Initial commit

CellProfiler/.ipynb_checkpoints/sort_images-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "from pprint import pprint\n",
+    "\n",
+    "import skimage.io\n",
+    "import sklearn.cluster\n",
+    "import sklearn.preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "image_list = glob.glob(\"../images/*.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([1382400,       0,       0,       0,       0,       0,       0,\n",
+      "             0,       0,       0], dtype=int64),\n",
+      " array([1382400,       0,       0,       0,       0,       0,       0,\n",
+      "             0,       0,       0], dtype=int64),\n",
+      " array([1376628,    4927,     697,     110,      23,      15,       0,\n",
+      "             0,       0,       0], dtype=int64)]\n",
+      "'break'\n",
+      "array([[1382400,       0,       0,       0,       0,       0,       0,\n",
+      "              0,       0,       0],\n",
+      "       [1382400,       0,       0,       0,       0,       0,       0,\n",
+      "              0,       0,       0],\n",
+      "       [1376628,    4927,     697,     110,      23,      15,       0,\n",
+      "              0,       0,       0]], dtype=int64)\n"
+     ]
+    }
+   ],
+   "source": [
+    "category_list = []\n",
+    "hist_list = []\n",
+    "\n",
+    "for im_name in image_list:\n",
+    "    im = skimage.io.imread(im_name)\n",
+    "    data, _ = np.histogram(im, range=(0,32767))\n",
+    "    hist_list.append(data)\n",
+    "\n",
+    "hist_list = np.asarray(hist_list)\n",
+    "pprint(hist_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = sklearn.cluster.KMeans(n_clusters=2, random_state=0).fit(hist_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 1])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans.labels_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\karhohs\\AppData\\Local\\Continuum\\Miniconda3\\envs\\bioimg\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: ../images/class_0\\testing_0.png is a low contrast image\n",
+      "  warn('%s is a low contrast image' % fname)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\karhohs\\AppData\\Local\\Continuum\\Miniconda3\\envs\\bioimg\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: ../images/class_0\\testing_1.png is a low contrast image\n",
+      "  warn('%s is a low contrast image' % fname)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\karhohs\\AppData\\Local\\Continuum\\Miniconda3\\envs\\bioimg\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: ../images/class_1\\testing_2.png is a low contrast image\n",
+      "  warn('%s is a low contrast image' % fname)\n"
+     ]
+    }
+   ],
+   "source": [
+    "os.makedirs(\"../images/class_0\", exist_ok = True)\n",
+    "os.makedirs(\"../images/class_1\", exist_ok = True)\n",
+    "\n",
+    "for ind, label in enumerate(kmeans.labels_):\n",
+    "    im = skimage.io.imread(image_list[ind])\n",
+    "    _, filename = os.path.split(image_list[ind])\n",
+    "    if label==0:\n",
+    "        skimage.io.imsave(os.path.join(\"../images/class_0\", filename), im)\n",
+    "    if label==1:\n",
+    "        skimage.io.imsave(os.path.join(\"../images/class_1\", filename), im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

CellProfiler/README.md

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+# A note about the software and hardware used to run the pipeline
+
+By Kyle W. Karhohs @karhohs 
+
+## software
+* CellProfiler, v3.0.0
+* OS: Microsoft Windows 10 Pro x64
+
+## hardware
+* Dell XPS 15 9550
+* Intel Core i7-6700HQ CPU @ 2.60GHz 4 Cores 8 Logical Processors
+* 16 GB Physical Memory/RAM
+
+