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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fastai.learner.Learner at 0x7f441528d0a0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fastai.vision.all import *\n",
    "import cv2\n",
    "import skimage as sm\n",
    "import skimage.io\n",
    "import tifffile\n",
    "import os\n",
    "import numpy as np\n",
    "path = Path('/notebooks/DL_paper') # replace with your own \n",
    "\n",
    "# Load model for testing\n",
    "\n",
    "# loads txt file with names of files in validation dataset\n",
    "valid_fnames = (path/'models/tissueModel/validTissue.txt').read_text().split('\\n')\n",
    "path_im = path/'train_images'\n",
    "path_lbl = path/'train_masks'\n",
    "\n",
    "# Names of class\n",
    "codes = np.loadtxt(path/'models/tissueModel/codesTissue.txt', dtype=str)\n",
    "fnames = get_image_files(path_im)\n",
    "lbl_names = get_image_files(path_lbl)\n",
    "\n",
    "# function to get mask using name of image\n",
    "get_msk = lambda o: path/f'dat_tissue/train_masks'/f'mask{o.stem.split(\"g\")[1]}{o.suffix}'\n",
    "\n",
    "# \n",
    "camvid = DataBlock(blocks=(ImageBlock, MaskBlock(codes)),\n",
    "                    get_items=get_image_files,\n",
    "                    splitter=FileSplitter(path/'models/tissueModel/validTissue.txt'),\n",
    "                    get_y=get_msk,\n",
    "                    batch_tfms=[*aug_transforms(), # augmentations\n",
    "                    Normalize.from_stats(*imagenet_stats)]) \n",
    "dls = camvid.dataloaders(path/f'dat_tissue/train_images', bs=4) # bs is the batch size\n",
    "dls.vocab = codes\n",
    "name2id = {v:k for k,v in enumerate(codes)}\n",
    "void_code = name2id['Void']\n",
    "\n",
    "# define metric to assess model performance \n",
    "def acc_camvid(inp, targ):\n",
    "  targ = targ.squeeze(1)\n",
    "  mask = targ != void_code\n",
    "  return (inp.argmax(dim=1)[mask]==targ[mask]).float().mean() \n",
    "opt = ranger\n",
    "\n",
    "# Makes the unet model from a resnet101 model\n",
    "learn = unet_learner(dls, resnet101, metrics=acc_camvid, self_attention=True, act_cls=Mish, opt_func=opt).to_fp16()\n",
    "\n",
    "# Load model\n",
    "learn.load(f'UNetTissue')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Applies model to testing data\n",
    "    \n",
    "def main():\n",
    "    \n",
    "    folder = \"dat_tissue/test_images\" # folder with test data images\n",
    "    \n",
    "    # collects names of files in the test folder\n",
    "    cwd = os.getcwd()\n",
    "    filenames = os.listdir(cwd + \"/\" + folder)\n",
    "    if \".DS_Store\" in filenames:\n",
    "        filenames.remove(\".DS_Store\")\n",
    "    if \".ipynb_checkpoints\" in filenames:\n",
    "        filenames.remove(\".ipynb_checkpoints\")\n",
    "    filenames.sort()\n",
    "\n",
    "    # runs the U-Nettissue model to test data images\n",
    "    for filename in filenames:\n",
    "#         print(filename)\n",
    "        out = learn.predict(f'{folder}/{filename}')[2][:4]\n",
    "    \n",
    "        # saves predation model output\n",
    "        out = np.asarray(out*255, \"uint8\")\n",
    "        tifffile.imwrite(f\"dat_tissue/test_output/pred_{filename}\", out, \n",
    "            imagej=True,)"
   ]
  },
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