Main.ipynb

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 "metadata": {
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9-final"
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   "name": "python3",
   "display_name": "Python 3.7.9 64-bit",
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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import cv2\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Activation, Dense, Flatten, BatchNormalization, Conv2D, MaxPool2D\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from keras import regularizers\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.metrics import categorical_crossentropy\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import itertools\n",
    "train_path = 'KneeXrayData/train'\n",
    "valid_path = 'KneeXrayData/val'\n",
    "test_path = 'KneeXrayData/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Found 8164 images belonging to 5 classes.\n",
      "Found 826 images belonging to 5 classes.\n",
      "Found 1656 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "def preprocess(img):\n",
    "    img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
    "    #img = tf.keras.preprocessing.image.img_to_array(img,dtype=np.uint8)\n",
    "    #img = np.expand_dims(img, axis=0)\n",
    "    img = np.array(img,dtype=np.uint8)\n",
    "    img = cv2.GaussianBlur(img, (3, 3), 7)\n",
    "    mean = int(np.mean(img))\n",
    "    for i in range(0,img.shape[0]):\n",
    "        for j in range(0,img.shape[1]):\n",
    "            img[i,j] = max(0,img[i,j]-mean)\n",
    "    clahe = cv2.createCLAHE()\n",
    "    img = clahe.apply(img)\n",
    "    img = img.reshape(-1, 224, 224, 1)\n",
    "    return img/255.0\n",
    "\n",
    "#Image loading and Preprocessing \n",
    "train_batches = ImageDataGenerator(preprocessing_function=preprocess).flow_from_directory(train_path, target_size=(224,224), classes=['0', '1','2','3','4'], batch_size=10,shuffle=True)\n",
    "valid_batches = ImageDataGenerator(preprocessing_function=preprocess).flow_from_directory(valid_path, target_size=(224,224), classes=['0', '1','2','3','4'], batch_size=10,shuffle=True)\n",
    "test_batches = ImageDataGenerator(preprocessing_function=preprocess).flow_from_directory(test_path, target_size=(224,224), classes=['0', '1','2','3','4'], batch_size=10,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plots images with labels within jupyter notebook\n",
    "def plots(ims, figsize=(12,6), rows=1, interp=False, titles=None):\n",
    "    if type(ims[0]) is np.ndarray:\n",
    "        ims = np.array(ims).astype(np.uint8)\n",
    "        if (ims.shape[-1] != 3):\n",
    "            ims = ims.transpose((0,2,3,1))\n",
    "    f = plt.figure(figsize=figsize)\n",
    "    cols = len(ims)//rows if len(ims) % 2 == 0 else len(ims)//rows + 1\n",
    "    for i in range(len(ims)):\n",
    "        sp = f.add_subplot(rows, cols, i+1)\n",
    "        sp.axis('Off')\n",
    "        if titles is not None:\n",
    "            sp.set_title(titles[i], fontsize=16)\n",
    "        plt.imshow(ims[i], interpolation=None if interp else 'none')\n",
    "imgs, labels = next(train_batches)\n",
    "plots(imgs, titles=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_batches = train_batches.reshape(-1, 224, 224, 1)\n",
    "imgs, labels = next(train_batches)\n",
    "print(imgs[0].shape)\n",
    "#print(type(train_batches))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Regularized Model\n",
    "model = Sequential()\n",
    "model.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\n",
    "model.add(MaxPool2D(pool_size=(2,2),strides=2))\n",
    "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(MaxPool2D(pool_size=(2,2),strides=2))\n",
    "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_regularizer=regularizers.l2(0.01)))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(MaxPool2D(pool_size=(2,2),strides=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(units=5, activation=\"softmax\"))\n",
    "model.compile(Adam(lr=.0001), loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extensive\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from keras import regularizers\n",
    "model = Sequential()\n",
    "model.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\", kernel_regularizer=regularizers.l2(0.1)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(MaxPool2D(pool_size=(2,2),strides=2))\n",
    "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_regularizer=regularizers.l2(0.1)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_regularizer=regularizers.l2(0.1)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(MaxPool2D(pool_size=(2,2),strides=2))\n",
    "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_regularizer=regularizers.l2(0.1)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\", kernel_regularizer=regularizers.l2(0.1)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(MaxPool2D(pool_size=(2,2),strides=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(units=5, activation=\"softmax\"))\n",
    "model.compile(Adam(lr=.0001), loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train_batches, batch_size=10 , validation_data=valid_batches, epochs=13, verbose=2)           "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2\n",
    "model.save('dataset/OF_2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_batches, batch_size=10, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grade_predictions = np.argmax(predictions,axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in Grade_predictions:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_labels = test_batches[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(test_labels, Grade_predictions)"
   ]
  }
 ]
}