test_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validate Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "from IPython.display import Image\n",
    "from PIL import Image\n",
    "from matplotlib import cm\n",
    "from tensorflow.python.client import device_lib\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import label_image\n",
    "import glob\n",
    "import math\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "#print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_image_list(folder):\n",
    "    image_list = {}\n",
    "    for directory in glob.glob(folder+'/*/'):\n",
    "        images = []\n",
    "        for filename in glob.glob(str(directory)+'*.jpg'): #assuming jpg\n",
    "            images.append(filename)\n",
    "        image_list.update({str(directory[directory.find('\\\\')+1:directory.rfind('\\\\')]):images})\n",
    "    return image_list\n",
    "\n",
    "def classify_cells(image_directory,model_file, \n",
    "                   label_file= \"output_files/output_labels.txt\", \n",
    "                   input_height=224, \n",
    "                   input_width=224,\n",
    "                   input_mean=0,\n",
    "                   input_std=255, \n",
    "                   input_layer='Placeholder',\n",
    "                   output_layer= 'final_result'):    \n",
    "    print('\\nClassifying directory...'+str(image_directory),'....')\n",
    "    image_list = get_image_list(image_directory)\n",
    "    graph = label_image.load_graph(model_file)\n",
    "    \n",
    "    #dict of validation images.\n",
    "    val_set = {}\n",
    "    print('READING IMAGES....')\n",
    "    for key in image_list.keys():\n",
    "        print(key+'.....',end='')\n",
    "        val_images = []\n",
    "        for file_name in image_list[key]:\n",
    "            val_images.append(label_image.read_tensor_from_image_file(\n",
    "                file_name,\n",
    "                input_height=input_height,\n",
    "                input_width=input_width,\n",
    "                input_mean=input_mean,\n",
    "                input_std=input_std))\n",
    "        val_set.update({str(key):val_images})\n",
    "        print('.....done')\n",
    "\n",
    "\n",
    "    input_name = \"import/\" + input_layer\n",
    "    output_name = \"import/\" + output_layer\n",
    "    input_operation = graph.get_operation_by_name(input_name)\n",
    "    output_operation = graph.get_operation_by_name(output_name)\n",
    "    \n",
    "    print('MAKING PREDICTIONS....')\n",
    "    predictions = {}\n",
    "    for key in val_set.keys():\n",
    "        print(key+'.....',end='')\n",
    "        val_pred = []\n",
    "        for img in val_set[key]:\n",
    "            with tf.Session(graph=graph) as sess:\n",
    "                results = sess.run(output_operation.outputs[0], {\n",
    "                input_operation.outputs[0]: img\n",
    "                })\n",
    "                results = np.squeeze(results)\n",
    "\n",
    "            val_pred.append(results.argsort()[-5:][::-1][0])\n",
    "        predictions.update({key:val_pred})\n",
    "        print('.....done')\n",
    "    labels = label_image.load_labels(label_file)\n",
    "    return predictions, labels\n",
    "\n",
    "def confusion_matrix(predictions,labels,image_directory,title=None):\n",
    "    n=len(labels)\n",
    "    confusion_matrix = np.zeros((n,n), dtype=np.float)\n",
    "    image_list = get_image_list(image_directory)\n",
    "    \n",
    "    pred_scores=[]\n",
    "    misclassified = []\n",
    "    for i,ctype in enumerate(labels):\n",
    "        err,num = 0.0,0\n",
    "        for j,pred in enumerate(predictions[ctype]):\n",
    "            confusion_matrix[pred,i]+=1/len(predictions[ctype])\n",
    "            num+=1\n",
    "            if(pred!=i):\n",
    "                misclassified.append(image_list[ctype][j])\n",
    "                err+=1\n",
    "        pred_scores.append(((num-err)/num,num))\n",
    "\n",
    "    for i,label in enumerate(labels):\n",
    "        print(label,'= ', str(round(pred_scores[i][0]*100,1))+'%,',pred_scores[i][1],'samples')\n",
    "\n",
    "    fig = plt.figure(figsize=(10,10))\n",
    "    ax = fig.add_subplot(111)\n",
    "    cfm = ax.matshow(confusion_matrix, cmap=plt.cm.get_cmap('hot',50))\n",
    "    divider = make_axes_locatable(ax)\n",
    "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "\n",
    "    plt.colorbar(cfm, cax=cax, ticks=np.arange(0,1.1,.1))\n",
    "\n",
    "    ax.set_xlabel('Cell Type', fontsize=20)\n",
    "    ax.set_ylabel('Prediction', fontsize=20)\n",
    "    ax.set_xticklabels(['']+labels, fontsize=12)\n",
    "    ax.set_yticklabels(['']+labels, fontsize=12)\n",
    "    ax.xaxis.set_label_position('top')\n",
    "    ax.xaxis.labelpad = 20\n",
    "    ax.yaxis.labelpad = 20\n",
    "\n",
    "    fig.tight_layout(pad=0, w_pad=1.5, h_pad=3)\n",
    "    fig.set_figheight(10)\n",
    "    fig.set_figwidth(10)\n",
    "    fig.suptitle(title, fontsize=26)\n",
    "    return misclassified\n",
    "    \n",
    "def vis_images(images):\n",
    "    #fig, axes = plt.subplots((len(images),1))\n",
    "    num=len(images)\n",
    "    \n",
    "    n_columns = 2\n",
    "    n_rows = math.ceil(num / n_columns) + 1\n",
    "    fig = plt.figure(1, figsize=(n_columns*8, n_rows*6))\n",
    "    for i,img in enumerate(images):\n",
    "        plt.subplot(n_rows, n_columns, i+1)\n",
    "        plt.title('File: '+img,fontsize=16)\n",
    "        plt.imshow(plt.imread(img))\n",
    "    plt.tight_layout()\n",
    "    \n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If not available in repo, first generate using retrain_helper.ipynb\n",
    "inception_v1 = \"output_files/output_graph_inception_v1.pb\" # Final Test Accuracy: 90.0%\n",
    "inception_v1_p = \"output_files/output_graph_inception_v1_partial.pb\" # Final Test Accuracy: 89.0%\n",
    "inception_v1_original_set_distort = \"output_files/output_graph_inception_v1_original_set_distort.pb\" # Final Test Accuracy: 90.2%\n",
    "inception_v3_original_set_adam_0001_feature = \"output_files/output_graph_inception_v3_original_set_adam_0001_feature.pb\"\n",
    "\n",
    "\n",
    "inception_v2 = \"output_files/output_graph_inception_v2.pb\" # Final Test Accuracy: 90.0%. Not available in repo.\n",
    "\n",
    "inception_v3 = \"output_files/output_graph_inception_v3.pb\" # Final Test Accuracy: 94.3%. Not available in repo.\n",
    "inception_v3_original_set = \"output_files/output_graph_inception_v3_original_set.pb\" # Final Test Accuracy: 94.3%. Not available in repo.\n",
    "inception_v3_original_set_adam = \"output_files/output_graph_inception_v3_original_set_adam.pb\"\n",
    "inception_v3_original_set_adam_0002 = \"output_files/output_graph_inception_v3_original_set_adam_0002.pb\"\n",
    "inception_v3_original_set_adam_0001 = \"output_files/output_graph_inception_v3_original_set_adam_0001.pb\"\n",
    "inception_v3_original_set_adam_0001_b200 = \"output_files/output_graph_inception_v3_original_set_adam_0001_b200.pb\"\n",
    "inception_v3_original_set_adam_00005 = \"output_files/output_graph_inception_v3_original_set_adam_00005.pb\"\n",
    "inception_v3_original_set_adam_00005_a = \"output_files/output_graph_inception_v3_original_set_adam_00005_a.pb\"\n",
    "inception_v3_original_set_adam_00005_feature = \"output_files/output_graph_inception_v3_original_set_adam_00005_feature.pb\"\n",
    "inception_v3_original_set_adam_0001_feature_alltrain = \"output_files/output_graph_inception_v3_original_set_adam_0001_feature_alltrain.pb\"\n",
    "inception_v3_original_set_adam_00005_feature_alltrain = \"output_files/output_graph_inception_v3_original_set_adam_00005_feature_alltrain.pb\"\n",
    "inception_v3_original_set_adam_00005_feature_alltrain2_7k = \"output_files/output_graph_inception_v3_original_set_adam_00005_feature_alltrain2_7k.pb\"\n",
    "\n",
    "\n",
    "inception_v3_original_set_adam_00005_distort = \"output_files/output_graph_inception_v3_original_set_adam_00005_distort.pb\"\n",
    "inception_v3_original_set_adam_000025 = \"output_files/output_graph_inception_v3_original_set_adam_000025.pb\"\n",
    "inception_v3_original_set_adam_000025_b32 = \"output_files/output_graph_inception_v3_original_set_adam_000025_b32.pb\"\n",
    "inception_v3_original_set_adam_000025_b32_30k = \"output_files/output_graph_inception_v3_original_set_adam_000025_b32_30k.pb\"\n",
    "inception_v3_original_set_adam_00005_b32 = \"output_files/output_graph_inception_v3_original_set_adam_00005_b32.pb\"\n",
    "\n",
    "mobilenet_v1 = 'output_files/output_graph_mobilenet_v1.pb' # Final Test Accuracy: 94.3%\n",
    "mobilenet_v1_original_set = 'output_files/output_graph_mobilenet_v1_original_set.pb' # Final Test Accuracy: 94.7% \n",
    "\n",
    "mobilenet_v2 = 'output_files/output_graph_mobilenet_v2.pb' # Final Test Accuracy: 92.2% \n",
    "\n",
    "inception_resnet_v2 = 'output_files/output_graph_inception_resnet_v2.pb' # Final Test Accuracy: 86.4%. Not available in repo."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Choose directory of images to classify and Model to use for classification (model_file)..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "unseen_dir = 'images/original_set/unseen_validation'\n",
    "train_dir = 'images/original_set/training_cell_type_images'\n",
    "model_file = inception_v3_original_set_adam_0001_feature_alltrain\n",
    "\n",
    "# other paramaters with default values\n",
    "label_file = \"output_files/output_labels.txt\"\n",
    "input_height = 299\n",
    "input_width = 299\n",
    "input_mean = 0\n",
    "input_std = 255\n",
    "input_layer = 'Placeholder'\n",
    "output_layer = 'final_result'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run Prediction..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classifying directory...images/original_set/unseen_validation ....\n",
      "READING IMAGES....\n",
      "actinedge..........done\n",
      "filopodia..........done\n",
      "hemisphere..........done\n",
      "lamellipodia..........done\n",
      "smallbleb..........done\n",
      "MAKING PREDICTIONS....\n",
      "actinedge..........done\n",
      "filopodia..........done\n",
      "hemisphere..........done\n",
      "lamellipodia..........done\n",
      "smallbleb..........done\n"
     ]
    }
   ],
   "source": [
    "#Unseen Validation Set\n",
    "unseen_predict, labels = classify_cells(unseen_dir,model_file, input_height=input_height, input_width=input_width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "actinedge =  57.1%, 7 samples\n",
      "filopodia =  77.8%, 9 samples\n",
      "hemisphere =  66.7%, 3 samples\n",
      "lamellipodia =  90.9%, 11 samples\n",
      "smallbleb =  100.0%, 6 samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db6cfd4390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "misclassified = confusion_matrix(unseen_predict, labels, unseen_dir, title='Unseen Validation Set')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display Misclassified Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "images/original_set/unseen_validation\\actinedge\\cell5.jpg\n",
      "images/original_set/unseen_validation\\actinedge\\cell6.jpg\n",
      "images/original_set/unseen_validation\\actinedge\\cell7.jpg\n",
      "images/original_set/unseen_validation\\filopodia\\cell2.jpg\n",
      "images/original_set/unseen_validation\\filopodia\\cell9.jpg\n",
      "images/original_set/unseen_validation\\hemisphere\\cell1.jpg\n",
      "images/original_set/unseen_validation\\lamellipodia\\cell10.jpg\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1db139e1c18>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for x in misclassified:\n",
    "    print(x)\n",
    "vis_images(misclassified)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Training Set Classification Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classifying directory...images/original_set/training_cell_type_images ....\n",
      "reading images....\n",
      "actinedge..........done\n",
      "filopodia..........done\n",
      "hemisphere..........done\n",
      "lamellipodia..........done\n",
      "smallbleb..........done\n",
      "making predictions....\n",
      "actinedge..........done\n",
      "filopodia..........done\n",
      "hemisphere..........done\n",
      "lamellipodia..........done\n",
      "smallbleb..........done\n"
     ]
    }
   ],
   "source": [
    "#Training Set\n",
    "train_predict, labels = classify_cells(train_dir,model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "actinedge =  97.7%, 131 samples\n",
      "filopodia =  99.8%, 460 samples\n",
      "hemisphere =  97.7%, 86 samples\n",
      "lamellipodia =  98.0%, 399 samples\n",
      "smallbleb =  99.5%, 222 samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x180735b4390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix(train_predict, labels, title='Training Set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}