{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据增强代码实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCtAngmentedCandidate(\n",
    "    augmentation_dict,\n",
    "    series_uid, center_xyz, width_irc, use_cache=True):\n",
    "    if use_cache:\n",
    "        ct_chunk, center_irc = getCtRawCandidate(series_uid, center_xyz, width_irc)\n",
    "    else:\n",
    "        ct = getCt(series_uid)\n",
    "        ct_chunk, center_irc = ct.getRawCandidate(center_xyz, width_irc)\n",
    "    ct_t = torch.tensor(ct_chunk).unsqueeze(0).unsqueeze(0).to(torch.float32)\n",
    "    \n",
    "    transform_t = torch.eye(4)\n",
    "    \n",
    "    for i in range(3):\n",
    "        #镜像方法\n",
    "        if 'flip' in augmentation_dict:\n",
    "            if random.random() > 0.5:\n",
    "                transform_t[i,i] *= -1\n",
    "        #offset 随机偏移\n",
    "        if 'offset' in augmentation_dict:\n",
    "            offset_float = augmentation_dict['offset']\n",
    "            random_float = (random.random() * 2 - 1)\n",
    "            transform_t[i,3] = offset_float * random_float\n",
    "            \n",
    "        #scale 随机缩放\n",
    "        if 'scale' in augmentation_dict:\n",
    "            scale_float = augmentation_dict['scale']\n",
    "            random_float = (random.random() * 2 - 1)\n",
    "            transform_t[i,i] *= 1.0 + scale_float * random_float\n",
    "            \n",
    "    #rotate 随机旋转\n",
    "    if 'rotate' in augmentation_dict:\n",
    "        angle_rad = random.random() * math.pi * 2\n",
    "        s = math.sin(angle_rad)\n",
    "        c = math.cos(angle_rad)\n",
    "        \n",
    "        rotation_t = torch.tensor([\n",
    "            [c, -s, 0, 0],\n",
    "            [s, c, 0, 0],\n",
    "            [0, 0, 1, 0],\n",
    "            [0, 0, 0, 1]\n",
    "        ])\n",
    "        \n",
    "        transform_t @= rotation_t  # @=操作符是将右侧的矩阵与左侧的矩阵进行矩阵乘法，并将结果赋值给左侧变量\n",
    "        \n",
    "    affine_t = F.affine_grid(\n",
    "        transform_t[:3].unsqueeze(0).to(torch.float32),\n",
    "        ct_t.size(),\n",
    "        align_corners=False,\n",
    "        )\n",
    "    augmented_chunk = F.grid_sample(\n",
    "            ct_t,\n",
    "        affine_t,\n",
    "        padding_mode = 'border',\n",
    "        align_corners = False,\n",
    "    ).to('cpu')\n",
    "    \n",
    "    #noise 加噪声\n",
    "    if 'noise' in augmenttation_dict:\n",
    "        noise_t = torch.randn_like(augmented_chunk)\n",
    "        noise_t *= augmentation_doct['noise']\n",
    "        \n",
    "        augmented_chunk += noise_t\n",
    "        \n",
    "    return augmented_chunk[0], center_irc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import torch\n",
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'  # 添加在导入任何库之前\n",
    "import copy\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "from code2.dsets import getCandidateInfoList, getCt, LunaDataset\n",
    "from util.util import xyz2irc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CandidateInfoTuple(isNodule_bool=True, diameter_mm=32.27003025, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.287966244644280690737019247886', center_xyz=(67.61451718, 85.02525992, -109.8084416))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidateInfo_list = getCandidateInfoList(requireOnDisk_bool=False)\n",
    "candidateInfo_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-05-03 18:00:29,589 INFO     pid:7692 code2.dsets:266:__init__ <code2.dsets.LunaDataset object at 0x0000027713A4DA60>: 164991 training samples, 164573 neg, 418 pos, unbalanced ratio\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 CandidateInfoTuple(isNodule_bool=True, diameter_mm=32.27003025, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.287966244644280690737019247886', center_xyz=(67.61451718, 85.02525992, -109.8084416))\n",
      "1 CandidateInfoTuple(isNodule_bool=True, diameter_mm=27.07544345, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.481278873893653517789960724156', center_xyz=(-102.571208, -5.186558766, -205.1033412))\n",
      "2 CandidateInfoTuple(isNodule_bool=True, diameter_mm=25.23320204, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.511347030803753100045216493273', center_xyz=(63.4740118048, 73.9174523314, -213.736128767))\n",
      "3 CandidateInfoTuple(isNodule_bool=True, diameter_mm=23.48374545, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.148447286464082095534651426689', center_xyz=(-90.1922382588, 32.9077042197, -127.738768532))\n",
      "4 CandidateInfoTuple(isNodule_bool=True, diameter_mm=23.48374545, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.148447286464082095534651426689', center_xyz=(-93.9441715503, 28.8165564567, -130.21511348))\n",
      "5 CandidateInfoTuple(isNodule_bool=True, diameter_mm=23.35064438, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.179049373636438705059720603192', center_xyz=(57.42, 81.14, -118.09))\n",
      "6 CandidateInfoTuple(isNodule_bool=True, diameter_mm=23.35064438, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.179049373636438705059720603192', center_xyz=(56.4889724157, 85.9418105037, -115.9731945))\n",
      "7 CandidateInfoTuple(isNodule_bool=True, diameter_mm=23.35064438, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.179049373636438705059720603192', center_xyz=(50.4361084414, 90.0424445754, -113.908439345))\n",
      "8 CandidateInfoTuple(isNodule_bool=True, diameter_mm=23.35064438, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.179049373636438705059720603192', center_xyz=(50.42, 85.34, -118.09))\n",
      "9 CandidateInfoTuple(isNodule_bool=True, diameter_mm=22.78152704, series_uid='1.3.6.1.4.1.14519.5.2.1.6279.6001.487745546557477250336016826588', center_xyz=(52.18, -21.02, -81.16))\n"
     ]
    }
   ],
   "source": [
    "from code2.vis import findPositiveSamples, showCandidate\n",
    "positiveSample_list = findPositiveSamples()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-05-03 18:00:31,889 INFO     pid:7692 code2.dsets:266:__init__ <code2.dsets.LunaDataset object at 0x0000027707E34B00>: 164991 training samples, 164573 neg, 418 pos, unbalanced ratio\n",
      "2025-05-03 18:00:31,920 INFO     pid:7692 code2.dsets:266:__init__ <code2.dsets.LunaDataset object at 0x0000027707E351C0>: 164991 training samples, 164573 neg, 418 pos, unbalanced ratio\n",
      "2025-05-03 18:00:31,943 INFO     pid:7692 code2.dsets:266:__init__ <code2.dsets.LunaDataset object at 0x0000027712468200>: 164991 training samples, 164573 neg, 418 pos, unbalanced ratio\n",
      "2025-05-03 18:00:31,970 INFO     pid:7692 code2.dsets:266:__init__ <code2.dsets.LunaDataset object at 0x0000027712494CB0>: 164991 training samples, 164573 neg, 418 pos, unbalanced ratio\n",
      "2025-05-03 18:00:31,994 INFO     pid:7692 code2.dsets:266:__init__ <code2.dsets.LunaDataset object at 0x00000277124F0E00>: 164991 training samples, 164573 neg, 418 pos, unbalanced ratio\n",
      "2025-05-03 18:00:32,020 INFO     pid:7692 code2.dsets:266:__init__ <code2.dsets.LunaDataset object at 0x0000027713BE0A10>: 164991 training samples, 164573 neg, 418 pos, unbalanced ratio\n",
      "2025-05-03 18:00:32,044 INFO     pid:7692 code2.dsets:266:__init__ <code2.dsets.LunaDataset object at 0x0000027707E34D70>: 164991 training samples, 164573 neg, 418 pos, unbalanced ratio\n"
     ]
    }
   ],
   "source": [
    "augmentation_dict = {}\n",
    "augmentation_list = [\n",
    "    ('None', {}),\n",
    "    ('flip', {'flip': True}),\n",
    "    ('offset', {'offset': 0.1}),\n",
    "    ('scale', {'scale': 0.2}),\n",
    "    ('rotate', {'rotate': True}),\n",
    "    ('noise', {'noise': 25.0}),    \n",
    "]\n",
    "ds_list = [\n",
    "    LunaDataset(sortby_str='label_and_size', augmentation_dict=augmentation_dict) \n",
    "    for title_str, augmentation_dict in augmentation_list\n",
    "]\n",
    "\n",
    "all_dict = {}\n",
    "for title_str, augmentation_dict in augmentation_list:\n",
    "    all_dict.update(augmentation_dict)\n",
    "all_ds = LunaDataset(sortby_str='label_and_size', augmentation_dict=all_dict)\n",
    "\n",
    "augmentation_list.extend([('All', augmentation_dict)] * 3)\n",
    "ds_list.extend([all_ds] * 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 32, 48, 48]) (tensor([0, 1]), '1.3.6.1.4.1.14519.5.2.1.6279.6001.197063290812663596858124411210', tensor([114, 280, 375]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x2000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_ndx = 100\n",
    "# sample_ndx = 154\n",
    "# sample_ndx = 155\n",
    "\n",
    "\n",
    "sample_tup = all_ds[sample_ndx]\n",
    "print(sample_tup[0].shape, sample_tup[1:])\n",
    "\n",
    "fig = plt.figure(figsize=(20, 20))\n",
    "\n",
    "clim=(-1000.0, 300)\n",
    "\n",
    "for i, ((title_str, _), ds) in enumerate(zip(augmentation_list, ds_list)):\n",
    "    sample_tup = ds[sample_ndx]\n",
    "    subplot = fig.add_subplot(3, 3, i+1)\n",
    "    subplot.set_title(title_str, fontsize=30)\n",
    "    for label in (subplot.get_xticklabels() + subplot.get_yticklabels()):\n",
    "        label.set_fontsize(20)\n",
    "    plt.imshow(sample_tup[0][0][16], clim=clim, cmap='gray')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}