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   "id": "741c0ad2-1f11-4981-b8d5-3f29e4fccde7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from typing import Any, Dict, Tuple, Union\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import PIL\n",
    "import SimpleITK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b21ce89f-c656-4137-b27c-8ed352fbd5ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "###########################################\n",
    "# PARAMETERS TO PLAY WITH\n",
    "\n",
    "# Select the patient identification (scalar value between 1 and 45)\n",
    "patient_id = 1\n",
    "time_id = \"ED\" # ED or ES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "840099b2-2cbb-45fa-909c-042ea9ad0173",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sitk_load(filepath: Union[str, Path]) -> Tuple[np.ndarray, Dict[str, Any]]:\n",
    "    \"\"\"Loads an image using SimpleITK and returns the image and its metadata.\n",
    "\n",
    "    Args:\n",
    "        filepath: Path to the image.\n",
    "\n",
    "    Returns:\n",
    "        - ([N], H, W), Image array.\n",
    "        - Collection of metadata.\n",
    "    \"\"\"\n",
    "    # Load image and save info\n",
    "    image = SimpleITK.ReadImage(str(filepath))\n",
    "    info = {\"origin\": image.GetOrigin(), \"spacing\": image.GetSpacing(), \"direction\": image.GetDirection()}\n",
    "\n",
    "    # Extract numpy array from the SimpleITK image object\n",
    "    im_array = np.squeeze(SimpleITK.GetArrayFromImage(image))\n",
    "\n",
    "    return im_array, info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4c0c7fb-374e-4b1a-854a-ca02ecd1651a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Specify the ultrasound/segmentation pair to be loaded\n",
    "patient_name = f\"patient{patient_id:02d}\"\n",
    "patient_dir = Path(f\"../dataset/{patient_name}\")\n",
    "path_to_bmode_image = patient_dir / f\"{patient_name}_{time_id}.nii.gz\"\n",
    "path_to_gt_segmentation = patient_dir / f\"{patient_name}_{time_id}_gt.nii.gz\"\n",
    "\n",
    "# Call of a specific function that reads the .nii.gz files and gives access to the corresponding images and metadata\n",
    "bmode, info = sitk_load(path_to_bmode_image)\n",
    "voxelspacing = info['spacing']\n",
    "depth, width, height = bmode.shape\n",
    "gt, info_gt = sitk_load(path_to_gt_segmentation)\n",
    "voxelspacing_gt = info_gt['spacing']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb09f80f-d055-40c0-8847-14d1778db212",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display the corresponding useful information\n",
    "print(f\"{type(bmode)=}\")\n",
    "print(f\"{bmode.dtype=}\")\n",
    "print(f\"{bmode.shape=}\")\n",
    "print(f\"{voxelspacing=}\")\n",
    "print('')\n",
    "\n",
    "print(f\"{type(gt)=}\")\n",
    "print(f\"{gt.dtype=}\")\n",
    "print(f\"{gt.shape=}\")\n",
    "print(f\"{voxelspacing_gt=}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c9503a-09f3-4fe7-9412-e0d4900a8455",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display one Y-slice\n",
    "\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "slice_id = width // 2\n",
    "\n",
    "px = 1/plt.rcParams['figure.dpi']  # pixel in inches\n",
    "fig = plt.figure(figsize=(depth*px*1.5, height*px*1.5))\n",
    "bmode_im = plt.imshow(bmode[:,slice_id,:], cmap='gray',vmin=0,vmax=255)\n",
    "gt_im = plt.imshow(np.ma.masked_where(gt[:,slice_id,:] == 0, gt[:,slice_id,:]), interpolation='none', cmap='jet', alpha=0.5)\n",
    "plt.axis(\"off\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c92aa98e-89dc-485d-84b3-77c47d9bbdae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display the set of Y-slices as a sequence \n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import animation\n",
    "from IPython.display import HTML\n",
    "\n",
    "px = 1/plt.rcParams['figure.dpi']  # pixel in inches\n",
    "fig = plt.figure(figsize=(depth*px*1.5, height*px*1.5))\n",
    "bmode_im = plt.imshow(bmode[:,0,:], cmap='gray', vmin=0, vmax=255)\n",
    "gt_im = plt.imshow(np.ma.masked_where(gt[:,0,:] == 0, gt[:,0,:]), interpolation='none', cmap='jet', alpha=0.5)\n",
    "plt.axis(\"off\")\n",
    "plt.tight_layout()\n",
    "plt.close() # this is required to not display the generated image\n",
    "\n",
    "def init():\n",
    "    \"\"\"Function that initializes the first frame of the video\"\"\"\n",
    "    bmode_im.set_data(bmode[:,0,:])\n",
    "    gt_im.set_data(gt[:,0,:])\n",
    "\n",
    "def animate(frame_idx):\n",
    "    \"\"\"Callback that fetches the data for subsequent frames.\"\"\"\n",
    "    bmode_im.set_data(bmode[:,frame_idx,:])\n",
    "    gt_im.set_data(np.ma.masked_where(gt[:,frame_idx,:] == 0, gt[:,frame_idx,:]))\n",
    "    return bmode_im, gt_im\n",
    "\n",
    "interval = 10000 / width # Adjust delay between frames so that animation lasts 10 seconds\n",
    "anim = animation.FuncAnimation(fig, animate, init_func=init, frames=len(bmode), interval=interval)\n",
    "HTML(anim.to_html5_video())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ff197f38-1095-4304-bebe-14365bdf0663",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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