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"execution_count": 3,
"metadata": {},
"outputs": [
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"execution_count": 3,
"metadata": {},
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"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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