png\n",
      "dataset/object/112.png\n",
      "dataset/object/33.png\n",
      "dataset/object/76.png\n",
      "dataset/object/59.png\n",
      "dataset/object/96.png\n",
      "dataset/object/91.png\n",
      "dataset/object/57.png\n",
      "dataset/object/2.png\n",
      "dataset/object/75.png\n",
      "dataset/object/107.png\n",
      "dataset/object/50.png\n",
      "dataset/object/16.png\n",
      "dataset/object/32.png\n",
      "dataset/object/15.png\n",
      "dataset/object/5.png\n",
      "dataset/object/72.png\n",
      "dataset/object/52.png\n",
      "dataset/object/4.png\n",
      "dataset/object/28.png\n",
      "dataset/object/43.png\n",
      "dataset/object/87.png\n",
      "dataset/object/98.png\n",
      "dataset/object/71.png\n",
      "dataset/object/102.png\n",
      "dataset/object/62.png\n",
      "dataset/object/9.png\n",
      "dataset/object/6.png\n",
      "dataset/object/85.png\n",
      "dataset/object/70.png\n",
      "dataset/object/42.png\n",
      "dataset/object/34.png\n",
      "dataset/object/81.png\n",
      "dataset/object/94.png\n",
      "dataset/object/26.png\n",
      "dataset/object/90.png\n",
      "dataset/object/44.png\n",
      "dataset/object/60.png\n",
      "dataset/object/17.png\n",
      "dataset/object/10.png\n",
      "dataset/object/53.png\n",
      "dataset/object/25.png\n",
      "dataset/object/21.png\n",
      "dataset/object/22.png\n",
      "dataset/object/30.png\n",
      "dataset/object/78.png\n",
      "dataset/object/118.png\n",
      "dataset/object/110.png\n",
      "dataset/object/79.png\n",
      "dataset/object/77.png\n",
      "dataset/object/12.png\n",
      "dataset/object/115.png\n",
      "dataset/object/67.png\n",
      "dataset/object/84.png\n",
      "dataset/object/11.png\n",
      "dataset/object/86.png\n",
      "dataset/object/89.png\n",
      "dataset/object/113.png\n",
      "dataset/noobject/image_0056.jpg\n",
      "dataset/noobject/image_0181.jpg\n",
      "dataset/noobject/image_0127.jpg\n",
      "dataset/noobject/image_0142.jpg\n",
      "dataset/noobject/image_0025.jpg\n",
      "dataset/noobject/image_0065.jpg\n",
      "dataset/noobject/image_0174.jpg\n",
      "dataset/noobject/image_0091.jpg\n",
      "dataset/noobject/image_0124.jpg\n",
      "dataset/noobject/image_0086.jpg\n",
      "dataset/noobject/image_0079.jpg\n",
      "dataset/noobject/image_0058.jpg\n",
      "dataset/noobject/image_0060.jpg\n",
      "dataset/noobject/image_0119.jpg\n",
      "dataset/noobject/image_0023.jpg\n",
      "dataset/noobject/image_0075.jpg\n",
      "dataset/noobject/image_0020.jpg\n",
      "dataset/noobject/image_0013.jpg\n",
      "dataset/noobject/image_0126.jpg\n",
      "dataset/noobject/image_0012.jpg\n",
      "dataset/noobject/image_0055.jpg\n",
      "dataset/noobject/image_0176.jpg\n",
      "dataset/noobject/image_0144.jpg\n",
      "dataset/noobject/image_0048.jpg\n",
      "dataset/noobject/image_0121.jpg\n",
      "dataset/noobject/image_0070.jpg\n",
      "dataset/noobject/image_0082.jpg\n",
      "dataset/noobject/image_0095.jpg\n",
      "dataset/noobject/image_0022.jpg\n",
      "dataset/noobject/image_0120.jpg\n",
      "dataset/noobject/image_0139.jpg\n",
      "dataset/noobject/image_0073.jpg\n",
      "dataset/noobject/image_0090.jpg\n",
      "dataset/noobject/image_0145.jpg\n",
      "dataset/noobject/image_0173.jpg\n",
      "dataset/noobject/image_0078.jpg\n",
      "dataset/noobject/image_0085.jpg\n",
      "dataset/noobject/image_0083.jpg\n",
      "dataset/noobject/image_0179.jpg\n",
      "dataset/noobject/image_0050.jpg\n",
      "dataset/noobject/image_0076.jpg\n",
      "dataset/noobject/image_0014.jpg\n",
      "dataset/noobject/image_0054.jpg\n",
      "dataset/noobject/image_0066.jpg\n",
      "dataset/noobject/image_0001.jpg\n",
      "dataset/noobject/image_0047.jpg\n",
      "dataset/noobject/image_0077.jpg\n",
      "dataset/noobject/image_0122.jpg\n",
      "dataset/noobject/image_0068.jpg\n",
      "dataset/noobject/image_0049.jpg\n",
      "dataset/noobject/image_0092.jpg\n",
      "dataset/noobject/image_0138.jpg\n",
      "dataset/noobject/image_0072.jpg\n",
      "dataset/noobject/image_0146.jpg\n",
      "dataset/noobject/image_0061.jpg\n",
      "dataset/noobject/image_0011.jpg\n",
      "dataset/noobject/image_0002.jpg\n",
      "dataset/noobject/image_0143.jpg\n",
      "dataset/noobject/image_0088.jpg\n",
      "dataset/noobject/image_0062.jpg\n",
      "dataset/noobject/image_0089.jpg\n",
      "dataset/noobject/image_0018.jpg\n",
      "dataset/noobject/image_0024.jpg\n",
      "dataset/noobject/image_0064.jpg\n",
      "dataset/noobject/image_0074.jpg\n",
      "dataset/noobject/image_0052.jpg\n",
      "dataset/noobject/image_0096.jpg\n",
      "dataset/noobject/image_0178.jpg\n",
      "dataset/noobject/image_0067.jpg\n",
      "dataset/noobject/image_0140.jpg\n",
      "dataset/noobject/image_0084.jpg\n",
      "dataset/noobject/image_0010.jpg\n",
      "dataset/noobject/image_0081.jpg\n",
      "dataset/noobject/image_0059.jpg\n",
      "dataset/noobject/image_0016.jpg\n",
      "dataset/noobject/image_0175.jpg\n",
      "dataset/noobject/image_0094.jpg\n",
      "dataset/noobject/image_0071.jpg\n",
      "dataset/noobject/image_0080.jpg\n",
      "dataset/noobject/image_0125.jpg\n",
      "dataset/noobject/image_0008.jpg\n",
      "dataset/noobject/image_0019.jpg\n",
      "dataset/noobject/image_0017.jpg\n",
      "dataset/noobject/image_0180.jpg\n"
      "reading dataset images files\n"
     ]
    }
   ],
   "source": [
    "print(\"reading dataset images files\")\n",
    "for directory in os.listdir(path):\n",
    "    for file in os.listdir(path+directory):\n",
    "        print(path+directory+\"/\"+file)\n",
    "        #print(path+directory+\"/\"+file)\n",
    "        img=Image.open(path+directory+\"/\"+file)\n",
    "        #resize\n",
    "        thumb = ImageOps.fit(img, size, Image.ANTIALIAS)\n",
@ -253,39 +85,20 @@
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf=AdaBoostClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores = cross_val_score(clf, X_train, y_train, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.77037037037\n"
      "0.762399355878\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "clf=AdaBoostClassifier(n_estimators=100)\n",
    "scores = cross_val_score(clf, X_train, y_train, cv=3)\n",
    "print(scores.mean())"
   ]
  },
@ -298,40 +111,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores = cross_val_score(clf, Xlist, Ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
@ -343,6 +123,9 @@
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "clf = GaussianNB()\n",
    "scores = cross_val_score(clf, Xlist, Ylist)\n",
    "print(scores.mean())"
   ]
  },
@ -355,40 +138,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = KNeighborsClassifier(n_neighbors=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores = cross_val_score(clf, Xlist, Ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
@ -400,6 +150,9 @@
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier(n_neighbors=10)\n",
    "scores = cross_val_score(clf, Xlist, Ylist)\n",
    "print(scores.mean())"
   ]
  },
@ -412,51 +165,21 @@
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = LinearSVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores = cross_val_score(clf, Xlist, Ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.638575605681\n"
      "0.66238512949\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "clf = LinearSVC()\n",
    "scores = cross_val_score(clf, Xlist, Ylist)\n",
    "print(scores.mean())"
   ]
  },
@ -469,40 +192,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores = cross_val_score(clf, Xlist, Ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
@ -514,6 +204,9 @@
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC()\n",
    "scores = cross_val_score(clf, Xlist, Ylist)\n",
    "print(scores.mean())"
   ]
  },
@ -526,40 +219,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = GaussianProcessClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores = cross_val_score(clf, Xlist, Ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
@ -571,6 +231,9 @@
    }
   ],
   "source": [
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "clf = GaussianProcessClassifier()\n",
    "scores = cross_val_score(clf, Xlist, Ylist)\n",
    "print(scores.mean())"
   ]
  },
@ -583,51 +246,21 @@
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scores = cross_val_score(clf, Xlist, Ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.710317460317\n"
      "0.775793650794\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier()\n",
    "scores = cross_val_score(clf, Xlist, Ylist)\n",
    "print(scores.mean())"
   ]
  },
@ -640,7 +273,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
@ -653,7 +286,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
@ -679,7 +312,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
@ -695,7 +328,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
@ -706,18 +339,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#grid_search.fit(Xlist, Ylist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
@ -738,7 +360,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
@ -763,26 +385,26 @@
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:    1.5s finished\n"
      "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:    0.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 2.38 seconds for 3 candidate parameter settings.\n",
      "GridSearchCV took 1.45 seconds for 3 candidate parameter settings.\n",
      "finished GridSearch\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.815 (std: 0.073)\n",
      "Mean validation score: 0.800 (std: 0.085)\n",
      "Parameters: {'clf__n_estimators': 100}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.763 (std: 0.093)\n",
      "Parameters: {'clf__n_estimators': 10}\n",
      "Mean validation score: 0.778 (std: 0.035)\n",
      "Parameters: {'clf__n_estimators': 3}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.756 (std: 0.110)\n",
      "Parameters: {'clf__n_estimators': 3}\n",
      "Mean validation score: 0.741 (std: 0.046)\n",
      "Parameters: {'clf__n_estimators': 10}\n",
      "\n",
      "-----\n",
      "classifier:\n",
@ -790,15 +412,15 @@
      "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
      "           weights='uniform')\n",
      "Fitting 3 folds for each of 2 candidates, totalling 6 fits\n",
      "GridSearchCV took 0.23 seconds for 2 candidate parameter settings.\n",
      "GridSearchCV took 0.35 seconds for 2 candidate parameter settings.\n",
      "finished GridSearch\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.778 (std: 0.048)\n",
      "Parameters: {'clf__n_neighbors': 3}\n",
      "Mean validation score: 0.756 (std: 0.056)\n",
      "Parameters: {'clf__n_neighbors': 10}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.704 (std: 0.010)\n",
      "Parameters: {'clf__n_neighbors': 10}\n",
      "Mean validation score: 0.748 (std: 0.111)\n",
      "Parameters: {'clf__n_neighbors': 3}\n",
      "\n",
      "-----\n",
      "classifier:\n",
@ -821,14 +443,14 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 0.36 seconds for 2 candidate parameter settings.\n",
      "GridSearchCV took 0.47 seconds for 2 candidate parameter settings.\n",
      "finished GridSearch\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.489 (std: 0.000)\n",
      "Mean validation score: 0.496 (std: 0.005)\n",
      "Parameters: {'clf__n_restarts_optimizer': 0}\n",
      "\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.489 (std: 0.000)\n",
      "Mean validation score: 0.496 (std: 0.005)\n",
      "Parameters: {'clf__n_restarts_optimizer': 1}\n",
      "\n",
      "-----\n",
@ -842,28 +464,27 @@
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.2s finished\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:    0.9s finished\n"
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.3s remaining:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Done   6 out of   6 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 1.16 seconds for 3 candidate parameter settings.\n",
      "GridSearchCV took 1.06 seconds for 3 candidate parameter settings.\n",
      "finished GridSearch\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.807 (std: 0.093)\n",
      "Mean validation score: 0.793 (std: 0.088)\n",
      "Parameters: {'clf__n_estimators': 3}\n",
      "\n",
      "Model with rank: 2\n",
      "Mean validation score: 0.756 (std: 0.048)\n",
      "Parameters: {'clf__n_estimators': 100}\n",
      "Mean validation score: 0.785 (std: 0.084)\n",
      "Parameters: {'clf__n_estimators': 10}\n",
      "\n",
      "Model with rank: 3\n",
      "Mean validation score: 0.733 (std: 0.054)\n",
      "Parameters: {'clf__n_estimators': 10}\n",
      "Mean validation score: 0.763 (std: 0.048)\n",
      "Parameters: {'clf__n_estimators': 100}\n",
      "\n",
      "-----\n",
      "classifier:\n",
@ -871,19 +492,32 @@
      "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n",
      "GridSearchCV took 0.35 seconds for 3 candidate parameter settings.\n",
      "Fitting 3 folds for each of 3 candidates, totalling 9 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   9 out of   9 | elapsed:    0.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV took 0.36 seconds for 3 candidate parameter settings.\n",
      "finished GridSearch\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.689 (std: 0.031)\n",
      "Mean validation score: 0.689 (std: 0.067)\n",
      "Parameters: {'clf__C': 3}\n",
      "\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.689 (std: 0.031)\n",
      "Mean validation score: 0.689 (std: 0.067)\n",
      "Parameters: {'clf__C': 10}\n",
      "\n",
      "Model with rank: 1\n",
      "Mean validation score: 0.689 (std: 0.031)\n",
      "Mean validation score: 0.689 (std: 0.067)\n",
      "Parameters: {'clf__C': 100}\n",
      "\n"
     ]
--- a/serverPredictor/serverPredictor.py
+++ b/serverPredictor/serverPredictor.py
@ -10,6 +10,7 @@ import numpy as np
 from flask import Flask
 from flask_restful import Resource, Api, request
 from flask_cors import CORS
 from PIL import Image, ImageOps
@ -23,6 +24,7 @@ size = 100, 100
 app = Flask(__name__)
 CORS(app)
 api = Api(app)
@ -61,4 +63,4 @@ if __name__ == '__main__':
    logging.basicConfig(filename=logfilename,level=logging.DEBUG)
    print("server running")
    app.run(port='3000')
    app.run(port='3200')
--- a/serverPredictor/test.sh
+++ b/serverPredictor/test.sh
@ -1,14 +1,14 @@
 echo "sending img1 to server"
 echo "server response:"
 curl -X POST -F file=@./test1.png http://127.0.0.1:3000/predict
 curl -X POST -F file=@./test1.png http://127.0.0.1:3200/predict
 echo ""
 echo "sending img2 to server"
 echo "server response:"
 curl -X POST -F file=@./test2.png http://127.0.0.1:3000/predict
 curl -X POST -F file=@./test2.png http://127.0.0.1:3200/predict
 echo ""
 echo "sending img to server"
 echo "server response:"
 curl -X POST -F file=@./test3.png http://127.0.0.1:3000/predict
 curl -X POST -F file=@./test3.png http://127.0.0.1:3200/predict
 echo ""
--- a/smartphoneApp/www/js/app.js
+++ b/smartphoneApp/www/js/app.js
@ -1,3 +1,4 @@
 var urlapi = "http://127.0.0.1:3200/";
 angular.module('app', [
    'ionic',
@ -35,7 +36,8 @@ angular.module('app', [
        url: '/main',
        views: {
          'menuContent': {
            templateUrl: 'templates/main.html'
            templateUrl: 'templates/main.html',
            controller: 'MainCtrl'
          }
        }
      });
--- a/smartphoneApp/www/js/main.js
+++ b/smartphoneApp/www/js/main.js
@ -1,5 +1,80 @@
 angular.module('app.main', [])
 .controller('MainCtrl', function($scope) {
 .controller('MainCtrl', function($scope, $http) {
    $scope.response = "";
    $scope.model_file={};
    $scope.uploadFile = function() {
        console.log("$scope.img_file");
        console.log($scope.img_file);
        var fd = new FormData();
        //Take the first selected file
        fd.append("file", $scope.img_file);
        console.log(fd);
        $http({
                url: urlapi + 'predict',
                method: "POST",
                headers: {
                    "Content-Type": undefined
                },
                data: fd
            })
            .then(function(data) {
                    console.log("response: ");
                    console.log(data.data);
                    // response reaction
                    $scope.response= data.data.result;
                },
                function(response) { // optional
                    // failed
                    console.log(response);
                });
    };
 });
    /*$scope.takePhoto = function() {
        alert("a");
        console.log("take photo");
        var options = {
                quality: 100,
                destinationType: Camera.DestinationType.DATA_URL,
                sourceType: Camera.sourceType,
                allowEdit: true,
                encodingType: Camera.EncodingType.PNG,
                targetWidth: 500,
                targetHeight: 500,
                popoverOptions: CameraPopoverOptions,
                saveToPhotoAlbum: false,
                correctOrientation:true
            };
            $cordovaCamera.getPicture(options).then(function(imageData) {
                //$scope.user.newAvatar = "data:image/jpeg;base64," + imageData;
                $scope.img.imgdata = "data:image/jpeg;base64," + imageData;
                $scope.img.img = imageData;
                }, function(err) {
                console.log(err);
            });
    };*/
 })
 .directive('fileModel', [
    '$parse',
    function($parse) {
        return {
            restrict: 'A',
            link: function(scope, element, attrs) {
                var model = $parse(attrs.fileModel);
                var modelSetter = model.assign;
                element.bind('change', function() {
                    scope.$apply(function() {
                        if (attrs.multiple) {
                            modelSetter(scope, element[0].files);
                        } else {
                            modelSetter(scope, element[0].files[0]);
                        }
                    });
                });
            }
        };
    }
 ]);
--- a/smartphoneApp/www/templates/main.html
+++ b/smartphoneApp/www/templates/main.html
@ -2,7 +2,7 @@
  <ion-content>
    <div class="row">
      <div class="col">
        <div class="button button-full button-stable">
        <div ng-click="takePhoto()" class="button button-full">
          <i class="icon ion-camera"></i> Take Photo
        </div>
      </div>
@ -12,14 +12,19 @@
        </div>
      </div>
    </div>
    <input type='file' file-model='img_file'>
    <div class="list card">
      <div class="item item-image">
        <img src="http://www.trueactivist.com/wp-content/uploads/2015/07/hotdog-300x300.jpg">
        <img ng-src="{{img_file}}">
      </div>
      <div ng-click="uploadFile()" class="button button-royal" href="#">
        Send
      </div>
      <a class="item item-icon-left balanced" href="#">
    </div>
    <a ng-show="response!=''" class="item item-icon-left balanced" href="#">
        <i class="icon ion-checkmark"></i>
        Is a Hotdog
        Is a {{response}}
        </a>
    </div>
  </ion-content>