contrib/MedicalSeg/visualize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Please install the itkwidgts before experiment\n",
    "details to install it can be found [here](https://github.com/InsightSoftwareConsortium/itkwidgets#installation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scipy in /ssd2/tangshiyu/anaconda3/lib/python3.8/site-packages (1.6.2)\r\n",
      "Requirement already satisfied: numpy<1.23.0,>=1.16.5 in /ssd2/tangshiyu/anaconda3/lib/python3.8/site-packages (from scipy) (1.20.1)\r\n"
     ]
    }
   ],
   "source": [
    "# Install dependencies for this example\n",
    "# Note: This does not include itkwidgets, itself\n",
    "import sys\n",
    "!{sys.executable} -m pip install scipy\n",
    "!wget https://bj.bcebos.com/paddleseg/paddleseg3d/lung_coronavirus/vnet_lung_coronavirus_128_128_128_15k_1e-3/prediction.npz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from itkwidgets import view, compare\n",
    "\n",
    "# load the compressed data img=a, label=b, pred=c, infer=d\n",
    "data = np.load(\"prediction.npz\")\n",
    "\n",
    "# the infer output \n",
    "infer_result = data['infer'].squeeze()\n",
    "print(infer_result.shape)\n",
    "infer_result = infer_result*100\n",
    "\n",
    "# Ascent has a range of values from 0 to 255, but it is stored with a int64 dtype\n",
    "infer_result = infer_result.astype(np.uint8)\n",
    "\n",
    "b = data['pred'].squeeze()\n",
    "print(b.shape)\n",
    "b = b*100\n",
    "\n",
    "# Ascent has a range of values from 0 to 255, but it is stored with a int64 dtype\n",
    "b = b.astype(np.uint8)\n",
    "\n",
    "c = data['label'].squeeze()\n",
    "print(b.shape)\n",
    "c = c*100\n",
    "\n",
    "# Ascent has a range of values from 0 to 255, but it is stored with a int64 dtype\n",
    "c = c.astype(np.uint8)\n",
    "\n",
    "d = data['img'].squeeze()\n",
    "print(d.shape)\n",
    "d = d*100\n",
    "\n",
    "# Ascent has a range of values from 0 to 255, but it is stored with a int64 dtype\n",
    "d = d.astype(np.uint8)\n",
    "\n",
    "# b is pred, c is label, d is img\n",
    "predlabel = np.concatenate((b, c), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 256, 128)\n"
     ]
    },
    {
     "data": {
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       "model_id": "762485dcc73340b0a2b23c6eee6eaf3f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(axes=True, geometries=[], gradient_opacity=1.0, point_sets=[], rendered_image=<itk.itkImagePython.itkIm…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(predlabel.shape)\n",
    "\n",
    "view(predlabel[:, 50:240, :],  axes=True, vmin=0, vmax=200, gradient_opacity=1.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2412dd34d0cf4e568b6ca1fe628485e6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(axes=True, cmap=['OrPu', 'BkGn'], geometries=[], gradient_opacity=1.0, interpolation=False, point_sets=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(label_image=predlabel[:, 50:240, :], rotate=True, axes=True, vmin=0, vmax=200, gradient_opacity=10.0, cmap=[\"OrPu\", 'BkGn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2168100f87274a93b1c1b2c331f06cb7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "AppLayout(children=(HBox(children=(Label(value='Link:'), Checkbox(value=True, description='cmap'), Checkbox(va…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare(b, c, link_cmap=True, rotate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "all the input arrays must have same number of dimensions, but the array at index 0 has 3 dimension(s) and the array at index 1 has 4 dimension(s)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-c5383c193e35>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mpredlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmri_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmri_label\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mpredlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: all the input arrays must have same number of dimensions, but the array at index 0 has 3 dimension(s) and the array at index 1 has 4 dimension(s)"
     ]
    }
   ],
   "source": [
    "#  visualize  MRI\n",
    "import numpy as np\n",
    "from itkwidgets import view, compare\n",
    "\n",
    "mri_img = np.load(\"saved_model/vnet_mri_spine_seg_128_128_24_15k_0309_rmmax/1_img.npy\")\n",
    "mri_label = np.load(\"saved_model/vnet_mri_spine_seg_128_128_24_15k_0309_rmmax/1_label.npy\") * 10\n",
    "mri_pred = np.load(\" saved_model/vnet_mri_spine_seg_128_128_24_15k_0309_rmmax/1_pred.npy\") * 10\n",
    "\n",
    "# Ascent has a range of values from 0 to 255, but it is stored with a int64 dtype\n",
    "mri_img = mri_img.astype(np.uint8)\n",
    "mri_label = mri_label.astype(np.uint8)\n",
    "mri_pred = mri_pred.astype(np.uint8)\n",
    "\n",
    "\n",
    "predlabel = np.concatenate((mri_pred, mri_label), axis=2)\n",
    "\n",
    "predlabel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6cf8328d4cdd4f4293c10ea116d261be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=1.0, point_sets=[], rendered_image=<itk.itkImagePython.itkImageUC3; pro…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(mri_pred, axes=False, rotate=True, vmin=0, vmax=200, gradient_opacity=190)"
   ]
  }
 ],
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