232 lines (231 with data), 58.6 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow\n",
    "from tensorflow import keras\n",
    "from keras.models import load_model\n",
    "from keras.utils import to_categorical\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "import segmentation_models as sm\n",
    "from keras.metrics import MeanIoU\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "# Note: Importing segmentation models library may give you generic_utils error on TF2.x\n",
    "# When the error shows up, click the __init__.py link in the error message and change..\n",
    "# keras.utils.generic_utils.get_custom_objects().update(custom_objects)\n",
    "# to\n",
    "# keras.utils.get_custom_objects().update(custom_objects)\n",
    "# Then save the init.py file and restart runtime and run this cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set compile=False as we are not loading it for training, only for prediction.\n",
    "model = load_model('D:/MRI - Tairawhiti (User POV)/Pre-Trained Models (Google Colab)/resnet(0.91 DSC, 3 Patients, 3 Epoches)/res34_backbone_n_epochs.hdf5', compile=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Images Shape:  (5, 512, 512, 3)\n",
      "Test Masks Shape:  (5, 512, 512, 1)\n",
      "Test Labels:  [0. 2.]\n"
     ]
    }
   ],
   "source": [
    "pred_name = 'msk_005'\n",
    "n_classes = 4\n",
    "\n",
    "img = nib.load((\"D:\\MRI - Tairawhiti (User POV)/nnUNet Data/scans/{}.nii.gz\").format(pred_name))\n",
    "img_data = img.get_fdata()\n",
    "X_test = np.repeat(img_data, 3, axis=3)\n",
    "X_test = X_test[0:5]\n",
    "\n",
    "msk = nib.load((\"D:\\MRI - Tairawhiti (User POV)/nnUNet Data/multiclass_masks/{}.nii.gz\").format(pred_name))\n",
    "y_test = msk.get_fdata()\n",
    "y_test = y_test[0:5]\n",
    "\n",
    "print(\"Test Images Shape: \", X_test.shape)\n",
    "print(\"Test Masks Shape: \", y_test.shape)\n",
    "print(\"Test Labels: \", np.unique(y_test))\n",
    "\n",
    "##Model \n",
    "BACKBONE = 'resnet34'\n",
    "preprocess_input = sm.get_preprocessing(BACKBONE)\n",
    "\n",
    "# preprocess input\n",
    "X_test_processed = preprocess_input(X_test)\n",
    "\n",
    "test_masks_cat = to_categorical(y_test, num_classes=n_classes)\n",
    "y_test_cat = test_masks_cat.reshape((y_test.shape[0], y_test.shape[1], y_test.shape[2], n_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Model \n",
    "BACKBONE = 'resnet34'\n",
    "preprocess_input = sm.get_preprocessing(BACKBONE)\n",
    "\n",
    "# preprocess input\n",
    "X_test_processed = preprocess_input(X_test)\n",
    "\n",
    "test_masks_cat = to_categorical(y_test, num_classes=n_classes)\n",
    "y_test_cat = test_masks_cat.reshape((y_test.shape[0], y_test.shape[1], y_test.shape[2], n_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 5s 5s/step\n",
      "Pred Mask Shape:  (5, 512, 512)\n",
      "Mean IoU: 0.9111407\n",
      "Dice Score - Tibia: nan\n",
      "Dice Score - Femur: 0.9\n",
      "Dice Score - Fibula: nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\GGPC\\AppData\\Local\\Temp\\ipykernel_9464\\1776725933.py:16: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  dice = 2.0 * intersection.sum() / (true_array.sum() + pred_array.sum())\n"
     ]
    }
   ],
   "source": [
    "y_pred=model.predict(X_test_processed)\n",
    "y_pred_argmax=np.argmax(y_pred, axis=3)\n",
    "\n",
    "print('Pred Mask Shape: ', y_pred_argmax.shape)\n",
    "\n",
    "# Calculate Mean IoU\n",
    "mean_iou = MeanIoU(num_classes=n_classes)  # Replace n_classes with the number of classes in your segmentation task\n",
    "mean_iou.update_state(y_test, y_pred_argmax)\n",
    "iou = mean_iou.result().numpy()\n",
    "\n",
    "def dice_coefficient(true_array, pred_array):\n",
    "    true_array = np.asarray(true_array).astype(bool)\n",
    "    pred_array = np.asarray(pred_array).astype(bool)\n",
    "\n",
    "    intersection = np.logical_and(true_array, pred_array)\n",
    "    dice = 2.0 * intersection.sum() / (true_array.sum() + pred_array.sum())\n",
    "    return round(dice, 2)\n",
    "\n",
    "\n",
    "\n",
    "y_pred_argmax = np.expand_dims(y_pred_argmax, axis = -1)\n",
    "\n",
    "tibia_seg_data_pred = np.where(y_pred_argmax != 1, 0, 1)\n",
    "femur_seg_data_pred = np.where(y_pred_argmax != 2, 0, 1)\n",
    "fibula_seg_data_pred = np.where(y_pred_argmax != 3, 0, 1)\n",
    "\n",
    "tibia_seg_data_gt = np.where(y_test != 1, 0, 1)\n",
    "femur_seg_data_gt = np.where(y_test != 2, 0, 1)\n",
    "fibula_seg_data_gt = np.where(y_test != 3, 0, 1)\n",
    "\n",
    "\n",
    "dice_score_tibia = dice_coefficient(tibia_seg_data_gt, tibia_seg_data_pred)\n",
    "dice_score_femur = dice_coefficient(femur_seg_data_gt, femur_seg_data_pred)\n",
    "dice_score_fibula = dice_coefficient(fibula_seg_data_gt, fibula_seg_data_pred)\n",
    "\n",
    "\n",
    "print('Mean IoU:', iou)\n",
    "print('Dice Score - Tibia:', dice_score_tibia)\n",
    "print('Dice Score - Femur:', dice_score_femur)\n",
    "print('Dice Score - Fibula:', dice_score_fibula)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_number = random.randint(0, len(X_test_processed)-1)\n",
    "img = X_test_processed[img_number]\n",
    "mask = y_test[img_number]\n",
    "prediction = y_pred_argmax[img_number]\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.subplot(231)\n",
    "plt.title('Image')\n",
    "plt.imshow(img, cmap='gray')\n",
    "plt.subplot(232)\n",
    "plt.title('Mask')\n",
    "plt.imshow(mask[:,:,0])\n",
    "plt.subplot(233)\n",
    "plt.title('Prediction')\n",
    "plt.imshow(prediction)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Py39-CNN",
   "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": "3.9.17"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}