\n"
]
}
],
@@ -324,7 +256,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -335,26 +267,23 @@
"-- Got MatchedInstancePair, will evaluate instances\n",
"\n",
"+++ MATCHING +++\n",
- "Number of instances in reference (num_ref_instances): 22\n",
- "Number of instances in prediction (num_pred_instances): 22\n",
- "True Positives (tp): 22\n",
+ "Number of instances in reference (num_ref_instances): 1\n",
+ "Number of instances in prediction (num_pred_instances): 1\n",
+ "True Positives (tp): 1\n",
"False Positives (fp): 0\n",
"False Negatives (fn): 0\n",
"Recognition Quality / F1-Score (rq): 1.0\n",
"\n",
"+++ GLOBAL +++\n",
"Global Binary Dice (global_bin_dsc): 0.9744370224078394\n",
- "Global Binary Centerline Dice (global_bin_cldsc): 0.9637064011802574\n",
- "Global Binary Average Symmetric Surface Distance (global_bin_assd): 0.1499152780072207\n",
- "Global Binary Relative Volume Difference (global_bin_rvd): -0.0006011763989268869\n",
"\n",
"+++ INSTANCE +++\n",
- "Segmentation Quality IoU (sq): 0.8328184295330796 +- 0.15186064004517466\n",
- "Panoptic Quality IoU (pq): 0.8328184295330796\n",
- "Segmentation Quality Dsc (sq_dsc): 0.900292616009954 +- 0.10253566174957332\n",
- "Panoptic Quality Dsc (pq_dsc): 0.900292616009954\n",
- "Segmentation Quality Assd (sq_assd): 0.250331887879225 +- 0.07696680402317076\n",
- "Segmentation Quality Relative Volume Difference (sq_rvd): 0.0028133049062930553 +- 0.034518928495505724\n",
+ "Segmentation Quality IoU (sq): 0.9501484001456879 +- 0.0\n",
+ "Panoptic Quality IoU (pq): 0.9501484001456879\n",
+ "Segmentation Quality Dsc (sq_dsc): 0.9744370224078394 +- 0.0\n",
+ "Panoptic Quality Dsc (pq_dsc): 0.9744370224078394\n",
+ "Segmentation Quality ASSD (sq_assd): 0.1499152780072207 +- 0.0\n",
+ "Segmentation Quality Relative Volume Difference (sq_rvd): -0.0006011763989268869 +- 0.0\n",
"\n"
]
}
diff --git a/panoptica/example_spine_matched_instance.ipynb b/panoptica/example_spine_matched_instance.ipynb
index 59d481c..e20a019 100644
--- a/panoptica/example_spine_matched_instance.ipynb
+++ b/panoptica/example_spine_matched_instance.ipynb
@@ -1,313 +1,405 @@
{
- "cells": [
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Use Case: Matched Instances Input\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Install Dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Use Case: Matched Instances Input\n"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: panoptica in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (1.0.0.post2.dev0+2f7d01f)\n",
+ "Requirement already satisfied: auxiliary in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 2)) (0.0.42)\n",
+ "Requirement already satisfied: rich in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (13.6.0)\n",
+ "Requirement already satisfied: numpy in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.25.2)\n",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -r requirements.txt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from auxiliary.nifti.io import read_nifti\n",
+ "from rich import print as pprint\n",
+ "from panoptica import InputType, Panoptica_Evaluator\n",
+ "from panoptica.metrics import Metric"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To demonstrate we use a reference and predicition of spine a segmentation with matched instances.\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Install Dependencies"
+ "data": {
+ "text/plain": [
+ "(array([ 0, 2, 3, 4, 5, 6, 7, 8, 26, 102, 103, 104, 105,\n",
+ " 106, 107, 108, 202, 203, 204, 205, 206, 207, 208], dtype=uint8),\n",
+ " array([ 0, 2, 3, 4, 5, 6, 7, 8, 26, 102, 103, 104, 105,\n",
+ " 106, 107, 108, 202, 203, 204, 205, 206, 207, 208], dtype=uint8))"
]
- },
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ref_masks = read_nifti(\"./spine_seg/matched_instance/ref.nii.gz\")\n",
+ "pred_masks = read_nifti(\"./spine_seg/matched_instance/pred.nii.gz\")\n",
+ "\n",
+ "# labels are matching\n",
+ "np.unique(ref_masks), np.unique(pred_masks)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Run Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: panoptica in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (0.6.6)\n",
- "Requirement already satisfied: auxiliary in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 2)) (0.0.42)\n",
- "Requirement already satisfied: rich in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (13.6.0)\n",
- "Requirement already satisfied: numpy in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.25.2)\n",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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",
- "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"
- ]
- }
+ "data": {
+ "text/html": [
+ "────────────────────────────────────────── Thank you for using panoptica ──────────────────────────────────────────\n",
+ "
\n"
],
- "source": [
- "!pip install -r requirements.txt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Setup Imports"
+ "text/plain": [
+ "\u001b[92m────────────────────────────────────────── \u001b[0mThank you for using \u001b[1mpanoptica\u001b[0m\u001b[92m ──────────────────────────────────────────\u001b[0m\n"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from auxiliary.nifti.io import read_nifti\n",
- "from rich import print as pprint\n",
- "from panoptica import InputType, Panoptica_Evaluator\n",
- "from panoptica.metrics import Metric"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Load Data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To demonstrate we use a reference and predicition of spine a segmentation with matched instances.\n",
- "\n",
- "\n",
- ""
+ "data": {
+ "text/html": [
+ " Please support our development by citing \n",
+ "
\n"
+ ],
+ "text/plain": [
+ " Please support our development by citing \n"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(array([ 0, 2, 3, 4, 5, 6, 7, 8, 26, 102, 103, 104, 105,\n",
- " 106, 107, 108, 202, 203, 204, 205, 206, 207, 208], dtype=uint8),\n",
- " array([ 0, 2, 3, 4, 5, 6, 7, 8, 26, 102, 103, 104, 105,\n",
- " 106, 107, 108, 202, 203, 204, 205, 206, 207, 208], dtype=uint8))"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
+ "data": {
+ "text/html": [
+ " https://github.com/BrainLesion/panoptica#citation -- Thank you! \n",
+ "
\n"
],
- "source": [
- "ref_masks = read_nifti(\"./spine_seg/matched_instance/ref.nii.gz\")\n",
- "pred_masks = read_nifti(\"./spine_seg/matched_instance/pred.nii.gz\")\n",
- "\n",
- "# labels are matching\n",
- "np.unique(ref_masks), np.unique(pred_masks)"
+ "text/plain": [
+ " \u001b[4;94mhttps://github.com/BrainLesion/panoptica#citation\u001b[0m -- Thank you! \n"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Run Evaluation"
+ "data": {
+ "text/html": [
+ "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\u001b[92m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Panoptic: Start Evaluation\n",
- "-- Got MatchedInstancePair, will evaluate instances\n"
- ]
- }
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
],
- "source": [
- "evaluator = Panoptica_Evaluator(\n",
- " expected_input=InputType.MATCHED_INSTANCE,\n",
- " decision_metric=Metric.IOU,\n",
- " decision_threshold=0.5,\n",
- ")\n",
- "\n",
- "result, debug_data = evaluator.evaluate(pred_masks, ref_masks)[\"ungrouped\"]"
+ "text/plain": [
+ "\n"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Inspect Results\n",
- "The results object allows access to individual metrics and provides helper methods for further processing"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Panoptic: Start Evaluation\n",
+ "-- Got MatchedInstancePair, will evaluate instances\n"
+ ]
+ }
+ ],
+ "source": [
+ "evaluator = Panoptica_Evaluator(\n",
+ " expected_input=InputType.MATCHED_INSTANCE,\n",
+ " decision_metric=Metric.IOU,\n",
+ " decision_threshold=0.5,\n",
+ ")\n",
+ "\n",
+ "result, intermediate_steps_data = evaluator.evaluate(pred_masks, ref_masks)[\"ungrouped\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Inspect Results\n",
+ "The results object allows access to individual metrics and provides helper methods for further processing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "+++ MATCHING +++\n",
- "Number of instances in reference (num_ref_instances): 22\n",
- "Number of instances in prediction (num_pred_instances): 22\n",
- "True Positives (tp): 22\n",
- "False Positives (fp): 0\n",
- "False Negatives (fn): 0\n",
- "Recognition Quality / F1-Score (rq): 1.0\n",
- "\n",
- "+++ GLOBAL +++\n",
- "Global Binary Dice (global_bin_dsc): 0.9744370224078394\n",
- "Global Binary Centerline Dice (global_bin_cldsc): 0.9637064011802574\n",
- "Global Binary Average Symmetric Surface Distance (global_bin_assd): 0.1499152780072207\n",
- "Global Binary Relative Volume Difference (global_bin_rvd): -0.0006011763989268869\n",
- "\n",
- "+++ INSTANCE +++\n",
- "Segmentation Quality IoU (sq): 0.8328184295330796 +- 0.15186064004517466\n",
- "Panoptic Quality IoU (pq): 0.8328184295330796\n",
- "Segmentation Quality Dsc (sq_dsc): 0.900292616009954 +- 0.10253566174957332\n",
- "Panoptic Quality Dsc (pq_dsc): 0.900292616009954\n",
- "Segmentation Quality Assd (sq_assd): 0.250331887879225 +- 0.07696680402317076\n",
- "Segmentation Quality Relative Volume Difference (sq_rvd): 0.0028133049062930553 +- 0.034518928495505724\n",
- "\n"
- ]
- }
- ],
- "source": [
- "# print all results\n",
- "print(result)"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "+++ MATCHING +++\n",
+ "Number of instances in reference (num_ref_instances): 22\n",
+ "Number of instances in prediction (num_pred_instances): 22\n",
+ "True Positives (tp): 22\n",
+ "False Positives (fp): 0\n",
+ "False Negatives (fn): 0\n",
+ "Recognition Quality / F1-Score (rq): 1.0\n",
+ "\n",
+ "+++ GLOBAL +++\n",
+ "Global Binary Dice (global_bin_dsc): 0.9744370224078394\n",
+ "\n",
+ "+++ INSTANCE +++\n",
+ "Segmentation Quality IoU (sq): 0.8328184295330796 +- 0.15186064004517466\n",
+ "Panoptic Quality IoU (pq): 0.8328184295330796\n",
+ "Segmentation Quality Dsc (sq_dsc): 0.900292616009954 +- 0.10253566174957332\n",
+ "Panoptic Quality Dsc (pq_dsc): 0.900292616009954\n",
+ "Segmentation Quality ASSD (sq_assd): 0.250331887879225 +- 0.07696680402317076\n",
+ "Segmentation Quality Relative Volume Difference (sq_rvd): 0.0028133049062930553 +- 0.034518928495505724\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# print all results\n",
+ "print(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "result.pq=0.8328184295330796\n",
- "
\n"
- ],
- "text/plain": [
- "result.\u001b[33mpq\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8328184295330796\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
+ "data": {
+ "text/html": [
+ "result.pq=0.8328184295330796\n",
+ "
\n"
],
- "source": [
- "# get specific metric, e.g. pq\n",
- "pprint(f\"{result.pq=}\")"
+ "text/plain": [
+ "result.\u001b[33mpq\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.8328184295330796\u001b[0m\n"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# get specific metric, e.g. pq\n",
+ "pprint(f\"{result.pq=}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "results dict: \n",
- "{\n",
- " 'num_ref_instances': 22,\n",
- " 'num_pred_instances': 22,\n",
- " 'tp': 22,\n",
- " 'fp': 0,\n",
- " 'fn': 0,\n",
- " 'prec': 1.0,\n",
- " 'rec': 1.0,\n",
- " 'rq': 1.0,\n",
- " 'global_bin_dsc': 0.9744370224078394,\n",
- " 'global_bin_cldsc': 0.9637064011802574,\n",
- " 'global_bin_assd': 0.1499152780072207,\n",
- " 'global_bin_rvd': -0.0006011763989268869,\n",
- " 'sq': 0.8328184295330796,\n",
- " 'sq_std': 0.15186064004517466,\n",
- " 'pq': 0.8328184295330796,\n",
- " 'sq_dsc': 0.900292616009954,\n",
- " 'sq_dsc_std': 0.10253566174957332,\n",
- " 'pq_dsc': 0.900292616009954,\n",
- " 'sq_assd': 0.250331887879225,\n",
- " 'sq_assd_std': 0.07696680402317076,\n",
- " 'sq_rvd': 0.0028133049062930553,\n",
- " 'sq_rvd_std': 0.034518928495505724\n",
- "}\n",
- "\n"
- ],
- "text/plain": [
- "results dict: \n",
- "\u001b[1m{\u001b[0m\n",
- " \u001b[32m'num_ref_instances'\u001b[0m: \u001b[1;36m22\u001b[0m,\n",
- " \u001b[32m'num_pred_instances'\u001b[0m: \u001b[1;36m22\u001b[0m,\n",
- " \u001b[32m'tp'\u001b[0m: \u001b[1;36m22\u001b[0m,\n",
- " \u001b[32m'fp'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
- " \u001b[32m'fn'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
- " \u001b[32m'prec'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
- " \u001b[32m'rec'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
- " \u001b[32m'rq'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
- " \u001b[32m'global_bin_dsc'\u001b[0m: \u001b[1;36m0.9744370224078394\u001b[0m,\n",
- " \u001b[32m'global_bin_cldsc'\u001b[0m: \u001b[1;36m0.9637064011802574\u001b[0m,\n",
- " \u001b[32m'global_bin_assd'\u001b[0m: \u001b[1;36m0.1499152780072207\u001b[0m,\n",
- " \u001b[32m'global_bin_rvd'\u001b[0m: \u001b[1;36m-0.0006011763989268869\u001b[0m,\n",
- " \u001b[32m'sq'\u001b[0m: \u001b[1;36m0.8328184295330796\u001b[0m,\n",
- " \u001b[32m'sq_std'\u001b[0m: \u001b[1;36m0.15186064004517466\u001b[0m,\n",
- " \u001b[32m'pq'\u001b[0m: \u001b[1;36m0.8328184295330796\u001b[0m,\n",
- " \u001b[32m'sq_dsc'\u001b[0m: \u001b[1;36m0.900292616009954\u001b[0m,\n",
- " \u001b[32m'sq_dsc_std'\u001b[0m: \u001b[1;36m0.10253566174957332\u001b[0m,\n",
- " \u001b[32m'pq_dsc'\u001b[0m: \u001b[1;36m0.900292616009954\u001b[0m,\n",
- " \u001b[32m'sq_assd'\u001b[0m: \u001b[1;36m0.250331887879225\u001b[0m,\n",
- " \u001b[32m'sq_assd_std'\u001b[0m: \u001b[1;36m0.07696680402317076\u001b[0m,\n",
- " \u001b[32m'sq_rvd'\u001b[0m: \u001b[1;36m0.0028133049062930553\u001b[0m,\n",
- " \u001b[32m'sq_rvd_std'\u001b[0m: \u001b[1;36m0.034518928495505724\u001b[0m\n",
- "\u001b[1m}\u001b[0m\n"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
+ "data": {
+ "text/html": [
+ "results dict: \n",
+ "{\n",
+ " 'num_ref_instances': 22,\n",
+ " 'num_pred_instances': 22,\n",
+ " 'tp': 22,\n",
+ " 'fp': 0,\n",
+ " 'fn': 0,\n",
+ " 'prec': 1.0,\n",
+ " 'rec': 1.0,\n",
+ " 'rq': 1.0,\n",
+ " 'sq': 0.8328184295330796,\n",
+ " 'sq_std': 0.15186064004517466,\n",
+ " 'pq': 0.8328184295330796,\n",
+ " 'sq_dsc': 0.900292616009954,\n",
+ " 'sq_dsc_std': 0.10253566174957332,\n",
+ " 'pq_dsc': 0.900292616009954,\n",
+ " 'sq_assd': 0.250331887879225,\n",
+ " 'sq_assd_std': 0.07696680402317076,\n",
+ " 'sq_rvd': 0.0028133049062930553,\n",
+ " 'sq_rvd_std': 0.034518928495505724,\n",
+ " 'global_bin_dsc': 0.9744370224078394\n",
+ "}\n",
+ "\n"
],
- "source": [
- "# get dict for further processing, e.g. for pandas\n",
- "pprint(\"results dict: \", result.to_dict())"
+ "text/plain": [
+ "results dict: \n",
+ "\u001b[1m{\u001b[0m\n",
+ " \u001b[32m'num_ref_instances'\u001b[0m: \u001b[1;36m22\u001b[0m,\n",
+ " \u001b[32m'num_pred_instances'\u001b[0m: \u001b[1;36m22\u001b[0m,\n",
+ " \u001b[32m'tp'\u001b[0m: \u001b[1;36m22\u001b[0m,\n",
+ " \u001b[32m'fp'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
+ " \u001b[32m'fn'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
+ " \u001b[32m'prec'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
+ " \u001b[32m'rec'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
+ " \u001b[32m'rq'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
+ " \u001b[32m'sq'\u001b[0m: \u001b[1;36m0.8328184295330796\u001b[0m,\n",
+ " \u001b[32m'sq_std'\u001b[0m: \u001b[1;36m0.15186064004517466\u001b[0m,\n",
+ " \u001b[32m'pq'\u001b[0m: \u001b[1;36m0.8328184295330796\u001b[0m,\n",
+ " \u001b[32m'sq_dsc'\u001b[0m: \u001b[1;36m0.900292616009954\u001b[0m,\n",
+ " \u001b[32m'sq_dsc_std'\u001b[0m: \u001b[1;36m0.10253566174957332\u001b[0m,\n",
+ " \u001b[32m'pq_dsc'\u001b[0m: \u001b[1;36m0.900292616009954\u001b[0m,\n",
+ " \u001b[32m'sq_assd'\u001b[0m: \u001b[1;36m0.250331887879225\u001b[0m,\n",
+ " \u001b[32m'sq_assd_std'\u001b[0m: \u001b[1;36m0.07696680402317076\u001b[0m,\n",
+ " \u001b[32m'sq_rvd'\u001b[0m: \u001b[1;36m0.0028133049062930553\u001b[0m,\n",
+ " \u001b[32m'sq_rvd_std'\u001b[0m: \u001b[1;36m0.034518928495505724\u001b[0m,\n",
+ " \u001b[32m'global_bin_dsc'\u001b[0m: \u001b[1;36m0.9744370224078394\u001b[0m\n",
+ "\u001b[1m}\u001b[0m\n"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "helm",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.11.6"
+ ],
+ "source": [
+ "# get dict for further processing, e.g. for pandas\n",
+ "pprint(\"results dict: \", result.to_dict())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "key SEMANTIC not in intermediate steps, maybe the step was skipped?\n"
+ ]
}
+ ],
+ "source": [
+ "# To inspect different phases, just use the returned intermediate_steps_data object\n",
+ "\n",
+ "intermediate_steps_data.original_prediction_arr # yields input prediction array\n",
+ "intermediate_steps_data.original_reference_arr # yields input reference array\n",
+ "\n",
+ "intermediate_steps_data.prediction_arr(\n",
+ " InputType.MATCHED_INSTANCE\n",
+ ") # yields prediction array after instances have been matched\n",
+ "intermediate_steps_data.reference_arr(\n",
+ " InputType.MATCHED_INSTANCE\n",
+ ") # yields reference array after instances have been matched\n",
+ "\n",
+ "# The other InputType do not work here, as the input was already a matched instance map, therefore the steps from instance approximation and matching have been skipped\n",
+ "try:\n",
+ " intermediate_steps_data.reference_arr(InputType.SEMANTIC)\n",
+ "except AssertionError as e:\n",
+ " print(e)\n",
+ " # Error will indicate the problem"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "helm",
+ "language": "python",
+ "name": "python3"
},
- "nbformat": 4,
- "nbformat_minor": 2
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
}
diff --git a/panoptica/example_spine_matching_algorithm.ipynb b/panoptica/example_spine_matching_algorithm.ipynb
new file mode 100644
index 0000000..b623947
--- /dev/null
+++ b/panoptica/example_spine_matching_algorithm.ipynb
@@ -0,0 +1,261 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Use Case: Unmatched Instances Input"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Install Dependencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: panoptica in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (1.0.0.post2.dev0+2f7d01f)\n",
+ "Requirement already satisfied: auxiliary in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 2)) (0.0.42)\n",
+ "Requirement already satisfied: rich in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (13.6.0)\n",
+ "Requirement already satisfied: numpy in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.25.2)\n",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install -r requirements.txt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Setup Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from auxiliary.nifti.io import read_nifti\n",
+ "from rich import print as pprint\n",
+ "from panoptica import NaiveThresholdMatching, Panoptica_Evaluator, InputType\n",
+ "from panoptica.utils.segmentation_class import LabelGroup, SegmentationClassGroups"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Load Example Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To demonstrate we use a reference and predicition of spine a segmentation with unmatched instances.\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([ 0, 2, 3, 4, 5, 6, 7, 8, 26, 102, 103, 104, 105,\n",
+ " 106, 107, 108, 202, 203, 204, 205, 206, 207, 208], dtype=uint8),\n",
+ " array([ 0, 3, 4, 5, 6, 7, 8, 9, 26, 103, 104, 105, 106,\n",
+ " 107, 108, 109, 203, 204, 205, 206, 207, 208, 209], dtype=uint8))"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ref_masks = read_nifti(\"./spine_seg/unmatched_instance/ref.nii.gz\")\n",
+ "pred_masks = read_nifti(\"./spine_seg/unmatched_instance/pred.nii.gz\")\n",
+ "\n",
+ "# labels are unmatched instances\n",
+ "pred_masks[pred_masks == 27] = 26 # For later\n",
+ "np.unique(ref_masks), np.unique(pred_masks)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Define Class Groups"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define (optionally) semantic groups\n",
+ "# This means that only instance within one group can be matched to each other\n",
+ "segmentation_class_groups = SegmentationClassGroups(\n",
+ " {\n",
+ " \"vertebra\": LabelGroup(list(range(1, 11))),\n",
+ " \"ivd\": LabelGroup(list(range(101, 111))),\n",
+ " \"sacrum\": ([26], True),\n",
+ " \"endplate\": LabelGroup(list(range(201, 211))),\n",
+ " }\n",
+ ")\n",
+ "# In this case, the label 26 can only be matched with label 26 (thats why have to ensure above that 26 exists in both masks, otherwise they wouldn't be matched)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let's do it ourselves!\n",
+ "Panoptica allows you to call everything yourself if you really want to"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "prediction_arr= [ 0 2 3 4 5 6 7 8 26 102 103 104 105 106 107 108 202 204\n",
+ " 206 207 209 210 211]\n",
+ "reference_arr= [ 0 2 3 4 5 6 7 8 26 102 103 104 105 106 107 108 202 203\n",
+ " 204 205 206 207 208]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Input are unmatched instances, so lets match em!\n",
+ "from panoptica import Metric\n",
+ "\n",
+ "# This will match based on IoU metric, will only match if instance have a IoU of 0.5 or higher and will not allow multiple predictions to be matched to the same reference\n",
+ "matcher = NaiveThresholdMatching(\n",
+ " matching_metric=Metric.IOU, matching_threshold=0.5, allow_many_to_one=False\n",
+ ")\n",
+ "\n",
+ "# Now we have to do our processing object ourselves\n",
+ "from panoptica import UnmatchedInstancePair\n",
+ "\n",
+ "unmatched_instance_input = UnmatchedInstancePair(pred_masks, ref_masks)\n",
+ "\n",
+ "matched_instance_output = matcher.match_instances(unmatched_instance_input)\n",
+ "print(\"prediction_arr=\", np.unique(matched_instance_output.prediction_arr))\n",
+ "print(\"reference_arr=\", np.unique(matched_instance_output.reference_arr))\n",
+ "\n",
+ "# Based of this, we see that some references are not sucessfully hit (203, 205, 208)\n",
+ "# We can also see that we indeed have the same number of prediction instances that got no match, they will be appended afterwards (209, 210, 211)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Let's match 'em all!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "prediction_arr= [ 0 2 3 4 5 6 7 8 26 102 103 104 105 106 107 108 202 203\n",
+ " 204 205 206 207 208]\n",
+ "reference_arr= [ 0 2 3 4 5 6 7 8 26 102 103 104 105 106 107 108 202 203\n",
+ " 204 205 206 207 208]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# This will match based on IoU metric, will only match if instance have a IoU of 0.0 or higher and will not allow multiple predictions to be matched to the same reference\n",
+ "matcher = NaiveThresholdMatching(\n",
+ " matching_metric=Metric.IOU, matching_threshold=0.0, allow_many_to_one=False\n",
+ ")\n",
+ "\n",
+ "matched_instance_output = matcher.match_instances(unmatched_instance_input)\n",
+ "print(\"prediction_arr=\", np.unique(matched_instance_output.prediction_arr))\n",
+ "print(\"reference_arr=\", np.unique(matched_instance_output.reference_arr))\n",
+ "\n",
+ "# With a threshold of 0.0, we ensure that we match as much as possible.\n",
+ "# We see, that contrary to before, instances 203, 205, and 208 are now matched"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Do it yourself\n",
+ "\n",
+ "Now it is up to you to explore the different matching algorithms and the best setup for your project\n",
+ "\n",
+ "Just remember, this setup can have drastic differences in the resulting metrics as well as interpretation of those results. For example, if you always match everything, of course your F1-Score will be 1.0. This becomes meaningless then. Also the choice of metric does matter!"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "brainles",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/panoptica/example_spine_semantic.ipynb b/panoptica/example_spine_semantic.ipynb
index 131cb48..44bafff 100644
--- a/panoptica/example_spine_semantic.ipynb
+++ b/panoptica/example_spine_semantic.ipynb
@@ -16,14 +16,14 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Requirement already satisfied: panoptica in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (0.6.6.post9.dev0+d25fabc)\n",
+ "Requirement already satisfied: panoptica in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (1.0.0.post2.dev0+2f7d01f)\n",
"Requirement already satisfied: auxiliary in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 2)) (0.0.42)\n",
"Requirement already satisfied: rich in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (13.6.0)\n",
"Requirement already satisfied: numpy in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.25.2)\n",
@@ -60,7 +60,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -92,7 +92,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -123,7 +123,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -144,7 +144,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -227,16 +227,13 @@
"\n",
"+++ GLOBAL +++\n",
"Global Binary Dice (global_bin_dsc): 0.9731641527805414\n",
- "Global Binary Centerline Dice (global_bin_cldsc): 0.9623567366028574\n",
- "Global Binary Average Symmetric Surface Distance (global_bin_assd): 0.15559863304671395\n",
- "Global Binary Relative Volume Difference (global_bin_rvd): 0.0021710740333888085\n",
"\n",
"+++ INSTANCE +++\n",
"Segmentation Quality IoU (sq): 0.7940127477906024 +- 0.11547745015679488\n",
"Panoptic Quality IoU (pq): 0.6586696657808406\n",
"Segmentation Quality Dsc (sq_dsc): 0.8802182546605446 +- 0.07728416427007168\n",
"Panoptic Quality Dsc (pq_dsc): 0.7301810521615881\n",
- "Segmentation Quality Assd (sq_assd): 0.2057371092494465 +- 0.1398348236766068\n",
+ "Segmentation Quality ASSD (sq_assd): 0.2057371092494465 +- 0.1398348236766068\n",
"Segmentation Quality Relative Volume Difference (sq_rvd): 0.011340219860617232 +- 0.1217805112447998\n",
"\n"
]
@@ -244,13 +241,15 @@
],
"source": [
"# print all results\n",
- "result, _ = evaluator.evaluate(pred_masks, ref_masks, verbose=False)[\"ungrouped\"]\n",
+ "result, intermediate_steps_data = evaluator.evaluate(\n",
+ " pred_masks, ref_masks, verbose=False\n",
+ ")[\"ungrouped\"]\n",
"print(result)"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -274,7 +273,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -290,10 +289,6 @@
" 'prec': 0.8202247191011236,\n",
" 'rec': 0.8390804597701149,\n",
" 'rq': 0.8295454545454546,\n",
- " 'global_bin_dsc': 0.9731641527805414,\n",
- " 'global_bin_cldsc': 0.9623567366028574,\n",
- " 'global_bin_assd': 0.15559863304671395,\n",
- " 'global_bin_rvd': 0.0021710740333888085,\n",
" 'sq': 0.7940127477906024,\n",
" 'sq_std': 0.11547745015679488,\n",
" 'pq': 0.6586696657808406,\n",
@@ -303,7 +298,8 @@
" 'sq_assd': 0.2057371092494465,\n",
" 'sq_assd_std': 0.1398348236766068,\n",
" 'sq_rvd': 0.011340219860617232,\n",
- " 'sq_rvd_std': 0.1217805112447998\n",
+ " 'sq_rvd_std': 0.1217805112447998,\n",
+ " 'global_bin_dsc': 0.9731641527805414\n",
"}\n",
"\n"
],
@@ -318,10 +314,6 @@
" \u001b[32m'prec'\u001b[0m: \u001b[1;36m0.8202247191011236\u001b[0m,\n",
" \u001b[32m'rec'\u001b[0m: \u001b[1;36m0.8390804597701149\u001b[0m,\n",
" \u001b[32m'rq'\u001b[0m: \u001b[1;36m0.8295454545454546\u001b[0m,\n",
- " \u001b[32m'global_bin_dsc'\u001b[0m: \u001b[1;36m0.9731641527805414\u001b[0m,\n",
- " \u001b[32m'global_bin_cldsc'\u001b[0m: \u001b[1;36m0.9623567366028574\u001b[0m,\n",
- " \u001b[32m'global_bin_assd'\u001b[0m: \u001b[1;36m0.15559863304671395\u001b[0m,\n",
- " \u001b[32m'global_bin_rvd'\u001b[0m: \u001b[1;36m0.0021710740333888085\u001b[0m,\n",
" \u001b[32m'sq'\u001b[0m: \u001b[1;36m0.7940127477906024\u001b[0m,\n",
" \u001b[32m'sq_std'\u001b[0m: \u001b[1;36m0.11547745015679488\u001b[0m,\n",
" \u001b[32m'pq'\u001b[0m: \u001b[1;36m0.6586696657808406\u001b[0m,\n",
@@ -331,7 +323,8 @@
" \u001b[32m'sq_assd'\u001b[0m: \u001b[1;36m0.2057371092494465\u001b[0m,\n",
" \u001b[32m'sq_assd_std'\u001b[0m: \u001b[1;36m0.1398348236766068\u001b[0m,\n",
" \u001b[32m'sq_rvd'\u001b[0m: \u001b[1;36m0.011340219860617232\u001b[0m,\n",
- " \u001b[32m'sq_rvd_std'\u001b[0m: \u001b[1;36m0.1217805112447998\u001b[0m\n",
+ " \u001b[32m'sq_rvd_std'\u001b[0m: \u001b[1;36m0.1217805112447998\u001b[0m,\n",
+ " \u001b[32m'global_bin_dsc'\u001b[0m: \u001b[1;36m0.9731641527805414\u001b[0m\n",
"\u001b[1m}\u001b[0m\n"
]
},
@@ -343,6 +336,68 @@
"# get dict for further processing, e.g. for pandas\n",
"pprint(\"results dict: \", result.to_dict())"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "InputType.SEMANTIC\n",
+ "Prediction array shape = (170, 512, 17) unique_values= [ 0 26 41 42 43 44 45 46 47 48 49 60 61 62 100]\n",
+ "Reference array shape = (170, 512, 17) unique_values= [ 0 26 41 42 43 44 45 46 47 48 49 60 61 62 100]\n",
+ "\n",
+ "InputType.UNMATCHED_INSTANCE\n",
+ "Prediction array shape = (170, 512, 17) unique_values= [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
+ " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
+ " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
+ " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89]\n",
+ "Reference array shape = (170, 512, 17) unique_values= [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
+ " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
+ " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
+ " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87]\n",
+ "\n",
+ "InputType.MATCHED_INSTANCE\n",
+ "Prediction array shape = (170, 512, 17) unique_values= [ 0 1 2 4 5 6 9 10 12 13 14 15 16 17 18 19 20 21\n",
+ " 22 23 24 25 26 27 28 29 30 32 33 34 35 36 37 38 39 41\n",
+ " 42 43 44 45 49 50 51 52 54 55 56 57 58 60 61 62 63 64\n",
+ " 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82\n",
+ " 83 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103]\n",
+ "Reference array shape = (170, 512, 17) unique_values= [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
+ " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
+ " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
+ " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# To inspect different phases, just use the returned intermediate_steps_data object\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "intermediate_steps_data.original_prediction_arr # yields input prediction array\n",
+ "intermediate_steps_data.original_reference_arr # yields input reference array\n",
+ "\n",
+ "intermediate_steps_data.prediction_arr(\n",
+ " InputType.MATCHED_INSTANCE\n",
+ ") # yields prediction array after instances have been matched\n",
+ "intermediate_steps_data.reference_arr(\n",
+ " InputType.MATCHED_INSTANCE\n",
+ ") # yields reference array after instances have been matched\n",
+ "\n",
+ "# This works with all InputType\n",
+ "for i in InputType:\n",
+ " print(i)\n",
+ " pred = intermediate_steps_data.prediction_arr(i)\n",
+ " ref = intermediate_steps_data.reference_arr(i)\n",
+ " print(\"Prediction array shape =\", pred.shape, \"unique_values=\", np.unique(pred))\n",
+ " print(\"Reference array shape =\", ref.shape, \"unique_values=\", np.unique(ref))\n",
+ " print()"
+ ]
}
],
"metadata": {
diff --git a/panoptica/example_spine_unmatched_instance.ipynb b/panoptica/example_spine_unmatched_instance.ipynb
index ced5e38..d994a5a 100644
--- a/panoptica/example_spine_unmatched_instance.ipynb
+++ b/panoptica/example_spine_unmatched_instance.ipynb
@@ -23,7 +23,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Requirement already satisfied: panoptica in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (0.6.6.post9.dev0+d25fabc)\n",
+ "Requirement already satisfied: panoptica in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (1.0.0.post2.dev0+2f7d01f)\n",
"Requirement already satisfied: auxiliary in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 2)) (0.0.42)\n",
"Requirement already satisfied: rich in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (13.6.0)\n",
"Requirement already satisfied: numpy in /opt/anaconda3/envs/seg11panoptdev/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.25.2)\n",
@@ -244,16 +244,13 @@
"\n",
"+++ GLOBAL +++\n",
"Global Binary Dice (global_bin_dsc): 0.9631786034883428\n",
- "Global Binary Centerline Dice (global_bin_cldsc): 0.9832727462458709\n",
- "Global Binary Average Symmetric Surface Distance (global_bin_assd): 0.16583233154456756\n",
- "Global Binary Relative Volume Difference (global_bin_rvd): -0.006522448092183933\n",
"\n",
"+++ INSTANCE +++\n",
"Segmentation Quality IoU (sq): 0.9259373047661901 +- 0.009654749671578153\n",
"Panoptic Quality IoU (pq): 0.9259373047661901\n",
"Segmentation Quality Dsc (sq_dsc): 0.9615183012231253 +- 0.005245540988039026\n",
"Panoptic Quality Dsc (pq_dsc): 0.9615183012231253\n",
- "Segmentation Quality Assd (sq_assd): 0.16832296646947947 +- 0.01828381629759957\n",
+ "Segmentation Quality ASSD (sq_assd): 0.16832296646947947 +- 0.01828381629759957\n",
"Segmentation Quality Relative Volume Difference (sq_rvd): -0.005930868093584259 +- 0.010871203881221219\n",
"\n",
"\n",
@@ -269,16 +266,13 @@
"\n",
"+++ GLOBAL +++\n",
"Global Binary Dice (global_bin_dsc): 0.9423566613429801\n",
- "Global Binary Centerline Dice (global_bin_cldsc): 0.9785654029223958\n",
- "Global Binary Average Symmetric Surface Distance (global_bin_assd): 0.2945667788167613\n",
- "Global Binary Relative Volume Difference (global_bin_rvd): 0.02117012587992792\n",
"\n",
"+++ INSTANCE +++\n",
"Segmentation Quality IoU (sq): 0.8897861147389462 +- 0.029181150423413706\n",
"Panoptic Quality IoU (pq): 0.8897861147389462\n",
"Segmentation Quality Dsc (sq_dsc): 0.9414254100052913 +- 0.016436031942319355\n",
"Panoptic Quality Dsc (pq_dsc): 0.9414254100052913\n",
- "Segmentation Quality Assd (sq_assd): 0.29013503272997326 +- 0.05544330133482135\n",
+ "Segmentation Quality ASSD (sq_assd): 0.29013503272997326 +- 0.05544330133482135\n",
"Segmentation Quality Relative Volume Difference (sq_rvd): 0.020603174193257762 +- 0.03071580120223084\n",
"\n",
"\n",
@@ -294,16 +288,13 @@
"\n",
"+++ GLOBAL +++\n",
"Global Binary Dice (global_bin_dsc): 0.9698239455931553\n",
- "Global Binary Centerline Dice (global_bin_cldsc): 1.0\n",
- "Global Binary Average Symmetric Surface Distance (global_bin_assd): 0.20907172118556794\n",
- "Global Binary Relative Volume Difference (global_bin_rvd): -0.011061174622567414\n",
"\n",
"+++ INSTANCE +++\n",
"Segmentation Quality IoU (sq): 0.941415733208399 +- 0.0\n",
"Panoptic Quality IoU (pq): 0.941415733208399\n",
"Segmentation Quality Dsc (sq_dsc): 0.9698239455931553 +- 0.0\n",
"Panoptic Quality Dsc (pq_dsc): 0.9698239455931553\n",
- "Segmentation Quality Assd (sq_assd): 0.20907172118556794 +- 0.0\n",
+ "Segmentation Quality ASSD (sq_assd): 0.20907172118556794 +- 0.0\n",
"Segmentation Quality Relative Volume Difference (sq_rvd): -0.011061174622567414 +- 0.0\n",
"\n",
"\n",
@@ -319,16 +310,13 @@
"\n",
"+++ GLOBAL +++\n",
"Global Binary Dice (global_bin_dsc): 0.6793787581594264\n",
- "Global Binary Centerline Dice (global_bin_cldsc): 0.7061452563556313\n",
- "Global Binary Average Symmetric Surface Distance (global_bin_assd): 0.3688658918466393\n",
- "Global Binary Relative Volume Difference (global_bin_rvd): 0.01340152790366438\n",
"\n",
"+++ INSTANCE +++\n",
"Segmentation Quality IoU (sq): 0.54301762284604 +- 0.01014458743300687\n",
"Panoptic Quality IoU (pq): 0.31029578448345146\n",
"Segmentation Quality Dsc (sq_dsc): 0.7037824449992637 +- 0.008529812661560601\n",
"Panoptic Quality Dsc (pq_dsc): 0.40216139714243637\n",
- "Segmentation Quality Assd (sq_assd): 0.33450703853088465 +- 0.010995297631511717\n",
+ "Segmentation Quality ASSD (sq_assd): 0.33450703853088465 +- 0.010995297631511717\n",
"Segmentation Quality Relative Volume Difference (sq_rvd): -0.009548043713894769 +- 0.05397632450411714\n",
"\n"
]
@@ -338,7 +326,7 @@
"# print all results\n",
"results = evaluator.evaluate(pred_masks, ref_masks, verbose=False)\n",
"# The groups will have the names specified above\n",
- "for groupname, (result, debug) in results.items():\n",
+ "for groupname, (result, intermediate_steps_data) in results.items():\n",
" print()\n",
" print(\"### Group\", groupname)\n",
" print(result)"
@@ -387,10 +375,6 @@
" 'prec': 1.0,\n",
" 'rec': 1.0,\n",
" 'rq': 1.0,\n",
- " 'global_bin_dsc': 0.9631786034883428,\n",
- " 'global_bin_cldsc': 0.9832727462458709,\n",
- " 'global_bin_assd': 0.16583233154456756,\n",
- " 'global_bin_rvd': -0.006522448092183933,\n",
" 'sq': 0.9259373047661901,\n",
" 'sq_std': 0.009654749671578153,\n",
" 'pq': 0.9259373047661901,\n",
@@ -400,7 +384,8 @@
" 'sq_assd': 0.16832296646947947,\n",
" 'sq_assd_std': 0.01828381629759957,\n",
" 'sq_rvd': -0.005930868093584259,\n",
- " 'sq_rvd_std': 0.010871203881221219\n",
+ " 'sq_rvd_std': 0.010871203881221219,\n",
+ " 'global_bin_dsc': 0.9631786034883428\n",
"}\n",
"\n"
],
@@ -415,10 +400,6 @@
" \u001b[32m'prec'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
" \u001b[32m'rec'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
" \u001b[32m'rq'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
- " \u001b[32m'global_bin_dsc'\u001b[0m: \u001b[1;36m0.9631786034883428\u001b[0m,\n",
- " \u001b[32m'global_bin_cldsc'\u001b[0m: \u001b[1;36m0.9832727462458709\u001b[0m,\n",
- " \u001b[32m'global_bin_assd'\u001b[0m: \u001b[1;36m0.16583233154456756\u001b[0m,\n",
- " \u001b[32m'global_bin_rvd'\u001b[0m: \u001b[1;36m-0.006522448092183933\u001b[0m,\n",
" \u001b[32m'sq'\u001b[0m: \u001b[1;36m0.9259373047661901\u001b[0m,\n",
" \u001b[32m'sq_std'\u001b[0m: \u001b[1;36m0.009654749671578153\u001b[0m,\n",
" \u001b[32m'pq'\u001b[0m: \u001b[1;36m0.9259373047661901\u001b[0m,\n",
@@ -428,7 +409,8 @@
" \u001b[32m'sq_assd'\u001b[0m: \u001b[1;36m0.16832296646947947\u001b[0m,\n",
" \u001b[32m'sq_assd_std'\u001b[0m: \u001b[1;36m0.01828381629759957\u001b[0m,\n",
" \u001b[32m'sq_rvd'\u001b[0m: \u001b[1;36m-0.005930868093584259\u001b[0m,\n",
- " \u001b[32m'sq_rvd_std'\u001b[0m: \u001b[1;36m0.010871203881221219\u001b[0m\n",
+ " \u001b[32m'sq_rvd_std'\u001b[0m: \u001b[1;36m0.010871203881221219\u001b[0m,\n",
+ " \u001b[32m'global_bin_dsc'\u001b[0m: \u001b[1;36m0.9631786034883428\u001b[0m\n",
"\u001b[1m}\u001b[0m\n"
]
},
@@ -443,10 +425,85 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "### Group vertebra\n",
+ "InputType.UNMATCHED_INSTANCE\n",
+ "Prediction array shape = (164, 399, 17) unique_values= [0 3 4 5 6 7 8 9]\n",
+ "Reference array shape = (164, 399, 17) unique_values= [0 2 3 4 5 6 7 8]\n",
+ "\n",
+ "InputType.MATCHED_INSTANCE\n",
+ "Prediction array shape = (164, 399, 17) unique_values= [0 2 3 4 5 6 7 8]\n",
+ "Reference array shape = (164, 399, 17) unique_values= [0 2 3 4 5 6 7 8]\n",
+ "\n",
+ "\n",
+ "### Group ivd\n",
+ "InputType.UNMATCHED_INSTANCE\n",
+ "Prediction array shape = (96, 406, 17) unique_values= [ 0 103 104 105 106 107 108 109]\n",
+ "Reference array shape = (96, 406, 17) unique_values= [ 0 102 103 104 105 106 107 108]\n",
+ "\n",
+ "InputType.MATCHED_INSTANCE\n",
+ "Prediction array shape = (96, 406, 17) unique_values= [ 0 102 103 104 105 106 107 108]\n",
+ "Reference array shape = (96, 406, 17) unique_values= [ 0 102 103 104 105 106 107 108]\n",
+ "\n",
+ "\n",
+ "### Group sacrum\n",
+ "InputType.UNMATCHED_INSTANCE\n",
+ "key UNMATCHED_INSTANCE not in intermediate steps, maybe the step was skipped?\n",
+ "InputType.MATCHED_INSTANCE\n",
+ "Prediction array shape = (140, 128, 17) unique_values= [ 0 26]\n",
+ "Reference array shape = (140, 128, 17) unique_values= [ 0 26]\n",
+ "\n",
+ "\n",
+ "### Group endplate\n",
+ "InputType.UNMATCHED_INSTANCE\n",
+ "Prediction array shape = (85, 385, 17) unique_values= [ 0 203 204 205 206 207 208 209]\n",
+ "Reference array shape = (85, 385, 17) unique_values= [ 0 202 203 204 205 206 207 208]\n",
+ "\n",
+ "InputType.MATCHED_INSTANCE\n",
+ "Prediction array shape = (85, 385, 17) unique_values= [ 0 202 204 206 207 209 210 211]\n",
+ "Reference array shape = (85, 385, 17) unique_values= [ 0 202 203 204 205 206 207 208]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# To inspect different phases, just use the returned intermediate_steps_data object\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "for groupname, (result, intermediate_steps_data) in results.items():\n",
+ " print()\n",
+ " print(\"### Group\", groupname)\n",
+ " intermediate_steps_data.original_prediction_arr # yields input prediction array\n",
+ " intermediate_steps_data.original_reference_arr # yields input reference array\n",
+ "\n",
+ " # This works with all phases\n",
+ " for i in [InputType.UNMATCHED_INSTANCE, InputType.MATCHED_INSTANCE]:\n",
+ " try:\n",
+ " print(i)\n",
+ " pred = intermediate_steps_data.prediction_arr(i)\n",
+ " ref = intermediate_steps_data.reference_arr(i)\n",
+ " print(\n",
+ " \"Prediction array shape =\",\n",
+ " pred.shape,\n",
+ " \"unique_values=\",\n",
+ " np.unique(pred),\n",
+ " )\n",
+ " print(\n",
+ " \"Reference array shape =\", ref.shape, \"unique_values=\", np.unique(ref)\n",
+ " )\n",
+ " print()\n",
+ " except AssertionError as e:\n",
+ " print(e)\n",
+ " # This happens because Sacrum class group was set to single_instance, hence the Matching phase is skipped and there is no intermediate result for UNMATCHED_INSTANCE"
+ ]
}
],
"metadata": {