test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import tempfile\n",
    "import shutil\n",
    "\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "\n",
    "from monai.config import print_config\n",
    "from monai.data import ITKReader, ITKWriter\n",
    "import matplotlib.pyplot as plt\n",
    "from monai.transforms import (\n",
    "    EnsureChannelFirstd,\n",
    "    Compose,\n",
    "    LoadImaged,\n",
    "    Spacingd,\n",
    "    CenterSpatialCropd,\n",
    "    MaskIntensityd,\n",
    "    ScaleIntensityd,\n",
    "    MapTransform,\n",
    "    Affined,\n",
    "    Resized,\n",
    "    Zoomd,\n",
    "    SpatialPadd,\n",
    ")\n",
    "from monai.data import DataLoader, Dataset\n",
    "import pandas as pd\n",
    "import xml.etree.ElementTree as ET\n",
    "from dicttoxml import dicttoxml\n",
    "from xml.dom.minidom import parseString\n",
    "import tools\n",
    "import torch\n",
    "# print_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 320, 240)\n",
      "(400, 320, 1)\n"
     ]
    }
   ],
   "source": [
    "import itk\n",
    "reader = ITKReader(reverse_indexing=True, affine_lps_to_ras=False)\n",
    "\n",
    "file_name = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Pre-Ablation_01.mha\"\n",
    "mask_file_name = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\moving_image_mask.mha\"\n",
    "filename_2DUS = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Image_0050_formated.mha\"\n",
    "mask_file_name_2DUS = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\fixed_image_mask.mha\"\n",
    "image_obj = reader.read(file_name)\n",
    "image_array, image_meta = reader.get_data(image_obj)\n",
    "\n",
    "mask_image_obj = reader.read(mask_file_name)\n",
    "mask_image_array, mask_image_meta = reader.get_data(mask_image_obj)\n",
    "print(image_array.shape)\n",
    "print(image_array.T.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = ITKWriter(output_dtype=np.float32, affine_lps_to_ras= False)\n",
    "writer.set_data_array(image_array.T, channel_dim=None)\n",
    "writer.set_metadata(image_meta)\n",
    "output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Pre-Ablation_01_output.mha\"\n",
    "writer.write(output_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "# data_dict_3DUS = {'image': file_name, 'image_mask':mask_file_name}\n",
    "# data_dict_2DUS = {'image': filename_2DUS, 'image_mask': mask_file_name_2DUS, 'case_ID_3DUS': '0'}\n",
    "\n",
    "\n",
    "# test_image = tfm_imageLoader(data_dict_2DUS)\n",
    "# origin_image = test_image['volume_name']\n",
    "# print(\"original image shape: \", origin_image.shape)\n",
    "\n",
    "# writer.set_data_array(origin_image.data, channel_dim=0)\n",
    "# writer.set_metadata(origin_image.meta, resample=False) # to decide whether the image needs to be resampled\n",
    "# output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\fixed_image_resampled.mha\"\n",
    "# # output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Image_0050_output_resample.mha\"\n",
    "# writer.write(output_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from monai.config import KeysCollection\n",
    "import SimpleITK as sitk\n",
    "def ConvertITKTransform2NumpyArray(transform):\n",
    "    \"\"\"ITK transfrom: transform_from_parent_LPS.\"\"\"\n",
    "    rotation = transform.GetMatrix()\n",
    "    translation = transform.GetTranslation()\n",
    "    center = transform.GetCenter()\n",
    "    rotation_array = np.array([[rotation[0], rotation[1], rotation[2]], [rotation[3], rotation[4], rotation[5]], [rotation[6], rotation[7], rotation[8]]])\n",
    "    offset = -rotation_array@center + center + translation\n",
    "\n",
    "    transform_array = np.array([[rotation[0], rotation[1], rotation[2], offset[0]], [rotation[3], rotation[4], rotation[5], offset[1]], [rotation[6], rotation[7], rotation[8], offset[2]], [0, 0, 0, 1]])\n",
    "    return transform_array\n",
    "\n",
    "def ConvertNumpyArray2ITKTransform(transform_array):\n",
    "    \"\"\"ITK transform: transfrom_from_parent_LPS\"\"\"\n",
    "    dimension = 3\n",
    "    transform = sitk.AffineTransform(dimension)\n",
    "    rotation = [transform_array[0][0], transform_array[0][1], transform_array[0][2], transform_array[1][0], transform_array[1][1], transform_array[1][2], transform_array[2][0], transform_array[2][1], transform_array[2][2]]\n",
    "    translation = [transform_array[0][3], transform_array[1][3], transform_array[2][3]]\n",
    "    transform.SetMatrix(rotation)\n",
    "    transform.SetTranslation(translation)\n",
    "    return transform\n",
    "\n",
    "def CreateLookupTable_backup(all_cases_metadata, save_flag = True):\n",
    "    num_of_cases = len(all_cases_metadata) # number of volumes\n",
    "    alldataset_LUT = []\n",
    "    volume_LUT = []\n",
    "    for case_index in range(0, num_of_cases):\n",
    "        case_tree = ET.parse(all_cases_metadata[case_index].text)\n",
    "        case_root = case_tree.getroot()\n",
    "\n",
    "        # get the volume name (moving image)\n",
    "        moving_image_metadata = case_root.find('moving_image')\n",
    "        moving_image_fileNAME = os.path.join(moving_image_metadata.find('directory').text, moving_image_metadata.find('name_raw_US_volume').text) \n",
    "\n",
    "        # get the mask of volume (moving image)\n",
    "        moving_image_mask_metadata = case_root.find('moving_image_mask')\n",
    "        moving_image_mask_fileNAME = os.path.join(moving_image_mask_metadata.find('directory').text, moving_image_mask_metadata.find('name_rawdata').text) \n",
    "\n",
    "        # get the fixed image\n",
    "        fixed_images_metadata = case_root.find('fixed_image')\n",
    "        rangeMin = int(fixed_images_metadata.find('rangeMin').text)\n",
    "        rangeMax = int(fixed_images_metadata.find('rangeMax').text)\n",
    "\n",
    "        # get the mask of fixed image\n",
    "        fixed_image_mask_metadata = case_root.find('fixed_image_mask')\n",
    "        fixed_image_mask_fileNAME = os.path.join(fixed_image_mask_metadata.find('directory').text, fixed_image_mask_metadata.find('name').text)\n",
    "\n",
    "        volume_case_dict = {'volume_ID': case_index,'volume_name': moving_image_fileNAME, \"volume_mask_name\": moving_image_mask_fileNAME}\n",
    "        volume_LUT.append(volume_case_dict)\n",
    "        # get the transform (3DUS to CT/MRI)\n",
    "        tfm_3DUS_CT_metadata = case_root.find('transform_3DUS_CT')\n",
    "        tfm_3DUS_CT_fileNAME = os.path.join(tfm_3DUS_CT_metadata.find('directory').text, tfm_3DUS_CT_metadata.find('name').text)\n",
    "\n",
    "        # get the frame flip flag\n",
    "        US_image_setting_metadata = case_root.find('US_image_setting')\n",
    "        frame_flip_flag = US_image_setting_metadata.find(\"flip\").text\n",
    "        \n",
    "        # get the transform metadata (slice to volume registration)\n",
    "        tfm_RegS2V_metadata = case_root.find('slice_to_volume_registration')\n",
    "        for frame_index in range(rangeMin, rangeMax + 1):\n",
    "            num_letters_frame_index = len(str(frame_index))\n",
    "            if num_letters_frame_index == 1:\n",
    "                full_frame_index = '000' + str(frame_index)       \n",
    "            elif num_letters_frame_index == 2:\n",
    "                full_frame_index = '00' + str(frame_index)      \n",
    "            elif num_letters_frame_index == 3:\n",
    "                full_frame_index = '0' + str(frame_index)\n",
    "            elif num_letters_frame_index == 4:\n",
    "                full_frame_index = str(frame_index)\n",
    "            else :\n",
    "                print(\"checking out the maximum length of the filename!\")\n",
    "            \n",
    "            # get the fixed image filename\n",
    "            fixed_image_filename = \"Image_\" + full_frame_index + \".mha\"\n",
    "            fixed_image_fileNAME = os.path.join(fixed_images_metadata.find('directory').text, fixed_image_filename) \n",
    "            \n",
    "            initial_frame_name = \"initial_\"+ full_frame_index + \".tfm\"\n",
    "            correction_frame_name = \"correction_\" + full_frame_index+ \".tfm\"\n",
    "            tfm_RegS2V_initial_fileNAME = os.path.join(tfm_RegS2V_metadata.find('US_directory').text, tfm_RegS2V_metadata.find('initial_folder').text, initial_frame_name)\n",
    "            tfm_RegS2V_correction_fileNAME = os.path.join(tfm_RegS2V_metadata.find('US_directory').text, tfm_RegS2V_metadata.find('correction_folder').text, correction_frame_name)\n",
    "\n",
    "            if frame_index == rangeMin:\n",
    "                fixed_image_fileNAME_pre = fixed_image_fileNAME\n",
    "                tfm_RegS2V_correction_fileNAME_pre = tfm_RegS2V_correction_fileNAME\n",
    "            else:\n",
    "                num_letters_frame_index_pre = len(str(frame_index-1))\n",
    "                if num_letters_frame_index_pre == 1:\n",
    "                    full_frame_index_pre = '000' + str(frame_index-1)       \n",
    "                elif num_letters_frame_index_pre == 2:\n",
    "                    full_frame_index_pre = '00' + str(frame_index-1)      \n",
    "                elif num_letters_frame_index_pre == 3:\n",
    "                    full_frame_index_pre = '0' + str(frame_index-1)\n",
    "                elif num_letters_frame_index_pre == 4:\n",
    "                    full_frame_index_pre = str(frame_index-1)\n",
    "                else :\n",
    "                    print(\"checking out the maximum length of the filename!\")\n",
    "                fixed_image_filename_pre = \"Image_\" + full_frame_index_pre + \".mha\"\n",
    "                fixed_image_fileNAME_pre = os.path.join(fixed_images_metadata.find('directory').text, fixed_image_filename_pre)\n",
    "\n",
    "                correction_frame_name = \"correction_\" + full_frame_index_pre+ \".tfm\"\n",
    "                tfm_RegS2V_correction_fileNAME_pre = os.path.join(tfm_RegS2V_metadata.find('US_directory').text, tfm_RegS2V_metadata.find('correction_folder').text, correction_frame_name)\n",
    "\n",
    "                # case_frame_pair = [case_index, frame_index, frame_index-1]\n",
    "            # case_dict = {'volume_ID': case_index, 'volume_name': moving_image_fileNAME, \"volume_mask_name\": moving_image_mask_fileNAME, \n",
    "            #              \"frame_name\":fixed_image_fileNAME, \"frame_name_pre\": fixed_image_fileNAME_pre, 'frame_mask_name': fixed_image_mask_fileNAME,\n",
    "            #              \"tfm_3DUS_CT_fileNAME\": tfm_3DUS_CT_fileNAME, \"tfm_RegS2V_initial_fileNAME\": tfm_RegS2V_initial_fileNAME, \"tfm_RegS2V_correction_fileNAME\": tfm_RegS2V_correction_fileNAME, \n",
    "            #              \"tfm_RegS2V_correction_fileNAME_pre\": tfm_RegS2V_correction_fileNAME_pre}\n",
    "            case_dict = {'volume_ID': case_index, \n",
    "                         \"frame_name\":fixed_image_fileNAME,'frame_mask_name': fixed_image_mask_fileNAME,\n",
    "                         \"tfm_RegS2V\": [tfm_3DUS_CT_fileNAME, tfm_RegS2V_initial_fileNAME, tfm_RegS2V_correction_fileNAME, tfm_RegS2V_correction_fileNAME_pre],\n",
    "                         \"tfm_gt_diff_mat\": None, \"tfm_RegS2V_initial_mat\": None, \"frame_flip_flag\": frame_flip_flag}\n",
    "            alldataset_LUT.append(case_dict)\n",
    "\n",
    "    data_LUT_np = np.array(alldataset_LUT)\n",
    "    # print(\"Lookuptable: \", data_LUT_np)\n",
    "    # print(\"size of lut:\", data_LUT_np.shape)\n",
    "    if save_flag:\n",
    "        # dataframe = pd.DataFrame(data_LUT_np, columns=['volume case ID', 'Transform ID (gt)', 'Transform ID (initial)'])\n",
    "        # dataframe.to_csv(r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\FVR-Net\\dataset_LUT.csv\")\n",
    "        alldataset_xml = dicttoxml(alldataset_LUT, custom_root=\"all_cases\")\n",
    "        dom = parseString(alldataset_xml)\n",
    "        dom.writexml( open(r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\FVR-Net\\alldataset_dict.xml\", 'w'),\n",
    "               indent=\"\\t\",\n",
    "               addindent=\"\\t\",\n",
    "               newl='\\n')\n",
    "        \n",
    "        volume_dict_xml = dicttoxml(volume_LUT, custom_root=\"volume_cases\")\n",
    "        dom = parseString(volume_dict_xml)\n",
    "        dom.writexml( open(r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\FVR-Net\\volume_dict.xml\", 'w'),\n",
    "               indent=\"\\t\",\n",
    "               addindent=\"\\t\",\n",
    "               newl='\\n')\n",
    "        \n",
    "        # print(dom.toprettyxml())\n",
    "    return alldataset_LUT, volume_LUT\n",
    "\n",
    "def CreateLookupTable(cases_metadata, dataset_dict_fileNAME, volume_dict_fileNAME, save_flag = True):\n",
    "    num_of_cases = len(cases_metadata) # number of volumes\n",
    "    alldataset_LUT = []\n",
    "    volume_LUT = []\n",
    "    for case_index in range(0, num_of_cases):\n",
    "        case_tree = ET.parse(os.path.join(project_data_dir, cases_metadata[case_index].text))\n",
    "        case_root = case_tree.getroot()\n",
    "\n",
    "        # get the volume name (moving image)\n",
    "        moving_image_metadata = case_root.find('moving_image')\n",
    "        # moving_image_fileNAME = os.path.join(project_data_dir, moving_image_metadata.find('directory').text, moving_image_metadata.find('name_raw_US_volume').text) \n",
    "        moving_image_fileNAME = os.path.join(project_data_dir, \"LHV\\LHV-06\\pre06_sweep02\\in_moving_volumes\", moving_image_metadata.find('name_raw_US_volume').text) \n",
    "        \n",
    "        print(\"moving_image_fileNAME: \", moving_image_fileNAME)\n",
    "        print(\"project_data_dir\", project_data_dir)\n",
    "        print(\"moving_image_metadata.find('directory').text\", moving_image_metadata.find('directory').text)\n",
    "        print(\"moving_image_metadata.find('name_raw_US_volume').text\", moving_image_metadata.find('name_raw_US_volume').text)\n",
    "        # get the mask of volume (moving image)\n",
    "        moving_image_mask_metadata = case_root.find('moving_image_mask')\n",
    "        moving_image_mask_fileNAME = os.path.join(project_data_dir, moving_image_mask_metadata.find('directory').text, moving_image_mask_metadata.find('name_rawdata').text) \n",
    "\n",
    "        # get the fixed image\n",
    "        fixed_images_metadata = case_root.find('fixed_image')\n",
    "        rangeMin = int(fixed_images_metadata.find('rangeMin').text)\n",
    "        rangeMax = int(fixed_images_metadata.find('rangeMax').text)\n",
    "\n",
    "        # get the mask of fixed image\n",
    "        fixed_image_mask_metadata = case_root.find('fixed_image_mask')\n",
    "        fixed_image_mask_fileNAME = os.path.join(project_data_dir, fixed_image_mask_metadata.find('directory').text, fixed_image_mask_metadata.find('name').text)\n",
    "\n",
    "        volume_case_dict = {'volume_ID': case_index,'volume_name': moving_image_fileNAME, \"volume_mask_name\": moving_image_mask_fileNAME}\n",
    "        volume_LUT.append(volume_case_dict)\n",
    "        # get the transform (3DUS to CT/MRI)\n",
    "        tfm_3DUS_CT_metadata = case_root.find('transform_3DUS_CT')\n",
    "        tfm_3DUS_CT_fileNAME = os.path.join(project_data_dir, tfm_3DUS_CT_metadata.find('directory').text, tfm_3DUS_CT_metadata.find('name').text)\n",
    "\n",
    "        # get the transform metadata (slice to volume registration)\n",
    "        tfm_RegS2V_metadata = case_root.find('slice_to_volume_registration')\n",
    "        for frame_index in range(rangeMin, rangeMax + 1):\n",
    "            num_letters_frame_index = len(str(frame_index))\n",
    "            if num_letters_frame_index == 1:\n",
    "                full_frame_index = '000' + str(frame_index)       \n",
    "            elif num_letters_frame_index == 2:\n",
    "                full_frame_index = '00' + str(frame_index)      \n",
    "            elif num_letters_frame_index == 3:\n",
    "                full_frame_index = '0' + str(frame_index)\n",
    "            elif num_letters_frame_index == 4:\n",
    "                full_frame_index = str(frame_index)\n",
    "            else :\n",
    "                print(\"checking out the maximum length of the filename!\")\n",
    "            \n",
    "            # get the fixed image filename\n",
    "            fixed_image_filename = \"Image_\" + full_frame_index + \".mha\"\n",
    "            fixed_image_fileNAME = os.path.join(project_data_dir, fixed_images_metadata.find('directory').text, fixed_image_filename) \n",
    "            \n",
    "            initial_frame_name = \"initial_\"+ full_frame_index + \".tfm\"\n",
    "            correction_frame_name = \"correction_\" + full_frame_index+ \".tfm\"\n",
    "            tfm_RegS2V_initial_fileNAME = os.path.join(project_data_dir, tfm_RegS2V_metadata.find('US_directory').text, tfm_RegS2V_metadata.find('initial_folder').text, initial_frame_name)\n",
    "            tfm_RegS2V_correction_fileNAME = os.path.join(project_data_dir, tfm_RegS2V_metadata.find('US_directory').text, tfm_RegS2V_metadata.find('correction_folder').text, correction_frame_name)\n",
    "\n",
    "            if frame_index == rangeMin:\n",
    "                # fixed_image_fileNAME_pre = fixed_image_fileNAME\n",
    "                tfm_RegS2V_correction_fileNAME_pre = tfm_RegS2V_correction_fileNAME\n",
    "            else:\n",
    "                num_letters_frame_index_pre = len(str(frame_index-1))\n",
    "                if num_letters_frame_index_pre == 1:\n",
    "                    full_frame_index_pre = '000' + str(frame_index-1)       \n",
    "                elif num_letters_frame_index_pre == 2:\n",
    "                    full_frame_index_pre = '00' + str(frame_index-1)      \n",
    "                elif num_letters_frame_index_pre == 3:\n",
    "                    full_frame_index_pre = '0' + str(frame_index-1)\n",
    "                elif num_letters_frame_index_pre == 4:\n",
    "                    full_frame_index_pre = str(frame_index-1)\n",
    "                else :\n",
    "                    print(\"checking out the maximum length of the filename!\")\n",
    "                # fixed_image_filename_pre = \"Image_\" + full_frame_index_pre + \".mha\"\n",
    "                # fixed_image_fileNAME_pre = os.path.join(fixed_images_metadata.find('directory').text, fixed_image_filename_pre)\n",
    "\n",
    "                correction_frame_name = \"correction_\" + full_frame_index_pre+ \".tfm\"\n",
    "                tfm_RegS2V_correction_fileNAME_pre = os.path.join(project_data_dir, tfm_RegS2V_metadata.find('US_directory').text, tfm_RegS2V_metadata.find('correction_folder').text, correction_frame_name)\n",
    "\n",
    "                # case_frame_pair = [case_index, frame_index, frame_index-1]\n",
    "            # case_dict = {'volume_ID': case_index, 'volume_name': moving_image_fileNAME, \"volume_mask_name\": moving_image_mask_fileNAME, \n",
    "            #              \"frame_name\":fixed_image_fileNAME, \"frame_name_pre\": fixed_image_fileNAME_pre, 'frame_mask_name': fixed_image_mask_fileNAME,\n",
    "            #              \"tfm_3DUS_CT_fileNAME\": tfm_3DUS_CT_fileNAME, \"tfm_RegS2V_initial_fileNAME\": tfm_RegS2V_initial_fileNAME, \"tfm_RegS2V_correction_fileNAME\": tfm_RegS2V_correction_fileNAME, \n",
    "            #              \"tfm_RegS2V_correction_fileNAME_pre\": tfm_RegS2V_correction_fileNAME_pre}\n",
    "            case_dict = {'volume_ID': case_index, \n",
    "                         \"frame_name\":fixed_image_fileNAME,'frame_mask_name': fixed_image_mask_fileNAME,\n",
    "                         \"tfm_RegS2V\": [tfm_3DUS_CT_fileNAME, tfm_RegS2V_initial_fileNAME, tfm_RegS2V_correction_fileNAME, tfm_RegS2V_correction_fileNAME_pre],\n",
    "                         \"tfm_gt_diff_mat\": None, \"tfm_gt_diff_dof\": None,\n",
    "                         \"tfm_RegS2V_initial_mat\": None, \"tfm_RegS2V_gt_mat\": None}\n",
    "            alldataset_LUT.append(case_dict)\n",
    "\n",
    "    data_LUT_np = np.array(alldataset_LUT)\n",
    "    # print(\"Lookuptable: \", data_LUT_np)\n",
    "    # print(\"size of lut:\", data_LUT_np.shape)\n",
    "    if save_flag:\n",
    "        # dataframe = pd.DataFrame(data_LUT_np, columns=['volume case ID', 'Transform ID (gt)', 'Transform ID (initial)'])\n",
    "        # dataframe.to_csv(r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\FVR-Net\\dataset_LUT.csv\")\n",
    "        alldataset_xml = dicttoxml(alldataset_LUT, custom_root=\"all_cases\")\n",
    "        dom = parseString(alldataset_xml)\n",
    "        dom.writexml( open(dataset_dict_fileNAME, 'w'),\n",
    "               indent=\"\\t\",\n",
    "               addindent=\"\\t\",\n",
    "               newl='\\n')\n",
    "        \n",
    "        volume_dict_xml = dicttoxml(volume_LUT, custom_root=\"volume_cases\")\n",
    "        dom = parseString(volume_dict_xml)\n",
    "        dom.writexml( open(volume_dict_fileNAME, 'w'),\n",
    "               indent=\"\\t\",\n",
    "               addindent=\"\\t\",\n",
    "               newl='\\n')\n",
    "        \n",
    "        # print(dom.toprettyxml())\n",
    "    return alldataset_LUT, volume_LUT\n",
    "\n",
    "# Note: direct to home directory\n",
    "\n",
    "project_dir = 'E:\\PROGRAM\\Project_PhD\\Registration\\DeepRegS2V'\n",
    "# print(project_dir)\n",
    "project_data_dir = project_dir + \"\\data\"\n",
    "project_src_dir = project_dir + \"\\src\"\n",
    "# data_tree_file = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\FVR-Net\\dataset_index_test.xml\"\n",
    "data_tree_file = os.path.join(project_src_dir, \"dataset_index_test.xml\")\n",
    "data_tree = ET.parse(data_tree_file)\n",
    "root = data_tree.getroot()\n",
    "all_cases_metadata = root.find('all_cases')\n",
    "training_cases_metadata = root.find(\"training_cases\")\n",
    "validation_cases_metadata = root.find(\"validation_cases\")\n",
    "\n",
    "training_dataset_dict_fileNAME = os.path.join(project_src_dir, \"training_dataset_dict.xml\")\n",
    "training_volume_dict_fileNAME = os.path.join(project_src_dir, \"training_volume_dict.xml\")\n",
    "validation_dataset_dict_fileNAME = os.path.join(project_src_dir, \"validation_dataset_dict.xml\")\n",
    "validation_volume_dict_fileNAME = os.path.join(project_src_dir, \"validation_volume_dict.xml\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "moving_image_fileNAME:  E:\\LHV\\LHV-06\\pre06_sweep02\\in_moving_volumes\\Pre-Ablation_06.mha\n",
      "project_data_dir E:\\PROGRAM\\Project_PhD\\Registration\\DeepRegS2V\\data\n",
      "moving_image_metadata.find('directory').text \\LHV\\LHV-06\\pre06_sweep02\\in_moving_volumes\n",
      "moving_image_metadata.find('name_raw_US_volume').text Pre-Ablation_06.mha\n",
      "moving_image_fileNAME:  E:\\LHV\\LHV-03\\pre04_sweep04\\in_moving_volumes\\Pre-Ablation_04.mha\n",
      "project_data_dir E:\\PROGRAM\\Project_PhD\\Registration\\DeepRegS2V\\data\n",
      "moving_image_metadata.find('directory').text \\LHV\\LHV-03\\pre04_sweep04\\in_moving_volumes\n",
      "moving_image_metadata.find('name_raw_US_volume').text Pre-Ablation_04.mha\n",
      "moving_image_fileNAME:  E:\\LHV\\LHV-03\\pre01_sweep04\\in_moving_volumes\\Pre-Ablation_01.mha\n",
      "project_data_dir E:\\PROGRAM\\Project_PhD\\Registration\\DeepRegS2V\\data\n",
      "moving_image_metadata.find('directory').text \\LHV\\LHV-03\\pre01_sweep04\\in_moving_volumes\n",
      "moving_image_metadata.find('name_raw_US_volume').text Pre-Ablation_01.mha\n",
      "[{'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0028.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0028.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0028.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0028.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0029.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0029.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0029.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0028.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0030.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0030.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0030.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0029.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0031.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0031.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0031.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0030.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0032.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0032.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0032.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0031.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0033.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0033.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0033.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0032.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0034.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0034.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0034.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0033.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0035.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0035.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0035.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0034.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0036.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0036.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0036.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0035.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0037.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0037.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0037.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0036.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0038.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0038.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0038.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0037.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0039.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0039.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0039.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0038.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0040.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0040.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0040.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0039.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0041.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0041.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0041.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0040.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0042.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0042.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0042.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0041.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0043.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0043.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0043.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0042.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0044.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0044.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0044.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0043.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0045.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0045.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0045.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0044.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0046.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0046.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0046.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0045.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0047.mha', 'frame_mask_name': 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'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0067.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0069.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0069.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0069.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0068.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0070.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0070.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0070.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0069.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}, {'volume_ID': 0, 'frame_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_reordered_images_mha\\\\Image_0071.mha', 'frame_mask_name': 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_fixed_image_mask\\\\fixed_image_mask.mha', 'tfm_RegS2V': ['E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\in_transform_3DUS_CT\\\\T_3DUS_CT.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\initial_transforms\\\\initial_0071.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0071.tfm', 'E:\\\\LHV\\\\LHV-03\\\\pre01_sweep04\\\\out_slice_to_3DUS_transforms\\\\correction_transforms\\\\correction_0070.tfm'], 'tfm_gt_diff_mat': None, 'tfm_gt_diff_dof': None, 'tfm_RegS2V_initial_mat': None, 'tfm_RegS2V_gt_mat': None}]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "# alldataset_dict, volume_dict = CreateLookupTable(all_cases_metadata, save_flag=True)\n",
    "training_dataset_dict, training_volume_dict = CreateLookupTable(training_cases_metadata,training_dataset_dict_fileNAME, training_volume_dict_fileNAME, save_flag=True)\n",
    "validation_dataset_dict, validation_volume_dict = CreateLookupTable(validation_cases_metadata, validation_dataset_dict_fileNAME, validation_volume_dict_fileNAME, save_flag=True)\n",
    "    \n",
    "# print(training_dataset_dict)\n",
    "print(validation_dataset_dict)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original image shape:  torch.Size([1, 480, 361, 365])\n"
     ]
    }
   ],
   "source": [
    "tfm_imageLoader = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"volume_name\", \"volume_mask_name\"], reader=ITKReader(reverse_indexing=False, affine_lps_to_ras=False),image_only=False),\n",
    "        MaskIntensityd(keys=[\"volume_name\"], mask_key= \"volume_mask_name\"),\n",
    "        EnsureChannelFirstd(keys = [\"volume_name\"]),\n",
    "        Spacingd(keys=[\"volume_name\"], pixdim=(0.5, 0.5, 0.5), mode=(\"bilinear\"))\n",
    "    ]\n",
    ")\n",
    "\n",
    "# test_image = tfm_imageLoader(validation_volume_dict)\n",
    "test_image = tfm_imageLoader(training_volume_dict)\n",
    "origin_image = test_image[0]['volume_name']\n",
    "print(\"original image shape: \", origin_image.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 365, 361, 480])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "origin_image.meta\n",
    "torch.permute(origin_image.data, (0, 3, 2, 1)).shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "writer = ITKWriter(output_dtype=np.float32, affine_lps_to_ras= False)\n",
    "writer.set_data_array(origin_image.data, channel_dim=0)\n",
    "writer.set_metadata(origin_image.meta, resample=False)\n",
    "output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Pre-Ablation_01_output.mha\"\n",
    "writer.write(output_filename)\n",
    "\n",
    "\n",
    "# writer = ITKWriter(output_dtype=np.float32, affine_lps_to_ras= False)\n",
    "# writer.set_data_array(torch.permute(origin_image.data, (0, 3, 2, 1)), channel_dim=0)\n",
    "# writer.set_metadata(origin_image.meta, resample=False)\n",
    "# output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Pre-Ablation_01_output.mha\"\n",
    "# writer.write(output_filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_conversion_pytorch_to_ITK(affine_transform_pytorch_initial, affine_transform_pytorch_correction, volume_size):\n",
    "    #note: affine_transform_ITK (to parent)\n",
    "    \"\"\" to calucate the affine_transorm_ITK, when you get the output from our network\"\"\"\n",
    "    \"\"\"our network is to cacluate the correction transform, assuming we already have a good iniital transform (affine_transform_ITK_initial)\"\"\"\n",
    "    affine_transform_pytorch = affine_transform_pytorch_correction@affine_transform_pytorch_initial\n",
    "    T_normalized = np.array([[2/volume_size[0], 0, 0, 0], [0, 2/volume_size[1], 0, 0], [0, 0, 2/volume_size[2], 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "    affine_transform_ITK = np.linalg.inv(T_normalized) @ affine_transform_pytorch @ T_normalized\n",
    "\n",
    "    return affine_transform_ITK\n",
    "\n",
    "def transform_conversion_pytorch_to_original_ITK(affine_transform_pytorch_initial, affine_transform_pytorch_correction, volume_size, frame_spacing, frame_size, scale):\n",
    "    # TODO: need to test the correctness\n",
    "    #note: affine_transform_ITK (to parent); affine_transform_original_ITK (to parent)\n",
    "    \"\"\" to calucate the affine_transorm_ITK, when you get the output from our network\"\"\"\n",
    "    \"\"\"our network is to cacluate the correction transform, assuming we already have a good iniital transform (affine_transform_ITK_initial)\"\"\"\n",
    "    affine_transform_pytorch = affine_transform_pytorch_correction@affine_transform_pytorch_initial\n",
    "    T_normalized = np.array([[2/volume_size[0], 0, 0, 0], [0, 2/volume_size[1], 0, 0], [0, 0, 2/volume_size[2], 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "    affine_transform_ITK = np.linalg.inv(T_normalized) @ affine_transform_pytorch @ T_normalized\n",
    "    \n",
    "    T_scale = np.array([[scale, 0, 0, 0], [0, scale, 0, 0], [0, 0, scale, 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "    T_scale_inv = np.linalg.inv(T_scale)\n",
    "    \n",
    "    T_translate = np.array([[1, 0, 0, 0], [0, 1, 0, -frame_spacing[1]*frame_size[1]*0.5], [0, 0, 1, 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "    T_translate_inv = np.linalg.inv(T_translate)\n",
    "    #tfm_RegS2V_gt_np_to = T_scale@T_translate@tfm_RegS2V_gt_np_to@T_scale_inv\n",
    "    affine_transform_original_ITK = T_translate_inv@T_scale_inv@affine_transform_ITK@T_scale\n",
    "\n",
    "    return affine_transform_original_ITK\n",
    "\n",
    "def transform_conversion_ITK_to_pytorch(affine_transform_ITK, volume_size):\n",
    "    # note: affine_transform_ITK (to parent)\n",
    "    \"\"\"to achieve the conversion of transform from the ITK transformation to pytorch transformation\"\"\"\n",
    "    \"\"\"For the input and output objects, both of them are rescaled to spacing (1mm, 1mm, 1mm) and recentered the volume, which is to fake the pytorch transform\"\"\"\n",
    "    \"\"\"For the pytorch, the input and output are the torches, which dont have the spacing information, so we can treat them as 1.\"\"\"\n",
    "    \"\"\"To understand how the transform_ITK and transform_pytorch can work well and know the relationship, we need to bring anther package (Scipy) to help us\"\"\"\n",
    "    \"\"\"The conversion workflow is: transform_ITK -> transform_scipy -> transform_pytorch\"\"\"\n",
    "    \"\"\"For the scipy, the input and outout dont have the spacing information, which are the same as the pytorch transform. Differently, the input and output are represented as [1, 2,..., N_w] by [1, 2, ..., N_h] by [1, 2, ..., N_d].\n",
    "    And the transform_scipy is applied around the first voxel [0, 0, 0] by default. If the volume center is set as the (0, 0, 0) in spatial domain, we need to translate the transform_ITK to the center of volume first T_translate, \n",
    "    then apply the transform_ITK, after that, we need to translate back the object.\"\"\"\n",
    "    \"\"\"For the pytorch, the transformation is similiar as the transform_scipy. Differently, the input and ouput need to be normalized first. That means that instead of representing by [1, 2,..., N_w] by [1, 2, ..., N_h] by [1, 2, ..., N_d], \n",
    "    the input and output are represented by [-1,,..., 0, ..., 1] by [-1,,..., 0, ..., 1] by [-1,,..., 0, ..., 1]. For the numpy array input, the grid_sample will recoginize them as \n",
    "    [-N_w/2, ..., -2, -1, 0, 1, 2, ..., N_w/2] by [-N_h/2, ..., -2, -1, 0, 1, 2, ..., N_h/2] by [-N_d/2, ..., -2, -1, 0, 1, 2, ..., N_d/2]\"\"\"\n",
    "    \n",
    "    # print(image_array_volume)\n",
    "    # affine_transform_ITK = data_2DUS[21][\"tfm_RegS2V_gt_mat\"].numpy()\n",
    "    # affine_transform = np.array([[np.cos(45*np.pi/180), -np.sin(45*np.pi/180), 0, 0], [np.sin(45*np.pi/180), np.cos(45*np.pi/180), 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "\n",
    "    \n",
    "    affine_transform_inverse = np.linalg.inv(affine_transform_ITK) # for the grid_sampling, the transform is inversed\n",
    "    \"\"\"transform_scipy -> transform_pytorch\"\"\"\n",
    "    # T_normalized = np.array([[2/volume_size[0], 0, 0, -1], [0, 2/volume_size[1], 0, -1], [0, 0, 2/volume_size[2], -1], [0, 0, 0, 1]]) # LPS to parent\n",
    "    T_normalized = np.array([[2/volume_size[0], 0, 0, 0], [0, 2/volume_size[1], 0, 0], [0, 0, 2/volume_size[2], 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "    affine_transform_pytorch = T_normalized @ affine_transform_inverse @ np.linalg.inv(T_normalized)\n",
    "    \n",
    "    return affine_transform_pytorch # 4 by 4\n",
    "\n",
    "def transform_conversion_ITK_to_scipy(affine_transform_ITK, volume_size):\n",
    "    #note: affine_transform_ITK (to parent)\n",
    "    \"\"\"to achieve the conversion of transform from the ITK transformation to pytorch transformation\"\"\"\n",
    "    \"\"\"For the input and output objects, both of them are rescaled to spacing (1mm, 1mm, 1mm) and recentered the volume, which is to fake the pytorch transform\"\"\"\n",
    "    \"\"\"For the pytorch, the input and output are the torches, which dont have the spacing information, so we can treat them as 1.\"\"\"\n",
    "    \"\"\"To understand how the transform_ITK and transform_pytorch can work well and know the relationship, we need to bring anther package (Scipy) to help us\"\"\"\n",
    "    \"\"\"The conversion workflow is: transform_ITK -> transform_scipy -> transform_pytorch\"\"\"\n",
    "    \"\"\"For the scipy, the input and outout dont have the spacing information, which are the same as the pytorch transform. Differently, the input and output are represented as [1, 2,..., N_w] by [1, 2, ..., N_h] by [1, 2, ..., N_d].\n",
    "    And the transform_scipy is applied around the first voxel [0, 0, 0] by default. If the volume center is set as the (0, 0, 0) in spatial domain, we need to translate the transform_ITK to the center of volume first T_translate, \n",
    "    then apply the transform_ITK, after that, we need to translate back the object.\"\"\"\n",
    "    \"\"\"For the pytorch, the transformation is similiar as the transform_scipy. Differently, the input and ouput need to be normalized first. That means that instead of representing by [1, 2,..., N_w] by [1, 2, ..., N_h] by [1, 2, ..., N_d], \n",
    "    the input and output are represented by [-1,,..., 0, ..., 1] by [-1,,..., 0, ..., 1] by [-1,,..., 0, ..., 1]. Therefore, based on transform_scipy, we need to renormalized the input first, then apply transform_scipy, after that re-normalized back the ojects\"\"\"\n",
    "    \n",
    "    # print(image_array_volume)\n",
    "    # affine_transform_ITK = data_2DUS[21][\"tfm_RegS2V_gt_mat\"].numpy()\n",
    "    # affine_transform = np.array([[np.cos(45*np.pi/180), -np.sin(45*np.pi/180), 0, 0], [np.sin(45*np.pi/180), np.cos(45*np.pi/180), 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "\n",
    "    \"\"\"transform_ITK -> transform_scipy\"\"\"\n",
    "    T_translate = np.array([[1, 0, 0, -volume_size[0]*0.5], [0, 1, 0, -volume_size[1]*0.5], [0, 0, 1, -volume_size[2]*0.5], [0, 0, 0, 1]]) # LPS to parent\n",
    "    affine_transform_scipy = np.linalg.inv(T_translate)@affine_transform_ITK@T_translate\n",
    "\n",
    "    return affine_transform_scipy # 4 by 4\n",
    "\n",
    "class LoadRegistrationTransformd(MapTransform):\n",
    "    def __init__(self, keys: KeysCollection, allow_missing_keys: bool = False, scale = 1, volume_size = None) -> None:\n",
    "        super().__init__(keys, allow_missing_keys)\n",
    "        self.keys = keys\n",
    "        self.scale = scale\n",
    "        self.volume_size = volume_size #\"W*H*D\"\n",
    "    def __call__(self, data):\n",
    "        \"\"\"Keys tfm_3DUS_CT_fileNAME: [\"tfm_3DUS_CT_fileNAME\", \"tfm_RegS2V_initial_fileNAME\", \"tfm_RegS2V_correction_fileNAME\", \"tfm_RegS2V_correction_fileNAME_pre\"]\"\"\" \n",
    "        for key in self.keys:\n",
    "            tfm_3DUS_CT = sitk.ReadTransform(data[key][0]) # transform (from parent) LPS\n",
    "            # print(tfm_3DUS_CT)\n",
    "            tfm_3DUS_CT_np = ConvertITKTransform2NumpyArray(tfm_3DUS_CT) # from parent\n",
    "            # print(\"tfm_3DUS_CT\", np.linalg.inv(tfm_3DUS_CT_np))\n",
    "\n",
    "            tfm_RegS2V_initial = sitk.ReadTransform(data[key][1]) # transform (from parent) LPS\n",
    "            # print(tfm_RegS2V_initial)\n",
    "            tfm_RegS2V_initial_np = ConvertITKTransform2NumpyArray(tfm_RegS2V_initial) # from parent\n",
    "            # print(\"tfm_RegS2V_initial\", np.linalg.inv(tfm_RegS2V_initial_np))\n",
    "\n",
    "            tfm_RegS2V_correction = sitk.ReadTransform(data[key][2]) # transform (from parent) LPS\n",
    "            tfm_RegS2V_correction_np = ConvertITKTransform2NumpyArray(tfm_RegS2V_correction) # from parent\n",
    "            # print(\"tfm_RegS2V_correction\", np.linalg.inv(tfm_RegS2V_correction_np))\n",
    "            \n",
    "            \"\"\"add fake scaling and translation, this is for training. since the networks don't support to identify the spacing of the data array, we rescaled the data to spacing 1\"\"\"\n",
    "            T_scale = np.array([[self.scale, 0, 0, 0], [0, self.scale, 0, 0], [0, 0, self.scale, 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "            T_scale_inv = np.linalg.inv(T_scale)\n",
    "            frame_fileNAME = data[\"frame_name\"]\n",
    "            # print(frame_fileNAME)\n",
    "            reader = sitk.ImageFileReader()\n",
    "            reader.SetFileName(frame_fileNAME)\n",
    "            reader.LoadPrivateTagsOn()\n",
    "            reader.ReadImageInformation()\n",
    "            spacing = reader.GetSpacing()\n",
    "            size = reader.GetSize()\n",
    "            T_translate = np.array([[1, 0, 0, 0], [0, 1, 0, -spacing[1]*size[1]*0.5], [0, 0, 1, 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "            # print(T_translate)\n",
    "\n",
    "            \"\"\"original transform_ITK (ground truth)\"\"\"\n",
    "            tfm_RegS2V_gt_np_from = tfm_3DUS_CT_np@tfm_RegS2V_initial_np@tfm_RegS2V_correction_np # from parent\n",
    "            tfm_RegS2V_gt_np_to = np.linalg.inv(tfm_RegS2V_gt_np_from) # (ground truth) to parent\n",
    "            # print(\"gt transform: {}\".format(tfm_RegS2V_gt_np_to))\n",
    "            # print(\"T_translate: {}\".format(T_scale_inv))\n",
    "            \"\"\"transform_ITK to fake the pytorch transform (rescaled and recentered the orignal one)\"\"\"\n",
    "            tfm_RegS2V_gt_np_to = T_scale@T_translate@tfm_RegS2V_gt_np_to@T_scale_inv # this is the affine_transform_ITK\n",
    "            # print(\"tfm_RegS2v_gt_np: {}\".format(tfm_RegS2V_gt_np_to))\n",
    "            \"\"\"get transform_pytorch\"\"\"\n",
    "            tfm_RegS2V_gt_pytorch = transform_conversion_ITK_to_pytorch(tfm_RegS2V_gt_np_to, self.volume_size) # this is the affine_transform_pytorch\n",
    "            tfm_RegS2V_gt_mat_tensor = torch.from_numpy(tfm_RegS2V_gt_pytorch) \n",
    "\n",
    "\n",
    "            \"\"\"original transform_ITK (initial one)\"\"\"\n",
    "            tfm_RegS2V_correction_pre = sitk.ReadTransform(data[key][3]) # transform (from parent) LPS\n",
    "            tfm_RegS2V_correction_np_pre = ConvertITKTransform2NumpyArray(tfm_RegS2V_correction_pre) # from parent\n",
    "            tfm_RegS2V_initial_np_from = tfm_3DUS_CT_np@tfm_RegS2V_initial_np@tfm_RegS2V_correction_np_pre # from parent\n",
    "            tfm_RegS2V_initial_np_to = np.linalg.inv(tfm_RegS2V_initial_np_from) # (initial transform) to parent\n",
    "            # print(\"initial transform:\", tfm_RegS2V_initial_np_to)\n",
    "            \"\"\"transform_ITK to fake the pytorch transform (rescaled and recentered the orignal one)\"\"\"\n",
    "            tfm_RegS2V_initial_np_to = T_scale@T_translate@tfm_RegS2V_initial_np_to@T_scale_inv  # this is the affine_transform_ITK\n",
    "            \"\"\"get transform_pytorch\"\"\"\n",
    "            tfm_RegS2V_initial_pytorch = transform_conversion_ITK_to_pytorch(tfm_RegS2V_initial_np_to, self.volume_size) # this is the affine_transform_pytorch\n",
    "            tfm_RegS2V_initial_mat_tensor = torch.from_numpy(tfm_RegS2V_initial_pytorch)\n",
    "            \"\"\"caculate the different between the initial one and ground truth, which is for training\"\"\"\n",
    "\n",
    "            # # \"\"\"this is based on the transform_ITK, we decided to use the transform_pytorch to train our model, see below\"\"\"\n",
    "            # tfm_gt_diff_mat = tfm_RegS2V_gt_np_to@np.linalg.inv(tfm_RegS2V_initial_np_to)\n",
    "            # tfm_gt_diff_mat_tensor = torch.from_numpy(tfm_gt_diff_mat)\n",
    "            # tfm_gt_diff_dof = tools.mat2dof_np(input_mat=tfm_gt_diff_mat)\n",
    "            # tfm_gt_diff_dof_tensor = torch.from_numpy(tfm_gt_diff_dof[:6])\n",
    "            # # tfm_RegS2V_initial_mat_tensor = torch.from_numpy(tfm_RegS2V_initial_np_to)\n",
    "\n",
    "            tfm_gt_diff_mat = tfm_RegS2V_gt_pytorch@np.linalg.inv(tfm_RegS2V_initial_pytorch)\n",
    "            tfm_gt_diff_mat_tensor = torch.from_numpy(tfm_gt_diff_mat)\n",
    "            \n",
    "            tfm_gt_diff_dof = tools.mat2dof_np(input_mat=tfm_gt_diff_mat)\n",
    "            tfm_gt_diff_dof_tensor = torch.from_numpy(tfm_gt_diff_dof[:6])\n",
    "            \n",
    "\n",
    "            data[\"tfm_gt_diff_mat\"] = tfm_gt_diff_mat_tensor\n",
    "            data[\"tfm_gt_diff_dof\"] = tfm_gt_diff_dof_tensor\n",
    "            data[\"tfm_RegS2V_initial_mat\"] = tfm_RegS2V_initial_mat_tensor\n",
    "            data[\"tfm_RegS2V_gt_mat\"] = tfm_RegS2V_gt_mat_tensor\n",
    "        return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "752\n",
      "spacing: (0.25099998712539673, 0.25099998712539673, 1.0), size: (752, 558, 1)\n",
      "[[2 0 0 0]\n",
      " [0 2 0 0]\n",
      " [0 0 2 0]\n",
      " [0 0 0 1]]\n",
      "[[0.5 0.  0.  0. ]\n",
      " [0.  0.5 0.  0. ]\n",
      " [0.  0.  0.5 0. ]\n",
      " [0.  0.  0.  1. ]]\n",
      "T_translate: [[  1.           0.           0.           0.        ]\n",
      " [  0.           1.           0.         -70.02899641]\n",
      " [  0.           0.           1.           0.        ]\n",
      " [  0.           0.           0.           1.        ]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'E:\\\\PROGRAM\\\\Project_PhD\\\\Registration\\\\Results\\\\3_slice2volumeRegistration\\\\LHV\\\\LHV-06\\\\pre06_sweep02\\\\in_reordered_images_mha\\\\Image_0016.mha'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader = sitk.ImageFileReader()\n",
    "\n",
    "reader.SetFileName(r\"E:\\PROGRAM\\Project_PhD\\Registration\\Results\\3_slice2volumeRegistration\\LHV\\LHV-06\\pre06_sweep02\\in_reordered_images_mha\\Image_0027.mha\")\n",
    "reader.LoadPrivateTagsOn()\n",
    "reader.ReadImageInformation()\n",
    "spacing = reader.GetSpacing()\n",
    "size = reader.GetSize()\n",
    "print(size[0])\n",
    "print(\"spacing: {}, size: {}\".format(spacing, size))\n",
    "scale = 2\n",
    "T_scale = np.array([[scale, 0, 0, 0], [0, scale, 0, 0], [0, 0, scale, 0], [0, 0, 0, 1]])\n",
    "print(T_scale)\n",
    "T_scale_inv = np.linalg.inv(T_scale)\n",
    "print(T_scale_inv)\n",
    "T_translate = np.array([[1, 0, 0, 0], [0, 1, 0, -spacing[1]*size[1]*0.5], [0, 0, 1, 0], [0, 0, 0, 1]]) # LPS to parent\n",
    "print(\"T_translate: {}\".format(T_translate))\n",
    "\n",
    "validation_dataset_dict[0][\"frame_name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "resample_spacing = 0.5\n",
    "resize_scale = 1/resample_spacing\n",
    "resize_scale\n",
    "spatial_size = [400, 320, 240]\n",
    "transform_3DUS = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"volume_name\", \"volume_mask_name\"], reader=ITKReader(reverse_indexing=False, affine_lps_to_ras=False), image_only=False), # N*C*D*H*W\n",
    "        # MaskIntensityd(keys=[\"image\",\"image_mask\"], mask_data= mask_image_array),\n",
    "        MaskIntensityd(keys=[\"volume_name\"], mask_key= \"volume_mask_name\"),\n",
    "        EnsureChannelFirstd(keys = [\"volume_name\"]),\n",
    "        Spacingd(keys=[\"volume_name\"], pixdim=(resample_spacing, resample_spacing, resample_spacing), mode=(\"bilinear\")),\n",
    "        SpatialPadd(keys=[\"volume_name\"], spatial_size=[spatial_size[0], spatial_size[1], spatial_size[2]], method=\"symmetric\", mode=\"constant\"),\n",
    "        CenterSpatialCropd(keys=[\"volume_name\"], roi_size=[spatial_size[0], spatial_size[1], spatial_size[2]]), # when the spacing is 0.5*0.5*0.5 (W*H*D)\n",
    "        # CenterSpatialCropd(keys=[\"volume_name\"], roi_size=[260, 300, 380]), # when the spacing is 0.5*0.5*0.5 (D*H*W)\n",
    "        ScaleIntensityd(keys=[\"volume_name\"], minv= 0.0, maxv = 1.0, dtype= np.float32),\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "# data_3DUS = transform_3DUS(validation_volume_dict)\n",
    "data_3DUS = transform_3DUS(training_volume_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 400, 320, 240])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "transformed_image_3DUS = data_3DUS[0]['volume_name']\n",
    "print(transformed_image_3DUS.shape)\n",
    "\n",
    "if False:\n",
    "    writer = ITKWriter(output_dtype=np.float32, affine_lps_to_ras= False)\n",
    "    writer.set_data_array(data_3DUS[0]['volume_name'].data, channel_dim=0)\n",
    "    writer.set_metadata(data_3DUS[0][\"volume_name\"].meta, resample=False)\n",
    "    output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Pre-Ablation_06_transformed.mha\"\n",
    "    writer.write(output_filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x287ad178750>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"visualize\", (12,8))\n",
    "plt.subplot(1,3,1)\n",
    "plt.title(\"original image\")\n",
    "plt.imshow(np.transpose(origin_image[0,:,:,int(origin_image.shape[3]/2)]), cmap=\"gray\")\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.title(\"transformed image\")\n",
    "plt.imshow(np.transpose(transformed_image_3DUS[0, :,:,int(transformed_image_3DUS.shape[3]/2)]), cmap=\"gray\")\n",
    "\n",
    "# plt.subplot(1,3,3)\n",
    "# plt.title(\"resampled image\")\n",
    "# plt.imshow(resampled_image[0,0,int(resampled_image.shape[2]/2), :,:], cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "volume_size = [400, 320, 240] # W*H*D\n",
    "transform_2DUS = Compose(\n",
    "    [\n",
    "        LoadRegistrationTransformd(keys=[\"tfm_RegS2V\"], scale=2, volume_size=volume_size),\n",
    "        LoadImaged(keys=[\"frame_name\", \"frame_mask_name\"], reader=ITKReader(reverse_indexing=False, affine_lps_to_ras=False), image_only=False),\n",
    "        # MaskIntensityd(keys=[\"image\",\"image_mask\"], mask_data= mask_image_array),\n",
    "        MaskIntensityd(keys=[\"frame_name\"], mask_key= \"frame_mask_name\"),\n",
    "        EnsureChannelFirstd(keys = [\"frame_name\"]),\n",
    "        Spacingd(keys=[\"frame_name\"], pixdim=(resample_spacing, resample_spacing, resample_spacing), mode=(\"bilinear\")),\n",
    "        SpatialPadd(keys=[\"frame_name\"], spatial_size=[spatial_size[0], spatial_size[1], 1], method=\"symmetric\", mode=\"constant\"),\n",
    "        CenterSpatialCropd(keys=[\"frame_name\"], roi_size=[spatial_size[0], spatial_size[1], 1]), # when the spacing is 0.5*0.5*0.5\n",
    "        ScaleIntensityd(keys=[\"frame_name\"], minv= 0.0, maxv = 1.0, dtype= np.float32)\n",
    "    ]\n",
    ")\n",
    "\n",
    "# data_2DUS = transform_2DUS(validation_dataset_dict)\n",
    "data_2DUS = transform_2DUS(training_dataset_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.9823,  0.1500, -0.0038, -0.0091],\n",
       "        [-0.2332,  0.9798,  0.0540,  0.1082],\n",
       "        [ 0.0329, -0.0928,  0.9974, -0.1182],\n",
       "        [ 0.0000,  0.0000,  0.0000,  1.0000]], dtype=torch.float64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2DUS[0][\"tfm_RegS2V_gt_mat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_2DUS[0][\"frame_name\"].shape\n",
    "# print(data_2DUS)\n",
    "\n",
    "if False:\n",
    "    writer = ITKWriter(output_dtype=np.float32, affine_lps_to_ras= False)\n",
    "    writer.set_data_array(data_2DUS[11]['frame_name'].data, channel_dim=0)\n",
    "    writer.set_metadata(data_2DUS[11][\"frame_name\"].meta, resample=False)\n",
    "    output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Image_27_transformed.mha\"\n",
    "    writer.write(output_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import transformations as tfms\n",
    "import math\n",
    "def computeScale(input_mat):\n",
    "    scale1 = np.linalg.norm(input_mat[:3, 0])\n",
    "    scale2 = np.linalg.norm(input_mat[:3, 1])\n",
    "    scale3 = np.linalg.norm(input_mat[:3, 2])\n",
    "    # print('scale1 {}'.format(scale1))\n",
    "    # print('scale2 {}'.format(scale2))\n",
    "    # print('scale3 {}'.format(scale3))\n",
    "    # print(0.478425 * 0.35)\n",
    "    # sys.exit()\n",
    "    return np.asarray([scale1, scale2, scale3])\n",
    "\n",
    "def mat2dof_np(input_mat):\n",
    "    # print('input_mat\\n{}'.format(input_mat))\n",
    "    translations = input_mat[:3, 3]\n",
    "    rotations_eulers = np.asarray(tfms.euler_from_matrix(input_mat, 'rxyz'))\n",
    "    rotations_degrees = (rotations_eulers / (2 * math.pi)) * 360\n",
    "    scales = computeScale(input_mat=input_mat)\n",
    "\n",
    "    dof = np.concatenate((translations, rotations_degrees, scales), axis=0)\n",
    "\n",
    "    # print('dof\\n{}\\n'.format(dof))\n",
    "    # sys.exit()\n",
    "    return dof\n",
    "\n",
    "def dof2mat_np(input_dof, scale=False):\n",
    "    \"\"\" Transfer degrees to euler \"\"\"\n",
    "    dof = input_dof\n",
    "    # print('deg {}'.format(dof[3:6]))\n",
    "    dof[3:6] = dof[3:6] * (2 * math.pi) / 360.0\n",
    "    # print('rad {}'.format(dof[3:6]))\n",
    "\n",
    "\n",
    "    rot_mat = tfms.euler_matrix(dof[3], dof[4], dof[5], 'rxyz')[:3, :3]\n",
    "\n",
    "    mat44 = np.identity(4)\n",
    "    mat44[:3, :3] = rot_mat\n",
    "    mat44[:3, 3] = dof[:3]\n",
    "\n",
    "    if scale:\n",
    "        scales = dof[6:]\n",
    "        mat_scale = np.diag([scales[1], scales[0], scales[2], 1])\n",
    "        mat44 = np.dot(mat44, np.linalg.inv(mat_scale))\n",
    "    # print('mat_scale\\n{}'.format(mat_scale))\n",
    "    # print('recon mat\\n{}'.format(mat44))\n",
    "    # sys.exit()\n",
    "    return mat44"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "affine_transform_ITK_gt: [[ 0.5        -0.14644661  0.85355339 10.        ]\n",
      " [ 0.5         0.85355339 -0.14644661 20.        ]\n",
      " [-0.70710678  0.5         0.5        30.        ]\n",
      " [ 0.          0.          0.          1.        ]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import scipy\n",
    "reader = ITKReader(reverse_indexing=False, affine_lps_to_ras=False)\n",
    "\n",
    "file_name = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Pre-Ablation_06_transformed_scaled.mha\"\n",
    "# filename_2DUS = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Image_27_transformed_scaled.mha\"\n",
    "filename_2DUS = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Results\\3_slice2volumeRegistration\\LHV\\LHV-06\\pre06_sweep02\\in_reordered_images_mha\\Image_0047.mha\"\n",
    "image_obj = reader.read(file_name)\n",
    "image_array_volume, image_meta_volume = reader.get_data(image_obj)\n",
    "image_obj = reader.read(filename_2DUS)\n",
    "image_array_frame, image_meta_frame = reader.get_data(image_obj)\n",
    "\n",
    "# print(image_array_volume.shape)\n",
    "# print(image_array_frame.shape)\n",
    "\n",
    "# print(image_array_volume)\n",
    "# affine_transform = data_2DUS[31][\"tfm_RegS2V_gt_mat\"].numpy()\n",
    "angle = math.pi/4\n",
    "direction_z = np.array([0, 0, 1])\n",
    "direction_y = np.array([0, 1, 0])\n",
    "direction_x = np.array([1, 0, 0])\n",
    "T0 = tfms.translation_matrix([10, 20, 30])\n",
    "affine_transform_ITK = T0@tfms.rotation_matrix(angle, direction_z, point=None) @ tfms.rotation_matrix(angle, direction_y, point=None)@tfms.rotation_matrix(angle, direction_x, point=None)\n",
    "print(\"affine_transform_ITK_gt: {}\".format(affine_transform_ITK))\n",
    "\n",
    "\n",
    "affine_transform_scipy = transform_conversion_ITK_to_scipy(affine_transform_ITK, volume_size=volume_size)\n",
    "# print(affine_transform_scipy)\n",
    "affine_transform_scipy_inv = np.linalg.inv(affine_transform_scipy)\n",
    "# print(affine_transform_scipy_inv)\n",
    "transformed_volume = scipy.ndimage.affine_transform(image_array_volume, affine_transform_scipy_inv, output_shape= (400, 320, 240), mode = 'constant')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "volume shape(torch): torch.Size([1, 1, 240, 320, 400])\n",
      "affine_transform_pytorch: [[ 0.5         0.4        -0.42426407  0.03106602]\n",
      " [-0.18305826  0.85355339  0.375      -0.19129126]\n",
      " [ 1.42258898 -0.19526215  0.5        -0.17172168]\n",
      " [ 0.          0.          0.          1.        ]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "volume = data_3DUS[0]['volume_name']\n",
    "# print(\"volume shape: {}\".format(volume.shape))\n",
    "volume = volume.unsqueeze(0).type(torch.FloatTensor)\n",
    "volume = torch.permute(volume, (0, 1, 4, 3, 2)).type(torch.FloatTensor)\n",
    "print(\"volume shape(torch): {}\".format(volume.shape))\n",
    "\n",
    "\n",
    "angle = -math.pi/4\n",
    "direction_z = np.array([0, 0, 1])\n",
    "direction_y = np.array([0, 1, 0])\n",
    "T0_inverse = tfms.translation_matrix([-10, -20, -30])\n",
    "\n",
    "\n",
    "affine_transform_pytorch = transform_conversion_ITK_to_pytorch(affine_transform_ITK, volume_size)\n",
    "# affine_transform_pytorch = data_2DUS[index][\"tfm_RegS2V_gt_mat\"].numpy()\n",
    "print(\"affine_transform_pytorch: {}\".format(affine_transform_pytorch))\n",
    "affine_transform_pytorch_theta = affine_transform_pytorch[0:3,:]\n",
    "T_affine_transform_theta = torch.from_numpy(affine_transform_pytorch_theta).type(torch.FloatTensor)\n",
    "T_affine_transform_theta = T_affine_transform_theta.unsqueeze(0)\n",
    "\n",
    "grid = F.affine_grid(T_affine_transform_theta, torch.Size([1, 1, volume_size[2], volume_size[1], volume_size[0]]), align_corners=True)\n",
    "# grid = F.affine_grid(T_affine_transform_theta, torch.Size([1, 1, volume_size[0], volume_size[1], volume_size[2]]), align_corners=True)\n",
    "resampled_volume = F.grid_sample(volume, grid=grid, align_corners=True)\n",
    "\n",
    "resampled_image_np = torch.Tensor.numpy(resampled_volume)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x287ad60bcd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "frame = data_2DUS[31][\"frame_name\"]\n",
    "frame_np = torch.Tensor.numpy(frame)\n",
    "# print(\"frame shape: {}\".format(frame.shape))\n",
    "\n",
    "plt.figure(\"visualize\", (12,8))\n",
    "plt.subplot(1,4,1)\n",
    "# plt.title(\"original image\")\n",
    "# plt.imshow(np.transpose(image_array_volume[:,:,int(image_array_volume.shape[2]/2)]), cmap=\"gray\")\n",
    "plt.title(\"transformed image\")\n",
    "plt.imshow(np.transpose(image_array_frame[:,:,int(image_array_frame.shape[2]/2)]), cmap=\"gray\")\n",
    "\n",
    "plt.subplot(1,4,2)\n",
    "# plt.title(\"transformed image\")\n",
    "# plt.imshow(np.transpose(image_array_frame[:,:,int(image_array_frame.shape[2]/2)]), cmap=\"gray\")\n",
    "\n",
    "plt.title(\"transformed image\")\n",
    "# plt.imshow(np.transpose(image_array_frame[:,:,int(image_array_frame.shape[2]/2)]), cmap=\"gray\")\n",
    "plt.imshow(np.transpose(frame_np[0, :,:,0]), cmap=\"gray\")\n",
    "\n",
    "plt.subplot(1,4,3)\n",
    "plt.title(\"resampled image\")\n",
    "plt.imshow(np.transpose(transformed_volume[:,:,int(transformed_volume.shape[2]/2)]), cmap=\"gray\")\n",
    "\n",
    "plt.subplot(1,4,4)\n",
    "plt.title(\"resampled image (affine_grid ground truth)\")\n",
    "plt.imshow(resampled_image_np[0, 0, int(resampled_image_np.shape[2]/2), :, :], cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b604375a0b694fe88022abd644a97034",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.5, description='alpha', max=1.0, step=0.05), Output()), _dom_classes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.display_images_with_alpha(alpha, fixed, moving)>"
      ]
     },
     "execution_count": 390,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interact, fixed\n",
    "import matplotlib.pyplot as plt\n",
    "def display_images_with_alpha(alpha, fixed, moving):\n",
    "    img = (1.0 - alpha) * fixed + alpha * moving\n",
    "    plt.imshow(img, cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "\n",
    "interact(display_images_with_alpha,alpha=(0.0, 1.0, 0.05),fixed=fixed(np.transpose(frame_np[0, :,:,0])),moving=fixed(np.transpose(transformed_volume[:,:,int(transformed_volume.shape[2]/2)])),)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "affine_transform_ITK_gt: [[ 0.93393807 -0.00565395 -0.21441591 -0.10503626]\n",
      " [-0.00859838  0.99925972 -0.02828816  0.01543987]\n",
      " [ 0.59561552  0.05026426  0.93320181  0.08379959]\n",
      " [ 0.          0.          0.          1.        ]]\n",
      "affine_transform_pytorch: [[ 0.93393807 -0.00565395 -0.21441591 -0.10503626]\n",
      " [-0.00859838  0.99925972 -0.02828816  0.01543987]\n",
      " [ 0.59561552  0.05026426  0.93320181  0.08379959]\n",
      " [ 0.          0.          0.          1.        ]]\n",
      "affine_transform_pytorch_initial: [[ 0.95080159 -0.00228386 -0.18586884 -0.0988564 ]\n",
      " [-0.01606876  0.99872888 -0.03647575  0.02145418]\n",
      " [ 0.51588935  0.06698415  0.94956025  0.07263372]\n",
      " [ 0.          0.          0.          1.        ]]\n",
      "volume shape(torch): torch.Size([1, 1, 240, 320, 400])\n"
     ]
    }
   ],
   "source": [
    "index = 67\n",
    "# calculate the affine transform\n",
    "affine_transform_pytorch = data_2DUS[index][\"tfm_RegS2V_gt_mat\"].numpy()\n",
    "\n",
    "print(\"affine_transform_ITK_gt: {}\".format(affine_transform_pytorch))\n",
    "\n",
    "volume_size = [400, 320, 240] # W*H*D\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\"\"\"ground truth transform\"\"\"\n",
    "# affine_transform_pytorch = transform_conversion_ITK_to_pytorch(affine_transform_ITK, volume_size)\n",
    "# affine_transform_pytorch = data_2DUS[index][\"tfm_RegS2V_gt_mat\"].numpy()\n",
    "print(\"affine_transform_pytorch: {}\".format(affine_transform_pytorch))\n",
    "affine_transform_pytorch_theta = affine_transform_pytorch[0:3,:]\n",
    "T_affine_transform_theta = torch.from_numpy(affine_transform_pytorch_theta).type(torch.FloatTensor)\n",
    "T_affine_transform_theta = T_affine_transform_theta.unsqueeze(0)\n",
    "\n",
    "\"\"\"initial transform\"\"\"\n",
    "affine_transform_pytorch_initial = data_2DUS[index][\"tfm_RegS2V_initial_mat\"].numpy()\n",
    "# print(\"affine_transform_ITK_initial: {}\".format(affine_transform_ITK_initial))\n",
    "# affine_transform_pytorch_initial = transform_conversion_ITK_to_pytorch(affine_transform_ITK_initial, volume_size)\n",
    "# affine_transform_pytorch_initial = data_2DUS[index][\"tfm_RegS2V_initial_mat\"].numpy()\n",
    "print(\"affine_transform_pytorch_initial: {}\".format(affine_transform_pytorch_initial))\n",
    "\n",
    "\n",
    "\n",
    "affine_transform_pytorch_initial_theta = affine_transform_pytorch_initial[0:3,:]\n",
    "T_affine_transform_initial_theta = torch.from_numpy(affine_transform_pytorch_initial_theta).type(torch.FloatTensor)\n",
    "T_affine_transform_initial_theta = T_affine_transform_initial_theta.unsqueeze(0)\n",
    "\n",
    "\n",
    "# get the volume and frame data\n",
    "frame = data_2DUS[index][\"frame_name\"]\n",
    "frame_np = torch.Tensor.numpy(frame)\n",
    "# print(\"frame shape: {}\".format(frame.shape))\n",
    "\n",
    "volume = data_3DUS[1]['volume_name']\n",
    "# print(\"volume shape: {}\".format(volume.shape))\n",
    "volume = volume.unsqueeze(0).type(torch.FloatTensor)\n",
    "volume = torch.permute(volume, (0, 1, 4, 3, 2)).type(torch.FloatTensor)\n",
    "print(\"volume shape(torch): {}\".format(volume.shape))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x287ad4d0c90>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# import torch.nn.functional as F\n",
    "# import torchgeometry as tgm\n",
    "grid = F.affine_grid(T_affine_transform_theta, torch.Size([1, 1, volume_size[2], volume_size[1], volume_size[0]]), align_corners=True)\n",
    "# grid = F.affine_grid(T_affine_transform_theta, torch.Size([1, 1, volume_size[0], volume_size[1], volume_size[2]]), align_corners=True)\n",
    "resampled_volume = F.grid_sample(volume, grid=grid, align_corners=True)\n",
    "\n",
    "resampled_image_np = torch.Tensor.numpy(resampled_volume)\n",
    "# print(\"resampled_image_np shape(torch): {}\".format(resampled_image_np.shape))\n",
    "\n",
    "\n",
    "grid_initial = F.affine_grid(T_affine_transform_initial_theta, torch.Size([1, 1, volume_size[2], volume_size[1], volume_size[0]]), align_corners=True)\n",
    "# grid_initial = F.affine_grid(T_affine_transform_initial_theta, torch.Size([1, 1, volume_size[0], volume_size[1], volume_size[2]]), align_corners=True)\n",
    "resampled_volume_initial = F.grid_sample(volume, grid=grid_initial, align_corners=True)\n",
    "resampled_image_initial_np = torch.Tensor.numpy(resampled_volume_initial)\n",
    "# print(\"resampled_image_initial_np shape(torch): {}\".format(resampled_image_initial_np.shape))\n",
    "\n",
    "\n",
    "\n",
    "plt.figure(\"visualize\", (12,8))\n",
    "plt.subplot(1,4,1)\n",
    "plt.title(\"original image\")\n",
    "plt.imshow(np.transpose(image_array_volume[:,:,int(image_array_volume.shape[2]/2)]), cmap=\"gray\")\n",
    "\n",
    "plt.subplot(1,4,2)\n",
    "plt.title(\"transformed image\")\n",
    "# plt.imshow(np.transpose(image_array_frame[:,:,int(image_array_frame.shape[2]/2)]), cmap=\"gray\")\n",
    "plt.imshow(np.transpose(frame_np[0, :,:,0]), cmap=\"gray\")\n",
    "\n",
    "plt.subplot(1,4,3)\n",
    "plt.title(\"resampled image (affine_grid initial)\")\n",
    "plt.imshow(resampled_image_initial_np[0,0,int(resampled_image_initial_np.shape[2]/2), :, :], cmap=\"gray\")\n",
    "# plt.imshow(np.transpose(resampled_image_initial_np[0,0,:, :, int(resampled_image_initial_np.shape[4]/2)]), cmap=\"gray\")\n",
    "\n",
    "plt.subplot(1,4,4)\n",
    "plt.title(\"resampled image (affine_grid ground truth)\")\n",
    "plt.imshow(resampled_image_np[0, 0, int(resampled_image_np.shape[2]/2), :, :], cmap=\"gray\")\n",
    "# plt.imshow(resampled_image_np[0, 0, 1, :, :], cmap=\"gray\")\n",
    "# plt.imshow(np.transpose(resampled_image_np[0,0,:, :, int(resampled_image_np.shape[4]/2)]), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b74f808a2f804d6094f634272c874c88",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.5, description='alpha', max=1.0, step=0.05), Output()), _dom_classes…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.display_images_with_alpha(alpha, fixed, moving)>"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interact, fixed\n",
    "import matplotlib.pyplot as plt\n",
    "def display_images_with_alpha(alpha, fixed, moving):\n",
    "    img = (1.0 - alpha) * fixed + alpha * moving\n",
    "    plt.imshow(img, cmap=plt.cm.Greys_r)\n",
    "    plt.axis(\"off\")\n",
    "    plt.show()\n",
    "\n",
    "interact(display_images_with_alpha,alpha=(0.0, 1.0, 0.05),fixed=fixed(np.transpose(frame_np[0, :,:,0])),moving=fixed(resampled_image_initial_np[0,0,int(resampled_image_initial_np.shape[2]/2), :, :]),)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "print(data_2DUS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "writer = ITKWriter(output_dtype=np.float32, affine_lps_to_ras= False)\n",
    "writer.set_data_array(data_3DUS[0]['volume_name'].data, channel_dim=0)\n",
    "writer.set_metadata(data_3DUS[0][\"volume_name\"].meta, resample=False)\n",
    "output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Pre-Ablation_01_output.mha\"\n",
    "writer.write(output_filename)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform_2DUS = Compose(\n",
    "    [\n",
    "        LoadRegistrationTransformd(keys=[\"tfm_RegS2V\"], scale=2),\n",
    "        LoadImaged(keys=[\"frame_name\", \"frame_mask_name\"], reader=ITKReader(reverse_indexing=False, affine_lps_to_ras=False), image_only=False),\n",
    "        # MaskIntensityd(keys=[\"image\",\"image_mask\"], mask_data= mask_image_array),\n",
    "        MaskIntensityd(keys=[\"frame_name\"], mask_key= \"frame_mask_name\"),\n",
    "        EnsureChannelFirstd(keys = [\"frame_name\"]),\n",
    "        Spacingd(keys=[\"frame_name\"], pixdim=(0.5, 0.5, 1.0), mode=(\"bilinear\")),\n",
    "        CenterSpatialCropd(keys=[\"frame_name\"], roi_size=[400, 320, 1]), # when the spacing is 0.5*0.5*0.5\n",
    "        # # ScaleIntensityRanged(keys=[\"image\"], a_min= 0, a_max= a_max, b_min = 0, b_max=1, dtype=np.float32)\n",
    "        ScaleIntensityd(keys=[\"frame_name\"], minv= 0.0, maxv = 1.0, dtype= np.float32)\n",
    "        \n",
    "        \n",
    "    ]\n",
    ")\n",
    "\n",
    "data_2DUS = transform_2DUS(validation_dataset_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "data_2DUS_image = data_2DUS[0][\"frame_name\"]\n",
    "data_2DUS[0][\"volume_ID\"]\n",
    "# data_2DUS[0][\"tfm_gt_diff_mat\"]\n",
    "# print(\"2D US image shape: \", data_2DUS_image.shape)\n",
    "# print(f\"image affine before Spacing:\\n{original_image['image'].meta['affine']}\")\n",
    "\n",
    "validation_dataloader_2DUS = DataLoader(dataset=data_2DUS, batch_size=2, shuffle=False)\n",
    "for batch_id, batch in enumerate(validation_dataloader_2DUS):\n",
    "    print(\"batch shape\",batch['tfm_RegS2V_initial_mat'].shape)\n",
    "    if batch_id == 1:\n",
    "\n",
    "        a = batch[\"tfm_gt_diff_dof\"]\n",
    "        print(a)\n",
    "\n",
    "a[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for batch_id, batch in enumerate(validation_dataloader_2DUS):\n",
    "    print(\"batch number: \", batch_id)\n",
    "    print(\"batch shape\",batch[\"frame_name\"].shape)\n",
    "    # print(\"batch size: \", len(batch['volume_ID']))\n",
    "    volume_size = data_3DUS[0]['volume_name'].shape\n",
    "    # if batch_id == 8:\n",
    "        \n",
    "        # actual_batch_size = len(batch[\"volume_ID\"])\n",
    "        \n",
    "        # batch_vol_tensor = torch.zeros(actual_batch_size, volume_size[0], volume_size[1], volume_size[2], volume_size[3])\n",
    "        # print(batch_vol_tensor.shape)\n",
    "    \n",
    "\n",
    "        # for i, volume_id in enumerate(batch[\"volume_ID\"]):\n",
    "        #     print(\"volume_id: \", volume_id)\n",
    "        #     batch_vol_tensor[i, :, :, :, :] = data_3DUS[volume_id]['volume_name']\n",
    "    \n",
    "    # print(\"case ID:\", batch[\"volume_ID\"])\n",
    "    # vol_tensor = volumes_tensor[batch[\"volume_ID\"]]\n",
    "    # print(batch[\"frame_name\"].shape)\n",
    "    \n",
    "    # volume_temp = batch[\"\"]\n",
    "\n",
    "    # volume_ID_tensor = batch[\"volume_ID\"]\n",
    "    # vol_tensor =  [volumes_tensor[i] \n",
    "    #               for i in volume_ID_tensor]\n",
    "    \n",
    "\n",
    "# vol_tensor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "x = torch.randn(3, 4)\n",
    "print(x)\n",
    "indices = torch.tensor([2])\n",
    "print(indices)\n",
    "torch.index_select(x, 0, indices)\n",
    "# torch.index_select(x, 1, indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_frame_tensor = batch[\"frame_name\"]\n",
    "\n",
    "batch_frame_tensor_permuted = torch.permute(batch[\"frame_name\"], (0, 1, 4, 3, 2))\n",
    "\n",
    "print(\"permuted shape: \",batch_frame_tensor_permuted.shape)\n",
    "\n",
    "# test_image = tfm_imageLoader(validation_volume_dict)\n",
    "# origin_image = test_image[0]['volume_name']\n",
    "# print(\"original image shape: \", origin_image.shape)\n",
    "\n",
    "plt.figure(\"visualize\", (12,8))\n",
    "plt.subplot(2,2,1)\n",
    "plt.title(\"original image\")\n",
    "plt.imshow(np.transpose(batch_frame_tensor[4, 0, :,:,0]), cmap=\"gray\")\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.title(\"transformed image\")\n",
    "plt.imshow(batch_frame_tensor_permuted[4, 0, 0, :,:], cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vol_tensor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfm_imageLoader_2DUS = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"frame_name\", \"frame_mask_name\"], reader=ITKReader(reverse_indexing=False, affine_lps_to_ras=False),image_only=False),\n",
    "        MaskIntensityd(keys=[\"frame_name\"], mask_key= \"frame_mask_name\"),\n",
    "        EnsureChannelFirstd(keys = [\"frame_name\"])\n",
    "    ]\n",
    ")\n",
    "\n",
    "test_image = tfm_imageLoader_2DUS(validation_dataset_dict)\n",
    "origin_image = test_image[0]['frame_name']\n",
    "\n",
    "# print(\"original image shape: \", origin_image.shape)\n",
    "\n",
    "\n",
    "plt.figure(\"visualize\", (12,8))\n",
    "plt.subplot(2,2,1)\n",
    "plt.title(\"original image\")\n",
    "plt.imshow(np.transpose(origin_image[0,:,:,int(origin_image.shape[3]/2)]), cmap=\"gray\")\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.title(\"transformed image\")\n",
    "plt.imshow(np.transpose(data_2DUS_image[0, :,:,int(data_2DUS_image.shape[3]/2)]), cmap=\"gray\")\n",
    "# plt.imshow(np.transpose(data_2DUS_image[0, :,:,int(data_2DUS_image.shape[3]/2)]), cmap=\"gray\")\n",
    "\n",
    "\n",
    "# plt.subplot(2,2,3)\n",
    "# plt.title(\"original image (mask)\")\n",
    "# plt.imshow(np.transpose(origin_image_mask[0,:,:,int(origin_image_mask.shape[3]/2)]), cmap=\"gray\")\n",
    "\n",
    "# plt.subplot(2,2,4)\n",
    "# plt.title(\"transformed image (mask)\")\n",
    "# plt.imshow(np.transpose(transformed_image_mask[0, :,:,int(transformed_image_mask.shape[3]/2)]), cmap=\"gray\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "writer = ITKWriter(output_dtype=np.float32, affine_lps_to_ras= False)\n",
    "writer.set_data_array(transformed_image.data, channel_dim=0)\n",
    "writer.set_metadata(transformed_image.meta, resample=False) # to decide whether the image needs to be resampled\n",
    "output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Pre-Ablation_01_output_resample_cropped.mha\"\n",
    "# output_filename = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Image_0050_output_resample.mha\"\n",
    "writer.write(output_filename)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename_2DUS = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Image_0050_1.mha\"\n",
    "\n",
    "data_tree_file = r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\FVR-Net\\dataset_index.xml\"\n",
    "data_tree = ET.parse(data_tree_file)\n",
    "root = data_tree.getroot()\n",
    "all_cases_metadata = root.find('all_cases')\n",
    "\n",
    "for case_index in range(0,len(all_cases_metadata)):\n",
    "# for case_index in range(0,1):\n",
    "    print(case_index)\n",
    "\n",
    "    case_tree = ET.parse(all_cases_metadata[case_index].text)\n",
    "    case_root = case_tree.getroot()\n",
    "\n",
    "\n",
    "    fixed_image_metadata = case_root.find('fixed_image')\n",
    "    in_renamed_directory = fixed_image_metadata.find('directory').text\n",
    "    \n",
    "    filenames = os.listdir(in_renamed_directory)\n",
    "    num = int(filenames.__len__()) # starting from 1\n",
    "    for index in range(1, num + 1):\n",
    "         \n",
    "        num_letters_filename = len(str(index))\n",
    "        if num_letters_filename == 1:\n",
    "            full_index = '000' + str(index)       \n",
    "        elif num_letters_filename == 2:\n",
    "            full_index = '00' + str(index)      \n",
    "        elif num_letters_filename == 3:\n",
    "            full_index = '0' + str(index)\n",
    "        elif num_letters_filename == 4:\n",
    "            full_index = str(index)\n",
    "        else :\n",
    "            print(\"checking out the maximum length of the filename!\") \n",
    "        \n",
    "        \n",
    "        imageName = \"Image_\"+full_index + \".mha\"\n",
    "    \n",
    "        fixed_image_fileNAME = os.path.join(fixed_image_metadata.find('directory').text, imageName)\n",
    "        img = sitk.ReadImage(fixed_image_fileNAME)\n",
    "\n",
    "        select = sitk.VectorIndexSelectionCastImageFilter()\n",
    "        channel1_image = select.Execute(img)\n",
    "        renamed_dir = fixed_image_metadata.find('directory').text + \"1\"\n",
    "        isExist = os.path.exists(renamed_dir)\n",
    "        if not isExist:\n",
    "            os.makedirs(renamed_dir)\n",
    "        sitk.WriteImage(channel1_image, os.path.join(renamed_dir, imageName))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -27.14329903   14.269207    -17.7978794  -176.39551264   -4.11050744\n",
      "  172.64089314    1.00000125    1.00000189    0.99999563]\n",
      "matrix:  tensor([[[ -0.9892,  -0.1278,  -0.0717, -27.1433],\n",
      "         [ -0.1323,   0.9892,   0.0627,  14.2692],\n",
      "         [  0.0629,   0.0715,  -0.9955, -17.7979],\n",
      "         [  0.0000,   0.0000,   0.0000,   1.0000]],\n",
      "\n",
      "        [[ -0.9892,  -0.1278,  -0.0717, -27.1433],\n",
      "         [ -0.1323,   0.9892,   0.0627,  14.2692],\n",
      "         [  0.0629,   0.0715,  -0.9955, -17.7979],\n",
      "         [  0.0000,   0.0000,   0.0000,   1.0000]]])\n",
      "original matrix:  [[ -0.98921406  -0.12775861  -0.07168005 -27.14329903]\n",
      " [ -0.13229723   0.98922525   0.06270669  14.269207  ]\n",
      " [  0.06289264   0.07151863  -0.9954502  -17.7978794 ]\n",
      " [  0.           0.           0.           1.        ]]\n"
     ]
    }
   ],
   "source": [
    "import torchgeometry as tgm\n",
    "def dof2mat_tensor(input_dof, device):\n",
    "    \"\"\"rotation order: matrix = rotx(ai) * roty(aj) * rotz(ak)\"\"\"\n",
    "    rad = tgm.deg2rad(input_dof[:, 3:])\n",
    "\n",
    "    ai = rad[:, 0]\n",
    "    aj = rad[:, 1]\n",
    "    ak = rad[:, 2]\n",
    "\n",
    "    si, sj, sk = torch.sin(ai), torch.sin(aj), torch.sin(ak)\n",
    "    ci, cj, ck = torch.cos(ai), torch.cos(aj), torch.cos(ak)\n",
    "    # cc, cs = ci*ck, ci*sk\n",
    "    # sc, ss = si*ck, si*sk\n",
    "\n",
    "    M = torch.zeros((input_dof.shape[0], 4, 4))\n",
    "\n",
    "    if device:\n",
    "        M = M.to(device)\n",
    "        M.requires_grad = True\n",
    "\n",
    "\n",
    "\n",
    "    M[:, 0, 0] = cj*ck\n",
    "    M[:, 0, 1] = -cj*sk\n",
    "    M[:, 0, 2] = sj\n",
    "    M[:, 1, 0] = si*sj*ck + ci*sk\n",
    "    M[:, 1, 1] = -si*sj*sk + ci*ck\n",
    "    M[:, 1, 2] = -si*cj\n",
    "    M[:, 2, 0] = -ci*sj*ck + si*sk\n",
    "    M[:, 2, 1] = ci*sj*sk + si*ck\n",
    "    M[:, 2, 2] = ci*cj\n",
    "    M[:, 3, 3] = 1\n",
    "    M[:, :3, 3] = input_dof[:, :3]\n",
    "\n",
    "    # print('out_mat {}\\n{}'.format(M.shape, M))\n",
    "    # sys.exit()\n",
    "    return M\n",
    "\n",
    "def mat2dof_np(input_mat):\n",
    "    # print('input_mat\\n{}'.format(input_mat))\n",
    "    translations = input_mat[:3, 3]\n",
    "    rotations_eulers = np.asarray(tfms.euler_from_matrix(input_mat, 'rxyz'))\n",
    "    rotations_degrees = (rotations_eulers / math.pi) * 180\n",
    "    scales = computeScale(input_mat=input_mat)\n",
    "\n",
    "    dof = np.concatenate((translations, rotations_degrees, scales), axis=0)\n",
    "\n",
    "    # print('dof\\n{}\\n'.format(dof))\n",
    "    # sys.exit()\n",
    "    return dof\n",
    "\n",
    "matrix44 = np.array([[-0.98921406,  -0.12775861,  -0.07168005, -27.14329903],\n",
    "     [-0.13229723,   0.98922525,   0.06270669,  14.269207],\n",
    "     [0.06289264,   0.07151863,  -0.9954502,  -17.7978794],\n",
    "     [0,           0,           0,           1        ]])\n",
    "dof = mat2dof_np(matrix44)\n",
    "# dof = torch.from_numpy(dof).unsqueeze(0)\n",
    "print(dof)\n",
    "dof = torch.tensor([[-27.1433,   14.2692,  -17.7979, -176.3955,   -4.1105,  172.6409, 1.0000,    1.0000,    1.0000], \n",
    "                    [-27.1433,   14.2692,  -17.7979, -176.3955,   -4.1105,  172.6409, 1.0000,    1.0000,    1.0000]])\n",
    "matrix = dof2mat_tensor(dof, device= None)\n",
    "print(\"matrix: \", matrix)\n",
    "print(\"original matrix: \", matrix44)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "x is leaf (guess true) True\n",
      "x required_grad (guess true):  True\n",
      "y is leaf (guess true) True\n",
      "y required_grad (guess false):  False\n",
      "y is leaf (guess false) False\n",
      "y required_grad (guess true):  True\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "w = torch.tensor([1.0, 2.0, 3.0])\n",
    "print(w.is_leaf)\n",
    "x = torch.tensor([1.0, 2.0, 3.0], requires_grad=True)\n",
    "print(\"x is leaf (guess true)\", x.is_leaf)\n",
    "print(\"x required_grad (guess true): \", x.requires_grad)\n",
    "# print(x.requires_grad)\n",
    "x_clone = x.clone()\n",
    "y = torch.tensor([1.0, 2.0, 3.0])\n",
    "print(\"y is leaf (guess true)\", y.is_leaf)\n",
    "print(\"y required_grad (guess false): \", y.requires_grad)\n",
    "y = x + 1\n",
    "print(\"y is leaf (guess false)\", y.is_leaf)\n",
    "print(\"y required_grad (guess true): \", y.requires_grad)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# itk.imread(filename_2DUS)\n",
    "img = sitk.ReadImage(filename_2DUS)\n",
    "# print(img.GetPixelIDTypeAsString())\n",
    "select = sitk.VectorIndexSelectionCastImageFilter()\n",
    "channel1_image = select.Execute(img)\n",
    "sitk.WriteImage(channel1_image, r\"E:\\PROGRAM\\Project_PhD\\Registration\\Deepcode\\test\\Image_0050_formated.mha\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DeepRegS2V",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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