01_python_basics/00_trailer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "casual-lunch",
   "metadata": {},
   "source": [
    "# Trailer: bio-image analysis, machine learning and bio-statistics with python\n",
    "During this semester we will dive into image analysis, machine learning and bio-statistics with python. This notebook serves as a trailer of what we will be doing until July. Every step will be explained in detail in future sessions. This is just a trailer.\n",
    "\n",
    "What typically comes first in Python Juptyer notebooks is a block of import statements. By executing it, we can make sure that everything we need is installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cfef5a8e-f39e-45a9-9793-e1e000935834",
   "metadata": {},
   "outputs": [],
   "source": [
    "from skimage.io import imread, imshow\n",
    "from csbdeep.utils import normalize\n",
    "from stardist.models import StarDist2D \n",
    "from skimage import measure\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pyclesperanto_prototype as cle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab85f75-6db3-487e-b8b5-83ec9b7db4cf",
   "metadata": {},
   "source": [
    "## Python basics\n",
    "Python is a functional programming language. It allows writing procedures for processing scientific data. Learning the basics are crucial for understanding advanced data analysis procedures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f9c071d-af32-46d9-aba3-bd5f8d3d2204",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 1\n",
    "b = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c970564-95aa-4717-9b85-99d50f84bca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "51ef9cf9-af1b-4b7f-afff-4f1723105904",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.2\n",
      "21.4\n",
      "48.3\n",
      "35.2\n",
      "11.2\n"
     ]
    }
   ],
   "source": [
    "measurements = [56.2, 21.4, 48.3, 35.2, 11.2]\n",
    "\n",
    "for m in measurements:\n",
    "    print(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08975148-8046-493a-a061-ea2a28424e56",
   "metadata": {},
   "source": [
    "## Working with image data\n",
    "We can load images into variables. The path \"../data/blobs.tif\" suggests, that from the current directory, where this notebook is saved, we need to go one folder up (\"..\") and there should be a \"data\" folder in which \"blobs.tif\" is stored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "helpful-purpose",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open an image file\n",
    "image = imread(\"../data/blobs.tif\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "pregnant-jenny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x263f44cff40>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# visualizing an image\n",
    "imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "meaning-campus",
   "metadata": {},
   "source": [
    "## Image segmentation\n",
    "There is an endless number of image segmentation techniques published. Current state-of-the-art segmentation algorithms for segmenting bright blobs in microscopy images use deep learning techniques. Here we demonstrate [StarDist](https://github.com/stardist/stardist)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "linear-estate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found model '2D_versatile_fluo' for 'StarDist2D'.\n",
      "Loading network weights from 'weights_best.h5'.\n",
      "Loading thresholds from 'thresholds.json'.\n",
      "Using default values: prob_thresh=0.479071, nms_thresh=0.3.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "matplotlib_plugin.py (150): Low image data range; displaying image with stretched contrast.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2638f2179a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# normalize image\n",
    "normalized_image = normalize(image, 1,99.8, axis=(0,1))\n",
    "\n",
    "# load pretrained deep-learning model\n",
    "model = StarDist2D.from_pretrained('2D_versatile_fluo')\n",
    "\n",
    "# predict labels\n",
    "label_image, details = model.predict_instances(normalized_image)\n",
    "imshow(label_image, cmap=\"jet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incredible-explorer",
   "metadata": {},
   "source": [
    "## Measurements and feature extraction\n",
    "From segmented images we can derive quantitative measurements. The process is called \"featue extraction\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "interstate-emperor",
   "metadata": {},
   "outputs": [],
   "source": [
    "# analyse objects\n",
    "table = measure.regionprops_table(label_image, intensity_image=image,\n",
    "                                  properties=('label', 'area', 'centroid', 'mean_intensity', 'feret_diameter_max'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "executive-centre",
   "metadata": {},
   "source": [
    "## Working with tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "indian-girlfriend",
   "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>centroid-0</th>\n",
       "      <th>centroid-1</th>\n",
       "      <th>mean_intensity</th>\n",
       "      <th>feret_diameter_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>564</td>\n",
       "      <td>33.423759</td>\n",
       "      <td>174.085106</td>\n",
       "      <td>179.843972</td>\n",
       "      <td>28.792360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>584</td>\n",
       "      <td>122.337329</td>\n",
       "      <td>37.251712</td>\n",
       "      <td>192.534247</td>\n",
       "      <td>31.048349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>579</td>\n",
       "      <td>217.272884</td>\n",
       "      <td>62.056995</td>\n",
       "      <td>184.110535</td>\n",
       "      <td>31.622777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>589</td>\n",
       "      <td>227.169779</td>\n",
       "      <td>234.027165</td>\n",
       "      <td>211.286927</td>\n",
       "      <td>29.832868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>625</td>\n",
       "      <td>127.454400</td>\n",
       "      <td>224.120000</td>\n",
       "      <td>205.542400</td>\n",
       "      <td>30.463092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>430</td>\n",
       "      <td>184.234884</td>\n",
       "      <td>233.339535</td>\n",
       "      <td>196.279070</td>\n",
       "      <td>26.419690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>606</td>\n",
       "      <td>197.270627</td>\n",
       "      <td>130.303630</td>\n",
       "      <td>196.858086</td>\n",
       "      <td>29.614186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>574</td>\n",
       "      <td>57.534843</td>\n",
       "      <td>28.611498</td>\n",
       "      <td>185.839721</td>\n",
       "      <td>29.546573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>485</td>\n",
       "      <td>163.105155</td>\n",
       "      <td>136.659794</td>\n",
       "      <td>168.461856</td>\n",
       "      <td>29.681644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>486</td>\n",
       "      <td>54.831276</td>\n",
       "      <td>138.421811</td>\n",
       "      <td>201.448560</td>\n",
       "      <td>28.284271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>483</td>\n",
       "      <td>84.060041</td>\n",
       "      <td>64.037267</td>\n",
       "      <td>174.741201</td>\n",
       "      <td>26.925824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>468</td>\n",
       "      <td>73.414530</td>\n",
       "      <td>98.982906</td>\n",
       "      <td>187.863248</td>\n",
       "      <td>29.732137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>399</td>\n",
       "      <td>111.453634</td>\n",
       "      <td>102.804511</td>\n",
       "      <td>189.273183</td>\n",
       "      <td>25.317978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>614</td>\n",
       "      <td>213.840391</td>\n",
       "      <td>102.618893</td>\n",
       "      <td>219.596091</td>\n",
       "      <td>32.756679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>678</td>\n",
       "      <td>181.890855</td>\n",
       "      <td>179.123894</td>\n",
       "      <td>196.389381</td>\n",
       "      <td>32.249031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>909</td>\n",
       "      <td>229.914191</td>\n",
       "      <td>180.221122</td>\n",
       "      <td>192.448845</td>\n",
       "      <td>41.976184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>447</td>\n",
       "      <td>168.928412</td>\n",
       "      <td>16.152125</td>\n",
       "      <td>193.413870</td>\n",
       "      <td>27.784888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>449</td>\n",
       "      <td>83.875278</td>\n",
       "      <td>129.380846</td>\n",
       "      <td>196.204900</td>\n",
       "      <td>34.205263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>753</td>\n",
       "      <td>41.334661</td>\n",
       "      <td>73.401062</td>\n",
       "      <td>163.580345</td>\n",
       "      <td>37.215588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>332</td>\n",
       "      <td>95.250000</td>\n",
       "      <td>222.605422</td>\n",
       "      <td>177.686747</td>\n",
       "      <td>22.847319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>313</td>\n",
       "      <td>70.520767</td>\n",
       "      <td>213.958466</td>\n",
       "      <td>187.680511</td>\n",
       "      <td>23.769729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>334</td>\n",
       "      <td>15.856287</td>\n",
       "      <td>197.149701</td>\n",
       "      <td>189.437126</td>\n",
       "      <td>22.825424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>309</td>\n",
       "      <td>86.029126</td>\n",
       "      <td>33.951456</td>\n",
       "      <td>168.181230</td>\n",
       "      <td>21.587033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>291</td>\n",
       "      <td>26.178694</td>\n",
       "      <td>44.632302</td>\n",
       "      <td>170.776632</td>\n",
       "      <td>21.189620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>420</td>\n",
       "      <td>171.845238</td>\n",
       "      <td>44.216667</td>\n",
       "      <td>186.780952</td>\n",
       "      <td>26.476405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>192</td>\n",
       "      <td>72.703125</td>\n",
       "      <td>167.244792</td>\n",
       "      <td>170.791667</td>\n",
       "      <td>18.027756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>515</td>\n",
       "      <td>14.285437</td>\n",
       "      <td>19.378641</td>\n",
       "      <td>174.586408</td>\n",
       "      <td>34.438351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>145</td>\n",
       "      <td>44.489655</td>\n",
       "      <td>7.841379</td>\n",
       "      <td>145.268966</td>\n",
       "      <td>16.124515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>471</td>\n",
       "      <td>10.135881</td>\n",
       "      <td>154.575372</td>\n",
       "      <td>208.662420</td>\n",
       "      <td>27.459060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>886</td>\n",
       "      <td>160.534989</td>\n",
       "      <td>89.668172</td>\n",
       "      <td>196.207675</td>\n",
       "      <td>53.150729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>285</td>\n",
       "      <td>146.821053</td>\n",
       "      <td>67.049123</td>\n",
       "      <td>177.908772</td>\n",
       "      <td>21.840330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>298</td>\n",
       "      <td>166.604027</td>\n",
       "      <td>213.892617</td>\n",
       "      <td>172.268456</td>\n",
       "      <td>21.954498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>271</td>\n",
       "      <td>47.162362</td>\n",
       "      <td>233.265683</td>\n",
       "      <td>182.110701</td>\n",
       "      <td>20.880613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>205</td>\n",
       "      <td>156.302439</td>\n",
       "      <td>188.248780</td>\n",
       "      <td>169.834146</td>\n",
       "      <td>18.788294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>652</td>\n",
       "      <td>131.253067</td>\n",
       "      <td>164.409509</td>\n",
       "      <td>190.110429</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>475</td>\n",
       "      <td>134.907368</td>\n",
       "      <td>124.025263</td>\n",
       "      <td>187.924211</td>\n",
       "      <td>38.078866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>324</td>\n",
       "      <td>233.466049</td>\n",
       "      <td>138.027778</td>\n",
       "      <td>179.111111</td>\n",
       "      <td>23.769729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>256</td>\n",
       "      <td>240.507812</td>\n",
       "      <td>46.070312</td>\n",
       "      <td>172.250000</td>\n",
       "      <td>21.540659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>187</td>\n",
       "      <td>202.764706</td>\n",
       "      <td>205.973262</td>\n",
       "      <td>175.058824</td>\n",
       "      <td>18.384776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>704</td>\n",
       "      <td>12.897727</td>\n",
       "      <td>108.149148</td>\n",
       "      <td>199.454545</td>\n",
       "      <td>33.600595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>250</td>\n",
       "      <td>122.948000</td>\n",
       "      <td>62.832000</td>\n",
       "      <td>164.192000</td>\n",
       "      <td>19.697716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>305</td>\n",
       "      <td>139.659016</td>\n",
       "      <td>7.416393</td>\n",
       "      <td>179.147541</td>\n",
       "      <td>23.409400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>340</td>\n",
       "      <td>28.029412</td>\n",
       "      <td>218.844118</td>\n",
       "      <td>161.505882</td>\n",
       "      <td>24.331050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>516</td>\n",
       "      <td>153.412791</td>\n",
       "      <td>245.230620</td>\n",
       "      <td>197.240310</td>\n",
       "      <td>35.846897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>389</td>\n",
       "      <td>73.035990</td>\n",
       "      <td>245.077121</td>\n",
       "      <td>182.334190</td>\n",
       "      <td>25.553865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>117</td>\n",
       "      <td>21.666667</td>\n",
       "      <td>137.119658</td>\n",
       "      <td>144.136752</td>\n",
       "      <td>13.416408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>562</td>\n",
       "      <td>216.574733</td>\n",
       "      <td>26.537367</td>\n",
       "      <td>157.309609</td>\n",
       "      <td>36.400549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>475</td>\n",
       "      <td>59.048421</td>\n",
       "      <td>194.930526</td>\n",
       "      <td>168.016842</td>\n",
       "      <td>37.696154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>536</td>\n",
       "      <td>97.565299</td>\n",
       "      <td>8.218284</td>\n",
       "      <td>193.388060</td>\n",
       "      <td>33.734256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>693</td>\n",
       "      <td>99.027417</td>\n",
       "      <td>175.282828</td>\n",
       "      <td>196.121212</td>\n",
       "      <td>36.769553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>221</td>\n",
       "      <td>4.823529</td>\n",
       "      <td>63.185520</td>\n",
       "      <td>167.384615</td>\n",
       "      <td>22.561028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>491</td>\n",
       "      <td>13.883910</td>\n",
       "      <td>246.456212</td>\n",
       "      <td>208.130346</td>\n",
       "      <td>31.016125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>212</td>\n",
       "      <td>226.084906</td>\n",
       "      <td>4.174528</td>\n",
       "      <td>196.037736</td>\n",
       "      <td>25.079872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>182</td>\n",
       "      <td>104.950549</td>\n",
       "      <td>250.626374</td>\n",
       "      <td>192.395604</td>\n",
       "      <td>20.615528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>33</td>\n",
       "      <td>242.575758</td>\n",
       "      <td>76.757576</td>\n",
       "      <td>99.878788</td>\n",
       "      <td>7.280110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>37</td>\n",
       "      <td>113.378378</td>\n",
       "      <td>83.432432</td>\n",
       "      <td>115.027027</td>\n",
       "      <td>8.062258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label  area  centroid-0  centroid-1  mean_intensity  feret_diameter_max\n",
       "0       1   564   33.423759  174.085106      179.843972           28.792360\n",
       "1       2   584  122.337329   37.251712      192.534247           31.048349\n",
       "2       3   579  217.272884   62.056995      184.110535           31.622777\n",
       "3       4   589  227.169779  234.027165      211.286927           29.832868\n",
       "4       5   625  127.454400  224.120000      205.542400           30.463092\n",
       "5       6   430  184.234884  233.339535      196.279070           26.419690\n",
       "6       7   606  197.270627  130.303630      196.858086           29.614186\n",
       "7       8   574   57.534843   28.611498      185.839721           29.546573\n",
       "8       9   485  163.105155  136.659794      168.461856           29.681644\n",
       "9      10   486   54.831276  138.421811      201.448560           28.284271\n",
       "10     11   483   84.060041   64.037267      174.741201           26.925824\n",
       "11     12   468   73.414530   98.982906      187.863248           29.732137\n",
       "12     13   399  111.453634  102.804511      189.273183           25.317978\n",
       "13     14   614  213.840391  102.618893      219.596091           32.756679\n",
       "14     15   678  181.890855  179.123894      196.389381           32.249031\n",
       "15     16   909  229.914191  180.221122      192.448845           41.976184\n",
       "16     17   447  168.928412   16.152125      193.413870           27.784888\n",
       "17     18   449   83.875278  129.380846      196.204900           34.205263\n",
       "18     19   753   41.334661   73.401062      163.580345           37.215588\n",
       "19     20   332   95.250000  222.605422      177.686747           22.847319\n",
       "20     21   313   70.520767  213.958466      187.680511           23.769729\n",
       "21     22   334   15.856287  197.149701      189.437126           22.825424\n",
       "22     23   309   86.029126   33.951456      168.181230           21.587033\n",
       "23     24   291   26.178694   44.632302      170.776632           21.189620\n",
       "24     25   420  171.845238   44.216667      186.780952           26.476405\n",
       "25     26   192   72.703125  167.244792      170.791667           18.027756\n",
       "26     27   515   14.285437   19.378641      174.586408           34.438351\n",
       "27     28   145   44.489655    7.841379      145.268966           16.124515\n",
       "28     29   471   10.135881  154.575372      208.662420           27.459060\n",
       "29     30   886  160.534989   89.668172      196.207675           53.150729\n",
       "30     31   285  146.821053   67.049123      177.908772           21.840330\n",
       "31     32   298  166.604027  213.892617      172.268456           21.954498\n",
       "32     33   271   47.162362  233.265683      182.110701           20.880613\n",
       "33     34   205  156.302439  188.248780      169.834146           18.788294\n",
       "34     35   652  131.253067  164.409509      190.110429           39.000000\n",
       "35     36   475  134.907368  124.025263      187.924211           38.078866\n",
       "36     37   324  233.466049  138.027778      179.111111           23.769729\n",
       "37     38   256  240.507812   46.070312      172.250000           21.540659\n",
       "38     39   187  202.764706  205.973262      175.058824           18.384776\n",
       "39     40   704   12.897727  108.149148      199.454545           33.600595\n",
       "40     41   250  122.948000   62.832000      164.192000           19.697716\n",
       "41     42   305  139.659016    7.416393      179.147541           23.409400\n",
       "42     43   340   28.029412  218.844118      161.505882           24.331050\n",
       "43     44   516  153.412791  245.230620      197.240310           35.846897\n",
       "44     45   389   73.035990  245.077121      182.334190           25.553865\n",
       "45     46   117   21.666667  137.119658      144.136752           13.416408\n",
       "46     47   562  216.574733   26.537367      157.309609           36.400549\n",
       "47     48   475   59.048421  194.930526      168.016842           37.696154\n",
       "48     49   536   97.565299    8.218284      193.388060           33.734256\n",
       "49     50   693   99.027417  175.282828      196.121212           36.769553\n",
       "50     51   221    4.823529   63.185520      167.384615           22.561028\n",
       "51     52   491   13.883910  246.456212      208.130346           31.016125\n",
       "52     53   212  226.084906    4.174528      196.037736           25.079872\n",
       "53     54   182  104.950549  250.626374      192.395604           20.615528\n",
       "54     55    33  242.575758   76.757576       99.878788            7.280110\n",
       "55     56    37  113.378378   83.432432      115.027027            8.062258"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show table\n",
    "dataframe = pd.DataFrame(table)\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aerial-release",
   "metadata": {},
   "source": [
    "## Statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "scheduled-motel",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean blob area is  427.60714285714283\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean blob area is \", np.mean(dataframe['area']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b029b0c-a4d8-4f7b-ba25-b2e3f34860f8",
   "metadata": {},
   "source": [
    "## Data visualization\n",
    "For understanding realationships between object properties, plotting is a commonly used technique. Here we plot area versus elongation of objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37424420-1467-4039-bc6a-2cd0574ee14e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2639c1699a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.scatter(dataframe['area'], dataframe['feret_diameter_max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f146c0e-7bbc-4eb9-967b-39924e07bfb7",
   "metadata": {},
   "source": [
    "Parametric maps allow us to visualize properties of objects in image space. In the following example, elongate objects are brighter than roundish objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7e122438-88c0-45d2-a224-a0e78be7d764",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "matplotlib_plugin.py (150): Float image out of standard range; displaying image with stretched contrast.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x263f495e700>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SaoQYpRBTdtx2YiEpNhXZHX/5fZ2RYqiRoYmwOE9/avDf+6Oubrcv3CZmG7Wpu1qlqBRDFSGYDIuSJ8L+662JUXsDKyFiQhwj+mWXCjJkCUK4Inz1nqN43XuebrsZ+zBKhv1l25CiAhMFnpXSBtELsWjabOyPiXB/Xr3nqIHnX/eepwdeS8+FIMYyMsze07wUw12pEr0QjfY58vwNTgdWmhBhnviqlu+/3rQgq8gwe2/TUgw1ZQ4zbi1JU1NxikzMDmVnmhg58vwN3mX46j1HlZZhyJ8D9WTYFqEu3euEEEfR9AqTcZRiXZGFGBXG+plVaFKqquEK0VLmgrS5bK/rgu2qDJsgxugQwp12MxYRIliU2ARVxBbqCLJLuirjOqgWP5pkbIQI1aVY9T4XUoxNrGUE15QMTUjhYSmzZ4pOv5m5fHXhlSsuospUaFVWsMQmw5RUdCE+QqAtfMxZDDVd3vE3r7Vrxhf2/zekNC+6onRGiGUYJDrf23uVEWOsIuxnXOU3rmRFuM+5z39nv/OBzroZLUQRuQl4L7BLVf8gOTcF+DYwB9gKXKCqLybXrgQuASaAv1LVu7y03DFN9TFmZWePHDVCoe48xEEyzEXD3dyhSB/izcDivnPLgFWqOhdYlbxHRE4ELgROSu75iohMctbajnH85feZDI3oKSXDFC1xNMhIIarqT4EX+k4vAVYmr1cC52fO36Kqu1X1SWAzsNBNU0fT5l6JhjGOVJIh4Q6qVB1lPkJVdwIkP6cl52cCz2TKbUvO7YeILBWRtSKyduI3/1axGYZhxMi4TLsZpPOBX0lVV6jqAlVdMOnwQxw3w6jCluWns2V59x6MHsLGCz5wtf64aj2Vo0PCjRCrjjI/KyLTVXWniEwHdiXntwGzM+VmAfa07gAZJr7+a8cui+eZI3nk7VYTO0ddvbrW9JumZQgkRgxzUKWqEO8ALgaWJz9vz5z/pohcB8wA5gIP1G2k4ZayUWC2fBfkaPRoc+fsaHe7EZFvAWuAt4jINhG5hJ4IzxaRTcDZyXtUdT1wK/A4cCdwqapO+Gp8P7Yv4mjqpsQxp9Rtps4+P/uoq1eXllvbjxEIdZR5ZISoqh/IuXRWTvlrgWvrNMrwQ8wyc0VWTF1LoYumz63L0Faq+Meiw+bYsvz0TqTOqRy7JMb2ZVeQWFNmoxu4jg67FG2+7j1P73P4qN/I4Hg/RBE5WEQeEJGHRWS9iPyP5PwUEblbRDYlP984qq5ORIguo8PNXz8599rxFz3k7HO6QFcixX66GDkGh9sIcTfwblX9rYgcCNwrIj8A3k9vRd1yEVlGb0XdFcMqil6IrmQ4TIT9ZUyM44ELMVp0mIe7PkRVVeC3ydsDk0PprZx7V3J+JfBjRghx7FPmzV8/uZAM++8JCTnlJOSUk9puRmepKrUuy3DQtl6lKDfKPDVd1ZYcS/urE5FJIrKO3pzou1X1fvJX1OUSRIR44BP/XrhskxHhqPvbjhT7JZi+1wfX73PeZ39fV9PmfspO7O6yDFNmfGF19Y0d9pSKEJ9T1QVDq+xN75svIm8AbhORPyjfsIgixBnvezwYGcbGOAirCYoMvPgamAmVqpGir7XMqvoreqnxYpIVdQB9K+pyCSJChH0jv3TXGh9TaVzKsEyU+MuP5f9PeuSXIpkqMYCQZfv7975p6PVfv/P5ynXHIr1NN5+y9/XcDz3o5TNSKQ6KFmd8YTUD/xU7HFQRkTcDr6jqr0Tk94D3AJ8lf0VdLsEIMUvX5hQOk2F6PWYphsgoGWbL1BFjSGTlV/S6S0mWihbdTsyeDqxM9l49ALhVVf9ZRNYAtyar654G/nRURUEK0RdtpMqjZJgtN65SnPyTI9n9R790Vl8RGQ4qH7MYR8lw1H2+osc8xGGEqKqPAPv941bV58lZUZdHNH2IdfElQ5f1FpVnij64fuAxCB+pbd06J//kSCb/5Mi9r11QVoau7m2LTTefUlmG/fU0RpkR5sj3QzQylBVc1XuK4lKKderKitAlMQotJFzJdTTSS5mLHg1iQgyQ0KVYV4ZVrhn740tejUjRIsT2GLdpNqNoa2S4iPCqSnHcokPf0vIuxUCFOFaDKjHhe5AllWLRSdsu+gsNYy+B7nZjQvSEz7TXJSHPIzQG09QAyKabT/Ez+tzBRwgYRmEsOjT6cTntxiVj0Yfok7bXM4dOWRm6nI9YhXHrixyFt2g00D5EE6Jh9GFSHF9MiJ4Y11UnWZpMlV2vMjEp+kW0+NEkJsRAMaG2T4hSbHRFiU8CnZhtgyqGF7oykNKFdc7B0ULfYFGCjxCfvmrR3qMqbQ18VI3yxjU6rDug4lNaIUaLURPooEqQEWKe/LLnj7qqfWn4EO24ytAVv37n852W19wPPdiJtNmm3RSkaCRYNmIMPUo88kurOyFDXxs3lMFXpNhl0TaORYjDqZISP33VolKR4vEXPdTKuuas6LIrWLogwCx1RNj2/MOi/P69b7L+RBcEGiEGI8SmcCXFqhFn1ySY0nZU2E/XU+em8LF0r43pNEUJImV+ecahjX5e3fTZVqfsS2gyTOlq6tz07tZeCHTaTRBCbIOqUjMZvoar/kKf6XJXpdgEXsUbaB/i2AoRenIrKrgyZUPkrh3ruGvHurab0Qpd7PPzHSVWrf+3dx7Lb+88dmQ52VP8aJKx60McRFZ0/f2LTUhw25X7DyjN+ky9vsY8+fWfP3fG/Er1h5om5+GjT7HtARZfU3DqyHDQa87tKxhwH2L0Qiw70jyKJqPAQSLsv1ZFjGUiwbRsVTHWJZbR5VBxLUUXMixEoEIc65S5LbZduWioDPvLlmFc0+JxxlX6XKWeoinyflgfogHlBVfmnjoyLHNvbOlyShf7ElPmfujBvUfVe8tSSYQJoe52E33KbLjjrh3rWkudYyRUwTYxLaeODEMm+ggxhDXNRakSHRa5N9YR5Db6D11JLFQZNoETGQaaMluE2BB1ZGiEhcmwJjbKbBj70uboctUpOOMsQnCcJgcqxKhT5pjS5S7Rhaky4y631gk0ZR4pRBG5SUR2ichjmXNXich2EVmXHOdlrl0pIptF5Bci0j8l0+gIdaQYilCLSvHX73x+7AXqMjoUwh1lLhIh3gwsHnD+71V1fnJ8H0BETgQuBE5K7vmKiExy1dgsMUWHPvsPYxtMCUWGRXEhwl2XLmLXpfH2IXsZUQ40QhzZh6iqPxWROQXrWwLcoqq7gSdFZDOwEFgz7KaDdvyuYPVxiTBl1mdWd3JQJZVb0XmJIcowrz+xqgiHiS+9Nu3Lcfwd9ja1pqODKpeJyAeBtcDHVfVFYCZwX6bMtuTcSFLR5W0UG6MIXVJ3bbNPdv/RL3OlGKIE+3GVDheNAndduih4KXqfZ9gxIV4PXEPva10D/B3w5/S6B/oZ+NVFZCmwFOBgDtl7ftzFFyu+xLfxhgX7vJ/34bVePqcuZVPikKXYyKTrLglRVZ9NX4vIPwH/nLzdBszOFJ0F7MipYwWwAuBwmRLoH487upo2u6ZfgHnXQxVj7DS1AiXUlLnStBsRmZ55+z4gHYG+A7hQRCaLyDHAXOCBek00Qk6XXbHxhgUjZdhfPhRiHjBpjUAHVYpMu/kWvUGRt4jINhG5BPiciDwqIo8AZwL/GUBV1wO3Ao8DdwKXquqEt9ZHxjiIrSxlRdh/r+GOxtYnl5FhaEJU1Q+o6nRVPVBVZ6nqjap6kaq+VVXfpqp/oqo7M+WvVdXjVPUtqvoDv82Pj7JS7LJEXQjNpBgnLuchishsEfmRiGwQkfUi8rHk/BQRuVtENiU/3ziqrqhXqsRKEcnN+szqxmXY5E43XRGZpcsVcRshvkpvpssJwGnApcmc6GXAKlWdC6xK3g8l+rXMm/7h1Nxrcy+7v8GWlCMru+xgS5cjwpSuyDAEfDx+oglcDqokGerO5PVLIrKB3nS/JcC7kmIrgR8DVwyrK1ohDhNhf5mQxQjV/wLHtkrFFxtvWDA2o85FZirUefxEY5QT4lQRyf6CVySzVPYjWURyMnA/cETanaeqO0Vk2qgPik6IRUTok83XnQbA8X9934iScdFUumzRYXXKTtvaduWi1qV42OIt+58sP1jynKqO/IsjIocB3wUuV9XfiJR/pnNUQqwqw03/cGrlKDEVYN75ronRJ12U4bQvr67Uj1h2UnaX5rAKg1dw1KpT5EB6MvyGqn4vOf2siExPosPpwK5R9UQzqNJGZJgnw7JlfGDpcrw0uUKlTZEOjA4TXD6XWXqh4I3ABlW9LnPpDuDi5PXFwO2j6opCiC5kWLaOMqJrS4ox0cXosClijA6HyRBwPcp8BnAR8O6+LQmXA2eLyCbg7OT9UKJKmZuiiuA2X3eapc9jSpm0OdT1yy4ZKUNwOuFaVe8lPws/q0xdwUeITafKdaI9ixQHMw7RYRHRhS7DQiJzUUeJSdn2GFKPjBpcMaHFSShTbnwIr+l0+bDFWyov4Ssl1C5t7tAUbU+xqYJJ1YidspHiYYu3lL7HIkQjWGJ+OH2T0eGTt7xt6PVjLnykoZb4JxVcXrRYO70ONEIcKyGGvmLFCJNRIuwv10UxuqZT+yHGSlMpuO+0ObY5iL6iON/R4ZO3vK2wDF3cNzbEvP1XW8TYf2g0RxMyDKGOzhKoEC1lHnNi7D8MZVTZqEb6XOYQCTZCNLqFK4k1IUOXkV3duupuztD25g65WIRoGNVpKir0keY+ecvbohhoyev79rECSzTMEHFshNildPncGfOdDKw0nS5npTZo9UrbqbDPPr86UkyjvDKTtEdFhlXX6juRYwuRX1GCFeLcy+53NrDSJRl2hbblFyP9kqu6W3YIiwdC7UMMVoiGX2IcTDH2pUr/YF0ZOtvEJFAhBj2o4iKyayM6DH3XG5PheBJCZJgS6tK9oIUI9YRW9t7QRWbETZvzEl3K0EldNspcnVRsRfsU2+wzDF2qFh0ardNC5FeUKISYMkh0dZ6XMojj//q+yv8Dhi5DI58uryoJKVXeS6BCDD5lHoWPaLCK2GKQoUWH7dNl8RYlXalifYgRUUZwJkMjVIKMDgFUix8NElXK3DRZ0Q36i9WmCItOzjYRGiFifYiRE2IUOEqKocmwzLNVujpxO4YlfN6xlSqGLwZJMSQRVn3AVPa+rspxnCnyvOU2MCF2gFSKXRBhXl0mxW4RqhBtUKUjdFWGPuvMYqlsgyjBDqqYEA2n+BTXODzfOQZc9KfbtBuj85iwwiPEwUDAlu4ZxrjThbTcSXRIuNNuLEI0nNCF6NCnsNqUoaso0Vm0Wab/0PoQDcNwTV2ZuU69rQ/RMCLAdSR3zIWPBJMqV5Wal35I60M0jDhIBVZnIwYXEvy/Hzl94Pk3X7+mcp1Fd3PyPRgTah+iCdGoTZP9h01O0u6X2iBB+or+8mSYXqsrxVZRYE+YRhwpRBGZDXwNOBLYA6xQ1S+JyBTg28AcYCtwgaq+mNxzJXAJMAH8lare5aX1htEgTaW+w2TYGcL0YaE+xFeBj6vqCcBpwKUiciKwDFilqnOBVcl7kmsXAicBi4GviMgkH403jK5RVIaxSzPUQZWREaKq7gR2Jq9fEpENwExgCfCupNhK4MfAFcn5W1R1N/CkiGwGFgLVY/wS7P7hnP3OTT5naxMfPbbM+/DaxtJmW9PcEbrwoHoRmQOcDNwPHJHIElXdKSLTkmIzgWwnxbbkXH9dS4GlAAdzSOmGD2KQDLPnTYyv8dStbx16/egLHm2oJUZK7FFfGaIfVBGRw4DvAper6m9EJLfogHP7fX1VXQGsADhcptT+48mT4aAy4yzGUSLsL2dibIZxkmHI+yEWmocoIgfSk+E3VPV7yelnRWR6cn06sCs5vw2Ynbl9FrDDTXONqjx161sLy7DKfZbK1qPOqHFs9JbuaeFjZH0iN4nILhF5LHNuiojcLSKbkp9vLNK2kUKUXih4I7BBVa/LXLoDuDh5fTFwe+b8hSIyWUSOAeYCDxRpTFWKRId1ytfliW/OH3g0RRUR+qjDMPayp8QxmpvpDeBmGTjoO4oiEeIZwEXAu0VkXXKcBywHzhaRTcDZyXtUdT1wK/A4cCdwqapOFGlMkzQhxVHia0OOdRglRYsSqzNWKTNuI0RV/SnwQt/pJfQGe0l+nl+kXSOFqKr3qqqo6ttUdX5yfF9Vn1fVs1R1bvLzhcw916rqcar6FlX9QZGGtIEvKVaRnC8puo7sLFL0wzilzKWW7VXva9xn0BeYNqI8YCtV2P3DOc4GWepKLXv/cX+2rlZd4E9eT9361tzBljRKdD0Nx6LPfYlboKV3sZkqItm/ACuSQVnn2OYOgRJ6Gt1k+jwuMoxbcuUoOTH7OVVdkDmKyDBv0HcoJkTcpM6hC8wHRaRYV2bjIsMydEKc/vdDzBv0HcrYp8wh88Q35ztJndsmK7WiqfS4ivDN16/xsstNUKjbp+6JyLforZqbKiLbgP9Ob5D3VhG5BHga+NMidZkQE+r0JfqMDkOX4rD+xEGMq+jKMEiKnZFhisOle6r6gZxLZ5Wty4RoGAHSOQH2IYFu/2V9iBmanrDdFWwqjlGaQJ+pEkyE+OTy11KEY5Z1+39HwwiNjTfm9+3Ou8RxN4dSdAVK4wQRIb4889B93mfl2DQhRolV+yhtYwZjGBtvXLD3GFXOJULxVSpFVqq4JAghDsKk6AaTojGIspJzLUVLmSvw5PLTC6XPk8/Z2qrEjvuzdWM5D7FrzH+oeNl1J/trh0/qiG3jjQvcpc+BbhAbbISY0makGAp1p91YlDic+Q+Vk2H2nv4jZFxEeU4ixbQP0d1uN84IOkJMKRopGkZZXEtsWH1tRpUuU14XkWLTfYNFCT5CbIsu9SOCRYmDaDqiayuSdN7/56LOQPsQoxGipc71MSmGQ1Ni9CHD+pSQoQmxGm0/JyXk5XVZfEgxRtGG0t8XQ99jHpVlqwQrxCj6EA23HH3Bo8GuLnnq6vxM4OhPuelHjlVAZQkzOkywidndJ5YoEdxFda7qeerq04fKMC3TVWKVdFXp2sRsB4zqR2w7bR43XMrQR9nYcCnFoKNDCDZljkqITVJVrq6jRJ9R59EXPFpZam3I0OgICuzR4keDmBCN0nJrexDFJBoW5aNRG2WOirqpt6uorsk+yaLRoksZ1hFbV6UYa19iaQIVYudGmdte15wS6/rmtqM/Y0ywlSrNUSfCczkwUyfCi2nEui26GiXGRullfAH3IUYVIfpcz1xGhNuXLdrn/czlq3PLpmIrGi2aCA0XzLtkbcAjzQoa5kTEqIRYhjKpcxEZ9ktw0LU6YjQRlqfqRO11J4fbVxfrtmKlCTRljkaIVaLDftFlBdnWnEUTnzH2pClzgHSyDzGPyeds3XuUYVh0WKWcYTSB82ehuPyMQEeZoxBim3shlpWcSTEOQkxNfbSpCSlWwoRYDdsY1hgHfAralxRrRYcTE8WPBglWiMcsW9O6DKtGexYlxkGIUaIvXEuxdn2BRohBDKoctP13rcvPaJ6jP7Wm8lxCV1uBtT3i3KSU+yVWdlqOU6naKLNRlwPefsLe13se3tBiS7pFW1JsO0IdJDinT9bLpfkJ10UJNmWOmWHzEatwwNtP2EeGXaJKpOcqOszStJzalmEejQzCKKjuKXw0iUWIkXLA208YyyjRhwxTspLyFTGGKsLGCTRCNCHmYAMjzVGnL9EXeeKqIkqT4ACsDzEuZi5fHbQUuxYdppFfaGLsx+TmAFXYY2uZxwLX/YfQk19X+xD7yabET119utcU2WgRixCNOnQtIiyCybC7aKAR4shRZhGZLSI/EpENIrJeRD6WnL9KRLaLyLrkOC9zz5UisllEfiEi5/r8Aj4pG+35iA4No3uE+wiBIhHiq8DHVfVfReT1wIMicndy7e9V9QvZwiJyInAhcBIwA7hHROaparNrcBrGZGgYBYl5txtV3amq/5q8fgnYAMwccssS4BZV3a2qTwKbgYUuGtsGJjrD8IDuKX40SKk+RBGZA5wM3A+cAVwmIh8E1tKLIl+kJ8v7MrdtY4BARWQpsBTgYA6p0vbGSKVYZqdswzAGo4AGGiEWFqKIHAZ8F7hcVX8jItcD19D7ftcAfwf8OSADbt/v26vqCmAFwOEyJcw/nT5MgIbhAHX/CAERWQx8CZgE3KCqy6vUU2jpnogcSE+G31DV7wGo6rOqOqG9tTX/xGtp8TZgdub2WcCOKo0zDKOb6B4tfIxCRCYBXwb+GDgR+EAyllGaIqPMAtwIbFDV6zLnp2eKvQ94LHl9B3ChiEwWkWOAucADVRpnGEZHcduHuBDYrKpbVPVl4BZ6YxmlKZIynwFcBDwqIuuSc/+VnoXn00uHtwJ/AaCq60XkVuBxeiPUl44aYX6JF5+7R7/zO+C5Ct8hFKYSd/sh/u9g7W+fQd/h6Oybl3jxrnv0O1NL1HmwiGR3nViRdLmlzASeybzfBpxaov69jBSiqt7L4H7B7w+551rg2qKNUNU3i8haVQ31uYkjib39EP93sPa3T5HvoKqLXX/soI+pUpFt/2UYRuw4G7cwIRqGETs/A+aKyDEichC9hSF3VKkopLXMK0YXCZrY2w/xfwdrf/s0/h1U9VURuQy4i960m5tUdX2VukQD3XXCMAyjaSxlNgzDSDAhGoZhJLQuRBFZnGwTtllElrXdnqKIyFYReTTZ+mxtcm6KiNwtIpuSn29su50pInKTiOwSkccy53LbG+IWbjnfIZpt6IZspRfF72EstgJU1dYOeh2gTwDHAgcBDwMnttmmEm3fCkztO/c5YFnyehnw2bbbmWnbHwLvAB4b1V56y58eBiYDxyS/o0mBfoergL8ZUDa47wBMB96RvH49sDFpZxS/hyHtj+Z3MOpoO0J0tuQmEJYAK5PXK4Hz22vKvqjqT4EX+k7ntTfILdxyvkMewX0Hzd9KL4rfw5D25xFU+4vQthAHLbkZ9gccEgr8UEQeTLYyAzhCVXdC7y8PMK211hUjr72x/V4uE5FHkpQ6TTeD/g59W+lF93voaz9E+DsYRNtCdLbkpgXOUNV30Nth41IR+cO2G+SQmH4v1wPHAfOBnfS2oYOAv0P/VnrDig441/p3GND+6H4HebQtxGi3ClPVHcnPXcBt9FKBZ9NdgJKfu9prYSHy2hvN70Uj24Zu0FZ6RPR76PpWgG0L0dmSmyYRkUOT58sgIocC59Db/uwO4OKk2MXA7e20sDB57Y1mC7eYtqHL20qPSH4PY7EVYNujOsB59EarngA+2XZ7Crb5WHqjZw8D69N2A28CVgGbkp9T2m5rps3fopfOvELvf+5LhrUX+GTyO/kF8Mdtt3/Id/g68CjwCL1/gNND/Q7AO+mljI8A65LjvFh+D0PaH83vYNRhS/cMwzAS2k6ZDcMwgsGEaBiGkWBCNAzDSDAhGoZhJJgQDcMwEkyIhmEYCSZEwzCMhP8PemoWrTMjbFkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "parametric_map = cle.replace_intensities(label_image, np.asarray([0] + list(dataframe['feret_diameter_max'])))\n",
    "imshow(parametric_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19ac58aa-0cec-4cee-9f79-f7c53e58fa72",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}