diff --git a/.github/workflows/linting.yml b/.github/workflows/linting.yml
new file mode 100644
index 0000000..45454fd
--- /dev/null
+++ b/.github/workflows/linting.yml
@@ -0,0 +1,35 @@
+name: Linting
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+
+jobs:
+  linting:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v3
+
+      - name: Cache pre-commit
+        uses: actions/cache@v3
+        with:
+          path: ~/.cache/pre-commit
+          key: pre-commit-${{ hashFiles('.pre-commit-config.yaml') }}
+
+      - name: Set up python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.x"
+          cache: "pip"
+          cache-dependency-path: "pyproject.toml"
+
+      - name: Install dependencies
+        run: |-
+          python -m pip install pre-commit
+          pre-commit install
+
+      - name: Run pre-commit
+        run: pre-commit run --all-files --color always
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
new file mode 100644
index 0000000..4d17277
--- /dev/null
+++ b/.github/workflows/test.yml
@@ -0,0 +1,36 @@
+name: Test
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    paths-ignore:
+      - "**.md"
+      - "**.rst"
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout source
+        uses: actions/checkout@v3
+
+      - name: Set up python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.10"
+          cache: "pip"
+          cache-dependency-path: "pyproject.toml"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install pytest
+
+      - name: Install umetrics
+        run: |
+          pip install -e .
+      - name: Run tests
+        run: |
+          pytest
diff --git a/.gitignore b/.gitignore
index 6355ce3..7d33481 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,6 @@
+*.ipynb_checkpoints
+*.egg-info/
 .DS_Store
-umetrics/__pycache__
-notebooks/.ipynb_checkpoints
+__pycache__
+notebooks/
+_version.py
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..2829773
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,26 @@
+repos:
+    - repo: https://github.com/charliermarsh/ruff-pre-commit
+      rev: v0.0.262
+      hooks:
+        - id: ruff
+    - repo: https://github.com/pre-commit/pre-commit-hooks
+      rev: v4.4.0
+      hooks:
+          - id: check-case-conflict
+          - id: check-docstring-first
+          - id: check-executables-have-shebangs
+          - id: check-merge-conflict
+          - id: check-toml
+          - id: end-of-file-fixer
+          - id: mixed-line-ending
+            args: [--fix=lf]
+          - id: trailing-whitespace
+            args: [--markdown-linebreak-ext=md]
+    - repo: https://github.com/psf/black
+      rev: 23.3.0
+      hooks:
+          - id: black
+    - repo: https://github.com/pappasam/toml-sort
+      rev: v0.23.0
+      hooks:
+        - id: toml-sort-fix
diff --git a/README.md b/README.md
index bb71fe3..2aaa1cf 100644
--- a/README.md
+++ b/README.md
@@ -20,7 +20,7 @@ TODO:
 ### Single image usage
 
 ```python
-import umetrics
+import umetrix
 from skimage.io import imread
 
 y_true = imread('true.tif')
@@ -29,7 +29,12 @@ y_pred = imread('pred.tif')
 
 # can now make the calculation strict, by only considering objects that have
 # an IoU above a theshold as being true positives
-result = umetrics.calculate(y_true, y_pred, strict=True, iou_threshold=0.5)
+result = umetrix.calculate(
+    y_true,
+    y_pred,
+    strict=True,
+    iou_threshold=0.5
+)
 
 print(result.results)
 ```
@@ -56,14 +61,16 @@ localization_error: 0.010
 ### Batch processing
 
 ```python
-import umetrics
+import umetrix
 
 # provide a list of file pairs ('true', 'prediction')
-files = [('true0.tif', 'pred0.tif'),
-         ('true1.tif', 'pred1.tif'),
-         ('true2.tif', 'pred2.tif')]
+files = [
+    ('true0.tif', 'pred0.tif'),
+    ('true1.tif', 'pred1.tif'),
+    ('true2.tif', 'pred2.tif')
+]
 
-batch_result = umetrics.batch(files)
+batch_result = umetrix.batch(files)
 ```
 
 Returns aggregate statistics over the batch. Jaccard index is calculated over
@@ -79,8 +86,8 @@ $ git clone https://github.com/quantumjot/unet_segmentation_metrics.git
 
 2. (Optional, but advised) Create a conda environment:
 ```sh
-$ conda create -n umetrics python=3.7
-$ conda activate umetrics
+$ conda create -n umetrix python=3.9
+$ conda activate umetrix
 ```
 
 3. Install the package
diff --git a/notebooks/unet_segmentation_metrics-napari.ipynb b/notebooks/unet_segmentation_metrics-napari.ipynb
index 8d5f2ab..1cfb8e2 100644
--- a/notebooks/unet_segmentation_metrics-napari.ipynb
+++ b/notebooks/unet_segmentation_metrics-napari.ipynb
@@ -26,12 +26,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "import sys\n",
-    "sys.path.append('..')\n",
-    "import umetrics\n",
+    "import umetrix\n",
     "\n",
-    "import numpy as np\n",
     "from skimage.io import imread\n",
     "\n",
     "import napari\n",
@@ -45,9 +41,10 @@
    "outputs": [],
    "source": [
     "# load a ground truth - prediction image pair\n",
-    "p = '/media/quantumjot/Data/TrainingData/UNet_training_scribble_v2b/set14/labels'\n",
-    "y_true = imread(os.path.join(p, '0014_mask.tif.modified.tif'))[0, ...]\n",
-    "y_pred = imread(os.path.join(p, '0014_mask.tif'))[0, ...]"
+    "p = \"../tests/data/unet.tif\"\n",
+    "s = imread(p)\n",
+    "y_true = s[-2, ...]\n",
+    "y_pred = s[-1, ...]"
    ]
   },
   {
@@ -63,7 +60,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = umetrics.calculate(y_true, y_pred, strict=True, iou_threshold=0.7)"
+    "result = umetrix.calculate(y_true, y_pred, strict=False, iou_threshold=0.7)"
    ]
   },
   {
@@ -84,16 +81,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Shapes layer 'bounding box' at 0x7fb2eb8fff90>"
+       "<Shapes layer 'bounding box' at 0x15017ecb0>"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -116,17 +113,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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alt=\"\"></img>"
+      ],
       "text/plain": [
-       "<napari.utils.notebook_display.NotebookScreenshot at 0x7fb2dd5c4410>"
+       "<napari.utils.notebook_display.NotebookScreenshot at 0x15017f850>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -137,47 +137,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================\n",
-      " Segmentation Metrics (n=1)\n",
-      "============================\n",
-      "Strict: True (IoU > 0.7)\n",
-      "n_true_labels: 354\n",
-      "n_pred_labels: 362\n",
-      "n_true_positives: 345\n",
-      "n_false_positives: 10\n",
-      "n_false_negatives: 0\n",
-      "IoU: 0.999\n",
-      "Jaccard: 0.972\n",
-      "pixel_identity: 0.998\n",
-      "localization_error: 0.010\n",
-      "\n"
-     ]
+     "data": {
+      "text/html": [
+       "<html><style>.row {display: inline-flex; align-items: flex-start;}  \n",
+       " .metrics {float: left;} \n",
+       " .confusion {width: 200px; float: left;}</style><body>\n",
+       "<p><h3>Segmentation Metrics</h3></p>\n",
+       "\n",
+       "<div class='row'><div class='metrics'>\n",
+       "<table><tr><th>Metric</th><th><Value></th></tr><tr><td>n_true_labels</td><td>13</td></tr><tr><td>n_pred_labels</td><td>14</td></tr><tr><td>n_true_positives</td><td>12</td></tr><tr><td>n_false_positives</td><td>2</td></tr><tr><td>n_false_negatives</td><td>1</td></tr><tr><td>IoU</td><td>0.807</td></tr><tr><td>Jaccard</td><td>0.800</td></tr><tr><td>pixel_identity</td><td>0.959</td></tr><tr><td>localization_error</td><td>15.524</td></tr></table>\n",
+       "</div><div class='confusion'>\n",
+       "<img 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' width=200px/>\n",
+       "</div></div>\n",
+       "</body></html>"
+      ],
+      "text/plain": [
+       "============================\n",
+       " Segmentation Metrics (n=1)\n",
+       "============================\n",
+       "n_true_labels: 13\n",
+       "n_pred_labels: 14\n",
+       "n_true_positives: 12\n",
+       "n_false_positives: 2\n",
+       "n_false_negatives: 1\n",
+       "IoU: 0.807\n",
+       "Jaccard: 0.800\n",
+       "pixel_identity: 0.959\n",
+       "localization_error: 15.524"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(result.results)"
+    "result"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:napari-dev]",
+   "display_name": "btrack",
    "language": "python",
-   "name": "conda-env-napari-dev-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -189,7 +196,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/unet_segmentation_metrics.ipynb b/notebooks/unet_segmentation_metrics.ipynb
index 8af93bd..f806400 100644
--- a/notebooks/unet_segmentation_metrics.ipynb
+++ b/notebooks/unet_segmentation_metrics.ipynb
@@ -25,10 +25,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "import sys\n",
-    "sys.path.append('..')\n",
-    "import umetrics\n",
+    "import umetrix\n",
     "\n",
     "import numpy as np\n",
     "from skimage.io import imread"
@@ -41,9 +38,10 @@
    "outputs": [],
    "source": [
     "# load a ground truth - prediction image pair\n",
-    "p = '/media/quantumjot/Data/TrainingData/UNet_training_scribble_v2b/set14/labels'\n",
-    "y_true = imread(os.path.join(p, '0014_mask.tif.modified.tif'))[0,...]\n",
-    "y_pred = imread(os.path.join(p, '0014_mask.tif'))[0,...]"
+    "p = \"../tests/data/unet.tif\"\n",
+    "s = imread(p)\n",
+    "y_true = s[-2, ...]\n",
+    "y_pred = s[-1, ...]"
    ]
   },
   {
@@ -52,24 +50,58 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "result = umetrics.calculate(y_true, y_pred)"
+    "result = umetrix.calculate(y_true, y_pred)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<html><style>.row {display: inline-flex; align-items: flex-start;}  \n",
+       " .metrics {float: left;} \n",
+       " .confusion {width: 200px; float: left;}</style><body>\n",
+       "<p><h3>Segmentation Metrics</h3></p>\n",
+       "\n",
+       "<div class='row'><div class='metrics'>\n",
+       "<table><tr><th>Metric</th><th><Value></th></tr><tr><td>n_true_labels</td><td>13</td></tr><tr><td>n_pred_labels</td><td>14</td></tr><tr><td>n_true_positives</td><td>12</td></tr><tr><td>n_false_positives</td><td>2</td></tr><tr><td>n_false_negatives</td><td>1</td></tr><tr><td>IoU</td><td>0.807</td></tr><tr><td>Jaccard</td><td>0.800</td></tr><tr><td>pixel_identity</td><td>0.959</td></tr><tr><td>localization_error</td><td>15.524</td></tr></table>\n",
+       "</div><div class='confusion'>\n",
+       "<img 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' width=200px/>\n",
+       "</div></div>\n",
+       "</body></html>"
+      ],
+      "text/plain": [
+       "============================\n",
+       " Segmentation Metrics (n=1)\n",
+       "============================\n",
+       "n_true_labels: 13\n",
+       "n_pred_labels: 14\n",
+       "n_true_positives: 12\n",
+       "n_false_positives: 2\n",
+       "n_false_negatives: 1\n",
+       "IoU: 0.807\n",
+       "Jaccard: 0.800\n",
+       "pixel_identity: 0.959\n",
+       "localization_error: 15.524"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "## visualize the metrics"
+    "result"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# uncomment for interactive\n",
-    "# %matplotlib qt"
+    "## visualize the metrics"
    ]
   },
   {
@@ -79,14 +111,12 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 1152x864 with 1 Axes>"
+       "<Figure size 1600x1200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -94,40 +124,57 @@
     "result.plot()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### now perform the calculation with strict matching only"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================\n",
-      " Segmentation Metrics (n=1)\n",
-      "============================\n",
-      "n_true_labels: 354\n",
-      "n_pred_labels: 362\n",
-      "n_true_positives: 350\n",
-      "n_false_positives: 5\n",
-      "n_false_negatives: 0\n",
-      "IoU: 0.988\n",
-      "Jaccard: 0.986\n",
-      "pixel_identity: 0.998\n",
-      "localization_error: 1.048\n",
-      "\n"
-     ]
+     "data": {
+      "text/html": [
+       "<html><style>.row {display: inline-flex; align-items: flex-start;}  \n",
+       " .metrics {float: left;} \n",
+       " .confusion {width: 200px; float: left;}</style><body>\n",
+       "<p><h3>Segmentation Metrics</h3></p>\n",
+       "<p>Strict matching (IoU threshold: 0.5)</p>\n",
+       "<div class='row'><div class='metrics'>\n",
+       "<table><tr><th>Metric</th><th><Value></th></tr><tr><td>n_true_labels</td><td>13</td></tr><tr><td>n_pred_labels</td><td>14</td></tr><tr><td>n_true_positives</td><td>11</td></tr><tr><td>n_false_positives</td><td>3</td></tr><tr><td>n_false_negatives</td><td>2</td></tr><tr><td>IoU</td><td>0.841</td></tr><tr><td>Jaccard</td><td>0.688</td></tr><tr><td>pixel_identity</td><td>0.959</td></tr><tr><td>localization_error</td><td>5.305</td></tr></table>\n",
+       "</div><div class='confusion'>\n",
+       "<img 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' width=200px/>\n",
+       "</div></div>\n",
+       "</body></html>"
+      ],
+      "text/plain": [
+       "============================\n",
+       " Segmentation Metrics (n=1)\n",
+       "============================\n",
+       "Strict: True (IoU > 0.5)\n",
+       "n_true_labels: 13\n",
+       "n_pred_labels: 14\n",
+       "n_true_positives: 11\n",
+       "n_false_positives: 3\n",
+       "n_false_negatives: 2\n",
+       "IoU: 0.841\n",
+       "Jaccard: 0.688\n",
+       "pixel_identity: 0.959\n",
+       "localization_error: 5.305"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(result.results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### now perform the calculation with strict matching only"
+    "result = umetrix.calculate(y_true, y_pred, strict=True, iou_threshold=0.5)\n",
+    "result"
    ]
   },
   {
@@ -136,37 +183,33 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============================\n",
-      " Segmentation Metrics (n=1)\n",
-      "============================\n",
-      "Strict: True (IoU > 0.7)\n",
-      "n_true_labels: 354\n",
-      "n_pred_labels: 362\n",
-      "n_true_positives: 345\n",
-      "n_false_positives: 10\n",
-      "n_false_negatives: 0\n",
-      "IoU: 0.999\n",
-      "Jaccard: 0.972\n",
-      "pixel_identity: 0.998\n",
-      "localization_error: 0.010\n",
-      "\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "result = umetrics.calculate(y_true, y_pred, strict=True, iou_threshold=0.7)\n",
-    "print(result.results)"
+    "result.plot()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:root] *",
+   "display_name": "napari",
    "language": "python",
-   "name": "conda-root-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -178,7 +221,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..7a9d0a6
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,33 @@
+[build-system]
+requires = ["setuptools", "setuptools-scm"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "umetrix"
+authors = [
+  {name = "Alan R. Lowe", email = "a.lowe@ucl.ac.uk"}
+]
+description = "UNet Segmentation Metrics"
+readme = "README.md"
+requires-python = ">=3.8"
+keywords = ["image analysis"]
+license = {text = "BSD-3-Clause"}
+classifiers = [
+  "Programming Language :: Python :: 3"
+]
+dependencies = [
+  "matplotlib",
+  "numpy",
+  "pandas",
+  "scikit-learn",
+  "scikit-image>=0.20.0" # to include the spacing argument in regionprops
+]
+dynamic = ["version"]
+
+[tool.setuptools.packages.find]
+where = ["src"]
+include = ["umetrix*"]
+
+[tool.setuptools_scm]
+local_scheme = "no-local-version"
+write_to = "src/umetrix/_version.py"
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 48c603c..0000000
--- a/setup.py
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/usr/bin/env python
-from setuptools import setup
-
-setup(name='unet_segmentation_metrics',
-      version='0.1',
-      description='Segmentation performance metrics',
-      author='Alan R. Lowe',
-      author_email='a.lowe@ucl.ac.uk',
-      url='https://github.com/quantumjot/unet_segmentation_metrics',
-      packages=['umetrics'],
-      install_requires=['numpy','scipy','matplotlib','scikit-image'],
-      python_requires='>=3.6'
-     )
diff --git a/src/umetrix/__init__.py b/src/umetrix/__init__.py
new file mode 100644
index 0000000..0af7969
--- /dev/null
+++ b/src/umetrix/__init__.py
@@ -0,0 +1 @@
+from .core import calculate, batch  # NOQA: F401
diff --git a/src/umetrix/core.py b/src/umetrix/core.py
new file mode 100644
index 0000000..09fcbe4
--- /dev/null
+++ b/src/umetrix/core.py
@@ -0,0 +1,446 @@
+from __future__ import annotations
+
+import enum
+import numpy as np
+import numpy.typing as npt
+
+from skimage.io import imread
+from scipy.ndimage import label
+from scipy.ndimage import center_of_mass
+from scipy.ndimage import find_objects
+from scipy.optimize import linear_sum_assignment
+
+from typing import Dict, Tuple
+
+from umetrix import render
+
+
+DEFAULT_MAXIMUM_COST = 1e8
+
+
+class Metrics(str, enum.Enum):
+    N_TRUE_LABELS = "n_true_labels"
+    N_PRED_LABELS = "n_pred_labels"
+    N_TRUE_POSITIVES = "n_true_positives"
+    N_FALSE_POSITIVES = "n_false_positives"
+    N_FALSE_NEGATIVES = "n_false_negatives"
+    IOU = "IoU"
+    JACCARD = "Jaccard"
+    PIXEL_IDENTITY = "pixel_identity"
+    LOCALIZATION_ERROR = "localization_error"
+
+
+METRICS = (
+    "n_true_labels",
+    "n_pred_labels",
+    "n_true_positives",
+    "n_false_positives",
+    "n_false_negatives",
+    "IoU",
+    "Jaccard",
+    "pixel_identity",
+    "localization_error",
+)
+
+
+def IoU(ref: npt.NDArray, pred: npt.NDArray) -> float:
+    """Calculate the IoU between two binary masks."""
+    intersection = np.sum(np.logical_and(ref, pred))
+    union = np.sum(np.logical_or(ref, pred))
+    iou = 0.0 if union == 0 else intersection / union
+    return iou
+
+
+def find_matches(
+    ref: LabeledSegmentation,
+    pred: LabeledSegmentation,
+    *,
+    strict: bool = False,
+    iou_threshold: float = 0.5,
+) -> Dict:
+    """Perform matching between the reference and the predicted image.
+
+    Parameters
+    ----------
+    ref :
+        The reference (ground truth) segmentation.
+    pred :
+        The predicted segmentation.
+    strict : bool
+        Whether to use strict matching, i.e. only allowing matches above a
+        threshold IoU value.
+    iou_threshold :
+        A threshold value to use when strict matching.
+
+    Return
+    ------
+    matches : dict
+        A dictionary of matches between the two images.
+    """
+
+    # make an infinite cost matrix, so that we only consider matches where
+    # there is some overlap in the masks
+    cost_matrix = np.full((len(ref.labels), len(pred.labels)), DEFAULT_MAXIMUM_COST)
+
+    for r_id, ref_label in enumerate(ref.labels):
+        mask = ref.labeled == ref_label
+        _matches = [m for m in np.unique(pred.labeled[mask]) if m > 0]
+        for pred_label in _matches:
+            p_id = pred.labels.index(pred_label)
+            reward = IoU(mask, pred.labeled == pred_label)
+            if (reward < iou_threshold) and strict:
+                continue
+            cost_matrix[r_id, p_id] = 1.0 - reward
+
+    # if it's strict, make sure every element is above the threshold
+    if strict:
+        cost_threshold = 1.0 - iou_threshold
+        cost_mask = cost_matrix == DEFAULT_MAXIMUM_COST
+        assert np.all(cost_matrix[~cost_mask] <= cost_threshold)
+
+    # solve it using JV
+    sol_row, sol_col = linear_sum_assignment(cost_matrix)
+
+    # remove infeasible solutions
+    edges = [
+        (ref.labels[r], pred.labels[c], 1.0 - cost_matrix[r, c])
+        for r, c in zip(sol_row, sol_col)
+        if cost_matrix[r, c] <= 1
+    ]
+
+    # return a default dictionary if there are no matches
+    if not edges:
+        matches = {
+            "true_matches": [],
+            "true_matches_IoU": [],
+            "in_ref_only": set(ref.labels),
+            "in_pred_only": set(pred.labels),
+        }
+        return matches
+
+    # find the labels that haven't been used
+    used_ref, used_pred, IoUs = zip(*edges)
+    in_ref_only = set(ref.labels).difference(used_ref)
+    in_pred_only = set(pred.labels).difference(used_pred)
+
+    # return a dictionary of found matches
+    matches = {
+        "true_matches": list(set(zip(used_ref, used_pred))),
+        "true_matches_IoU": IoUs,
+        "in_ref_only": in_ref_only,
+        "in_pred_only": in_pred_only,
+    }
+
+    return matches
+
+
+class MetricResults(object):
+    def __init__(self, metrics):
+        assert isinstance(metrics, SegmentationMetrics)
+        self._images = 1
+        self._metrics = metrics
+
+        # list of metrics that are aggregated
+        self._agg = (
+            "n_true_labels",
+            "n_pred_labels",
+            "n_true_positives",
+            "n_false_positives",
+            "n_false_negatives",
+            "per_object_IoU",
+            "per_object_localization_error",
+            "per_image_pixel_identity",
+        )
+
+    def __getattr__(self, key):
+        return getattr(self._metrics, key)
+
+    @property
+    def n_images(self) -> int:
+        if any([getattr(self, m) is None for m in self._agg]):
+            return 0
+        else:
+            return self._images
+
+    def __add__(self, result: MetricResults) -> MetricResults:
+        assert isinstance(result, MetricResults)
+        for m in self._agg:
+            setattr(self, m, getattr(result, m) + getattr(self, m))
+        self._images += 1
+        return self
+
+    def __repr__(self) -> str:
+        title = f" Segmentation Metrics (n={self.n_images})\n"
+        hbar = "=" * len(title) + "\n"
+        r = hbar + title + hbar
+        if self.strict:
+            r += f"Strict: {self.strict} (IoU > {self.iou_threshold})\n"
+        for m in METRICS:
+            mval = getattr(self, m)
+            if isinstance(mval, float):
+                r += f"{m}: {mval:.3f}\n"
+            else:
+                r += f"{m}: {mval}\n"
+        return r
+
+    def _repr_html_(self):
+        from umetrix.notebooks import render_metrics_html
+
+        return render_metrics_html(self)
+
+    @property
+    def localization_error(self) -> float:
+        return np.mean(self.per_object_localization_error)
+
+    @property
+    def IoU(self) -> float:
+        return np.mean(self.per_object_IoU)
+
+    @property
+    def Jaccard(self) -> float:
+        """Jaccard metric"""
+        tp = self.n_true_positives
+        fn = self.n_false_negatives
+        fp = self.n_false_positives
+        return tp / (tp + fn + fp)
+
+    @property
+    def pixel_identity(self) -> float:
+        return np.mean(self.per_image_pixel_identity)
+
+    @staticmethod
+    def merge(results: list) -> MetricResults:
+        """Merge n results together and return a single object."""
+        merged = results.pop(0)
+        for result in results:
+            assert isinstance(result, MetricResults)
+            assert result.n_images == 1
+            merged = merged + result
+        return merged
+
+
+class SegmentationMetrics:
+    """A class for calculating various segmentation metrics to assess the
+    accuracy of a trained model.
+
+    Parameters
+    ----------
+    reference : array
+        An array containing labeled objects from the ground truth.
+    predicted : array
+        An array containing labeled objects from the segmentation algorithm.
+    strict : bool
+        Whether to disregard matches with a low IoU score.
+    iou_threshold : float
+      Threshold IoU for strict matching.
+
+    Properties
+    ----------
+    Jaccard : float
+        The Jaccard index calculated according to the notes below.
+    IoU : float
+        The Intersection over Union metric.
+    localisation_precision : float
+        The localisation precision.
+    true_positives : int
+        Number of TP predictions.
+    false_positives : int
+        Number of FP predictions.
+    false_negatives : int
+        Number of FN predicitons.
+
+
+    Notes
+    -----
+    The Jaccard metric is calculated accordingly:
+
+        FP = number of objects in predicted but not in reference
+        TP = number of objects in both
+        TN = background correctly segmented (not used)
+        FN = number of objects in true but not in predicted
+
+        J = TP / (TP+FP+FN)
+
+    The IoU is calculated as the intersection of the binary segmentation
+    divided by the union.
+    """
+
+    def __init__(
+        self, reference: LabeledSegmentation, predicted: LabeledSegmentation, **kwargs
+    ):
+        assert isinstance(predicted, LabeledSegmentation)
+        assert isinstance(reference, LabeledSegmentation)
+
+        self._reference = reference
+        self._predicted = predicted
+        self._strict = kwargs.get("strict", False)
+        self._iou_threshold = kwargs.get("iou_threshold", 0.5)
+
+        if self.iou_threshold < 0.0 or self.iou_threshold > 1.0:
+            raise ValueError(
+                f"IoU Threshold shoud be in (0, 1), found: {self.iou_threshold:.2f}"
+            )
+        assert isinstance(self.strict, bool)
+
+        # find the matches
+        self._matches = find_matches(
+            self._reference,
+            self._predicted,
+            strict=self.strict,
+            iou_threshold=self.iou_threshold,
+        )
+
+    @property
+    def strict(self) -> bool:
+        return self._strict
+
+    @property
+    def iou_threshold(self) -> float:
+        return self._iou_threshold
+
+    @property
+    def results(self):
+        return MetricResults(self)
+
+    @property
+    def image_overlay(self):
+        # n_labels = max([self._predicted.n_labels, self._reference.n_labels])
+        # scale = int(255 / n_labels)
+        return (
+            np.stack(
+                [self._predicted.image, self._reference.image, self._predicted.image],
+                axis=-1,
+            )
+            * 127
+        )
+
+    @property
+    def n_true_labels(self):
+        return self._reference.n_labels
+
+    @property
+    def n_pred_labels(self):
+        return self._predicted.n_labels
+
+    @property
+    def true_positives(self):
+        """Only one match between reference and predicted."""
+        return self._matches["true_matches"]
+
+    @property
+    def false_negatives(self):
+        """No match in predicted for reference object."""
+        return self._matches["in_ref_only"]
+
+    @property
+    def false_positives(self):
+        """Combination of non unique matches and unmatched objects."""
+        return self._matches["in_pred_only"]
+
+    @property
+    def n_true_positives(self):
+        return len(self.true_positives)
+
+    @property
+    def n_false_negatives(self):
+        return len(self.false_negatives)
+
+    @property
+    def n_false_positives(self):
+        return len(self.false_positives)
+
+    @property
+    def per_object_IoU(self):
+        """Intersection over Union (IoU) metric"""
+        return self._matches["true_matches_IoU"]
+
+    @property
+    def per_image_pixel_identity(self):
+        """Calculate the per-image pixel identity."""
+        n_tot = np.prod(self._reference.image.shape)
+        return [np.sum(self._reference.image == self._predicted.image) / n_tot]
+
+    @property
+    def per_object_localization_error(self):
+        """Calculate the per-object localization error."""
+        ref_centroids = self._reference.centroids
+        tgt_centroids = self._predicted.centroids
+        positional_error = []
+        for m in self.true_positives:
+            true_centroid = np.array(ref_centroids[m[0] - 1])
+            pred_centroid = np.array(tgt_centroids[m[1] - 1])
+            err = np.sum((true_centroid - pred_centroid) ** 2)
+            positional_error.append(err)
+        return positional_error
+
+    def plot(self):
+        render.plot_metrics(self)
+
+    def to_napari(self):
+        return render.render_metrics_napari(self)
+
+    def __repr__(self):
+        return self.results.__repr__()
+
+    def _repr_html_(self):
+        return self.results._repr_html_()
+
+
+class LabeledSegmentation:
+    """A helper class to enable simple calculation of accuracy statistics for
+    image segmentation output.
+    """
+
+    def __init__(self, image: npt.NDArray):
+        self.image = image
+        self.labeled, self.n_labels = label(image.astype(bool))
+
+    @property
+    def shape(self) -> Tuple[int]:
+        return self.image.shape
+
+    @property
+    def bboxes(self):
+        return [find_objects(self.labeled == label)[0] for label in self.labels]
+
+    @property
+    def labels(self):
+        return range(1, self.n_labels + 1)
+
+    @property
+    def centroids(self):
+        return [center_of_mass(self.labeled == label) for label in self.labels]
+
+    @property
+    def areas(self):
+        return [np.sum(self.labeled == label) for label in self.labels]
+
+
+def calculate(reference, predicted, **kwargs):
+    """Take a predicted image and compare with the reference image.
+
+    Compute various metrics.
+    """
+
+    ref = LabeledSegmentation(reference)
+    tgt = LabeledSegmentation(predicted)
+
+    # make sure they are the same size
+    assert ref.shape == tgt.shape
+
+    return SegmentationMetrics(ref, tgt, **kwargs)
+
+
+def batch(files, **kwargs):
+    """batch process a list of files"""
+    metrix = []
+    for f_ref, f_pred in files:
+        true = imread(f_ref)
+        pred = imread(f_pred)
+        result = calculate(true, pred, **kwargs).results
+        metrix.append(result)
+    return MetricResults.merge(metrix)
+
+
+if __name__ == "__main__":
+    pass
diff --git a/src/umetrix/notebooks.py b/src/umetrix/notebooks.py
new file mode 100644
index 0000000..a774090
--- /dev/null
+++ b/src/umetrix/notebooks.py
@@ -0,0 +1,112 @@
+import base64
+import io
+import itertools
+import matplotlib.pyplot as plt
+import numpy as np
+
+from matplotlib import colors, colormaps
+
+from umetrix.core import METRICS
+
+
+DARK_CONTEXT = {
+    "axes.edgecolor": "gray",
+    "xtick.color": "gray",
+    "ytick.color": "gray",
+    "axes.labelcolor": "gray",
+    "font.size": 18,
+}
+
+
+def _text_color_based_on_value(value: float, cmap: colors.Colormap) -> str:
+    rgb = cmap(value)
+    luminance = 0.299 * rgb[0] + 0.587 * rgb[1] + 0.114 * rgb[2]
+    return "k" if luminance > 0.5 else "w"
+
+
+def _header() -> str:
+    css = (
+        ".row {display: inline-flex; align-items: flex-start;}  \n "
+        ".metrics {float: left;} \n "
+        ".confusion {width: 200px; float: left;}"
+    )
+    return f"<html><style>{css}</style><body>"
+
+
+def _footer() -> str:
+    return "</body></html>"
+
+
+def render_metrics_html(metrics) -> str:
+    """Render the metrics to HTML"""
+
+    html_table = _render_table(metrics)
+    encoded_cm = _render_confusion(metrics)
+    html_strict = (
+        f"<p>Strict matching (IoU threshold: {metrics.iou_threshold})</p>"
+        if metrics.strict
+        else ""
+    )
+
+    html = (
+        _header(),
+        "<p><h3>Segmentation Metrics</h3></p>",
+        html_strict,
+        "<div class='row'><div class='metrics'>",
+        html_table,
+        "</div><div class='confusion'>",
+        f"<img src='data:image/png;base64,{encoded_cm}' width=200px/>",
+        "</div></div>",
+        _footer(),
+    )
+
+    return html
+
+
+def _render_table(metrics) -> str:
+    """Render the table of results"""
+
+    def _get_f_string(m):
+        val = getattr(metrics, m)
+        return f"{val:.3f}" if isinstance(val, float) else f"{val:d}"
+
+    return (
+        "<table><tr><th>Metric</th><th><Value></th></tr>"
+        + "".join(
+            [f"<tr><td>{m}</td><td>" + _get_f_string(m) + "</td></tr>" for m in METRICS]
+        )
+        + "</table>"
+    )
+
+
+def _render_confusion(metrics, *, cmap: str = "Blues") -> str:
+    """Render a confusion matrix as an image"""
+    grid = np.zeros((2, 2), dtype=float)
+    grid[1, 1] = metrics.n_true_positives
+    grid[0, 1] = metrics.n_false_positives
+    grid[1, 0] = metrics.n_false_negatives
+    cmap = colormaps[cmap]
+
+    with plt.rc_context(DARK_CONTEXT):
+        _, ax = plt.subplots(figsize=(4, 4))
+        ax.pcolor(grid, cmap=cmap)
+        ax.set_xticks([0.5, 1.5], labels=["Negative", "Positive"])
+        ax.set_yticks(
+            [0.5, 1.5], labels=["Negative", "Positive"], rotation=90, va="center"
+        )
+
+        for i, j in itertools.product(range(2), range(2)):
+            ax.text(
+                i + 0.5,
+                j + 0.5,
+                grid[i, j].astype(int),
+                ha="center",
+                va="center",
+                color=_text_color_based_on_value(grid[i, j] / np.max(grid), cmap),
+            )
+        ax.set_ylabel("Predicted")
+        ax.set_xlabel("Ground truth")
+    stream = io.BytesIO()
+    plt.savefig(stream, format="png", bbox_inches="tight", transparent=True)
+    plt.close()
+    return base64.b64encode(stream.getvalue()).decode("utf-8")
diff --git a/src/umetrix/render.py b/src/umetrix/render.py
new file mode 100644
index 0000000..6f0f924
--- /dev/null
+++ b/src/umetrix/render.py
@@ -0,0 +1,105 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+
+
+def plot_metrics(seg_metrics):
+    pred = seg_metrics._predicted
+    ref = seg_metrics._reference
+
+    iou = [None] * len(ref.labels)
+    IoU = seg_metrics.per_object_IoU
+    for i, tp in enumerate(seg_metrics.true_positives):
+        iou[tp[0] - 1] = "{:.2f}".format(IoU[i])
+
+    fig, ax = plt.subplots(1, figsize=(16, 12))
+    ax.imshow(seg_metrics.image_overlay)
+
+    for i, (sy, sx) in enumerate(ref.bboxes):
+        r = patches.Rectangle(
+            (sx.start, sy.start),
+            sx.stop - sx.start,
+            sy.stop - sy.start,
+            edgecolor="g",
+            facecolor="None",
+        )
+        ax.add_patch(r)
+        ax.text(
+            sx.start, sy.start, "{}, IoU: {}".format(i, iou[i]), fontsize=6, color="w"
+        )
+    for i, (sy, sx) in enumerate(pred.bboxes):
+        r = patches.Rectangle(
+            (sx.start, sy.start),
+            sx.stop - sx.start,
+            sy.stop - sy.start,
+            edgecolor="m",
+            facecolor="None",
+        )
+        ax.add_patch(r)
+        # ax.text(sx.start, sy.start, '{}'.format(i), fontsize=6, color='w')
+
+    bboxes = pred.bboxes
+    for fp in seg_metrics.false_positives:
+        sy, sx = bboxes[fp - 1]
+        w, h = sx.stop - sx.start, sy.stop - sy.start
+        r = patches.Rectangle(
+            (sx.start, sy.start),
+            w,
+            h,
+            edgecolor="r",
+            facecolor=(1.0, 0.0, 0.0, 0.0),
+            linewidth=2,
+        )
+        ax.add_patch(r)
+
+    bboxes = ref.bboxes
+    for fn in seg_metrics.false_negatives:
+        sy, sx = bboxes[fn - 1]
+        w, h = sx.stop - sx.start, sy.stop - sy.start
+        r = patches.Rectangle(
+            (sx.start, sy.start),
+            w,
+            h,
+            edgecolor="c",
+            facecolor=(0.0, 1.0, 1.0, 0.0),
+            linewidth=2,
+        )
+        ax.add_patch(r)
+    plt.axis("off")
+    plt.show()
+
+
+def make_bboxes(bbox_slices):
+    """Calculate bboxes for napari"""
+    minr = [sxy[0].start for sxy in bbox_slices]
+    minc = [sxy[1].start for sxy in bbox_slices]
+    maxr = [sxy[0].stop for sxy in bbox_slices]
+    maxc = [sxy[1].stop for sxy in bbox_slices]
+
+    bbox_rect = np.array([[minr, minc], [maxr, minc], [maxr, maxc], [minr, maxc]])
+    bbox_rect = np.moveaxis(bbox_rect, 2, 0)
+    return bbox_rect
+
+
+def render_metrics_napari(seg_metrics):
+    """Render the segmentation metrics for visualization in Napari."""
+
+    # pred = seg_metrics._predicted
+    ref = seg_metrics._reference
+
+    properties = {"iou": seg_metrics.per_object_IoU}
+
+    bboxes = make_bboxes(ref.bboxes)
+    tp_idx = np.asarray(seg_metrics.true_positives)[:, 0] - 1
+    bboxes = bboxes[tp_idx, ...]
+
+    # specify the display parameters for the text
+    text_parameters = {
+        "text": "IoU: {iou:.2f}\n",
+        "size": 8,
+        "color": "white",
+        "anchor": "upper_left",
+        "translation": [-2, 0],
+    }
+
+    return bboxes, properties, text_parameters
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..f1dd701
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,99 @@
+import pytest
+import numpy as np
+import numpy.typing as npt
+
+from pathlib import Path
+from skimage.io import imread
+from skimage.util import montage
+from typing import Tuple
+
+from umetrix.core import IoU
+
+SEED = 12343
+RNG = np.random.default_rng(seed=SEED)
+
+
+def _synthetic_image(sz: int = 32) -> npt.NDArray:
+    image = np.zeros((sz, sz), dtype=np.uint8)
+    boxsz = RNG.integers(low=sz // 4, high=sz - 1)
+    xlo, ylo = RNG.integers(low=1, high=sz - boxsz, size=(2,))
+    image[xlo : xlo + boxsz, ylo : ylo + boxsz] = 1
+    return image
+
+
+# def _IoU(y_true: npt.NDArray, y_pred: npt.NDArray) -> float:
+#     union = np.sum(np.logical_or(y_true, y_pred))
+#     intersection = np.sum(np.logical_and(y_true, y_pred))
+#     return intersection / union
+
+
+@pytest.fixture
+def image_grid(N: int = 3, sz: int = 32) -> Tuple[npt.NDArray, npt.NDArray, dict]:
+    image_types = RNG.choice(
+        ["pair", "missing_true", "missing_pred"], size=(N * N,)
+    ).tolist()
+    true_stack = np.zeros((N * N, sz, sz), dtype=np.uint8)
+    pred_stack = np.zeros((N * N, sz, sz), dtype=np.uint8)
+
+    ious = []
+
+    n_true_positive = 0
+    n_false_positive = 0
+    n_false_negative = 0
+
+    for idx, img_type in enumerate(image_types):
+        if img_type == "pair":
+            true_stack[idx, ...] = _synthetic_image()
+            pred_stack[idx, ...] = _synthetic_image()
+            iou = IoU(true_stack[idx, ...], pred_stack[idx, ...])
+            ious.append(iou)
+            if iou > 0:
+                n_true_positive += 1
+            else:
+                n_false_positive += 1
+                n_false_negative += 1
+        elif img_type == "missing_true":
+            pred_stack[idx, ...] = _synthetic_image()
+            ious.append(0.0)
+            n_false_positive += 1
+        else:
+            true_stack[idx, ...] = _synthetic_image()
+            ious.append(0.0)
+            n_false_negative += 1
+
+    # number of pairs where there is some overlap
+    n_pairs = image_types.count("pair")
+    n_missing_true = image_types.count("missing_true")
+    n_missing_pred = image_types.count("missing_pred")
+
+    stats = {
+        "n_pairs": n_pairs,
+        "n_true": n_pairs + n_missing_pred,
+        "n_pred": n_pairs + n_missing_true,
+        "n_true_positive": n_true_positive,
+        "n_false_positive": n_false_positive,
+        "n_false_negative": n_false_negative,
+        "n_total": len(image_types),
+        "IoU": ious,
+    }
+
+    return (
+        montage(true_stack, rescale_intensity=False, grid_shape=(N, N)),
+        montage(pred_stack, rescale_intensity=False, grid_shape=(N, N)),
+        stats,
+    )
+
+
+@pytest.fixture
+def image_pair() -> Tuple[npt.NDArray, npt.NDArray, dict]:
+    y_true = _synthetic_image()
+    y_pred = _synthetic_image()
+    stats = {"IoU": IoU(y_true, y_pred)}
+    return y_true, y_pred, stats
+
+
+@pytest.fixture
+def real_image_pair() -> Tuple[npt.NDArray, npt.NDArray]:
+    filename = Path(__file__).parent.resolve() / "data" / "unet.tif"
+    img = (imread(filename) > 0).astype(np.uint8)
+    return img[0, ...], img[1, ...]
diff --git a/tests/data/unet.tif b/tests/data/unet.tif
new file mode 100644
index 0000000..d369122
Binary files /dev/null and b/tests/data/unet.tif differ
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
new file mode 100644
index 0000000..fcf005a
--- /dev/null
+++ b/tests/test_metrics.py
@@ -0,0 +1,84 @@
+import pytest
+import numpy as np
+
+import umetrix
+
+
+STRICT_PARAMS = [(False, 0.0), (True, 0.1), (True, 0.2), (True, 0.5), (True, 0.7)]
+
+
+@pytest.mark.parametrize("strict,iou_threshold", STRICT_PARAMS)
+def test_calculate(image_pair, strict, iou_threshold):
+    """Run the metrics on a pair of images."""
+    y_true, y_pred, stats = image_pair
+    IoU = stats["IoU"]
+
+    result = umetrix.calculate(
+        y_true, y_pred, strict=strict, iou_threshold=iou_threshold
+    )
+
+    # calculate the real number of true postives based on strict matching
+    real_tp = int(IoU > result.iou_threshold) if strict else int(IoU > 0)
+
+    assert result.n_true_labels == 1
+    assert result.n_pred_labels == 1
+    assert result.n_true_positives == real_tp
+    assert result.n_false_positives == 1 - real_tp
+
+
+def test_calculate_no_true(image_pair):
+    """Test a pair of images where there is no object in the GT."""
+    y_true, y_pred, _ = image_pair
+    y_true = np.zeros_like(y_pred)
+
+    result = umetrix.calculate(y_true, y_pred)
+    assert result.n_true_labels == 0
+    assert result.n_pred_labels == 1
+    assert result.n_true_positives == 0
+    assert result.n_false_negatives == 0
+    assert result.n_false_positives == 1
+
+
+def test_calculate_no_pred(image_pair):
+    """Test a pair of images where there is no object in the prediction."""
+    y_true, y_pred, _ = image_pair
+    y_pred = np.zeros_like(y_true)
+
+    result = umetrix.calculate(y_true, y_pred)
+    assert result.n_true_labels == 1
+    assert result.n_pred_labels == 0
+    assert result.n_true_positives == 0
+    assert result.n_false_negatives == 1
+    assert result.n_false_positives == 0
+
+
+@pytest.mark.parametrize("strict,iou_threshold", STRICT_PARAMS)
+def test_calculate_grid(image_grid, strict, iou_threshold):
+    """Test a multi-instance segmentation."""
+    y_true, y_pred, stats = image_grid
+    result = umetrix.calculate(
+        y_true, y_pred, strict=strict, iou_threshold=iou_threshold
+    )
+
+    n_iou_over_threshold = sum([iou > iou_threshold for iou in stats["IoU"]])
+    n_iou_under_threshold = stats["n_pairs"] - n_iou_over_threshold if strict else 0
+    n_tp = n_iou_over_threshold
+    n_fp = stats["n_false_positive"] + n_iou_under_threshold
+    n_fn = stats["n_false_negative"] + n_iou_under_threshold
+
+    assert result.n_true_labels == stats["n_true"]
+    assert result.n_pred_labels == stats["n_pred"]
+    assert result.n_true_positives == n_tp
+    assert result.n_false_positives == n_fp
+    assert result.n_false_negatives == n_fn
+
+
+@pytest.mark.parametrize("strict,iou_threshold", STRICT_PARAMS)
+def test_real(real_image_pair, strict, iou_threshold):
+    """Test a real image pair."""
+    y_true, y_pred = real_image_pair
+    result = umetrix.calculate(
+        y_true, y_pred, strict=strict, iou_threshold=iou_threshold
+    )
+    assert result.n_true_labels == 13
+    assert result.n_pred_labels == 14
diff --git a/umetrics/__init__.py b/umetrics/__init__.py
deleted file mode 100644
index bb67a43..0000000
--- a/umetrics/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .core import *
diff --git a/umetrics/core.py b/umetrics/core.py
deleted file mode 100644
index c0ae0aa..0000000
--- a/umetrics/core.py
+++ /dev/null
@@ -1,404 +0,0 @@
-
-__author__ = "Alan R. Lowe"
-__email__ = "a.lowe@ucl.ac.uk"
-
-import os
-
-import numpy as np
-
-from skimage.io import imread
-from scipy.ndimage import label
-from scipy.ndimage import center_of_mass
-from scipy.ndimage import find_objects
-
-from . import render
-
-
-METRICS = ('n_true_labels',
-           'n_pred_labels',
-           'n_true_positives',
-           'n_false_positives',
-           'n_false_negatives',
-           'IoU',
-           'Jaccard',
-           'pixel_identity',
-           'localization_error')
-
-
-
-def _find_matches(ref, pred):
-    """ find potential matches between objects in the reference and
-    predicted images. These need to have at least 1 pixel of overlap.
-    """
-    matches = {}
-    for label in ref.labels:
-        mask = ref.labeled == label
-        matches[label] = [m for m in np.unique(pred.labeled[mask]) if m>0]
-    return matches
-
-
-def find_matches(ref, pred):
-    """ Find matches between the reference image and the predicted image.
-
-    Args:
-        ref
-        pred
-    """
-
-    # do forward and reverse matching
-    matches_rp = _find_matches(ref, pred)
-    matches_pr = _find_matches(pred, ref)
-
-    true_matches = []
-    in_ref_only = []
-    in_pred_only = []
-
-    for m0, match in matches_rp.items():
-        # no matches
-        if len(match) < 1:
-            in_ref_only.append(m0)
-        # if there is only one match, check that there is only one reverse match
-        elif len(match) == 1:
-            if match[0] not in matches_pr:
-                raise Exception('Something doesn\'t make sense...')
-            elif len(matches_pr[match[0]]) == 1:
-                # one to one
-                true_matches.append((m0, match[0]))
-            elif len(matches_pr[match[0]]) > 1:
-                # two (or more) objects in the prediction match one in the ref
-                in_pred_only += match
-        elif len(match) > 1:
-            in_pred_only += match
-
-    # sanity check that all are accounted for
-    ref_found_labels = set(in_ref_only + [m[0] for m in true_matches])
-    pred_found_labels = set(in_pred_only + [m[1] for m in true_matches])
-
-    assert( len(ref_found_labels.difference(set(ref.labels))) == 0 )
-    assert( len(pred_found_labels.difference(set(pred.labels))) == 0 )
-
-    # return a dictionary of found matches
-    matches = {'true_matches': true_matches,
-               'in_ref_only': list(set(in_ref_only)),
-               'in_pred_only': list(set(in_pred_only))}
-    return matches
-
-
-
-class MetricResults(object):
-    def __init__(self, metrics):
-        assert(isinstance(metrics, SegmentationMetrics))
-        self._images = 1
-        self._metrics = metrics
-
-        # list of metrics that are aggregated
-        self._agg = ('n_true_labels',
-                     'n_pred_labels',
-                     'n_true_positives',
-                     'n_false_positives',
-                     'n_false_negatives',
-                     'per_object_IoU',
-                     'per_object_localization_error',
-                     'per_image_pixel_identity')
-
-    def __getattr__(self, key):
-        return getattr(self._metrics, key)
-
-    @property
-    def n_images(self):
-        if any([getattr(self, m) is None for m in self._agg]):
-            return 0
-        else:
-            return self._images
-
-    def __add__(self, result):
-        assert(isinstance(result, MetricResults))
-        for m in self._agg:
-            setattr(self, m, getattr(result, m)+getattr(self, m))
-        self._images+=1
-        return self
-
-    def __repr__(self):
-        title = f' Segmentation Metrics (n={self.n_images})\n'
-        hbar = '='*len(title)+'\n'
-        r = hbar + title + hbar
-        if self.strict:
-            r += f'Strict: {self.strict} (IoU > {self.iou_threshold})\n'
-        for m in METRICS:
-            mval = getattr(self,m)
-            if isinstance(mval, float):
-                r+= f'{m}: {mval:.3f}\n'
-            else:
-                r+= f'{m}: {mval}\n'
-        return r
-
-
-    @property
-    def localization_error(self):
-        return np.mean(self.per_object_localization_error)
-
-    @property
-    def IoU(self):
-        return np.mean(self.per_object_IoU)
-
-    @property
-    def Jaccard(self):
-        """ Jaccard metric """
-        tp = self.n_true_positives
-        fn = self.n_false_negatives
-        fp = self.n_false_positives
-        return tp / (tp+fn+fp)
-
-    @property
-    def pixel_identity(self):
-        return np.mean(self.per_image_pixel_identity)
-
-
-    @staticmethod
-    def merge(results):
-        """ merge n results together and return a single object """
-        assert(isinstance(results, list))
-        merged = results.pop(0)
-        for result in results:
-            assert(isinstance(result, MetricResults))
-            assert(result.n_images == 1)
-            merged = merged + result
-        return merged
-
-
-
-class SegmentationMetrics:
-    """ SegmentationMetrics
-
-        A class for calculating various segmentation metrics to assess the
-        accuracy of a trained model.
-
-        Args:
-            reference - a numpy array (wxh) containing labeled objects from the
-                ground truth
-            predicted - a numpy array (wxh) containing labeled objects from the
-                segmentation algorithm
-
-            strict - (bool) whether to disregard matches with a low IoU score
-            iou_threshold - (float) threshold for strict matching
-
-        Properties:
-            Jaccard: the Jaccard index calculated according to the notes below
-            IoU: the Intersection over Union metric
-            localisation_precision:
-
-            true_positives:
-            false_positives:
-            false_negatives:
-
-
-        Notes:
-            The Jaccard metric is calculated accordingly:
-
-                FP = number of objects in predicted but not in reference
-                TP = number of objects in both
-                TN = background correctly segmented (not used)
-                FN = number of objects in true but not in predicted
-
-                J = TP / (TP+FP+FN)
-
-            The IoU is calculated as the intersection of the binary segmentation
-            divided by the union.
-
-            TODO(arl): need to address undersegmentation detection
-
-    """
-    def __init__(self,
-                 reference,
-                 predicted,
-                 **kwargs):
-
-        assert(isinstance(predicted, LabeledSegmentation))
-        assert(isinstance(reference, LabeledSegmentation))
-
-
-        self._reference = reference
-        self._predicted = predicted
-        self._strict = kwargs.get('strict', False)
-        self._iou_threshold = kwargs.get('iou_threshold', 0.5)
-
-        assert(self.iou_threshold>=0. and self.iou_threshold<=1.)
-        assert(isinstance(self.strict, bool))
-
-        # find the matches
-        self._matches = find_matches(self._reference, self._predicted)
-
-        # if we're in strict mode, prune the matches
-        if self.strict:
-            iou = self.per_object_IoU
-            tp = [self.true_positives[i] for i, ov in enumerate(iou) if ov > self.iou_threshold]
-            fp = list(set(self.true_positives).difference(tp))
-
-            self._matches['true_matches'] = tp
-            self._matches['in_pred_only'] += [m[1] for m in fp]
-
-    @property
-    def strict(self):
-        return self._strict
-
-    @property
-    def iou_threshold(self):
-        return self._iou_threshold
-
-    @property
-    def results(self):
-        return MetricResults(self)
-
-    @property
-    def image_overlay(self):
-        n_labels = max([self._predicted.n_labels, self._reference.n_labels])
-        scale = int(255/n_labels)
-        return np.stack([self._predicted.image,
-                         self._reference.image,
-                         self._predicted.image],axis=-1)*127
-
-    @property
-    def n_true_labels(self):
-        return self._reference.n_labels
-
-    @property
-    def n_pred_labels(self):
-        return self._predicted.n_labels
-
-    @property
-    def true_positives(self):
-        """ only one match between reference and predicted """
-        return self._matches['true_matches']
-
-    @property
-    def false_negatives(self):
-        """ no match in predicted for reference object """
-        return self._matches['in_ref_only']
-
-    @property
-    def false_positives(self):
-        """ combination of non unique matches and unmatched objects """
-        return self._matches['in_pred_only']
-
-    @property
-    def n_true_positives(self): return len(self.true_positives)
-    @property
-    def n_false_negatives(self): return len(self.false_negatives)
-    @property
-    def n_false_positives(self): return len(self.false_positives)
-
-    @property
-    def per_object_IoU(self):
-        """ Intersection over Union (IoU) metric """
-        iou = []
-        for m in self.true_positives:
-            mask_ref = self._reference.labeled == m[0]
-            mask_pred = self._predicted.labeled == m[1]
-
-            intersection = np.logical_and(mask_ref, mask_pred)
-            union = np.logical_or(mask_ref, mask_pred)
-
-            iou.append(np.sum(intersection)/np.sum(union))
-        return iou
-
-    @property
-    def per_image_pixel_identity(self):
-        n_tot = np.prod(self._reference.image.shape)
-        return [np.sum(self._reference.image == self._predicted.image) / n_tot]
-
-    @property
-    def per_object_localization_error(self):
-        """ localization error """
-        ref_centroids = self._reference.centroids
-        tgt_centroids = self._predicted.centroids
-        positional_error = []
-        for m in self.true_positives:
-            true_centroid = np.array(ref_centroids[m[0]-1])
-            pred_centroid = np.array(tgt_centroids[m[1]-1])
-            err = np.sum((true_centroid-pred_centroid)**2)
-            positional_error.append(err)
-        return positional_error
-
-    def plot(self):
-        render.plot_metrics(self)
-
-    def to_napari(self):
-        return render.render_metrics_napari(self)
-
-
-
-
-class LabeledSegmentation(object):
-    """ LabeledSegmentation
-
-    A helper class to enable simple calculation of accuracy statistics for
-    image segmentation output.
-
-    """
-    def __init__(self, image):
-        assert(isinstance(image, np.ndarray))
-        self.image = image
-
-        # label it
-        self.labeled, self.n_labels = label(image.astype(bool))
-
-    @property
-    def shape(self):
-        return self.image.shape
-
-    @property
-    def bboxes(self):
-        return [find_objects(self.labeled==label)[0] for label in self.labels]
-
-    @property
-    def labels(self):
-        return range(1, self.n_labels+1)
-
-    @property
-    def centroids(self):
-        return [center_of_mass(self.labeled==label) for label in self.labels]
-
-    @property
-    def areas(self):
-        return [np.sum(self.labeled==label) for label in self.labels]
-
-
-
-
-
-
-def calculate(reference, predicted, **kwargs):
-    """ Take a predicted image and compare with the reference image.
-
-    Compute various metrics.
-    """
-
-    ref = LabeledSegmentation(reference)
-    tgt = LabeledSegmentation(predicted)
-
-    # make sure they are the same size
-    assert(ref.shape == tgt.shape)
-
-    return SegmentationMetrics(ref, tgt, **kwargs)
-
-
-def batch(files, **kwargs):
-    """ batch process a list of files """
-    metrix = []
-    for f_ref, f_pred in files:
-        print(f_pred)
-        true = imread(f_ref)
-        pred = imread(f_pred)
-        result = calculate(true, pred, **kwargs).results
-        metrix.append(result)
-    return MetricResults.merge(metrix)
-
-
-
-
-
-
-
-
-if __name__ == "__main__":
-    pass
diff --git a/umetrics/render.py b/umetrics/render.py
deleted file mode 100644
index 9738005..0000000
--- a/umetrics/render.py
+++ /dev/null
@@ -1,92 +0,0 @@
-
-__author__ = "Alan R. Lowe"
-__email__ = "a.lowe@ucl.ac.uk"
-
-import os
-
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.patches as patches
-
-def plot_metrics(seg_metrics):
-
-    pred = seg_metrics._predicted
-    ref = seg_metrics._reference
-
-    iou = [None] * len(ref.labels)
-    IoU = seg_metrics.per_object_IoU
-    for i, tp in enumerate(seg_metrics.true_positives):
-        iou[tp[0]-1] = '{:.2f}'.format(IoU[i])
-
-    fig, ax = plt.subplots(1, figsize=(16,12))
-    # plt.imshow(J_image)
-    ax.imshow(seg_metrics.image_overlay)
-
-
-    for i, (sy, sx) in enumerate(ref.bboxes):
-        r = patches.Rectangle((sx.start,sy.start), sx.stop-sx.start,sy.stop-sy.start, edgecolor='g', facecolor='None')
-        ax.add_patch(r)
-        ax.text(sx.start, sy.start, '{}, IoU: {}'.format(i, iou[i]), fontsize=6, color='w')
-    for i, (sy, sx) in enumerate(pred.bboxes):
-        r = patches.Rectangle((sx.start,sy.start), sx.stop-sx.start,sy.stop-sy.start, edgecolor='m', facecolor='None')
-        ax.add_patch(r)
-        # ax.text(sx.start, sy.start, '{}'.format(i), fontsize=6, color='w')
-
-    bboxes = pred.bboxes
-    for fp in seg_metrics.false_positives:
-        sy, sx = bboxes[fp-1]
-        w, h = sx.stop-sx.start,sy.stop-sy.start
-        r = patches.Rectangle((sx.start,sy.start), w, h, edgecolor='r', facecolor=(1.,0.,0.,0.0), linewidth=2)
-        ax.add_patch(r)
-
-    bboxes = ref.bboxes
-    for fn in seg_metrics.false_negatives:
-        sy, sx = bboxes[fn-1]
-        w, h = sx.stop-sx.start,sy.stop-sy.start
-        r = patches.Rectangle((sx.start,sy.start), w, h, edgecolor='c', facecolor=(0.,1.,1.,0.0), linewidth=2)
-        ax.add_patch(r)
-    plt.axis('off')
-    plt.show()
-
-
-
-
-def make_bboxes(bbox_slices):
-    """ Calculate bboxes for napari """
-    minr = [sxy[0].start for sxy in bbox_slices]
-    minc = [sxy[1].start for sxy in bbox_slices]
-    maxr = [sxy[0].stop for sxy in bbox_slices]
-    maxc = [sxy[1].stop for sxy in bbox_slices]
-
-    bbox_rect = np.array(
-        [[minr, minc], [maxr, minc], [maxr, maxc], [minr, maxc]]
-    )
-    bbox_rect = np.moveaxis(bbox_rect, 2, 0)
-    return bbox_rect
-
-
-
-def render_metrics_napari(seg_metrics):
-    """ Render the segmentation metrics for visualization in Napari. """
-
-    pred = seg_metrics._predicted
-    ref = seg_metrics._reference
-
-
-
-    properties = {'iou': seg_metrics.per_object_IoU}
-
-    bboxes = make_bboxes(ref.bboxes)
-    tp_idx = np.asarray(seg_metrics.true_positives)[:, 0] - 1
-    bboxes = bboxes[tp_idx, ...]
-
-    # specify the display parameters for the text
-    text_parameters = {
-        'text': 'IoU: {iou:.2f}\n',
-        'size': 8,
-        'color': 'white',
-        'anchor': 'upper_left',
-        'translation': [-2, 0],
-    }
-
-    return bboxes, properties, text_parameters