scripts/Figure_S2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10979286-4456-443b-a990-913c26f15c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import numpy as np\n",
    "import time, os, sys\n",
    "from urllib.parse import urlparse\n",
    "import skimage.io\n",
    "\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "\n",
    "mpl.rcParams['figure.dpi'] = 300\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap, rgb2hex\n",
    "plt.style.use('dark_background')\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "\n",
    "import scipy.ndimage as ndimg\n",
    "\n",
    "from cellpose_omni import utils\n",
    "from skimage.io import imread\n",
    "import scipy\n",
    "from skimage.morphology import binary_erosion, medial_axis, disk, medial_axis,remove_small_holes,remove_small_objects\n",
    "from scipy.ndimage import binary_dilation, binary_opening\n",
    "from skimage.measure import label, regionprops\n",
    "\n",
    "# import skfmm\n",
    "import cv2\n",
    "from skimage import measure\n",
    "\n",
    "import fastremap #important for relabeling! Could move just to color function perhaps, but idk of other parts of cellpose require sequential lebel matrices; probably worth it\n",
    "import edt\n",
    "\n",
    "from cellpose_omni import models, io, dynamics, plot, transforms, metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c01a16a4-74a8-4bd1-a213-ccbb9ab1b0f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage import uniform_filter\n",
    "import numpy.ma as ma\n",
    "import cv2\n",
    "golden = (1 + 5 ** 0.5) / 2\n",
    "sz = 1\n",
    "\n",
    "\n",
    "l = 24\n",
    "n = 1\n",
    "a = 3\n",
    "b = .5\n",
    "r = l/2\n",
    "\n",
    "L = np.ceil(l*b).astype(int)+1\n",
    "pad = (L/2).astype(int)\n",
    "\n",
    "x, y = np.indices((l, l))\n",
    "center = (l/2-.5, l/2-.5)\n",
    "circle = np.abs(x - center[0])**a + np.abs((y - center[1])/b)**a < r**a\n",
    "mask = np.pad(circle,pad).astype(int)\n",
    "print('mask area',np.sum(mask))\n",
    "\n",
    "Ly, Lx = mask.shape\n",
    "Y,X = np.nonzero(mask)\n",
    "plt.imshow(mask)\n",
    "plt.axis('off')\n",
    "plt.show()\n",
    "\n",
    "# basedir = '/home/kcutler/DataDrive/omnipose_paper/Figure S2'\n",
    "basedir = '/home/kcutler/DataDrive/Dissertation/figures'\n",
    "\n",
    "io.check_dir(basedir)\n",
    "\n",
    "ious = np.zeros(L)\n",
    "pics = [[]]*(L)\n",
    "for dx in range(L):\n",
    "    mask2 = np.zeros_like(mask).astype(int)\n",
    "    mask2[Y,X-dx] = 1\n",
    "    iou =  metrics._intersection_over_union(mask,mask2)[-1,-1]\n",
    "    ious[dx] = iou\n",
    "    print(iou,np.sum(mask),'%.2f'%iou)\n",
    "    bd = edt.edt(mask) == 1\n",
    "    bd2 = edt.edt(mask2) == 1\n",
    "    p = np.stack([mask*255/3]*4,axis=-1).astype(float)\n",
    "    pic = plot.outline_view(p,bd2,skip_formatting=1,color=[1.,0,0,0])/255\n",
    "    # pic = np.concatenate([pic,np.zeros(pic.shape[:2])])\n",
    "    # y,x = np.nonzero(1-pic[:,:,1]>0)\n",
    "    pic[...,-1] = np.any(pic,axis=-1) # set opacity\n",
    "#     pic = pic[:,max(x.min()-5,0):min(x.max()+1+5,Lx)]\n",
    "    pics[dx] = pic\n",
    "    # plt.imshow(pic)\n",
    "    # plt.axis('off')\n",
    "    # plt.show()\n",
    "    # io.imsave(os.path.join(basedir,'iou_%.2f'%iou+'_cell_'+str(l)+'.png'),((pic)*255).astype(np.uint8))\n",
    "from omnipose.plot import imshow\n",
    "imshow(pic)\n",
    "pic.shape, p.shape, np.unique(pic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48e193cb-4b17-432b-918c-c9aca1cc002d",
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = np.hstack(pics)\n",
    "ly,lx = fp.shape[:2]\n",
    "fig = plt.figure(figsize=(sz, 2*sz*ly/lx)) \n",
    "ax = plt.axes()\n",
    "plt.imshow(fp)\n",
    "plt.axis('off')\n",
    "# plt.axis('off')\n",
    "w = pic.shape[1]\n",
    "ax.set_xticks(np.linspace(w/2,L*w,L))\n",
    "ax.set_xticklabels(['%.2f'%i for i in ious])\n",
    "ax.set_facecolor(background_color)\n",
    "# pics[0][0]\n",
    "# io.imsave(os.path.join(basedir,'iou_%.2f'%iou+'_stack_cell_'+str(l)+'.png'),((fp)*255).astype(np.uint8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20460510-7d0c-4684-9400-50a48cac0a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import omnipose\n",
    "sz = 9\n",
    "labelsize = 9\n",
    "fig,axs = plt.subplots(3,4,figsize=(sz, sz*3/4), frameon=False) \n",
    "axs = axs.flatten()\n",
    "for l in range(min(12,L)):\n",
    "    # for l in range(L):\n",
    "    ax = axs[l]\n",
    "    # ax.imshow(np.round(pics[l]).astype(int),interpolation='nearest')\n",
    "    ax.imshow(omnipose.utils.to_8_bit(pics[l]),interpolation='nearest',vmin=0,vmax=1)\n",
    "    \n",
    "    ax.axis('off')\n",
    "    # ax.set_xticks([])\n",
    "    # ax.set_yticks([])\n",
    "    # ax.set_xlabel('%.2f'%ious[l],size=labelsize)\n",
    "    for s in ax.spines.values():\n",
    "        s.set_visible(False)\n",
    "    ax.set_facecolor([0]*4)\n",
    "    ax.set_facecolor(background_color)\n",
    "plt.subplots_adjust(bottom=0,left=0,wspace=0,hspace=0)\n",
    "# fig.patch.set_facecolor(None)\n",
    "plt.show()\n",
    "\n",
    "fig.savefig(os.path.join(basedir,'cells.eps'),bbox_inches='tight')#,transparent=True)\n",
    "fig.savefig(os.path.join(basedir,'cells.png'),bbox_inches='tight')#,transparent=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aefc4347-b3ab-41f2-8ac4-9947949dde55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imshow(pics[l].astype(float))\n",
    "# np.unique((pics[l]/255).astype(int))\n",
    "np.unique(pics[l])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8986721c-2b02-466f-9ae7-6a942ee601b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "# mpl.rcParams[\"text.usetex\"] = False\n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42\n",
    "mpl.rcParams['svg.fonttype'] = 'none'\n",
    "mpl.rcParams.update({\n",
    "    # \"text.usetex\": True,\n",
    "    \"font.family\": \"sans-serif\",\n",
    "    # \"font.sans-serif\": [\"Helvetica\"]\n",
    "})\n",
    "# # matplotlib_inline.backend_inline.set_matplotlib_formats('retina', 'png')\n",
    "# mpl.rcParams['figure.dpi'] = 72\n",
    "# mpl.rcParams[\"ps.usedistiller\"] = 'xpdf'\n",
    "# # rc('text.latex', preamble=r'\\usepackage{cmbright}')\n",
    "\n",
    "\n",
    "ly,lx = fp.shape[:2]\n",
    "labelsize = 9\n",
    "\n",
    "axcol = [.5]*4\n",
    "background_color = [0]*4\n",
    "sz = 3\n",
    "fig = plt.figure(figsize=(sz, sz*3/4), frameon=False) \n",
    "ax = plt.axes()\n",
    "plt.plot(ious,color='r')\n",
    "ax.set_facecolor(background_color)\n",
    "for s in ax.spines.values():\n",
    "    s.set_color(axcol)\n",
    "    \n",
    "ax.tick_params(axis='both', which='major', labelsize=labelsize,length=3, \n",
    "               direction=\"out\",colors=axcol,bottom=True,left=True)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=labelsize,length=3, \n",
    "               direction=\"out\",colors=axcol,bottom=True,left=True)\n",
    "ax.set_ylabel('IoU', fontsize = labelsize, c=axcol)\n",
    "ax.set_xlabel('Mask shift (px)', fontsize = labelsize, c=axcol)\n",
    "ax.set_ylim(0,1)\n",
    "ax.set_xlim(0,L-1)\n",
    "ax.set_xticks(range(0,L,2))\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.xaxis.get_major_formatter()._usetex = False\n",
    "ax.yaxis.get_major_formatter()._usetex = False\n",
    "\n",
    "fig.patch.set_facecolor(None)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "fig.savefig(os.path.join(basedir,'IoU.eps'),bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bbf4a3c-50ce-427f-ba41-153e191a6b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "bd = edt.edt(mask) == 1\n",
    "mask2 = np.zeros_like(mask).astype(int)\n",
    "mask3 = np.zeros_like(mask).astype(int)\n",
    "y0 = np.percentile(y,50).astype(int)\n",
    "select1 = Y>=y0\n",
    "select2 = Y<y0\n",
    "mask2[Y[select1],X[select1]] = 1.\n",
    "mask3[Y[select2],X[select2]] = 1.\n",
    "\n",
    "plt.imshow(np.hstack((mask2,mask3)))\n",
    "plt.show()\n",
    "\n",
    "masks = mask2+2*mask3\n",
    "iou =  metrics._intersection_over_union(mask,masks)[1:,1:]\n",
    "print(iou,np.sum(mask),'%.2f'%np.mean(iou))\n",
    "bd = edt.edt(mask) == 1\n",
    "bd2 = edt.edt(masks) == 1\n",
    "p = np.stack([mask]*3,axis=-1)\n",
    "pic = plot.outline_view(p,bd2)*1.\n",
    "plt.imshow(pic)\n",
    "plt.show()\n",
    "\n",
    "# skimage.io.imsave(os.path.join(basedir,'iou_%.2f'%iou+'_split.eps'),((pic)*255).astype(np.uint8),dpi=1200)\n",
    "plt.imsave(os.path.join(basedir,'iou_%.2f'%iou+'_split.png'),((pic)*255).astype(np.uint8),dpi=1200,interpolation='nearest')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "354df7c9-8264-4860-ad99-697bed24412b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sub2ind(array_shape, rows, cols):\n",
    "    return rows*array_shape[1] + cols\n",
    "\n",
    "ly,lx,ch= pics[0].shape\n",
    "pic = np.zeros((ly*3,lx*4,ch))\n",
    "for j in range(3):\n",
    "    for k in range(4):\n",
    "        pic[j*ly:(j+1)*ly,k*lx:(k+1)*lx] = pics[sub2ind((3,4),j,k)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e67a8633-c20e-48d8-bcd0-f205695005fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(pic)\n",
    "io.imsave(os.path.join(basedir,'cell'+str(l)+'_grid.png'),((pic)*255).astype(np.uint8))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}