DeepCell_explore_tissuenet_1.0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3041296",
   "metadata": {},
   "source": [
    "**Examine TissueNet 1.0**<br>\n",
    "Nuclear channels are 0 in img and 1 in mask.<br>\n",
    "Cyto channels are 1 in img and 0 in mask.<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "583bab47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import skimage.io as io\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.image\n",
    "%matplotlib inline\n",
    "\n",
    "from deepcell.utils.plot_utils import create_rgb_image\n",
    "from deepcell.utils.plot_utils import make_outline_overlay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9925aac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2601, 512, 512, 2)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dict = np.load('/fh/fast/fong_y/tissuenet_v1.0/tissuenet_v1.0_train.npz')\n",
    "train_X, train_y = train_dict['X'], train_dict['y']\n",
    "train_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce8cb341",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3140, 256, 256, 2)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_dict = np.load('/fh/fast/fong_y/tissuenet_v1.0/tissuenet_v1.0_val.npz')\n",
    "val_X, val_y = val_dict['X'], val_dict['y']\n",
    "val_y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "156c11c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dict = np.load('/fh/fast/fong_y/tissuenet_v1.0/tissuenet_v1.0_test.npz')\n",
    "test_X, test_y = test_dict['X'], test_dict['y']\n",
    "train_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7cb893",
   "metadata": {},
   "source": [
    "**Train image 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6a3fb6b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 2, 1) \n",
    "io.imshow(train_X[0,:,:,0])\n",
    "plt.title(\"Nucleus\")\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "io.imshow(train_X[0,:,:,1])\n",
    "plt.title(\"Cyto\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b0671e7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/io/_plugins/matplotlib_plugin.py:150: UserWarning: Low image data range; displaying image with stretched contrast.\n",
      "  lo, hi, cmap = _get_display_range(image)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 2, 1) # row 1, col 2 index 1\n",
    "io.imshow(train_y[0,:,:,0])\n",
    "plt.title(\"Cyto\")\n",
    "plt.subplot(2, 2, 2) # row 1, col 2 index 1\n",
    "io.imshow(train_y[0,:,:,1])\n",
    "plt.title(\"Nucleus\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a7108b94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b25975fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "io.imsave(\"/home/yfong/deeplearning/dense_cell_segmentation/train0_masks_0.png\", np.array(train_y[0,:,:,0], dtype=np.uint8))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fef83a7",
   "metadata": {},
   "source": [
    "**Train image 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da317c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 2, 3) # row 1, col 2 index 1\n",
    "io.imshow(train_X[1,:,:,0])\n",
    "plt.title(\"Nucleus\")\n",
    "\n",
    "plt.subplot(2, 2, 4) # row 1, col 2 index 1\n",
    "io.imshow(train_X[1,:,:,1])\n",
    "plt.title(\"Cyto\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e5db76a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 2, 3) # row 1, col 2 index 1\n",
    "io.imshow(train_y[1,:,:,0])\n",
    "plt.title(\"Cyto\")\n",
    "plt.subplot(2, 2, 4) # row 1, col 2 index 1\n",
    "io.imshow(train_y[1,:,:,1])\n",
    "plt.title(\"Nucleus\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95286fb5",
   "metadata": {},
   "source": [
    "**Test image 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2d6654b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.subplot(2, 2, 1) # row 1, col 2 index 1\n",
    "io.imshow(test_X[0,:,:,0])\n",
    "plt.title(\"Nucleus\")\n",
    "\n",
    "plt.subplot(2, 2, 2) # row 1, col 2 index 1\n",
    "io.imshow(test_X[0,:,:,1])\n",
    "plt.title(\"Cyto\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24de5fcc",
   "metadata": {},
   "source": [
    "**Write nuclear channel training images and masks**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "29a58947",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2601, 512, 512, 2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(train_X.shape)\n",
    "io.imshow(img_as_ubyte(train_X[0,:,:,0]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c3d178f",
   "metadata": {},
   "source": [
    "**Write nuclear images and masks to files**<br>\n",
    "Files are split into train and val. Images are wxhx1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2bb13996",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_14848\\4129865617.py\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain_X\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\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      5\u001b[0m \u001b[0mval_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m \u001b[1;31m# fraction of data saved as validation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'train_X' is not defined"
     ]
    }
   ],
   "source": [
    "seed = 0 \n",
    "import random\n",
    "\n",
    "n=train_X.shape[0]\n",
    "val_size = 0.2 # fraction of data saved as validation\n",
    "\n",
    "tmp = random.sample(range(n), int(val_size*n))\n",
    "X_val = train_X[tmp,:,:,:]\n",
    "y_val = train_y[tmp,:,:,:]\n",
    "\n",
    "tmp1 = list(set(range(n)).difference (set(tmp)))\n",
    "X_train = train_X[tmp1,:,:,:]\n",
    "y_train = train_y[tmp1,:,:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f16fdce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage import img_as_ubyte, img_as_uint\n",
    "\n",
    "for img in range(X_train.shape[0]):\n",
    "    tmpimg = X_train[img,:,:,0]\n",
    "    tmpimg = np.expand_dims(tmpimg, -1)\n",
    "    io.imsave('/fh/fast/fong_y/tissuenet_v1.0/images/train_nuclear/train'+str(img)+'_img.png', img_as_ubyte(tmpimg))\n",
    "    \n",
    "    tmpmask= y_train[img,:,:,1]\n",
    "    io.imsave('/fh/fast/fong_y/tissuenet_v1.0/images/train_nuclear/train'+str(img)+'_masks.png', img_as_uint(tmpmask), check_contrast=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d08189a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a strange thing is that if this cell is merged into the last cell, warnings about casting appear\n",
    "for img in range(X_val.shape[0]):\n",
    "    tmpimg = X_val[img,:,:,0]\n",
    "    tmpimg = np.expand_dims(tmpimg, -1)\n",
    "    io.imsave('/fh/fast/fong_y/tissuenet_v1.0/images/val_nuclear/val'+str(img)+'_img.png', img_as_ubyte(tmpimg))\n",
    "    \n",
    "    tmpmask= y_val[img,:,:,1]\n",
    "    io.imsave('/fh/fast/fong_y/tissuenet_v1.0/images/val_nuclear/val'+str(img)+'_masks.png', img_as_uint(tmpmask), check_contrast=False)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80968b06",
   "metadata": {},
   "source": [
    "**Write nuclear images and masks to files**<br>\n",
    "All files into one folder. Images are W x H x 3. Following the cellpose convention, the three channels are R: nucleus, G: cyto, B: blank. But we treat tissunet nuclear channel as cyto. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8cc0e5a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 213 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1275 1276 1277 "
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 94 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1278 1279 1280 1281 1282 1283 1284 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 123 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1285 1286 1287 1288 1289 1290 1291 1292 1293 "
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 24 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1294 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 118 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1295 1296 1297 "
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 35 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1298 1299 1300 1301 1302 1303 "
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 155 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n",
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 23 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 "
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 43 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1317 1318 1319 1320 1321 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 153 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1322 1323 1324 1325 1326 1327 1328 1329 1330 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 107 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 "
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 228 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1349 1350 1351 1352 1353 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 162 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 171 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1378 1379 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 130 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1380 1381 1382 1383 1384 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 31 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 172 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1395 1396 1397 1398 1399 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 104 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1400 1401 1402 1403 1404 1405 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 173 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1406 1407 1408 1409 1410 1411 1412 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 164 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1413 1414 1415 1416 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 610 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1417 1418 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 868 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1419 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 803 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1420 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 863 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1421 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 875 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1422 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1060 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1423 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 750 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1424 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 924 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1425 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 947 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1426 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 954 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1427 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1021 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1428 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 828 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1429 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1056 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1430 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1007 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1431 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 854 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1432 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 887 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1433 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 956 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1434 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1048 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1435 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 856 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1436 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1055 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1437 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 839 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1438 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 895 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1439 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 812 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1440 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 425 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1441 1442 1443 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 569 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1444 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 682 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1445 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 981 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1446 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 534 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1447 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1155 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1448 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 911 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1449 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 702 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1450 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 528 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1451 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 737 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1452 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 788 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1453 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 833 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1454 1455 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1017 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1456 1457 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1154 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1458 1459 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 704 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1460 1461 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 937 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1462 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 567 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1463 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 619 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1464 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 708 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1465 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 712 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1466 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1071 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1467 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 661 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1468 1469 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1353 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1470 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 546 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1471 1472 1473 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 141 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1474 1475 1476 1477 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 749 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1478 1479 1480 1481 1482 1483 1484 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 18 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1485 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 521 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1486 1487 1488 1489 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 587 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1490 1491 1492 1493 1494 1495 1496 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 728 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1497 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 39 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1498 1499 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 187 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1500 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 673 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1501 1502 1503 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 7 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1504 1505 1506 1507 1508 1509 1510 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 648 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1511 1512 1513 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 617 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1514 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 458 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 631 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 194 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 19 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1554 1555 1556 1557 1558 1559 1560 1561 1562 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 486 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1563 1564 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 22 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 705 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1577 1578 1579 1580 1581 1582 1583 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 53 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1584 1585 1586 1587 1588 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 154 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1589 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 169 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1590 1591 1592 1593 1594 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 452 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 512 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1605 1606 1607 1608 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 128 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1609 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 479 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 599 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1621 1622 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 197 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1623 1624 1625 1626 1627 1628 1629 1630 1631 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 513 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1632 1633 1634 1635 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 897 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1636 1637 1638 1639 1640 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 445 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 681 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1674 1675 1676 1677 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 147 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1678 1679 1680 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 199 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1681 1682 1683 1684 1685 1686 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 603 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1687 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 466 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1688 1689 1690 1691 1692 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 417 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1693 1694 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 580 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1695 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 49 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1696 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 427 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1697 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 733 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1698 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 677 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1699 1700 1701 1702 1703 1704 1705 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 114 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1706 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 929 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1707 1708 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 442 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1709 1710 1711 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 27 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1712 1713 1714 1715 1716 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 422 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1717 1718 1719 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 473 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 29 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1735 1736 1737 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 601 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1738 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 495 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1739 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 771 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1740 1741 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1391 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1742 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 979 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1743 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1566 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1744 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 982 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1745 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 955 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1746 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 692 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1747 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1069 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1748 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 926 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1749 1750 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1120 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1751 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1293 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1752 1753 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 993 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1754 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1434 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1755 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1010 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1756 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1166 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1757 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1078 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1758 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1202 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1759 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 574 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1760 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1047 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1761 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1339 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1762 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 735 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1763 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1341 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1764 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1194 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1765 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 974 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1766 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1179 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1767 1768 1769 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1042 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1770 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 793 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1771 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1397 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1772 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1288 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1773 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1242 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1774 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 517 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1775 1776 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 946 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1777 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1343 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1778 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1193 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1779 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1257 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1780 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1419 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1781 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 641 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1782 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1090 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1783 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 802 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1784 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1052 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1785 1786 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 990 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1787 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1111 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1788 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 800 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1789 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1424 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1790 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1209 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1791 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1407 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1792 1793 1794 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 989 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1795 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1112 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1796 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1098 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1797 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1095 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1798 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 978 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1799 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1428 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1800 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 1024 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1801 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 558 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1802 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 589 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1803 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 424 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1804 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 476 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1805 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 609 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1806 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 662 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1807 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 604 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1808 1809 1810 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 634 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1811 1812 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 618 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1813 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 34 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1814 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 514 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1815 1816 1817 1818 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 647 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1819 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 564 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 583 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1830 1831 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 731 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1832 1833 1834 1835 1836 1837 1838 1839 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 519 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1840 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 454 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1841 1842 1843 1844 1845 1846 1847 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 531 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1848 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 446 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1849 1850 1851 1852 1853 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 436 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 559 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1868 1869 1870 1871 1872 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 630 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1873 1874 1875 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 809 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1876 1877 1878 1879 1880 1881 1882 1883 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 212 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 477 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1902 1903 1904 1905 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 536 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1906 1907 1908 1909 1910 1911 1912 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 453 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1913 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 483 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1914 1915 1916 1917 1918 1919 1920 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 472 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 100 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1938 1939 1940 1941 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 507 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1942 1943 1944 1945 1946 1947 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 497 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 570 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1960 1961 1962 1963 1964 1965 1966 1967 1968 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 493 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1969 1970 1971 1972 1973 1974 1975 1976 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 557 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1977 1978 1979 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 515 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1980 1981 1982 1983 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 492 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1984 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 751 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1985 1986 1987 1988 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 740 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1989 1990 1991 1992 1993 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 553 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1994 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 501 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1995 1996 1997 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 620 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1998 1999 2000 2001 2002 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 450 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2003 2004 2005 2006 2007 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 529 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2008 2009 2010 2011 2012 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 602 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2013 2014 2015 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 568 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016 2017 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 538 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 525 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 547 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 522 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 551 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022 2023 2024 2025 2026 2027 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 577 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2028 2029 2030 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 544 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2031 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 560 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2032 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 561 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 461 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2050 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 605 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2051 2052 2053 2054 2055 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 633 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2056 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 572 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2057 2058 2059 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 651 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2060 2061 2062 2063 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 622 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2064 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 588 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2065 2066 2067 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 524 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2068 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 575 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2069 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 686 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2070 2071 2072 2073 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 444 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2074 2075 2076 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 530 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2077 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 650 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2078 2079 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 629 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2080 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 606 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2081 2082 2083 2084 2085 2086 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 565 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2087 2088 2089 2090 2091 2092 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 714 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2093 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 593 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2094 2095 2096 2097 2098 2099 2100 2101 2102 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 47 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2103 2104 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 490 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2105 2106 2107 2108 2109 2110 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 549 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2111 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 635 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2112 2113 2114 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 639 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2115 2116 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 548 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2117 2118 2119 2120 2121 2122 2123 2124 2125 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 607 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2126 2127 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 550 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2128 2129 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 573 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2130 2131 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 540 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2132 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 537 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2133 2134 2135 2136 2137 2138 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 592 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2139 2140 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 566 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2141 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 437 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2142 2143 2144 2145 2146 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 636 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2147 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 632 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2148 2149 2150 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 438 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 527 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2164 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 621 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 611 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2181 2182 2183 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 718 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2184 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 463 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2185 2186 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 430 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2187 2188 2189 2190 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 481 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2191 2192 2193 2194 2195 2196 2197 2198 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 571 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2199 2200 2201 2202 2203 2204 2205 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 626 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2206 2207 2208 2209 2210 2211 2212 2213 2214 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 584 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2215 2216 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 754 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2217 2218 2219 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 533 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2220 2221 2222 2223 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 600 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2224 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 579 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2225 2226 2227 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 668 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2228 2229 2230 2231 2232 2233 2234 2235 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 710 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2236 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 679 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2237 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 542 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2238 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 658 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2239 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 526 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2240 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 597 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2241 2242 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 624 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2243 2244 2245 2246 2247 2248 2249 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 638 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2250 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 505 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2251 2252 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 616 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2253 2254 2255 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 586 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2256 2257 2258 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 585 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2259 2260 2261 2262 2263 2264 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 582 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2265 2266 2267 2268 2269 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 494 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2270 2271 2272 2273 2274 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 539 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2275 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 470 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2276 2277 2278 2279 2280 2281 2282 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 676 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2283 2284 2285 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 627 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2286 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 596 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2287 2288 2289 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 741 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2290 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 535 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2291 2292 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 484 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 594 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2304 2305 2306 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 665 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2307 2308 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 578 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2309 2310 2311 2312 2313 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 38 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2314 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 48 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2315 2316 2317 2318 2319 2320 2321 2322 2323 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 628 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2324 2325 2326 2327 2328 2329 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 516 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2330 2331 2332 2333 2334 2335 2336 2337 2338 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 576 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2339 2340 2341 2342 2343 2344 2345 2346 2347 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 656 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 900 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2360 2361 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 645 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2362 2363 2364 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 664 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 774 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2379 2380 2381 2382 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 591 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2383 2384 2385 2386 2387 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 532 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2388 2389 2390 2391 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 805 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2392 2393 2394 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 792 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2395 2396 2397 2398 2399 2400 2401 2402 2403 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 373 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2404 2405 2406 2407 2408 2409 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 435 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 687 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2422 2423 2424 2425 2426 2427 2428 2429 2430 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 168 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 590 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2441 2442 2443 2444 2445 2446 2447 2448 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 808 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2449 2450 2451 2452 2453 2454 2455 2456 2457 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 660 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2458 2459 2460 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 614 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 746 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 713 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2503 2504 2505 2506 2507 2508 2509 2510 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 105 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2511 2512 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 685 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2513 2514 2515 2516 2517 2518 2519 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 545 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 655 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2530 2531 2532 2533 2534 2535 2536 2537 2538 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 678 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 667 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 543 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2573 2574 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 657 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 722 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2589 2590 2591 2592 2593 2594 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 474 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2595 2596 2597 2598 2599 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/app/software/scikit-image/0.19.1-foss-2021b/lib/python3.9/site-packages/skimage/util/dtype.py:492: UserWarning: Downcasting int32 to uint16 without scaling because max value 196 fits in uint16\n",
      "  return _convert(image, np.uint16, force_copy)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2600 "
     ]
    }
   ],
   "source": [
    "from skimage import img_as_ubyte, img_as_uint\n",
    "\n",
    "rgb_data = np.zeros(train_X.shape[1:3] + (3,), dtype='ubyte') \n",
    "\n",
    "for img in range(train_X.shape[0]):\n",
    "    print(img, end=' ')\n",
    "    rgb_data[:,:,1] = img_as_ubyte(train_X[img,:,:,0]) # put the nucleus image in the cyto channel\n",
    "    io.imsave('/fh/fast/fong_y/tissuenet_v1.0/images/train_nuclear_rgb/train'+str(img)+'_img.png', rgb_data, check_contrast=False)\n",
    "    \n",
    "    tmpmask= train_y[img,:,:,1]\n",
    "    io.imsave('/fh/fast/fong_y/tissuenet_v1.0/images/train_nuclear_rgb/train'+str(img)+'_masks.png', img_as_uint(tmpmask), check_contrast=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d67f132a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "196\n"
     ]
    }
   ],
   "source": [
    "# It is not clear why there are warnings about downcasting. Not reproducible outside the loop\n",
    "img=2599\n",
    "rgb_data[:,:,1] = img_as_ubyte(train_X[img,:,:,0]) # put the nucleus image in the cyto channel\n",
    "io.imsave('/fh/fast/fong_y/tissuenet_v1.0/images/train_nuclear_rgb/train'+str(img)+'_img.png', rgb_data, check_contrast=False)\n",
    "\n",
    "tmpmask= train_y[img,:,:,1]\n",
    "print(tmpmask.max())\n",
    "io.imsave('/fh/fast/fong_y/tissuenet_v1.0/images/train_nuclear_rgb/train'+str(img)+'_masks.png', img_as_uint(tmpmask), check_contrast=False)"
   ]
  }
 ],
 "metadata": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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