denoiser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pycromanager import Dataset as PDataset\n",
    "from glob import glob\n",
    "from os import path\n",
    "import os\n",
    "import numpy as np\n",
    "from skimage import io as skio\n",
    "from skimage import img_as_float, img_as_uint, exposure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "cannot squeeze axis with size other than one",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mf:\\Documents\\sp-project\\denoiser.ipynb Cell 2'\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Documents/sp-project/denoiser.ipynb#ch0000001?line=3'>4</a>\u001b[0m \u001b[39mfor\u001b[39;00m data_path \u001b[39min\u001b[39;00m training_data:\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Documents/sp-project/denoiser.ipynb#ch0000001?line=4'>5</a>\u001b[0m     dataset \u001b[39m=\u001b[39m PDataset(data_path)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Documents/sp-project/denoiser.ipynb#ch0000001?line=5'>6</a>\u001b[0m     d_array \u001b[39m=\u001b[39m dataset\u001b[39m.\u001b[39;49mas_array(stitched\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\u001b[39m.\u001b[39;49msqueeze(\u001b[39m0\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Documents/sp-project/denoiser.ipynb#ch0000001?line=6'>7</a>\u001b[0m     \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(d_array\u001b[39m.\u001b[39mshape[\u001b[39m0\u001b[39m]):\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Documents/sp-project/denoiser.ipynb#ch0000001?line=7'>8</a>\u001b[0m         np_stack \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(d_array[i])\n",
      "File \u001b[1;32mc:\\Users\\lociuser\\Miniconda3\\envs\\pyimagej\\lib\\site-packages\\dask\\array\\core.py:2640\u001b[0m, in \u001b[0;36mArray.squeeze\u001b[1;34m(self, axis)\u001b[0m\n\u001b[0;32m   2636\u001b[0m \u001b[39m@derived_from\u001b[39m(np\u001b[39m.\u001b[39mndarray)\n\u001b[0;32m   2637\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39msqueeze\u001b[39m(\u001b[39mself\u001b[39m, axis\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m   2638\u001b[0m     \u001b[39mfrom\u001b[39;00m \u001b[39mdask\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39marray\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mroutines\u001b[39;00m \u001b[39mimport\u001b[39;00m squeeze\n\u001b[1;32m-> 2640\u001b[0m     \u001b[39mreturn\u001b[39;00m squeeze(\u001b[39mself\u001b[39;49m, axis)\n",
      "File \u001b[1;32mc:\\Users\\lociuser\\Miniconda3\\envs\\pyimagej\\lib\\site-packages\\dask\\array\\routines.py:1931\u001b[0m, in \u001b[0;36msqueeze\u001b[1;34m(a, axis)\u001b[0m\n\u001b[0;32m   1928\u001b[0m     axis \u001b[39m=\u001b[39m (axis,)\n\u001b[0;32m   1930\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39many\u001b[39m(a\u001b[39m.\u001b[39mshape[i] \u001b[39m!=\u001b[39m \u001b[39m1\u001b[39m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m axis):\n\u001b[1;32m-> 1931\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mcannot squeeze axis with size other than one\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m   1933\u001b[0m axis \u001b[39m=\u001b[39m validate_axis(axis, a\u001b[39m.\u001b[39mndim)\n\u001b[0;32m   1935\u001b[0m sl \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m(\u001b[39m0\u001b[39m \u001b[39mif\u001b[39;00m i \u001b[39min\u001b[39;00m axis \u001b[39melse\u001b[39;00m \u001b[39mslice\u001b[39m(\u001b[39mNone\u001b[39;00m) \u001b[39mfor\u001b[39;00m i, s \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(a\u001b[39m.\u001b[39mshape))\n",
      "\u001b[1;31mValueError\u001b[0m: cannot squeeze axis with size other than one"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "### parse images from pmm dataset\n",
    "data_folder = 'denoiser'\n",
    "training_data = glob(path.join(data_folder, 'training', '*', '*\\\\'))\n",
    "for data_path in training_data:\n",
    "    dataset = PDataset(data_path)\n",
    "    d_array = dataset.as_array(stitched=False).squeeze(0)\n",
    "    for i in range(d_array.shape[0]):\n",
    "        np_stack = np.array(d_array[i])\n",
    "        for z in range(np_stack.shape[0]):\n",
    "            save_fname = path.join(data_folder, 'training', data_path.split(os.sep)[-3], data_path.split(os.sep)[-2]+f'---{i}---{z}.tif')\n",
    "            skio.imsave(save_fname, np_stack[z])\n",
    "\n",
    "### normalize 16-bit image\n",
    "training_images = glob(path.join(data_folder, 'training', '*', '*.tif'))\n",
    "max_val = []\n",
    "min_val = []\n",
    "for img_fname in training_images:\n",
    "    img = skio.imread(img_fname)\n",
    "    max_val.append(np.percentile(img, 99.8))\n",
    "    min_val.append(np.percentile(img, 2))\n",
    "max_val = np.mean(max_val)\n",
    "min_val = np.mean(min_val)\n",
    "for img_fname in training_images:\n",
    "    img = skio.imread(img_fname)\n",
    "    img = img_as_uint( exposure.rescale_intensity(img, in_range=(min_val, max_val), out_range=(0, 1)) )\n",
    "    skio.imsave(img_fname, img)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import random\n",
    "from torch.utils.data import DataLoader, Dataset\n",
    "import skimage.transform as skt\n",
    "from torchvision.utils import make_grid, save_image\n",
    "from torch.optim import Adam\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ImagePairDataset(Dataset): \n",
    "    def __init__(self, img_fnames, **transform_args): \n",
    "        self.img_fnames = img_fnames \n",
    "        self.transform_args = transform_args \n",
    "    \n",
    "    def __len__(self): return len(self.img_fnames) \n",
    "    \n",
    "    def __getitem__(self, idx): \n",
    "        img1 = skio.imread(self.img_fnames[idx][0])\n",
    "        img2 = skio.imread(self.img_fnames[idx][1]) \n",
    "        img1 = skt.resize(img1, (self.transform_args['img_size'], self.transform_args['img_size']), order=0) \n",
    "        img1 = img_as_float(img1) \n",
    "        img1 = torch.from_numpy(img1).float()[None, :] \n",
    "        img2 = skt.resize(img2, (self.transform_args['img_size'], self.transform_args['img_size']), order=0) \n",
    "        img2 = img_as_float(img2) \n",
    "        img2 = torch.from_numpy(img2).float()[None, :]\n",
    "        return img1, img2 \n",
    "        \n",
    "def prepare_train_valid_loader(file_dirs, split=0.2, bs=64, nw=0, **transform_args): \n",
    "    random.shuffle(file_dirs) \n",
    "    split_idx = int(np.floor(len(file_dirs)*split)) \n",
    "    train_dataset = ImagePairDataset(file_dirs[split_idx:], **transform_args) \n",
    "    train_loader = DataLoader(train_dataset, batch_size=bs, num_workers=nw) \n",
    "    if split>0: \n",
    "        valid_dataset = ImagePairDataset(file_dirs[:split_idx], **transform_args) \n",
    "        valid_loader = DataLoader(valid_dataset, batch_size=bs, num_workers=nw) \n",
    "        return train_loader, valid_loader \n",
    "    else: valid_loader=None \n",
    "    return train_loader, valid_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in C:\\Users\\lociuser/.cache\\torch\\hub\\mateuszbuda_brain-segmentation-pytorch_master\n"
     ]
    }
   ],
   "source": [
    "unet = torch.hub.load('mateuszbuda/brain-segmentation-pytorch', 'unet',\n",
    "    in_channels=1, out_channels=1, init_features=8, pretrained=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "in_fnames = glob(path.join(data_folder, 'training', 'noisy', '*.tif'))\n",
    "out_fnames = glob(path.join(data_folder, 'training', 'clean', '*.tif'))\n",
    "pair_fnams = list(zip(in_fnames, out_fnames))\n",
    "train_loader, valid_loader = prepare_train_valid_loader(pair_fnams, split=0.2, bs=8, nw=0, img_size=512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "import torchvision.transforms.functional as F\n",
    "def show(imgs):\n",
    "    if not isinstance(imgs, list):\n",
    "        imgs = [imgs]\n",
    "    fix, axs = plt.subplots(ncols=len(imgs), squeeze=False, figsize=(20, 20))\n",
    "    for i, img in enumerate(imgs):\n",
    "        img = img.detach()\n",
    "        img = F.to_pil_image(img)\n",
    "        axs[0, i].imshow(np.asarray(img))\n",
    "        axs[0, i].set(xticklabels=[], yticklabels=[], xticks=[], yticks=[])\n",
    "for in_img, out_img in train_loader:      \n",
    "    in_grid = make_grid(in_img)\n",
    "    out_grid = make_grid(out_img)\n",
    "    show(in_grid)\n",
    "    show(out_grid)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Denoiser:\n",
    "    def __init__(self, working_dir, backbone, loss_fn, lr, train_loader, valid_loader=None, cuda=True):\n",
    "        self.backbone = backbone\n",
    "        self.loss_fn = loss_fn\n",
    "        self.optimizer = Adam(self.backbone.parameters(), lr=lr)\n",
    "        self.train_loader = train_loader\n",
    "        self.valid_loader = valid_loader\n",
    "        self.train_loss_log = []\n",
    "        self.valid_loss_log = []\n",
    "        self.working_dir = working_dir\n",
    "        os.makedirs(path.join(self.working_dir, 'output'), exist_ok=True)\n",
    "        self.cuda = cuda\n",
    "        if self.cuda:\n",
    "            self.loss_fn.cuda()\n",
    "            self.backbone.cuda()\n",
    "\n",
    "    def train(self, num_epoch):\n",
    "        self.backbone.train()\n",
    "        for e in range(num_epoch):\n",
    "            for i, batch in enumerate(self.train_loader):\n",
    "                self.optimizer.zero_grad()\n",
    "                if self.cuda():\n",
    "                    in_img = batch[0].cuda()\n",
    "                    target_img = batch[1].cuda()\n",
    "                else:\n",
    "                    in_img = batch[0]\n",
    "                    target_img = batch[1]\n",
    "                out_img = self.backbone[in_img]\n",
    "                loss = self.loss_fn(out_img, target_img)\n",
    "                loss.backward()\n",
    "                self.optimizer.step()\n",
    "                log = f'[train] epoch {e}/{num_epoch} iter {i}/{len(self.train_loader)} loss: {loss.item()}'\n",
    "                self.train_loss_log.append(log)\n",
    "                print(log)\n",
    "            if e % 5 == 0:\n",
    "                self.test(e, num_epoch)\n",
    "\n",
    "    def test(self, e, num_epoch, print=True):\n",
    "        with torch.no_grad():\n",
    "            self.backbone.eval()\n",
    "            for i, batch in enumerate(self.valid_loader):\n",
    "                if self.cuda():\n",
    "                    in_img = batch[0].cuda()\n",
    "                    target_img = batch[1].cuda()\n",
    "                else:\n",
    "                    in_img = batch[0]\n",
    "                    target_img = batch[1]\n",
    "                out_img = self.backbone[in_img]\n",
    "                loss = self.loss_fn(out_img, target_img)\n",
    "                log = f'[test] epoch {e}/{num_epoch} iter {i}/{len(self.test_loader)} loss: {loss.item()}'\n",
    "                self.valid_loss_log.append(log)\n",
    "                print(log)\n",
    "            if print:\n",
    "                out_dir = path.join(self.working_dir, 'output')\n",
    "                in_fname = path.join(out_dir, f'input_epoch_{e}/{num_epoch}.tif')\n",
    "                out_fname = path.join(out_dir, f'output_epoch_{e}/{num_epoch}.tif')\n",
    "                target_fname = path.join(out_dir, f'target_epoch_{e}/{num_epoch}.tif')\n",
    "                save_image(in_img, in_fname)\n",
    "                save_image(out_img, out_fname)\n",
    "                save_image(target_img, target_fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.MSELoss()\n",
    "lr = 0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Torch not compiled with CUDA enabled",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-b11c28d038ba>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdenoiser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDenoiser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'denoiser'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munet\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalid_loader\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0mcuda\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-10-f573a86b657c>\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, working_dir, backbone, loss_fn, lr, train_loader, valid_loader, cuda)\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackbone\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_epoch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\lociuser\\Miniconda3\\envs\\pyimagej\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36mcuda\u001b[1;34m(self, device)\u001b[0m\n\u001b[0;32m    678\u001b[0m             \u001b[0mModule\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    679\u001b[0m         \"\"\"\n\u001b[1;32m--> 680\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    682\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mxpu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\lociuser\\Miniconda3\\envs\\pyimagej\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m    568\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    569\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 570\u001b[1;33m             \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    572\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\lociuser\\Miniconda3\\envs\\pyimagej\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m    568\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    569\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 570\u001b[1;33m             \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    572\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtensor_applied\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\lociuser\\Miniconda3\\envs\\pyimagej\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m(self, fn)\u001b[0m\n\u001b[0;32m    591\u001b[0m             \u001b[1;31m# `with torch.no_grad():`\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 593\u001b[1;33m                 \u001b[0mparam_applied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    594\u001b[0m             \u001b[0mshould_use_set_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompute_should_use_set_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam_applied\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    595\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mshould_use_set_data\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\lociuser\\Miniconda3\\envs\\pyimagej\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m    678\u001b[0m             \u001b[0mModule\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    679\u001b[0m         \"\"\"\n\u001b[1;32m--> 680\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    681\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    682\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mxpu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mUnion\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mT\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\lociuser\\Miniconda3\\envs\\pyimagej\\lib\\site-packages\\torch\\cuda\\__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[1;34m()\u001b[0m\n\u001b[0;32m    206\u001b[0m                 \"multiprocessing, you must use the 'spawn' start method\")\n\u001b[0;32m    207\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_cuda_getDeviceCount'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Torch not compiled with CUDA enabled\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0m_cudart\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             raise AssertionError(\n",
      "\u001b[1;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
     ]
    }
   ],
   "source": [
    "denoiser = Denoiser('denoiser', unet, criterion, lr, train_loader, valid_loader , cuda=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import img_as_float, img_as_uint\n",
    "from skimage import io \n",
    "from glob import glob\n",
    "from skimage import transform, filters\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_003_a-lsm--12157_10338.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_003_a-lsm--12157_10338.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_003_a-lsm--12157_10338.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_003_a-lsm--12157_10338.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_003_a-lsm--12157_10338.ome___6_9.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_003_a-lsm--12157_10338.ome___7_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_003_a-lsm--12157_10338.ome___7_8.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_003_a-lsm--16382_8206.ome___0_6.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_017-lsm--6416_5033.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--12967_13752.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--14860_1085.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--14860_1085.ome___2_9.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--14860_1085.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--14860_1085.ome___6_7.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--14860_1085.ome___6_8.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--15223_7517.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--16627_8679.ome___0_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--20933_4944.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--20933_4944.ome___1_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--20933_4944.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_025-lsm--6779_6331.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_029-lsm--12000_1985.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--14048_11553.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--14048_11553.ome___2_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--14048_11553.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--14048_11553.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--14048_11553.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--14048_11553.ome___5_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--17385_7772.ome___0_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--17866_11225.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--17866_11225.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--17866_11225.ome___0_6.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--17866_11225.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--17866_11225.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--17866_11225.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--17866_11225.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_063-lsm--17866_11225.ome___1_6.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--13591_12563.ome___0_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--16199_9407.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--16199_9407.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--16199_9407.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--16199_9407.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--16199_9407.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--16199_9407.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--16199_9407.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--9284_8452.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--9284_8452.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_074-lsm--9284_8452.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--12928_5350.ome___0_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--12928_5350.ome___1_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___0_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___2_6.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--19034_5480.ome___5_7.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--22388_13753.ome___5_6.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_088-lsm--22388_13753.ome___6_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N302L5-lsm--14471_3503.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--11448_5928.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--11448_5928.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--11448_5928.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--11448_5928.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--11448_5928.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--11508_8061.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--11508_8061.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--12933_7645.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--13683_8489.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--15849_6904.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--8392_6434.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--8392_6434.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--8392_6434.ome___1_8.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--8392_6434.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--8392_6434.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N311L55-lsm--8392_6434.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N352L42-lsm--2904_4443.ome___0_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N362L124-lsm--1537_1856.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N362L124-lsm--1879_2582.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N362L124-lsm--4116_3466.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N362L124-lsm--5505_1777.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N362L124-lsm--5505_1777.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N369L117-lsm--4050_6157.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--11445_5997.ome___0_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--11445_5997.ome___1_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--11817_2951.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--11817_2951.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--11817_2951.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--11817_2951.ome___1_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--11817_2951.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--12098_3996.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--14808_2751.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--8869_5447.ome___0_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--9156_8364.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--9156_8364.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--9383_7144.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N376L110-lsm--9383_7144.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\PCCP___PDAC_MetroHealth_N381L66-lsm--16902_12405.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___2_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-1.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-10.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-12.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-2.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-2.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-2.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-2.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-2.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-2.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-3.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-3.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-4.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-5.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-5.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-5.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-5.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-5.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-5.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-7.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-7.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-9.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--A-9.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-1.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-10.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-10.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-10.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-10.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-11.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-11.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-11.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-12.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-12.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-12.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-12.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-12.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-12.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-2.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-5.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-7.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-7.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--B-8.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-10.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-10.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-10.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-10.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-10.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-10.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-11.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-11.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-11.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-11.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-11.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-11.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-11.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-11.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-11.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-12.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-12.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-12.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-12.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-12.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-12.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-2.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-2.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-2.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-2.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-2.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-2.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-2.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-5.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-5.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-5.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-5.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-5.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-5.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-6.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-6.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-6.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-6.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-6.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-6.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-6.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-7.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-7.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-7.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--C-7.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___2_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-1.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___2_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-10.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-11.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-11.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-11.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-11.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-11.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-12.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-12.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-2.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-2.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-2.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-2.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-2.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-2.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-2.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-2.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-3.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-3.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-3.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-4.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-5.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-7.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--D-9.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-10.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-11.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-11.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-11.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-12.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-2.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-2.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-2.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-3.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-3.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-3.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-3.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-3.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-3.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-3.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-4.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-4.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-4.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-4.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-4.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-5.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-5.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-5.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-5.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-5.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-6.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-6.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-7.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-7.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-7.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-7.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-9.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-9.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-9.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-9.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-9.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--E-9.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-10.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-10.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-10.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-10.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-10.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-10.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-10.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-11.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-11.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-11.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-11.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-11.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-11.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-11.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-11.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-12.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-12.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-12.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-12.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-12.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-2.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-2.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-3.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-3.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--F-3.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-10.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-10.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-10.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-10.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-10.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-11.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-11.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-12.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-12.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-12.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-12.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-12.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-12.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-12.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-12.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-2.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-2.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-2.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-3.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-3.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-4.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-4.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-4.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-4.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-4.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-5.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-5.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-5.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-5.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-5.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-5.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-6.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-6.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-7.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-7.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-7.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-8.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-8.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--G-9.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-10.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-10.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-11.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-11.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-11.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-11.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-11.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-12.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-2.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-2.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-2.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-2.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-2.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-2.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-2.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-4.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-4.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-4.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-5.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-5.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-5.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-5.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-5.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-6.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-6.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-6.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-7.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-7.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-7.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--H-9.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-10.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-10.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-10.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-10.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-10.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-11.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-11.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-12.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-2.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-2.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-2.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-2.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-3.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-3.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-3.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-3.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-3.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-3.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-4.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-4.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-4.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-5.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-6.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-6.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-7.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-8.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-8.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--I-8.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-11.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-11.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-11.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-11.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-11.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-11.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-12.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-12.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-2.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-2.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-2.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-2.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-2.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-8.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--J-9.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-10.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-10.ome___1_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-10.ome___2_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-11.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-11.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-11.ome___2_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-11.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-11.ome___3_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-11.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-12.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-12.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-3.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-3.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-3.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-4.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-5.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-6.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-7.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-8.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-8.ome___3_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained-lsm--K-8.ome___4_5.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--A-18.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--B-18.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--B-18.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--B-18.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--B-18.ome___2_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--B-18.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--B-18.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--C-15.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--D-17.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--D-17.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--D-17.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--D-17.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--D-17.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--D-17.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--D-17.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--D-17.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--E-14.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--E-16.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--E-16.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-13.ome___0_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-13.ome___0_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-13.ome___1_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-13.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-13.ome___1_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-13.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-13.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-13.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-17.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--F-17.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--G-14.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--G-14.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--G-15.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--G-16.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--G-16.ome___1_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--G-16.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--G-17.ome___2_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--G-18.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--H-13.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--H-16.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--H-16.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--H-16.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--H-18.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--H-19.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--H-19.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--H-19.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--I-18.ome___0_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--I-18.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--I-18.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--I-18.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--I-18.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-17.ome___1_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-17.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-17.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-17.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-17.ome___2_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-17.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-18.ome___2_0.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-18.ome___2_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-18.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-18.ome___3_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-18.ome___3_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-18.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-18.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--J-18.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-18.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-18.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-19.ome___1_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-19.ome___2_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-19.ome___2_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-19.ome___3_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-19.ome___3_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-19.ome___4_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-19.ome___4_2.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-19.ome___4_3.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___PDAC.3.A_Unstained_part2-lsm--K-19.ome___4_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___rl-PDAC.3.A_Unstained-lsm--I-3.ome___0_1.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n",
      "C:\\Users\\bli346\\AppData\\Local\\Temp\\ipykernel_10372\\2941263670.py:8: UserWarning: datasets/lsm-pair/input\\Unstained___rl-PDAC.3.A_Unstained-lsm--I-3.ome___0_4.tif is a low contrast image\n",
      "  io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))\n"
     ]
    }
   ],
   "source": [
    "inputs = glob('datasets/lsm-pair/target/*.tif')\n",
    "output_dir = 'datasets/lsm-pair/input'\n",
    "for fname in inputs:\n",
    "    img = io.imread(fname)\n",
    "    img = img_as_float(img)\n",
    "    img = transform.rescale(img, 0.25, order=0)\n",
    "    # img = filters.median(img)\n",
    "    io.imsave(os.path.join(output_dir, os.path.basename(fname)), img_as_uint(img))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lsm_utils import process_pmm_datasets\n",
    "from glob import glob\n",
    "from pycromanager import Dataset as PDataset\n",
    "from os import path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Dataset opened                 \n"
     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Dataset opened                 \n"
     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
     ]
    },
    {
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     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
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    {
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    {
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    {
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    {
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     "name": "stderr",
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     "text": [
      " 87%|████████▋ | 78/90 [34:05<02:54, 14.54s/it]"
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     "text": [
      "Dataset opened                 \n"
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     "name": "stderr",
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     "text": [
      " 88%|████████▊ | 79/90 [34:18<02:37, 14.31s/it]"
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    },
    {
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     "text": [
      "Dataset opened                 \n"
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     "text": [
      " 89%|████████▉ | 80/90 [34:36<02:32, 15.27s/it]"
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     "text": [
      "Dataset opened                 \n"
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     "name": "stderr",
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     "text": [
      " 90%|█████████ | 81/90 [34:49<02:12, 14.74s/it]"
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     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 91%|█████████ | 82/90 [35:02<01:53, 14.15s/it]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 83/90 [35:16<01:37, 13.96s/it]"
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    },
    {
     "name": "stdout",
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     "text": [
      "Dataset opened                 \n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 84/90 [35:29<01:22, 13.75s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 85/90 [35:43<01:08, 13.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████▌| 86/90 [35:55<00:53, 13.47s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 97%|█████████▋| 87/90 [36:08<00:39, 13.33s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset opened                 \n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 88/90 [36:21<00:26, 13.03s/it]"
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    },
    {
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     "text": [
      "Dataset opened                 \n"
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     "text": [
      " 99%|█████████▉| 89/90 [36:35<00:13, 13.50s/it]"
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    {
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     "text": [
      "Dataset opened                 \n"
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     "text": [
      "100%|██████████| 90/90 [36:49<00:00, 24.55s/it]\n"
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   "source": [
    "process_pmm_datasets('pmm_datasets/PA2081b_clean', 'datasets/PA_2081b/clean')"
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  {
   "cell_type": "code",
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   "metadata": {},
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     "text": [
      "Dataset opened                 \n"
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    "training_data = glob(path.join('pmm_datasets/PA2081b_noisy', '*/'))\n",
    "dataset = PDataset(training_data[-1])"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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     },
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     "metadata": {},
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    }
   ],
   "source": [
    "dataset.as_array()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "from os import path \n",
    "clean_data = [path.basename(i)[:33]+path.basename(i)[34:] for i in glob(path.join('datasets/PA_2081b/clean', '*'))]\n",
    "noisy_data = [path.basename(i)[:33]+path.basename(i)[34:] for i in glob(path.join('datasets/PA_2081b/noisy', '*'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PA2081b_0709_train-lsm-slow--M-7_---99---3.tif'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_data[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PA2081b_0709_train-lsm-slow--M-7_---3---99.tif'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_data[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41312"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_files = list(set(clean_data)-set(noisy_data))\n",
    "len(diff_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PA2081b_0709_train-lsm-slow--A-1_---0---0.tif'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname[:33]+fname[34:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = 0\n",
    "for i in glob(path.join('datasets/PA_2081b/noisy', '*')):\n",
    "    fname = path.basename(i)\n",
    "    striped_name = fname[:30]+fname[34:]\n",
    "    if striped_name in diff_files:\n",
    "        c += 1\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PA2081b_0709_train-lsm-slow--A-1_3---0---0.tif'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PA2081b_0709_train-lsm-slow--A---0---0.tif'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "striped_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PA2081b_0709_train-lsm-slow--M-7_1---3---99.tif'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn \n",
    "import torch \n",
    "loss = nn.MSELoss(reduction='none')\n",
    "input = torch.randn((4, 1, 64, 64), requires_grad=False)\n",
    "output = torch.randn((4, 1, 64, 64), requires_grad=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "torch.Size([4])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ms.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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