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Merge pull request #56 from BrainLesion/config
updated to config branch, addded config example notebook
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‎panoptica/example_config.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Use Case: Matched Instances Input\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Install Dependencies"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: panoptica in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (0.6.6.post13.dev0+a2f5dd3)\n",
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"Requirement already satisfied: auxiliary in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 2)) (0.0.42)\n",
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"Requirement already satisfied: rich in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (13.6.0)\n",
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"Requirement already satisfied: numpy in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.25.2)\n",
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"Requirement already satisfied: connected-components-3d<4.0.0,>=3.12.3 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from panoptica->-r requirements.txt (line 1)) (3.12.3)\n",
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"Requirement already satisfied: ruamel.yaml<0.19.0,>=0.18.6 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from panoptica->-r requirements.txt (line 1)) (0.18.6)\n",
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"Requirement already satisfied: scikit-image<0.23.0,>=0.22.0 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from panoptica->-r requirements.txt (line 1)) (0.22.0)\n",
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"Requirement already satisfied: scipy<2.0.0,>=1.7.0 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from panoptica->-r requirements.txt (line 1)) (1.11.2)\n",
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"Requirement already satisfied: nibabel>=3.0 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from auxiliary->-r requirements.txt (line 2)) (5.1.0)\n",
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"Requirement already satisfied: path>=16.10.0 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from auxiliary->-r requirements.txt (line 2)) (17.0.0)\n",
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"Requirement already satisfied: pathlib>=1.0 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from auxiliary->-r requirements.txt (line 2)) (1.0.1)\n",
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"Requirement already satisfied: pillow>=10.0.0 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from auxiliary->-r requirements.txt (line 2)) (10.0.0)\n",
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"Requirement already satisfied: tifffile>=2023.8.25 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from auxiliary->-r requirements.txt (line 2)) (2023.8.30)\n",
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"Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from rich->-r requirements.txt (line 3)) (3.0.0)\n",
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"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from rich->-r requirements.txt (line 3)) (2.17.2)\n",
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"Requirement already satisfied: mdurl~=0.1 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from markdown-it-py>=2.2.0->rich->-r requirements.txt (line 3)) (0.1.2)\n",
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"Requirement already satisfied: packaging>=17 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from nibabel>=3.0->auxiliary->-r requirements.txt (line 2)) (23.1)\n",
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"Requirement already satisfied: ruamel.yaml.clib>=0.2.7 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from ruamel.yaml<0.19.0,>=0.18.6->panoptica->-r requirements.txt (line 1)) (0.2.8)\n",
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"Requirement already satisfied: networkx>=2.8 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from scikit-image<0.23.0,>=0.22.0->panoptica->-r requirements.txt (line 1)) (3.1)\n",
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"Requirement already satisfied: imageio>=2.27 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from scikit-image<0.23.0,>=0.22.0->panoptica->-r requirements.txt (line 1)) (2.31.3)\n",
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"Requirement already satisfied: lazy_loader>=0.3 in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from scikit-image<0.23.0,>=0.22.0->panoptica->-r requirements.txt (line 1)) (0.3)\n"
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]
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}
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],
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"source": [
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"!pip install -r requirements.txt"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from auxiliary.nifti.io import read_nifti\n",
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"from rich import print as pprint\n",
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"from panoptica import InputType, Panoptica_Evaluator\n",
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"from panoptica.metrics import Metric"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"To demonstrate we use a reference and predicition of spine a segmentation with matched instances.\n",
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"\n",
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"\n",
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"![matched_figure](figures/matched_instance.png)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(array([ 0, 2, 3, 4, 5, 6, 7, 8, 26, 102, 103, 104, 105,\n",
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" 106, 107, 108, 202, 203, 204, 205, 206, 207, 208], dtype=uint8),\n",
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" array([ 0, 2, 3, 4, 5, 6, 7, 8, 26, 102, 103, 104, 105,\n",
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" 106, 107, 108, 202, 203, 204, 205, 206, 207, 208], dtype=uint8))"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ref_masks = read_nifti(\"./spine_seg/matched_instance/ref.nii.gz\")\n",
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"pred_masks = read_nifti(\"./spine_seg/matched_instance/pred.nii.gz\")\n",
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"\n",
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"# labels are matching\n",
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"np.unique(ref_masks), np.unique(pred_masks)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Build Evaluator object"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">────────────────────────────────────────── </span>Thank you for using <span style=\"font-weight: bold\">panoptica</span><span style=\"color: #00ff00; text-decoration-color: #00ff00\"> ──────────────────────────────────────────</span>\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[92m────────────────────────────────────────── \u001b[0mThank you for using \u001b[1mpanoptica\u001b[0m\u001b[92m ──────────────────────────────────────────\u001b[0m\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"> Please support our development by citing \n",
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"</pre>\n"
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],
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"text/plain": [
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" Please support our development by citing \n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"> <span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://github.com/BrainLesion/panoptica#citation</span> -- Thank you! \n",
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"</pre>\n"
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],
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"text/plain": [
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" \u001b[4;94mhttps://github.com/BrainLesion/panoptica#citation\u001b[0m -- Thank you! \n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ff00; text-decoration-color: #00ff00\">───────────────────────────────────────────────────────────────────────────────────────────────────────────────────</span>\n",
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"</pre>\n"
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"text/plain": [
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"\u001b[92m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n"
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"metadata": {},
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"output_type": "display_data"
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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"</pre>\n"
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],
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"text/plain": [
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"\n"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Panoptic: Start Evaluation\n",
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"-- Got MatchedInstancePair, will evaluate instances\n"
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]
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}
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],
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"source": [
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"evaluator = Panoptica_Evaluator(\n",
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" expected_input=InputType.MATCHED_INSTANCE,\n",
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" decision_metric=Metric.IOU,\n",
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" decision_threshold=0.5,\n",
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")\n",
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"\n",
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"result, debug_data = evaluator.evaluate(pred_masks, ref_masks)[\"ungrouped\"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Inspect Results\n",
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"The results object allows access to individual metrics and provides helper methods for further processing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"+++ MATCHING +++\n",
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"Number of instances in reference (num_ref_instances): 22\n",
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"Number of instances in prediction (num_pred_instances): 22\n",
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"True Positives (tp): 22\n",
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"False Positives (fp): 0\n",
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"False Negatives (fn): 0\n",
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"Recognition Quality / F1-Score (rq): 1.0\n",
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"\n",
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"+++ GLOBAL +++\n",
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"Global Binary Dice (global_bin_dsc): 0.9744370224078394\n",
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"Global Binary Centerline Dice (global_bin_cldsc): 0.9637064011802574\n",
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"Global Binary Average Symmetric Surface Distance (global_bin_assd): 0.1499152780072207\n",
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"Global Binary Relative Volume Difference (global_bin_rvd): -0.0006011763989268869\n",
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"\n",
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"+++ INSTANCE +++\n",
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"Segmentation Quality IoU (sq): 0.8328184295330796 +- 0.15186064004517466\n",
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"Panoptic Quality IoU (pq): 0.8328184295330796\n",
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"Segmentation Quality Dsc (sq_dsc): 0.900292616009954 +- 0.10253566174957332\n",
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"Panoptic Quality Dsc (pq_dsc): 0.900292616009954\n",
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"Segmentation Quality Assd (sq_assd): 0.250331887879225 +- 0.07696680402317076\n",
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"Segmentation Quality Relative Volume Difference (sq_rvd): 0.0028133049062930553 +- 0.034518928495505724\n",
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"\n"
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]
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}
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],
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"source": [
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"# print all results\n",
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"print(result)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Save Evaluator as config\n",
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"\n",
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"You can save your settings and arguments for nearly all panoptica-related objects as yaml configs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Saved config into /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages/panoptica/test_config.yaml\n"
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]
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}
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],
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"source": [
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"evaluator.save_to_config_by_name(\"test_config\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Configs\n",
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"\n",
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"You can simply load the same object by using its class.load_config:\n",
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"\n",
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"Notably: \n",
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"\n",
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"- load_from_config() uses an absolute path, while\n",
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"- load_from_config_by_name() uses some default path\n",
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"\n",
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"The same is true for saving configs:\n",
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"\n",
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"- save_to_config() saves to a given absolute path, while\n",
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"- save_to_config_by_name() uses a default path"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<panoptica.panoptica_evaluator.Panoptica_Evaluator object at 0x7fbe999a9c90>\n"
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]
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}
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],
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"source": [
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"eval = Panoptica_Evaluator.load_from_config_name(\"test_config\")\n",
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"print(eval)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Panoptic: Start Evaluation\n",
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"-- Got MatchedInstancePair, will evaluate instances\n",
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"\n",
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"+++ MATCHING +++\n",
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"Number of instances in reference (num_ref_instances): 22\n",
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"Number of instances in prediction (num_pred_instances): 22\n",
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"True Positives (tp): 22\n",
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"False Positives (fp): 0\n",
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"False Negatives (fn): 0\n",
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"Recognition Quality / F1-Score (rq): 1.0\n",
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"\n",
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"+++ GLOBAL +++\n",
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"Global Binary Dice (global_bin_dsc): 0.9744370224078394\n",
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"Global Binary Centerline Dice (global_bin_cldsc): 0.9637064011802574\n",
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"Global Binary Average Symmetric Surface Distance (global_bin_assd): 0.1499152780072207\n",
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"Global Binary Relative Volume Difference (global_bin_rvd): -0.0006011763989268869\n",
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"\n",
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"+++ INSTANCE +++\n",
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"Segmentation Quality IoU (sq): 0.8328184295330796 +- 0.15186064004517466\n",
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"Panoptic Quality IoU (pq): 0.8328184295330796\n",
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"Segmentation Quality Dsc (sq_dsc): 0.900292616009954 +- 0.10253566174957332\n",
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"Panoptic Quality Dsc (pq_dsc): 0.900292616009954\n",
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"Segmentation Quality Assd (sq_assd): 0.250331887879225 +- 0.07696680402317076\n",
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"Segmentation Quality Relative Volume Difference (sq_rvd): 0.0028133049062930553 +- 0.034518928495505724\n",
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"\n"
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]
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}
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],
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"source": [
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"# Now use it as normal\n",
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"result, debug_data = evaluator.evaluate(pred_masks, ref_masks)[\"ungrouped\"]\n",
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"print(result) # yields same results as the evaluator object manually constructed"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "helm",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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