projection_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fd11fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pydicom\n",
    "from pydicom import dcmread\n",
    "from pydicom.uid import ExplicitVRLittleEndian\n",
    "from pydicom.data import get_testdata_file\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "from numba import jit\n",
    "import cv2\n",
    "\n",
    "from skimage.transform import rescale\n",
    "\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0266d97f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_dicom(in_dir_path):\n",
    "    #names the path to the input model directory, and lists the files in this directory\n",
    "\n",
    "    in_dir_list = sorted(os.listdir(in_dir_path))\n",
    "    model_depth = len(in_dir_list)\n",
    "    model_data = []\n",
    "\n",
    "\n",
    "    i = 0\n",
    "    while i < model_depth:\n",
    "        path = in_dir_path + \"/\" + in_dir_list[i]\n",
    "        current_slice = pydicom.dcmread(path)\n",
    "        model_slice = current_slice.pixel_array\n",
    "        model_slice = np.asarray(model_slice)\n",
    "        model_data.append(model_slice)\n",
    "        i = i + 1\n",
    "    model_data = np.asarray(model_data, dtype = np.int16)\n",
    "    \n",
    "        \n",
    "    return model_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0034dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit\n",
    "def add_img(img_1, img_2):\n",
    "    \n",
    "    #tests which of the inputs is the larger image\n",
    "    if len(img_1) > len(img_2):\n",
    "        lar_img = img_1\n",
    "        sml_img = img_2\n",
    "    else:\n",
    "        lar_img = img_2\n",
    "        sml_img = img_1\n",
    "\n",
    "    #finds the distance (in the x-axis direction) between the left\n",
    "    #corners of the two images, if the smaller image was placed\n",
    "    #in the middle of the larger image\n",
    "    lar_centre_index_x = round(len(lar_img)/2) - 1\n",
    "    sml_centre_index_x = round(len(sml_img)/2) - 1\n",
    "    edge_x = lar_centre_index_x - sml_centre_index_x\n",
    "       \n",
    "    #finds the distance (in the y-axis direction) between the left\n",
    "    #corners of the two images, if the smaller image was placed\n",
    "    #in the middle of the larger image\n",
    "    lar_centre_index_y = round(len(lar_img[0])/2) - 1\n",
    "    sml_centre_index_y = round(len(sml_img[0])/2) - 1\n",
    "    edge_y = lar_centre_index_y - sml_centre_index_y\n",
    "\n",
    "    output = lar_img\n",
    "    \n",
    "    x = edge_x\n",
    "    \n",
    "    #works through each pixel where the two images overlap\n",
    "    #and adds the value of the pixels from each image together\n",
    "    while x < (edge_x + len(sml_img)):\n",
    "        i = edge_y     \n",
    "        while i < (edge_y + len(sml_img[0])):\n",
    "                out_pixel = (lar_img[x][i] + sml_img[x - edge_x][i - edge_y])\n",
    "                output[x][i]= out_pixel            \n",
    "                i = i + 1\n",
    "        x = x + 1\n",
    "    \n",
    "    return output\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "481dd497",
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit\n",
    "def project(model, source_object_distance, object_image_distance):\n",
    "    source_image_distance = source_object_distance + len(model) + object_image_distance\n",
    "    \n",
    "    model_depth = len(model)\n",
    "    model_height = len(model[0])\n",
    "    model_length = len(model[0][0])\n",
    "    \n",
    "    \n",
    "    max_mag = ((source_image_distance)/(source_object_distance))\n",
    "\n",
    "    \n",
    "\n",
    "    base_cube = np.zeros((model_depth, int(np.ceil(max_mag * model_height)), int(np.ceil(max_mag * model_length))))\n",
    "    enclosed_cube = np.zeros((model_depth, int(np.ceil(max_mag * model_height)), int(np.ceil(max_mag * model_length))))\n",
    "  \n",
    "    i = 0\n",
    "    while i < len(model):\n",
    "        slice_mag = (source_image_distance)/(source_object_distance + i)\n",
    "        temp = cv2.resize(model[i], dsize = (round(model_length * slice_mag), round(model_height * slice_mag)), interpolation = cv2.INTER_LINEAR)\n",
    "        \n",
    "        enclosed_cube[i] = add_img(base_cube[i], temp)\n",
    "        \n",
    "        i = i + 1\n",
    "        \n",
    "    \n",
    "    projection = np.sum(base_cube, axis = 0)\n",
    "    return projection\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "22b14959",
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_dicom(image, filename):\n",
    "    dicom_out_path = get_testdata_file(\"CT_small.dcm\")\n",
    "    dicom_out = dcmread(dicom_out_path)\n",
    "    dicom_out.file_meta.TransferSyntaxUID = ExplicitVRLittleEndian\n",
    "    dicom_out.PixelData = image.astype(np.float16).tobytes()\n",
    "    dicom_out.save_as(filename)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c322deb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Katie\\anaconda3\\envs\\Testing\\lib\\site-packages\\numba\\core\\dispatcher.py:289: UserWarning: Numba extension module 'numba_dppy.numpy_usm_shared' failed to load due to 'ImportError(cannot import name 'TargetConfig' from 'numba.core.dispatcher' (C:\\Users\\Katie\\anaconda3\\envs\\Testing\\lib\\site-packages\\numba\\core\\dispatcher.py))'.\n",
      "  entrypoints.init_all()\n",
      "<ipython-input-4-c67faf352868>:1: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"project\" failed type inference due to: \u001b[1m\u001b[1mUnknown attribute 'resize' of type Module(<module 'cv2' from 'C:\\\\Users\\\\Katie\\\\anaconda3\\\\envs\\\\Testing\\\\lib\\\\site-packages\\\\cv2.cp38-win_amd64.pyd'>)\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-c67faf352868>\", line 20:\u001b[0m\n",
      "\u001b[1mdef project(model, source_object_distance, object_image_distance):\n",
      "    <source elided>\n",
      "        slice_mag = (source_image_distance)/(source_object_distance + i)\n",
      "\u001b[1m        temp = cv2.resize(model[i], dsize = (round(model_length * slice_mag), round(model_height * slice_mag)), interpolation = cv2.INTER_LINEAR)\n",
      "\u001b[0m        \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of get attribute at <ipython-input-4-c67faf352868> (20)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-c67faf352868>\", line 20:\u001b[0m\n",
      "\u001b[1mdef project(model, source_object_distance, object_image_distance):\n",
      "    <source elided>\n",
      "        slice_mag = (source_image_distance)/(source_object_distance + i)\n",
      "\u001b[1m        temp = cv2.resize(model[i], dsize = (round(model_length * slice_mag), round(model_height * slice_mag)), interpolation = cv2.INTER_LINEAR)\n",
      "\u001b[0m        \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @jit\n",
      "C:\\Users\\Katie\\anaconda3\\envs\\Testing\\lib\\site-packages\\numba\\core\\object_mode_passes.py:151: NumbaWarning: \u001b[1mFunction \"project\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-c67faf352868>\", line 2:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef project(model, source_object_distance, object_image_distance):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "C:\\Users\\Katie\\anaconda3\\envs\\Testing\\lib\\site-packages\\numba\\core\\object_mode_passes.py:161: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-c67faf352868>\", line 2:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef project(model, source_object_distance, object_image_distance):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
     ]
    }
   ],
   "source": [
    "#Initialize @jit\n",
    "ini_model = np.ones((2,2,2))\n",
    "ini_project = project(ini_model, 100, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13a35e74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-4-c67faf352868>:1: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"project\" failed type inference due to: \u001b[1m\u001b[1mUnknown attribute 'resize' of type Module(<module 'cv2' from 'C:\\\\Users\\\\Katie\\\\anaconda3\\\\envs\\\\Testing\\\\lib\\\\site-packages\\\\cv2.cp38-win_amd64.pyd'>)\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-c67faf352868>\", line 20:\u001b[0m\n",
      "\u001b[1mdef project(model, source_object_distance, object_image_distance):\n",
      "    <source elided>\n",
      "        slice_mag = (source_image_distance)/(source_object_distance + i)\n",
      "\u001b[1m        temp = cv2.resize(model[i], dsize = (round(model_length * slice_mag), round(model_height * slice_mag)), interpolation = cv2.INTER_LINEAR)\n",
      "\u001b[0m        \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of get attribute at <ipython-input-4-c67faf352868> (20)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-c67faf352868>\", line 20:\u001b[0m\n",
      "\u001b[1mdef project(model, source_object_distance, object_image_distance):\n",
      "    <source elided>\n",
      "        slice_mag = (source_image_distance)/(source_object_distance + i)\n",
      "\u001b[1m        temp = cv2.resize(model[i], dsize = (round(model_length * slice_mag), round(model_height * slice_mag)), interpolation = cv2.INTER_LINEAR)\n",
      "\u001b[0m        \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @jit\n",
      "C:\\Users\\Katie\\anaconda3\\envs\\Testing\\lib\\site-packages\\numba\\core\\object_mode_passes.py:151: NumbaWarning: \u001b[1mFunction \"project\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-c67faf352868>\", line 2:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef project(model, source_object_distance, object_image_distance):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "C:\\Users\\Katie\\anaconda3\\envs\\Testing\\lib\\site-packages\\numba\\core\\object_mode_passes.py:161: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"<ipython-input-4-c67faf352868>\", line 2:\u001b[0m\n",
      "\u001b[1m@jit\n",
      "\u001b[1mdef project(model, source_object_distance, object_image_distance):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "<ipython-input-3-93ed7f67225a>:1: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"add_img\" failed type inference due to: \u001b[1m\u001b[1mCannot unify array(int16, 2d, C) and array(float64, 2d, C) for 'sml_img.2', defined at <ipython-input-3-93ed7f67225a> (15)\n",
      "\u001b[1m\n",
      "File \"<ipython-input-3-93ed7f67225a>\", line 15:\u001b[0m\n",
      "\u001b[1mdef add_img(img_1, img_2):\n",
      "    <source elided>\n",
      "    #in the middle of the larger image\n",
      "\u001b[1m    lar_centre_index_x = round(len(lar_img)/2) - 1\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of assignment at <ipython-input-3-93ed7f67225a> (15)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"<ipython-input-3-93ed7f67225a>\", line 15:\u001b[0m\n",
      "\u001b[1mdef add_img(img_1, img_2):\n",
      "    <source elided>\n",
      "    #in the middle of the larger image\n",
      "\u001b[1m    lar_centre_index_x = round(len(lar_img)/2) - 1\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @jit\n",
      "<ipython-input-3-93ed7f67225a>:1: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"add_img\" failed type inference due to: \u001b[1m\u001b[1mCannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'>\u001b[0m\n",
      "\u001b[1m\n",
      "File \"<ipython-input-3-93ed7f67225a>\", line 15:\u001b[0m\n",
      "\u001b[1mdef add_img(img_1, img_2):\n",
      "    <source elided>\n",
      "    #in the middle of the larger image\n",
      "\u001b[1m    lar_centre_index_x = round(len(lar_img)/2) - 1\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\u001b[0m\n",
      "  @jit\n",
      "C:\\Users\\Katie\\anaconda3\\envs\\Testing\\lib\\site-packages\\numba\\core\\object_mode_passes.py:151: NumbaWarning: \u001b[1mFunction \"add_img\" was compiled in object mode without forceobj=True, but has lifted loops.\n",
      "\u001b[1m\n",
      "File \"<ipython-input-3-93ed7f67225a>\", line 5:\u001b[0m\n",
      "\u001b[1mdef add_img(img_1, img_2):\n",
      "    <source elided>\n",
      "    #tests which of the inputs is the larger image\n",
      "\u001b[1m    if len(img_1) > len(img_2):\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "C:\\Users\\Katie\\anaconda3\\envs\\Testing\\lib\\site-packages\\numba\\core\\object_mode_passes.py:161: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"<ipython-input-3-93ed7f67225a>\", line 5:\u001b[0m\n",
      "\u001b[1mdef add_img(img_1, img_2):\n",
      "    <source elided>\n",
      "    #tests which of the inputs is the larger image\n",
      "\u001b[1m    if len(img_1) > len(img_2):\n",
      "\u001b[0m    \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
     ]
    }
   ],
   "source": [
    "model = import_dicom(\"./infile\")\n",
    "projection = project(model, 100,10)\n",
    "save_dicom(projection, \"./Outfile\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbfa7160",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}