2D_seed_pixelflow_demo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kzr3_Gfs_SUQ"
      },
      "source": [
        "# Automated Extraction of 2D Seed Phenotype Data using a pre-trained `StarDist` model, `Scivision`, and `Pixelflow`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### This notebook demonstrates how to use pixelflow to extract measurements of segmented objects from 2D imagery. This demo will use 2D lightbox images of Oilseed rape (*Brassica napus*) seedpods detected and segmented using an adapted StarDist model as described in [Corcoran et al. 2023](https://www.frontiersin.org/articles/10.3389/fpls.2023.1120182/full).\n",
        "\n",
        "### Example images required to run this notebook can be found [here](https://zenodo.org/records/8355920)\n",
        "\n",
        "### R code used to run valve sorting in this notebook is available from the [Scivision gallery github page](https://github.com/scivision-gallery/pixelflow_seed_demo/tree/main) seed (`'seedpod_2D_valve_lowess_single.R'`)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CbZyHOm7xSj"
      },
      "source": [
        "## 1. Load dependencies including `Scivision` and `Pixelflow`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XnTG8vvG3ZSt",
        "outputId": "15bff507-c364-456f-c050-b9edc2589f30"
      },
      "outputs": [],
      "source": [
        "from pixelflow import pixelflow\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from tifffile import imread\n",
        "from pathlib import Path\n",
        "\n",
        "from glob import glob\n",
        "import re\n",
        "\n",
        "from scivision import default_catalog, load_pretrained_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TYmbhpkE8H0S"
      },
      "source": [
        "## 2. Find `StarDist Seed` model in `Scivision` model catalog"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3l0Tumw1NdjJ",
        "outputId": "98b3f805-dbcd-41c5-b2b5-1187d08267e5"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>description</th>\n",
              "      <th>tasks</th>\n",
              "      <th>pkg_url</th>\n",
              "      <th>url</th>\n",
              "      <th>scivision_usable</th>\n",
              "      <th>institution</th>\n",
              "      <th>tags</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>stardist</td>\n",
              "      <td>Single class object detection and segementatio...</td>\n",
              "      <td>(TaskEnum.object_detection, TaskEnum.segmentat...</td>\n",
              "      <td>git+https://github.com/stardist/stardist.git@m...</td>\n",
              "      <td>https://github.com/stardist/stardist</td>\n",
              "      <td>False</td>\n",
              "      <td>(epfl,)</td>\n",
              "      <td>(2D, 3D, optical-microscopy, xray, microtomogr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PlantCV</td>\n",
              "      <td>Open-source image analysis software package ta...</td>\n",
              "      <td>(TaskEnum.object_detection, TaskEnum.segmentat...</td>\n",
              "      <td>git+https://github.com/danforthcenter/plantcv@...</td>\n",
              "      <td>https://github.com/danforthcenter/plantcv</td>\n",
              "      <td>False</td>\n",
              "      <td>(danforthcenter,)</td>\n",
              "      <td>(2D, hyperspectral, multispectral, near-infrar...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>greenotyper</td>\n",
              "      <td>Greenotyper is an image analysis tool for larg...</td>\n",
              "      <td>(TaskEnum.segmentation)</td>\n",
              "      <td>git+https://github.com/MarniTausen/Greenotyper...</td>\n",
              "      <td>https://github.com/MarniTausen/Greenotyper</td>\n",
              "      <td>False</td>\n",
              "      <td>(MarniTausen,)</td>\n",
              "      <td>(2D, multispectral, plant-phenotyping, climate...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>mapreader-plant</td>\n",
              "      <td>automated detection of plant patches in images...</td>\n",
              "      <td>(TaskEnum.object_detection, TaskEnum.classific...</td>\n",
              "      <td>git+https://github.com/alan-turing-institute/m...</td>\n",
              "      <td>https://github.com/alan-turing-institute/mapre...</td>\n",
              "      <td>True</td>\n",
              "      <td>(alan-turing-institute,)</td>\n",
              "      <td>(2D, plant, phenotype, rgb, biology, agriculture)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>resnet50-plankton</td>\n",
              "      <td>automated classification of plankton images co...</td>\n",
              "      <td>(TaskEnum.classificiation)</td>\n",
              "      <td>git+https://github.com/alan-turing-institute/p...</td>\n",
              "      <td>https://github.com/alan-turing-institute/plank...</td>\n",
              "      <td>True</td>\n",
              "      <td>(alan-turing-institute, cefas, plankton-analyt...</td>\n",
              "      <td>(2D, plankton, ecology, environmental-science)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>vedge-detector</td>\n",
              "      <td>automated detection of coastal vegetation edge...</td>\n",
              "      <td>(TaskEnum.segmentation)</td>\n",
              "      <td>git+https://github.com/MartinSJRogers/VEdge_De...</td>\n",
              "      <td>https://github.com/MartinSJRogers/VEdge_Detect...</td>\n",
              "      <td>True</td>\n",
              "      <td>(cambridge-university, birkbeck-university)</td>\n",
              "      <td>(2D, satellite, remote-sensing, ecology, envir...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>image-classifiers</td>\n",
              "      <td>Scivision plugin for the image-classifiers pac...</td>\n",
              "      <td>(TaskEnum.classificiation)</td>\n",
              "      <td>git+https://github.com/alan-turing-institute/s...</td>\n",
              "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
              "      <td>True</td>\n",
              "      <td>(alan-turing-institute,)</td>\n",
              "      <td>(classification, 2D, image)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>huggingface-classifiers</td>\n",
              "      <td>Selection of image classifiers taken from http...</td>\n",
              "      <td>(TaskEnum.classificiation)</td>\n",
              "      <td>git+https://github.com/alan-turing-institute/s...</td>\n",
              "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
              "      <td>True</td>\n",
              "      <td>(alan-turing-institute, huggingface)</td>\n",
              "      <td>(classification, 2D, image)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>huggingface-segmentation</td>\n",
              "      <td>Selection of segmentation algorithms taken fro...</td>\n",
              "      <td>(TaskEnum.segmentation)</td>\n",
              "      <td>git+https://github.com/alan-turing-institute/s...</td>\n",
              "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
              "      <td>True</td>\n",
              "      <td>(alan-turing-institute, huggingface)</td>\n",
              "      <td>()</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>huggingface-object-detection</td>\n",
              "      <td>Selection of object detection algorithms taken...</td>\n",
              "      <td>(TaskEnum.object_detection)</td>\n",
              "      <td>git+https://github.com/alan-turing-institute/s...</td>\n",
              "      <td>https://github.com/alan-turing-institute/scivi...</td>\n",
              "      <td>True</td>\n",
              "      <td>(alan-turing-institute, huggingface)</td>\n",
              "      <td>()</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>detectreeRGB-forest</td>\n",
              "      <td>Detect changes in tall tree height with machin...</td>\n",
              "      <td>(TaskEnum.object_detection, TaskEnum.segmentat...</td>\n",
              "      <td>git+https://github.com/shmh40/detectreeRGB-tre...</td>\n",
              "      <td>https://github.com/shmh40/detectreeRGB-treecro...</td>\n",
              "      <td>True</td>\n",
              "      <td>(cambridge-university,)</td>\n",
              "      <td>(2D, satellite, remote-sensing, ecology, envir...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>flower-classification-model</td>\n",
              "      <td>Identify flower species</td>\n",
              "      <td>(TaskEnum.classificiation)</td>\n",
              "      <td>git+https://github.com/alan-turing-institute/f...</td>\n",
              "      <td>https://github.com/alan-turing-institute/flowe...</td>\n",
              "      <td>True</td>\n",
              "      <td>(alan-turing-institute,)</td>\n",
              "      <td>(flowers, ecology)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>butterfly-classification-model</td>\n",
              "      <td>Identify butterfly species</td>\n",
              "      <td>(TaskEnum.classificiation)</td>\n",
              "      <td>git+https://github.com/alan-turing-institute/b...</td>\n",
              "      <td>https://github.com/alan-turing-institute/butte...</td>\n",
              "      <td>True</td>\n",
              "      <td>(alan-turing-institute,)</td>\n",
              "      <td>(butterflies, insects, animals, ecology)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Affinity-VAE</td>\n",
              "      <td>A framework for automatic clustering and class...</td>\n",
              "      <td>(TaskEnum.classificiation)</td>\n",
              "      <td>https://github.com/alan-turing-institute/affin...</td>\n",
              "      <td>https://github.com/alan-turing-institute/affin...</td>\n",
              "      <td>False</td>\n",
              "      <td>(The Alan Turing Institute, Rutherford Appleto...</td>\n",
              "      <td>(Protein, Tomography, Cryo-EM, Shape regularis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>btrack</td>\n",
              "      <td>btrack is a Python library for multi object tr...</td>\n",
              "      <td>()</td>\n",
              "      <td>btrack</td>\n",
              "      <td>https://github.com/quantumjot/btrack</td>\n",
              "      <td>False</td>\n",
              "      <td>(The Alan Turing Institute, University College...</td>\n",
              "      <td>(cell tracking, bio imaging , optimisation, ba...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>StarDist Seed</td>\n",
              "      <td>Pre-trained StarDist model for detection and s...</td>\n",
              "      <td>(TaskEnum.object_detection, TaskEnum.segmentat...</td>\n",
              "      <td>stardist_seed_pod</td>\n",
              "      <td>https://raw.githubusercontent.com/alan-turing-...</td>\n",
              "      <td>True</td>\n",
              "      <td>(The Alan Turing Institute, Rothamsted Research)</td>\n",
              "      <td>(2D, 3D, plant-phenotyping, agriculture, rgb, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "                              name  \\\n",
              "0                         stardist   \n",
              "1                          PlantCV   \n",
              "2                      greenotyper   \n",
              "3                  mapreader-plant   \n",
              "4                resnet50-plankton   \n",
              "5                   vedge-detector   \n",
              "6                image-classifiers   \n",
              "7          huggingface-classifiers   \n",
              "8         huggingface-segmentation   \n",
              "9     huggingface-object-detection   \n",
              "10             detectreeRGB-forest   \n",
              "11     flower-classification-model   \n",
              "12  butterfly-classification-model   \n",
              "13                    Affinity-VAE   \n",
              "14                          btrack   \n",
              "15                   StarDist Seed   \n",
              "\n",
              "                                          description  \\\n",
              "0   Single class object detection and segementatio...   \n",
              "1   Open-source image analysis software package ta...   \n",
              "2   Greenotyper is an image analysis tool for larg...   \n",
              "3   automated detection of plant patches in images...   \n",
              "4   automated classification of plankton images co...   \n",
              "5   automated detection of coastal vegetation edge...   \n",
              "6   Scivision plugin for the image-classifiers pac...   \n",
              "7   Selection of image classifiers taken from http...   \n",
              "8   Selection of segmentation algorithms taken fro...   \n",
              "9   Selection of object detection algorithms taken...   \n",
              "10  Detect changes in tall tree height with machin...   \n",
              "11                            Identify flower species   \n",
              "12                         Identify butterfly species   \n",
              "13  A framework for automatic clustering and class...   \n",
              "14  btrack is a Python library for multi object tr...   \n",
              "15  Pre-trained StarDist model for detection and s...   \n",
              "\n",
              "                                                tasks  \\\n",
              "0   (TaskEnum.object_detection, TaskEnum.segmentat...   \n",
              "1   (TaskEnum.object_detection, TaskEnum.segmentat...   \n",
              "2                             (TaskEnum.segmentation)   \n",
              "3   (TaskEnum.object_detection, TaskEnum.classific...   \n",
              "4                          (TaskEnum.classificiation)   \n",
              "5                             (TaskEnum.segmentation)   \n",
              "6                          (TaskEnum.classificiation)   \n",
              "7                          (TaskEnum.classificiation)   \n",
              "8                             (TaskEnum.segmentation)   \n",
              "9                         (TaskEnum.object_detection)   \n",
              "10  (TaskEnum.object_detection, TaskEnum.segmentat...   \n",
              "11                         (TaskEnum.classificiation)   \n",
              "12                         (TaskEnum.classificiation)   \n",
              "13                         (TaskEnum.classificiation)   \n",
              "14                                                 ()   \n",
              "15  (TaskEnum.object_detection, TaskEnum.segmentat...   \n",
              "\n",
              "                                              pkg_url  \\\n",
              "0   git+https://github.com/stardist/stardist.git@m...   \n",
              "1   git+https://github.com/danforthcenter/plantcv@...   \n",
              "2   git+https://github.com/MarniTausen/Greenotyper...   \n",
              "3   git+https://github.com/alan-turing-institute/m...   \n",
              "4   git+https://github.com/alan-turing-institute/p...   \n",
              "5   git+https://github.com/MartinSJRogers/VEdge_De...   \n",
              "6   git+https://github.com/alan-turing-institute/s...   \n",
              "7   git+https://github.com/alan-turing-institute/s...   \n",
              "8   git+https://github.com/alan-turing-institute/s...   \n",
              "9   git+https://github.com/alan-turing-institute/s...   \n",
              "10  git+https://github.com/shmh40/detectreeRGB-tre...   \n",
              "11  git+https://github.com/alan-turing-institute/f...   \n",
              "12  git+https://github.com/alan-turing-institute/b...   \n",
              "13  https://github.com/alan-turing-institute/affin...   \n",
              "14                                             btrack   \n",
              "15                                  stardist_seed_pod   \n",
              "\n",
              "                                                  url  scivision_usable  \\\n",
              "0                https://github.com/stardist/stardist             False   \n",
              "1           https://github.com/danforthcenter/plantcv             False   \n",
              "2          https://github.com/MarniTausen/Greenotyper             False   \n",
              "3   https://github.com/alan-turing-institute/mapre...              True   \n",
              "4   https://github.com/alan-turing-institute/plank...              True   \n",
              "5   https://github.com/MartinSJRogers/VEdge_Detect...              True   \n",
              "6   https://github.com/alan-turing-institute/scivi...              True   \n",
              "7   https://github.com/alan-turing-institute/scivi...              True   \n",
              "8   https://github.com/alan-turing-institute/scivi...              True   \n",
              "9   https://github.com/alan-turing-institute/scivi...              True   \n",
              "10  https://github.com/shmh40/detectreeRGB-treecro...              True   \n",
              "11  https://github.com/alan-turing-institute/flowe...              True   \n",
              "12  https://github.com/alan-turing-institute/butte...              True   \n",
              "13  https://github.com/alan-turing-institute/affin...             False   \n",
              "14               https://github.com/quantumjot/btrack             False   \n",
              "15  https://raw.githubusercontent.com/alan-turing-...              True   \n",
              "\n",
              "                                          institution  \\\n",
              "0                                             (epfl,)   \n",
              "1                                   (danforthcenter,)   \n",
              "2                                      (MarniTausen,)   \n",
              "3                            (alan-turing-institute,)   \n",
              "4   (alan-turing-institute, cefas, plankton-analyt...   \n",
              "5         (cambridge-university, birkbeck-university)   \n",
              "6                            (alan-turing-institute,)   \n",
              "7                (alan-turing-institute, huggingface)   \n",
              "8                (alan-turing-institute, huggingface)   \n",
              "9                (alan-turing-institute, huggingface)   \n",
              "10                            (cambridge-university,)   \n",
              "11                           (alan-turing-institute,)   \n",
              "12                           (alan-turing-institute,)   \n",
              "13  (The Alan Turing Institute, Rutherford Appleto...   \n",
              "14  (The Alan Turing Institute, University College...   \n",
              "15   (The Alan Turing Institute, Rothamsted Research)   \n",
              "\n",
              "                                                 tags  \n",
              "0   (2D, 3D, optical-microscopy, xray, microtomogr...  \n",
              "1   (2D, hyperspectral, multispectral, near-infrar...  \n",
              "2   (2D, multispectral, plant-phenotyping, climate...  \n",
              "3   (2D, plant, phenotype, rgb, biology, agriculture)  \n",
              "4      (2D, plankton, ecology, environmental-science)  \n",
              "5   (2D, satellite, remote-sensing, ecology, envir...  \n",
              "6                         (classification, 2D, image)  \n",
              "7                         (classification, 2D, image)  \n",
              "8                                                  ()  \n",
              "9                                                  ()  \n",
              "10  (2D, satellite, remote-sensing, ecology, envir...  \n",
              "11                                 (flowers, ecology)  \n",
              "12           (butterflies, insects, animals, ecology)  \n",
              "13  (Protein, Tomography, Cryo-EM, Shape regularis...  \n",
              "14  (cell tracking, bio imaging , optimisation, ba...  \n",
              "15  (2D, 3D, plant-phenotyping, agriculture, rgb, ...  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# load full scivision model catalog\n",
        "models_catalog = default_catalog.models.to_dataframe()\n",
        "models_catalog"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "lwNcgpjlNwiO",
        "outputId": "a4d13e7f-a7d3-4afd-dde8-484f810d0f9f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "FlexibleUrl('https://raw.githubusercontent.com/alan-turing-institute/stardist-seed-pod/main/.scivision/model.yml', scheme='https', host='raw.githubusercontent.com', tld='com', host_type='domain', path='/alan-turing-institute/stardist-seed-pod/main/.scivision/model.yml')"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# get github repository link for the model we want to use (StarDist Seed)\n",
        "model_repo = models_catalog[models_catalog.name == \"StarDist Seed\"].url.item()\n",
        "model_repo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6GkKvJyQ8m8K"
      },
      "source": [
        "## 3. Load `StarDist Seed` model from `Scivision` model catalog"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBOSjoE4PfGf",
        "outputId": "9e46437b-9ca0-48c9-f652-91081e9aba2e"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[11:59:50] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"color: #808000; text-decoration-color: #808000\">Call</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'load_pretrained_model'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">module</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'scivision.io.reader'</span>, <span style=\"color: #808000; text-decoration-color: #808000\">signature</span>=<span style=\"font-weight: bold\">(</span>path:        <a href=\"file:///Users/ifenton/opt/anaconda3/envs/test_pxsd4/lib/python3.10/site-packages/scivision/koala.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">koala.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///Users/ifenton/opt/anaconda3/envs/test_pxsd4/lib/python3.10/site-packages/scivision/koala.py#49\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">49</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         os.PathLike, branch: str = <span style=\"color: #008000; text-decoration-color: #008000\">'main'</span>, allow_install: bool = <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, model_selection:    <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         str = <span style=\"color: #008000; text-decoration-color: #008000\">'default'</span>, load_multiple: bool = <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>, *args, **kwargs<span style=\"font-weight: bold\">)</span> -&gt;                   <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>         scivision.io.wrapper.PretrainedModel, <span style=\"color: #808000; text-decoration-color: #808000\">kwargs_given</span>=<span style=\"color: #008000; text-decoration-color: #008000\">'model_selection, allow_install'</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">           </span>\n",
              "</pre>\n"
            ],
            "text/plain": [
              "\u001b[2;36m[11:59:50]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[33mCall\u001b[0m=\u001b[32m'load_pretrained_model'\u001b[0m, \u001b[33mmodule\u001b[0m=\u001b[32m'scivision.io.reader'\u001b[0m, \u001b[33msignature\u001b[0m=\u001b[1m(\u001b[0mpath:        \u001b]8;id=735983;file:///Users/ifenton/opt/anaconda3/envs/test_pxsd4/lib/python3.10/site-packages/scivision/koala.py\u001b\\\u001b[2mkoala.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=267354;file:///Users/ifenton/opt/anaconda3/envs/test_pxsd4/lib/python3.10/site-packages/scivision/koala.py#49\u001b\\\u001b[2m49\u001b[0m\u001b]8;;\u001b\\\n",
              "\u001b[2;36m           \u001b[0m         os.PathLike, branch: str = \u001b[32m'main'\u001b[0m, allow_install: bool = \u001b[3;91mFalse\u001b[0m, model_selection:    \u001b[2m           \u001b[0m\n",
              "\u001b[2;36m           \u001b[0m         str = \u001b[32m'default'\u001b[0m, load_multiple: bool = \u001b[3;91mFalse\u001b[0m, *args, **kwargs\u001b[1m)\u001b[0m ->                   \u001b[2m           \u001b[0m\n",
              "\u001b[2;36m           \u001b[0m         scivision.io.wrapper.PretrainedModel, \u001b[33mkwargs_given\u001b[0m=\u001b[32m'model_selection, allow_install'\u001b[0m \u001b[2m           \u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2024-02-05 11:59:50.685966: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-02-05 11:59:53.301841: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2\n",
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loading network weights from 'weights_best.h5'.\n",
            "Loading thresholds from 'thresholds.json'.\n",
            "Using default values: prob_thresh=0.718646, nms_thresh=0.3.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "scivision.PretrainedModel( \n",
              "  module='stardist_seed_pod', \n",
              "  model='model_2d', \n",
              "  source='https://github.com/alan-turing-institute/stardist-seed-pod' \n",
              "  pipe='DataPipe(input=<Parameter \"X: numpy.ndarray\">, output=<Parameter \"image: numpy.ndarray\">)' \n",
              ")"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# load pretrained seed model\n",
        "model = load_pretrained_model(model_repo, model_selection= \"model_2d\", allow_install=True)\n",
        "model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6k4ikoKfFtC0"
      },
      "source": [
        "# Extract seed size and shape metrics from a single image (one seed pod) with `Pixelflow`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5vLXtHqo85jR"
      },
      "source": [
        "## 4. Load images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "izc8BpE_b_9I"
      },
      "outputs": [],
      "source": [
        "# load image\n",
        "img = imread('01_Shengliyoucai_main_A.tif')\n",
        "pod_name = '01_Shengliyoucai_main_A'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCChEOIp8-Sp"
      },
      "source": [
        "## 5. Predict location, size and shape of seeds with `StarDist Seed` model using `Scivision `core function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "hPmIhnyzcHB0"
      },
      "outputs": [],
      "source": [
        "# detect and segment seeds in image\n",
        "pred = model.predict(img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "73LPibkofq72"
      },
      "outputs": [],
      "source": [
        "# save seed labels created with Stardist Seed model\n",
        "np.save(Path('content/' + pod_name + '_pred.npy'), pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I4KnObP6-TlB"
      },
      "source": [
        "## 6. Calculate seed size, shape and arrangement metrics with `Pixelflow`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "AtMqE9BD3ZSw",
        "outputId": "984165ff-97d1-40a3-c2dd-acfcc38a5036"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x114087c10>"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# load image and mask\n",
        "img = imread('01_Shengliyoucai_main_A.tif')\n",
        "mask = pred\n",
        "\n",
        "fig, ax = plt.subplots(1, 2)\n",
        "ax[0].imshow(img)\n",
        "ax[1].imshow(mask)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6S0OaG83ZS0",
        "outputId": "d804c9cc-74d6-4813-cac7-80aca876860c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "YX image detected\n"
          ]
        }
      ],
      "source": [
        "# create table of size and shape metrics\n",
        "features = pixelflow(\n",
        "    mask,\n",
        "    img,\n",
        "    spacing=(0.03,0.03),\n",
        "    features=('label', 'bbox', 'centroid', 'area', 'equivalent_diameter', 'major_axis_length', 'minor_axis_length', 'orientation', 'solidity', 'image_intensity')\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "WWRR6VC33ZS1",
        "outputId": "d789d562-a52e-4075-fb75-d778f16a2061"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>bbox-0</th>\n",
              "      <th>bbox-1</th>\n",
              "      <th>bbox-2</th>\n",
              "      <th>bbox-3</th>\n",
              "      <th>centroid-0</th>\n",
              "      <th>centroid-1</th>\n",
              "      <th>area</th>\n",
              "      <th>equivalent_diameter</th>\n",
              "      <th>major_axis_length</th>\n",
              "      <th>minor_axis_length</th>\n",
              "      <th>orientation</th>\n",
              "      <th>solidity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>835</td>\n",
              "      <td>3742</td>\n",
              "      <td>1007</td>\n",
              "      <td>3946</td>\n",
              "      <td>27.659814</td>\n",
              "      <td>115.256195</td>\n",
              "      <td>26.5113</td>\n",
              "      <td>5.809926</td>\n",
              "      <td>6.408291</td>\n",
              "      <td>5.293776</td>\n",
              "      <td>-1.531574</td>\n",
              "      <td>0.991785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>846</td>\n",
              "      <td>3470</td>\n",
              "      <td>1014</td>\n",
              "      <td>3652</td>\n",
              "      <td>27.888554</td>\n",
              "      <td>106.812965</td>\n",
              "      <td>23.2209</td>\n",
              "      <td>5.437441</td>\n",
              "      <td>5.688197</td>\n",
              "      <td>5.217015</td>\n",
              "      <td>1.524893</td>\n",
              "      <td>0.992079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>859</td>\n",
              "      <td>3185</td>\n",
              "      <td>1023</td>\n",
              "      <td>3350</td>\n",
              "      <td>28.205184</td>\n",
              "      <td>98.013696</td>\n",
              "      <td>20.5407</td>\n",
              "      <td>5.114023</td>\n",
              "      <td>5.160864</td>\n",
              "      <td>5.084435</td>\n",
              "      <td>1.232456</td>\n",
              "      <td>0.991442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>861</td>\n",
              "      <td>4054</td>\n",
              "      <td>1022</td>\n",
              "      <td>4221</td>\n",
              "      <td>28.218190</td>\n",
              "      <td>124.127465</td>\n",
              "      <td>20.5902</td>\n",
              "      <td>5.120181</td>\n",
              "      <td>5.244345</td>\n",
              "      <td>5.017989</td>\n",
              "      <td>-1.373028</td>\n",
              "      <td>0.992280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>877</td>\n",
              "      <td>2725</td>\n",
              "      <td>1060</td>\n",
              "      <td>2948</td>\n",
              "      <td>29.050008</td>\n",
              "      <td>85.017869</td>\n",
              "      <td>30.9213</td>\n",
              "      <td>6.274569</td>\n",
              "      <td>6.983425</td>\n",
              "      <td>5.664964</td>\n",
              "      <td>-1.470105</td>\n",
              "      <td>0.992719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>881</td>\n",
              "      <td>4416</td>\n",
              "      <td>1030</td>\n",
              "      <td>4600</td>\n",
              "      <td>28.647920</td>\n",
              "      <td>135.176317</td>\n",
              "      <td>20.8566</td>\n",
              "      <td>5.153198</td>\n",
              "      <td>5.761219</td>\n",
              "      <td>4.633875</td>\n",
              "      <td>-1.469292</td>\n",
              "      <td>0.991189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>888</td>\n",
              "      <td>4241</td>\n",
              "      <td>1026</td>\n",
              "      <td>4417</td>\n",
              "      <td>28.731418</td>\n",
              "      <td>129.842832</td>\n",
              "      <td>18.3555</td>\n",
              "      <td>4.834351</td>\n",
              "      <td>5.516655</td>\n",
              "      <td>4.257249</td>\n",
              "      <td>-1.566841</td>\n",
              "      <td>0.989376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>907</td>\n",
              "      <td>4796</td>\n",
              "      <td>1064</td>\n",
              "      <td>4988</td>\n",
              "      <td>29.551040</td>\n",
              "      <td>146.720336</td>\n",
              "      <td>23.0472</td>\n",
              "      <td>5.417066</td>\n",
              "      <td>5.982115</td>\n",
              "      <td>4.933751</td>\n",
              "      <td>-1.492895</td>\n",
              "      <td>0.991597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>910</td>\n",
              "      <td>2463</td>\n",
              "      <td>1070</td>\n",
              "      <td>2615</td>\n",
              "      <td>29.664914</td>\n",
              "      <td>76.141714</td>\n",
              "      <td>18.5391</td>\n",
              "      <td>4.858468</td>\n",
              "      <td>5.006396</td>\n",
              "      <td>4.731858</td>\n",
              "      <td>-0.154559</td>\n",
              "      <td>0.991433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>924</td>\n",
              "      <td>4993</td>\n",
              "      <td>1093</td>\n",
              "      <td>5161</td>\n",
              "      <td>30.250303</td>\n",
              "      <td>152.281324</td>\n",
              "      <td>21.5325</td>\n",
              "      <td>5.236032</td>\n",
              "      <td>5.288282</td>\n",
              "      <td>5.204249</td>\n",
              "      <td>0.673634</td>\n",
              "      <td>0.991710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>928</td>\n",
              "      <td>2178</td>\n",
              "      <td>1132</td>\n",
              "      <td>2370</td>\n",
              "      <td>30.876890</td>\n",
              "      <td>68.204226</td>\n",
              "      <td>29.6649</td>\n",
              "      <td>6.145773</td>\n",
              "      <td>6.356039</td>\n",
              "      <td>5.963699</td>\n",
              "      <td>0.027018</td>\n",
              "      <td>0.992174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>928</td>\n",
              "      <td>5166</td>\n",
              "      <td>1124</td>\n",
              "      <td>5354</td>\n",
              "      <td>30.747954</td>\n",
              "      <td>157.770568</td>\n",
              "      <td>28.1178</td>\n",
              "      <td>5.983368</td>\n",
              "      <td>6.119983</td>\n",
              "      <td>5.870473</td>\n",
              "      <td>-0.122183</td>\n",
              "      <td>0.992976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>1003</td>\n",
              "      <td>5390</td>\n",
              "      <td>1160</td>\n",
              "      <td>5544</td>\n",
              "      <td>32.399653</td>\n",
              "      <td>163.984284</td>\n",
              "      <td>18.4554</td>\n",
              "      <td>4.847489</td>\n",
              "      <td>4.983515</td>\n",
              "      <td>4.733016</td>\n",
              "      <td>-0.726204</td>\n",
              "      <td>0.991730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>1014</td>\n",
              "      <td>1816</td>\n",
              "      <td>1163</td>\n",
              "      <td>2005</td>\n",
              "      <td>32.672187</td>\n",
              "      <td>57.230858</td>\n",
              "      <td>21.1680</td>\n",
              "      <td>5.191525</td>\n",
              "      <td>5.919740</td>\n",
              "      <td>4.578597</td>\n",
              "      <td>-1.524870</td>\n",
              "      <td>0.989483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>1026</td>\n",
              "      <td>1022</td>\n",
              "      <td>1198</td>\n",
              "      <td>1209</td>\n",
              "      <td>33.324746</td>\n",
              "      <td>33.445462</td>\n",
              "      <td>24.3837</td>\n",
              "      <td>5.571920</td>\n",
              "      <td>5.854066</td>\n",
              "      <td>5.324035</td>\n",
              "      <td>-1.540617</td>\n",
              "      <td>0.992345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1035</td>\n",
              "      <td>3548</td>\n",
              "      <td>1234</td>\n",
              "      <td>3745</td>\n",
              "      <td>34.025832</td>\n",
              "      <td>109.394479</td>\n",
              "      <td>29.2050</td>\n",
              "      <td>6.097947</td>\n",
              "      <td>6.158044</td>\n",
              "      <td>6.053905</td>\n",
              "      <td>-0.286532</td>\n",
              "      <td>0.993144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>1049</td>\n",
              "      <td>4168</td>\n",
              "      <td>1243</td>\n",
              "      <td>4357</td>\n",
              "      <td>34.322070</td>\n",
              "      <td>127.856191</td>\n",
              "      <td>27.1971</td>\n",
              "      <td>5.884592</td>\n",
              "      <td>6.038060</td>\n",
              "      <td>5.751526</td>\n",
              "      <td>-0.487346</td>\n",
              "      <td>0.991567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>1053</td>\n",
              "      <td>3779</td>\n",
              "      <td>1246</td>\n",
              "      <td>3987</td>\n",
              "      <td>34.448106</td>\n",
              "      <td>116.472861</td>\n",
              "      <td>30.0879</td>\n",
              "      <td>6.189435</td>\n",
              "      <td>6.425920</td>\n",
              "      <td>5.974747</td>\n",
              "      <td>-1.522360</td>\n",
              "      <td>0.993285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>1057</td>\n",
              "      <td>3063</td>\n",
              "      <td>1208</td>\n",
              "      <td>3205</td>\n",
              "      <td>33.941341</td>\n",
              "      <td>94.005297</td>\n",
              "      <td>16.1892</td>\n",
              "      <td>4.540124</td>\n",
              "      <td>4.683783</td>\n",
              "      <td>4.416628</td>\n",
              "      <td>-0.189601</td>\n",
              "      <td>0.989983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>1059</td>\n",
              "      <td>2869</td>\n",
              "      <td>1220</td>\n",
              "      <td>3023</td>\n",
              "      <td>34.165903</td>\n",
              "      <td>88.331937</td>\n",
              "      <td>18.5319</td>\n",
              "      <td>4.857525</td>\n",
              "      <td>4.998235</td>\n",
              "      <td>4.734433</td>\n",
              "      <td>0.266099</td>\n",
              "      <td>0.991668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>21</td>\n",
              "      <td>1085</td>\n",
              "      <td>4398</td>\n",
              "      <td>1258</td>\n",
              "      <td>4569</td>\n",
              "      <td>35.037302</td>\n",
              "      <td>134.469559</td>\n",
              "      <td>22.4838</td>\n",
              "      <td>5.350445</td>\n",
              "      <td>5.498830</td>\n",
              "      <td>5.236273</td>\n",
              "      <td>-0.763711</td>\n",
              "      <td>0.992018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>22</td>\n",
              "      <td>1097</td>\n",
              "      <td>2593</td>\n",
              "      <td>1271</td>\n",
              "      <td>2788</td>\n",
              "      <td>35.495790</td>\n",
              "      <td>80.648438</td>\n",
              "      <td>25.8336</td>\n",
              "      <td>5.735186</td>\n",
              "      <td>6.124204</td>\n",
              "      <td>5.389607</td>\n",
              "      <td>-1.542840</td>\n",
              "      <td>0.993321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>23</td>\n",
              "      <td>1099</td>\n",
              "      <td>4697</td>\n",
              "      <td>1309</td>\n",
              "      <td>4908</td>\n",
              "      <td>36.059014</td>\n",
              "      <td>144.046770</td>\n",
              "      <td>32.9904</td>\n",
              "      <td>6.481102</td>\n",
              "      <td>6.593189</td>\n",
              "      <td>6.387528</td>\n",
              "      <td>-0.916836</td>\n",
              "      <td>0.993011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>1126</td>\n",
              "      <td>4949</td>\n",
              "      <td>1327</td>\n",
              "      <td>5128</td>\n",
              "      <td>36.777866</td>\n",
              "      <td>151.144384</td>\n",
              "      <td>26.9064</td>\n",
              "      <td>5.853058</td>\n",
              "      <td>6.254687</td>\n",
              "      <td>5.490873</td>\n",
              "      <td>0.052111</td>\n",
              "      <td>0.992398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>25</td>\n",
              "      <td>1130</td>\n",
              "      <td>1979</td>\n",
              "      <td>1319</td>\n",
              "      <td>2136</td>\n",
              "      <td>36.722909</td>\n",
              "      <td>61.707084</td>\n",
              "      <td>22.5612</td>\n",
              "      <td>5.359647</td>\n",
              "      <td>5.932554</td>\n",
              "      <td>4.864765</td>\n",
              "      <td>-0.113745</td>\n",
              "      <td>0.991692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>1176</td>\n",
              "      <td>549</td>\n",
              "      <td>1318</td>\n",
              "      <td>702</td>\n",
              "      <td>37.368127</td>\n",
              "      <td>18.757046</td>\n",
              "      <td>16.4781</td>\n",
              "      <td>4.580455</td>\n",
              "      <td>4.783871</td>\n",
              "      <td>4.400699</td>\n",
              "      <td>1.379798</td>\n",
              "      <td>0.990961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>1176</td>\n",
              "      <td>1642</td>\n",
              "      <td>1358</td>\n",
              "      <td>1818</td>\n",
              "      <td>37.984530</td>\n",
              "      <td>51.900157</td>\n",
              "      <td>23.7411</td>\n",
              "      <td>5.498009</td>\n",
              "      <td>5.657482</td>\n",
              "      <td>5.356424</td>\n",
              "      <td>-0.449678</td>\n",
              "      <td>0.991990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>28</td>\n",
              "      <td>1210</td>\n",
              "      <td>1312</td>\n",
              "      <td>1386</td>\n",
              "      <td>1523</td>\n",
              "      <td>38.901697</td>\n",
              "      <td>42.474331</td>\n",
              "      <td>28.4454</td>\n",
              "      <td>6.018123</td>\n",
              "      <td>6.635741</td>\n",
              "      <td>5.480809</td>\n",
              "      <td>-1.484604</td>\n",
              "      <td>0.992620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>29</td>\n",
              "      <td>1250</td>\n",
              "      <td>5726</td>\n",
              "      <td>1398</td>\n",
              "      <td>5859</td>\n",
              "      <td>39.691402</td>\n",
              "      <td>173.777640</td>\n",
              "      <td>14.8824</td>\n",
              "      <td>4.353029</td>\n",
              "      <td>4.651191</td>\n",
              "      <td>4.088903</td>\n",
              "      <td>-0.281760</td>\n",
              "      <td>0.990476</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "    label  bbox-0  bbox-1  bbox-2  bbox-3  centroid-0  centroid-1     area  \\\n",
              "0       1     835    3742    1007    3946   27.659814  115.256195  26.5113   \n",
              "1       2     846    3470    1014    3652   27.888554  106.812965  23.2209   \n",
              "2       3     859    3185    1023    3350   28.205184   98.013696  20.5407   \n",
              "3       4     861    4054    1022    4221   28.218190  124.127465  20.5902   \n",
              "4       5     877    2725    1060    2948   29.050008   85.017869  30.9213   \n",
              "5       6     881    4416    1030    4600   28.647920  135.176317  20.8566   \n",
              "6       7     888    4241    1026    4417   28.731418  129.842832  18.3555   \n",
              "7       8     907    4796    1064    4988   29.551040  146.720336  23.0472   \n",
              "8       9     910    2463    1070    2615   29.664914   76.141714  18.5391   \n",
              "9      10     924    4993    1093    5161   30.250303  152.281324  21.5325   \n",
              "10     11     928    2178    1132    2370   30.876890   68.204226  29.6649   \n",
              "11     12     928    5166    1124    5354   30.747954  157.770568  28.1178   \n",
              "12     13    1003    5390    1160    5544   32.399653  163.984284  18.4554   \n",
              "13     14    1014    1816    1163    2005   32.672187   57.230858  21.1680   \n",
              "14     15    1026    1022    1198    1209   33.324746   33.445462  24.3837   \n",
              "15     16    1035    3548    1234    3745   34.025832  109.394479  29.2050   \n",
              "16     17    1049    4168    1243    4357   34.322070  127.856191  27.1971   \n",
              "17     18    1053    3779    1246    3987   34.448106  116.472861  30.0879   \n",
              "18     19    1057    3063    1208    3205   33.941341   94.005297  16.1892   \n",
              "19     20    1059    2869    1220    3023   34.165903   88.331937  18.5319   \n",
              "20     21    1085    4398    1258    4569   35.037302  134.469559  22.4838   \n",
              "21     22    1097    2593    1271    2788   35.495790   80.648438  25.8336   \n",
              "22     23    1099    4697    1309    4908   36.059014  144.046770  32.9904   \n",
              "23     24    1126    4949    1327    5128   36.777866  151.144384  26.9064   \n",
              "24     25    1130    1979    1319    2136   36.722909   61.707084  22.5612   \n",
              "25     26    1176     549    1318     702   37.368127   18.757046  16.4781   \n",
              "26     27    1176    1642    1358    1818   37.984530   51.900157  23.7411   \n",
              "27     28    1210    1312    1386    1523   38.901697   42.474331  28.4454   \n",
              "28     29    1250    5726    1398    5859   39.691402  173.777640  14.8824   \n",
              "\n",
              "    equivalent_diameter  major_axis_length  minor_axis_length  orientation  \\\n",
              "0              5.809926           6.408291           5.293776    -1.531574   \n",
              "1              5.437441           5.688197           5.217015     1.524893   \n",
              "2              5.114023           5.160864           5.084435     1.232456   \n",
              "3              5.120181           5.244345           5.017989    -1.373028   \n",
              "4              6.274569           6.983425           5.664964    -1.470105   \n",
              "5              5.153198           5.761219           4.633875    -1.469292   \n",
              "6              4.834351           5.516655           4.257249    -1.566841   \n",
              "7              5.417066           5.982115           4.933751    -1.492895   \n",
              "8              4.858468           5.006396           4.731858    -0.154559   \n",
              "9              5.236032           5.288282           5.204249     0.673634   \n",
              "10             6.145773           6.356039           5.963699     0.027018   \n",
              "11             5.983368           6.119983           5.870473    -0.122183   \n",
              "12             4.847489           4.983515           4.733016    -0.726204   \n",
              "13             5.191525           5.919740           4.578597    -1.524870   \n",
              "14             5.571920           5.854066           5.324035    -1.540617   \n",
              "15             6.097947           6.158044           6.053905    -0.286532   \n",
              "16             5.884592           6.038060           5.751526    -0.487346   \n",
              "17             6.189435           6.425920           5.974747    -1.522360   \n",
              "18             4.540124           4.683783           4.416628    -0.189601   \n",
              "19             4.857525           4.998235           4.734433     0.266099   \n",
              "20             5.350445           5.498830           5.236273    -0.763711   \n",
              "21             5.735186           6.124204           5.389607    -1.542840   \n",
              "22             6.481102           6.593189           6.387528    -0.916836   \n",
              "23             5.853058           6.254687           5.490873     0.052111   \n",
              "24             5.359647           5.932554           4.864765    -0.113745   \n",
              "25             4.580455           4.783871           4.400699     1.379798   \n",
              "26             5.498009           5.657482           5.356424    -0.449678   \n",
              "27             6.018123           6.635741           5.480809    -1.484604   \n",
              "28             4.353029           4.651191           4.088903    -0.281760   \n",
              "\n",
              "    solidity  \n",
              "0   0.991785  \n",
              "1   0.992079  \n",
              "2   0.991442  \n",
              "3   0.992280  \n",
              "4   0.992719  \n",
              "5   0.991189  \n",
              "6   0.989376  \n",
              "7   0.991597  \n",
              "8   0.991433  \n",
              "9   0.991710  \n",
              "10  0.992174  \n",
              "11  0.992976  \n",
              "12  0.991730  \n",
              "13  0.989483  \n",
              "14  0.992345  \n",
              "15  0.993144  \n",
              "16  0.991567  \n",
              "17  0.993285  \n",
              "18  0.989983  \n",
              "19  0.991668  \n",
              "20  0.992018  \n",
              "21  0.993321  \n",
              "22  0.993011  \n",
              "23  0.992398  \n",
              "24  0.991692  \n",
              "25  0.990961  \n",
              "26  0.991990  \n",
              "27  0.992620  \n",
              "28  0.990476  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# show table of size and shape metrics in millimetres\n",
        "features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "UMOw40kT3ZS3"
      },
      "outputs": [],
      "source": [
        "# output csv with measurements\n",
        "features.to_csv('01_Shengliyoucai_main_A.csv', header=True, index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xa2lGO0p-gKA"
      },
      "source": [
        "## 7. Save segmented images of each individual seed with `Pixelflow`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "vg9DhBwgB4XB"
      },
      "outputs": [],
      "source": [
        "# make list of all seeds in mask, shift backwards by one to match index values\n",
        "X = list(features.features.label)\n",
        "for i in range(len(X)):\n",
        "    X[i] -= 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "BoC7MGJ_CKLS"
      },
      "outputs": [],
      "source": [
        "# save segmented images of all seeds within a seed pod\n",
        "for file in X:\n",
        "    np.save(pod_name + '_seed_' + str(features.features.label[file]) + '.npy', features.image_intensity[file])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "ZueuWsKyzooB",
        "outputId": "8acac810-a330-49a4-a977-adfa5def43fc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# load single seed image\n",
        "seed_image = np.load('01_Shengliyoucai_main_A_seed_1.npy')\n",
        "\n",
        "fig = plt.imshow(seed_image)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k4emQXRsJIQX"
      },
      "source": [
        "# Extract seed size and shape metrics and single seed images from multiple images (one seed pod per image) with `Pixelflow`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IMpQ-5MFGUhH",
        "outputId": "307a1b80-431f-4c26-b054-079058f87dd5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['content/DSC_0008.tif',\n",
              " 'content/DSC_0017.tif',\n",
              " 'content/DSC_0035.tif',\n",
              " 'content/DSC_0039.tif']"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# load all input images\n",
        "img_list = sorted(glob('content/*.tif'))\n",
        "img_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qzg_sCzDmk-c",
        "outputId": "7d6997f6-7947-459e-c71e-2551e46353bd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "YX image detected\n",
            "YX image detected\n",
            "YX image detected\n",
            "YX image detected\n"
          ]
        }
      ],
      "source": [
        "for file in img_list:\n",
        "  # load image and mask\n",
        "  img = imread(file)\n",
        "  pod_name = re.split(\"[/.]\", file)[-2]\n",
        "  pred = model.predict(img)\n",
        "  np.save(Path('content/'+ pod_name + '_pred.npy'), pred)\n",
        "  \n",
        "  # create table of size and shape metrics\n",
        "  features = pixelflow(\n",
        "      mask=pred,\n",
        "      image=img,\n",
        "      spacing=(0.03,0.03),\n",
        "      features=('label', 'bbox', 'centroid', 'area', 'equivalent_diameter', 'major_axis_length', 'minor_axis_length', 'orientation', 'solidity', 'image_intensity'),\n",
        "  )\n",
        "  # output csv with measurements\n",
        "  features.to_csv(pod_name + '.csv', header=True, index=False)\n",
        "\n",
        "  # make list of all seeds in mask, shift backwards by one to match index values\n",
        "  X = list(features.features.label)\n",
        "  for i in range(len(X)):\n",
        "      X[i] -= 1\n",
        "\n",
        "  # save segmented images of all seeds within a seed pod\n",
        "  for file in X:\n",
        "      np.save(pod_name + '_seed_' + str(features.features.label[file]) + '.npy', features.image_intensity[file])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDLKLxbBQGLq"
      },
      "source": [
        "##  8. Sort detected seeds into 2 seeds pod valves, and calculated related measurements (order in valve from seed pod beak to pedicel, space between seeds in each valve) with `'seedpod_2D_valve_lowess_single.R'`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "22Hf8rk6gKd8"
      },
      "outputs": [],
      "source": [
        "# import pandas as pd\n",
        "import rpy2.robjects as robjects\n",
        "from rpy2.robjects import pandas2ri\n",
        "from rpy2.robjects import conversion, default_converter\n",
        "import rpy2.robjects.packages as rpackages\n",
        "from rpy2.robjects.vectors import StrVector"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2m0qMXDUyVf0",
        "outputId": "ff77f730-fe54-486d-e65e-afbb3366a01f"
      },
      "outputs": [],
      "source": [
        "# R packages to install\n",
        "with conversion.localconverter(default_converter):\n",
        "  utils = rpackages.importr('utils')\n",
        "  utils.chooseCRANmirror(ind=1)\n",
        "  packnames = ('gplots', 'tidyverse')\n",
        "  # Selectively install what needs to be install.\n",
        "  names_to_install = [x for x in packnames if not rpackages.isinstalled(x)]\n",
        "  if len(names_to_install) > 0:\n",
        "      utils.install_packages(StrVector(names_to_install))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "GsTPZuFb3zqy"
      },
      "outputs": [],
      "source": [
        "# Defining the R script and loading the instance in Python\n",
        "r = robjects.r"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bxEwHtRNyzfk",
        "outputId": "3c0515f1-63d4-4c2f-d91a-f276c80c4c46"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "YX image detected\n",
            "── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──\n",
            "✔ dplyr     1.1.4     ✔ readr     2.1.5\n",
            "✔ forcats   1.0.0     ✔ stringr   1.5.1\n",
            "✔ ggplot2   3.4.4     ✔ tibble    3.2.1\n",
            "✔ lubridate 1.9.3     ✔ tidyr     1.3.1\n",
            "✔ purrr     1.0.2     \n",
            "── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──\n",
            "✖ dplyr::filter() masks stats::filter()\n",
            "✖ dplyr::lag()    masks stats::lag()\n",
            "ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n"
          ]
        },
        {
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              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>             lowess                                                                     <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                </span>\n",
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        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "YX image detected\n",
            "YX image detected\n",
            "YX image detected\n"
          ]
        }
      ],
      "source": [
        "for file in img_list:\n",
        "  # Calculate the image data in pixelflow\n",
        "  img2 = imread(file)\n",
        "  pod_name = re.split(\"[/.]\", file)[-2]\n",
        "  mask2 = np.load(Path('content/'+ pod_name + '_pred.npy'))\n",
        "  features2 = pixelflow(mask2, img2, features=('label', 'bbox', 'centroid', 'area', 'equivalent_diameter', 'major_axis_length', 'minor_axis_length', 'orientation', 'solidity', 'image_intensity'))\n",
        "\n",
        "  # Loading the function we have defined in R.\n",
        "  with conversion.localconverter(default_converter):\n",
        "      r['source']('seedpod_2D_valve_lowess_single.R')\n",
        "      seedpod_2D_single_r = robjects.globalenv['seedpod_2D_single']\n",
        "\n",
        "  #converting dataframe into r object for passing into r function\n",
        "  with (robjects.default_converter + pandas2ri.converter).context():\n",
        "    r_from_pd_df = robjects.conversion.get_conversion().py2rpy(features2.features)\n",
        "\n",
        "  #Invoking the R function and getting the result\n",
        "  with conversion.localconverter(default_converter):\n",
        "      df_result_r = seedpod_2D_single_r(r_from_pd_df, \"plot_\" + pod_name + \".png\")\n",
        "\n",
        "  #Converting it back to a pandas dataframe\n",
        "  with (robjects.default_converter + pandas2ri.converter).context():\n",
        "    df_result = robjects.conversion.get_conversion().rpy2py(df_result_r)\n"
      ]
    }
  ],
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