demo/analysis_workflow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4dbec23f-4c29-4123-bcd9-68a5592b3c63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import os\n",
    "#os.environ['OPENAI_API_KEY'] = 'sk-...'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28e1cd02-5f49-4522-89ae-677a0a764e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.9.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bia_bob import bob\n",
    "bob.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17c8334d-2590-4a8b-96d4-84c674eafe98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "I will load the image file \"blobs.tif\" and display it.\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%bob load blobs.tif and show it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0bc826c-5ecc-4ee8-a1e6-2539aa356aca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<img 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\"></img>\n",
       "</td>\n",
       "<td style=\"text-align: center; vertical-align: top;\">\n",
       "\n",
       "<table>\n",
       "<tr><td>shape</td><td>(254,&nbsp;256)</td></tr>\n",
       "<tr><td>dtype</td><td>uint8</td></tr>\n",
       "<tr><td>size</td><td>63.5 kB</td></tr>\n",
       "<tr><td>min</td><td>8</td></tr><tr><td>max</td><td>248</td></tr>\n",
       "</table>\n",
       "<img src=\"data:image/png;base64,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\"></img>\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "StackViewNDArray([[ 40,  32,  24, ..., 216, 200, 200],\n",
       "                  [ 56,  40,  24, ..., 232, 216, 216],\n",
       "                  [ 64,  48,  24, ..., 240, 232, 232],\n",
       "                  ...,\n",
       "                  [ 72,  80,  80, ...,  48,  48,  48],\n",
       "                  [ 80,  80,  80, ...,  48,  48,  48],\n",
       "                  [ 96,  88,  80, ...,  48,  48,  48]], dtype=uint8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skimage.io import imread\n",
    "import stackview\n",
    "\n",
    "# Load the image\n",
    "filename = 'blobs.tif'\n",
    "image = imread(filename)\n",
    "\n",
    "# Display the image\n",
    "stackview.insight(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52f5322c-843a-47fd-ac1c-2ffd45a0827c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "I am going to segment the provided image using Otsu's method for thresholding and then label the connected components.\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%bob \n",
    "Segment the image stored in the variable `image` using \n",
    "thresholding and connected component labeling. \n",
    "Show the resulting label image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc00184b-de83-4187-a614-085c2fcc15a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<img 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\"></img>\n",
       "</td>\n",
       "<td style=\"text-align: center; vertical-align: top;\">\n",
       "\n",
       "<table>\n",
       "<tr><td>shape</td><td>(254,&nbsp;256)</td></tr>\n",
       "<tr><td>dtype</td><td>int32</td></tr>\n",
       "<tr><td>size</td><td>254.0 kB</td></tr>\n",
       "<tr><td>min</td><td>0</td></tr><tr><td>max</td><td>64</td></tr>\n",
       "</table>\n",
       "\n",
       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "StackViewNDArray([[0, 0, 0, ..., 5, 5, 5],\n",
       "                  [0, 0, 0, ..., 5, 5, 5],\n",
       "                  [0, 0, 0, ..., 5, 5, 5],\n",
       "                  ...,\n",
       "                  [0, 0, 0, ..., 0, 0, 0],\n",
       "                  [0, 0, 0, ..., 0, 0, 0],\n",
       "                  [0, 0, 0, ..., 0, 0, 0]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skimage.filters import threshold_otsu\n",
    "from skimage.measure import label\n",
    "import stackview\n",
    "\n",
    "# Apply Otsu's thresholding\n",
    "thresh = threshold_otsu(image)\n",
    "binary = image > thresh\n",
    "\n",
    "# Label connected components\n",
    "label_image = label(binary)\n",
    "\n",
    "# Display the label image\n",
    "stackview.insight(label_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ed8ffad-e1b3-4e96-bf1c-0136cf196051",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "I will measure the size and shape properties of the segmented objects using `regionprops_table` and store the results in a pandas DataFrame. I will then display this DataFrame.\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%bob\n",
    "Measure size and shape of the segmented objects using `regionprops_table` and \n",
    "store the results in a pandas dataframe. \n",
    "Show this dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ed1ca83f-dfef-4de3-936e-c292f3ba16fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>area</th>\n",
       "      <th>perimeter</th>\n",
       "      <th>major_axis_length</th>\n",
       "      <th>minor_axis_length</th>\n",
       "      <th>eccentricity</th>\n",
       "      <th>orientation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>433</td>\n",
       "      <td>91.254834</td>\n",
       "      <td>34.957399</td>\n",
       "      <td>16.819060</td>\n",
       "      <td>0.876649</td>\n",
       "      <td>-0.445057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>185</td>\n",
       "      <td>53.556349</td>\n",
       "      <td>21.061417</td>\n",
       "      <td>11.803854</td>\n",
       "      <td>0.828189</td>\n",
       "      <td>-1.518883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>658</td>\n",
       "      <td>95.698485</td>\n",
       "      <td>30.212552</td>\n",
       "      <td>28.278264</td>\n",
       "      <td>0.352060</td>\n",
       "      <td>-0.151090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>434</td>\n",
       "      <td>76.870058</td>\n",
       "      <td>24.535398</td>\n",
       "      <td>23.064079</td>\n",
       "      <td>0.341084</td>\n",
       "      <td>-0.513943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>477</td>\n",
       "      <td>83.798990</td>\n",
       "      <td>31.162612</td>\n",
       "      <td>19.833058</td>\n",
       "      <td>0.771328</td>\n",
       "      <td>-0.045020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.785398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>81</td>\n",
       "      <td>40.727922</td>\n",
       "      <td>18.558405</td>\n",
       "      <td>5.920690</td>\n",
       "      <td>0.947745</td>\n",
       "      <td>-1.565594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>90</td>\n",
       "      <td>46.278175</td>\n",
       "      <td>22.458271</td>\n",
       "      <td>5.369081</td>\n",
       "      <td>0.971003</td>\n",
       "      <td>-1.547086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>53</td>\n",
       "      <td>31.899495</td>\n",
       "      <td>14.811463</td>\n",
       "      <td>5.065719</td>\n",
       "      <td>0.939695</td>\n",
       "      <td>1.565945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64</td>\n",
       "      <td>49</td>\n",
       "      <td>34.485281</td>\n",
       "      <td>17.127524</td>\n",
       "      <td>3.843548</td>\n",
       "      <td>0.974495</td>\n",
       "      <td>1.548303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    label  area  perimeter  major_axis_length  minor_axis_length  \\\n",
       "0       1   433  91.254834          34.957399          16.819060   \n",
       "1       2   185  53.556349          21.061417          11.803854   \n",
       "2       3   658  95.698485          30.212552          28.278264   \n",
       "3       4   434  76.870058          24.535398          23.064079   \n",
       "4       5   477  83.798990          31.162612          19.833058   \n",
       "..    ...   ...        ...                ...                ...   \n",
       "59     60     1   0.000000           0.000000           0.000000   \n",
       "60     61    81  40.727922          18.558405           5.920690   \n",
       "61     62    90  46.278175          22.458271           5.369081   \n",
       "62     63    53  31.899495          14.811463           5.065719   \n",
       "63     64    49  34.485281          17.127524           3.843548   \n",
       "\n",
       "    eccentricity  orientation  \n",
       "0       0.876649    -0.445057  \n",
       "1       0.828189    -1.518883  \n",
       "2       0.352060    -0.151090  \n",
       "3       0.341084    -0.513943  \n",
       "4       0.771328    -0.045020  \n",
       "..           ...          ...  \n",
       "59      0.000000     0.785398  \n",
       "60      0.947745    -1.565594  \n",
       "61      0.971003    -1.547086  \n",
       "62      0.939695     1.565945  \n",
       "63      0.974495     1.548303  \n",
       "\n",
       "[64 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from skimage.measure import regionprops_table\n",
    "import pandas as pd\n",
    "\n",
    "# Measure properties of labeled regions\n",
    "properties = regionprops_table(label_image, image, \n",
    "                               properties=('label',\n",
    "                                           'area', \n",
    "                                           'perimeter', \n",
    "                                           'major_axis_length',\n",
    "                                           'minor_axis_length',\n",
    "                                           'eccentricity',\n",
    "                                           'orientation'))\n",
    "\n",
    "# Create a pandas DataFrame\n",
    "df = pd.DataFrame(properties)\n",
    "\n",
    "# Display the DataFrame\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d2c9c839-b233-498a-a51a-fc081beff2cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "I will create a scatter plot with perimeter on the x-axis and eccentricity on the y-axis using the seaborn library.\n",
       "\n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%bob\n",
    "plot the perimeter against eccentricity using the seaborn library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c87b911a-8533-4d8f-a985-fe1c42ae3ee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create a scatter plot of perimeter versus eccentricity\n",
    "sns.scatterplot(x='perimeter', y='eccentricity', data=df)\n",
    "\n",
    "# Show the plot\n",
    "plt.xlabel('Perimeter')\n",
    "plt.ylabel('Eccentricity')\n",
    "plt.title('Perimeter vs Eccentricity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4102e986-2d7a-407a-859b-47c6ce03ffa7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}