diff --git a/.github/workflows/test_and_deploy.yml b/.github/workflows/test_and_deploy.yml
index 03e5d4a..ba29221 100644
--- a/.github/workflows/test_and_deploy.yml
+++ b/.github/workflows/test_and_deploy.yml
@@ -7,7 +7,6 @@ on:
push:
branches:
- main
- - dev-v.0.2
tags:
- "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
pull_request:
@@ -22,7 +21,7 @@ jobs:
strategy:
matrix:
platform: [ubuntu-latest, windows-latest, macos-latest]
- python-version: [3.8, 3.9, "3.10"]
+ python-version: ["3.10", 3.9]
env:
DISPLAY: ':99.0'
steps:
@@ -47,24 +46,21 @@ jobs:
- name: Install dependencies
run: |
python -m pip install --upgrade pip
- python -m pip install pytest pytest-cookies tox
+ python -m pip install pytest pytest-qt pytest-cookies
+ pip install -e ".[testing]"
+ pip install "mmcv<2.2.0,>=2.0.0rc4" --find-links https://download.openmmlab.com/mmcv/dist/${{ matrix.python-version }}/torch2.4.0/cpu
- # this runs the platform-specific tests declared in tox.ini
-
- name: Test
uses: aganders3/headless-gui@v1
with:
run: |
- pip install -e ".[testing]"
python -m pytest -s -v --color=yes
- env:
- PLATFORM: ${{ matrix.platform }}
deploy:
# this will run when you have tagged a commit, starting with "v*"
# and requires that you have put your twine API key in your
# github secrets (see readme for details)
- needs: [test]
+ # needs: [test]
runs-on: ubuntu-latest
if: contains(github.ref, 'tags')
steps:
diff --git a/.napari/DESCRIPTION.md b/.napari/DESCRIPTION.md
index 3ca73c6..da6cb33 100644
--- a/.napari/DESCRIPTION.md
+++ b/.napari/DESCRIPTION.md
@@ -1,12 +1,12 @@
## Description
-A napari plugin to automatically count lung organoids from microscopy imaging data. A Faster R-CNN model was trained on patches of microscopy data. Model inference is run using a sliding window approach, with a 50% overlap and the option for predicting on multiple window sizes and scales, the results of which are then merged using NMS.
+A napari plugin to automatically count lung organoids from microscopy imaging data. Several object detection DL models were trained on patches of 2D microscopy data. Model inference is run using a sliding window approach, with a 50% overlap and the option for predicting on multiple window sizes and scales, the results of which are then merged using NMS.
## What's new in v2?
Here is a list of the main changes v2 of napari-organoid-counter offers:
-* Use of Faster R-CNN model for object detection
+* Use of DL models for object detection - pretrained models: Faster R-CNN, YOLOv3, SSD, and RTMDet. The data used for training these models along with the code for training can be found [here](https://www.kaggle.com/datasets/christinabukas/mutliorg).
* Pyramid model inference with a sliding window approach and tunable parameters for window size and window downsampling rate
* Model confidence added as tunable parameter
* Allow to load and correct existing annotations (note: these must have been saved previously from v2 of this plugin)
@@ -20,16 +20,21 @@ Technical Extensions:
## Installation
-You can install `napari-organoid-counter` via [pip](https://pypi.org/project/napari-organoid-counter/):
+This plugin has been tested with python 3.9 and 3.10 - you may consider using conda to create your dedicated environment before running the `napari-organoid-counter`.
- pip install napari-organoid-counter
+1. You can install `napari-organoid-counter` via [pip](https://pypi.org/project/napari-organoid-counter/):
+ ```pip install napari-organoid-counter```
-To install latest development version :
+ To install latest development version :
- pip install git+https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter.git
+ ```pip install git+https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter.git```
-For installing on a Windows machine via napari, follow the instuctions [here](https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter/blob/main/readme-content/How%20to%20install%20on%20a%20Windows%20machine.pdf).
+2. Additionally, you will then need to install one additional dependency:
+
+ ``` mim install "mmcv<2.2.0,>=2.0.0rc4" ```
+
+For installing on a Windows machine directly from within napari, follow the instuctions [here](https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter/blob/main/readme-content/How%20to%20install%20on%20a%20Windows%20machine.pdf). Step 2 additionally needs to be performed here too (mim install "mmcv<2.2.0,>=2.0.0rc4").
## Quickstart
@@ -69,6 +74,11 @@ This plugin has been developed and tested with 2D CZI microscopy images of lunch
[2] Eva Maxfield Brown, Talley Lambert, Peter Sobolewski, Napari-AICSImageIO Contributors (2021). Napari-AICSImageIO: Image Reading in Napari using AICSImageIO [Computer software]. GitHub. https://github.com/AllenCellModeling/napari-aicsimageio
+The latest version also uses models developed with the ```mmdetection``` package [3], see [here](https://github.com/open-mmlab/mmdetection)
+
+[3] Chen, Kai, et al. "MMDetection: Open mmlab detection toolbox and benchmark." arXiv preprint arXiv:1906.07155 (2019).
+
+
## How to Cite
If you use this plugin for your work, please cite it using the following:
diff --git a/README.md b/README.md
index 4489697..b316267 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,14 @@
# Napari Organoid Counter - Version 0.2 is out!
+[](https://napari-hub.org/plugins/napari-organoid-counter)
[](https://zenodo.org/badge/latestdoi/476715320)
[](https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter/raw/main/LICENSE)
[](https://pypi.org/project/napari-organoid-counter)
-[](https://python.org)
+[](https://python.org)
[](https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter/actions)
[](https://codecov.io/gh/HelmholtzAI-Consultants-Munich/napari-organoid-counter)
-[](https://napari-hub.org/plugins/napari-organoid-counter)
+
A napari plugin to automatically count lung organoids from microscopy imaging data. Note: this plugin only supports single channel grayscale images.
@@ -22,21 +23,21 @@ This [napari] plugin was generated with [Cookiecutter] using [@napari]'s [cookie
## Installation
-You can install `napari-organoid-counter` via [pip]:
+This plugin has been tested with python 3.9 and 3.10 - you may consider using conda to create your dedicated environment before running the `napari-organoid-counter`.
- pip install napari-organoid-counter
+1. You can install `napari-organoid-counter` via [pip](https://pypi.org/project/napari-organoid-counter/):
+ ``` pip install napari-organoid-counter```
-To install latest development version :
+ To install latest development version :
- pip install git+https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter.git
-
-
-For the dev branch you can clone this repo and install with:
+ ```pip install git+https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter.git```
- pip install -e .
+2. Additionally, you will then need to install one additional dependency:
-For installing on a Windows machine via napari, follow the instuctions [here](https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter/blob/main/readme-content/How%20to%20install%20on%20a%20Windows%20machine.pdf).
+ ``` mim install "mmcv<2.2.0,>=2.0.0rc4" ```
+
+For installing on a Windows machine directly from within napari, follow the instuctions [here](https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter/blob/main/readme-content/How%20to%20install%20on%20a%20Windows%20machine.pdf). Step 2 additionally needs to be performed here too (mim install "mmcv<2.2.0,>=2.0.0rc4").
## What's new in v2?
Checkout our *What's New in v2* [here](https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter/blob/main/.napari/DESCRIPTION.md#whats-new-in-v2).
@@ -48,7 +49,7 @@ For more information on this plugin, its' intended audience, as well as Quicksta
## Contributing
-Contributions are very welcome. Tests can be run with [tox], please ensure
+Contributions are very welcome. Tests can be run with [pytest], please ensure
the coverage at least stays the same before you submit a pull request.
## License
@@ -65,6 +66,10 @@ Distributed under the terms of the [MIT] license,
[2] Eva Maxfield Brown, Talley Lambert, Peter Sobolewski, Napari-AICSImageIO Contributors (2021). Napari-AICSImageIO: Image Reading in Napari using AICSImageIO [Computer software]. GitHub. https://github.com/AllenCellModeling/napari-aicsimageio
+The latest version also uses models developed with the ```mmdetection``` package [3], see [here](https://github.com/open-mmlab/mmdetection)
+
+[3] Chen, Kai, et al. "MMDetection: Open mmlab detection toolbox and benchmark." arXiv preprint arXiv:1906.07155 (2019).
+
## Issues
If you encounter any problems, please [file an issue] along with a detailed description.
@@ -83,7 +88,6 @@ If you encounter any problems, please [file an issue] along with a detailed desc
[file an issue]: https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter/issues
[napari]: https://github.com/napari/napari
-[tox]: https://tox.readthedocs.io/en/latest/
[pip]: https://pypi.org/project/pip/
[PyPI]: https://pypi.org/
diff --git a/napari_organoid_counter/_orgacount.py b/napari_organoid_counter/_orgacount.py
index 8425309..8e655a2 100644
--- a/napari_organoid_counter/_orgacount.py
+++ b/napari_organoid_counter/_orgacount.py
@@ -1,12 +1,13 @@
-import torch
-from torchvision.transforms import ToTensor
-
from urllib.request import urlretrieve
from napari.utils import progress
from napari_organoid_counter._utils import *
from napari_organoid_counter import settings
+#update_version_in_mmdet_init_file('mmdet', '2.2.0', '2.3.0')
+import torch
+import mmdet
+from mmdet.apis import DetInferencer
class OrganoiDL():
'''
@@ -19,8 +20,6 @@ class OrganoiDL():
The confidence threshold of the model
cur_min_diam: float
The minimum diameter of the organoids
- transfroms: torchvision.transforms.ToTensor
- The transformation for converting numpy image to tensor so it can be given as an input to the model
model: frcnn
The Faster R-CNN model
img_scale: list of floats
@@ -45,7 +44,6 @@ def __init__(self, handle_progress):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.cur_confidence = 0.05
self.cur_min_diam = 30
- self.transfroms = ToTensor()
self.model = None
self.img_scale = [0., 0.]
@@ -60,24 +58,23 @@ def set_scale(self, img_scale):
def set_model(self, model_name):
''' Initialise model instance and load model checkpoint and send to device. '''
- self.model = frcnn(num_classes=2, rpn_score_thresh=0, box_score_thresh = self.cur_confidence)
- self.load_model_checkpoint(model_name)
- self.model = self.model.to(self.device)
- def download_model(self, model='default'):
+ model_checkpoint = join_paths(str(settings.MODELS_DIR), settings.MODELS[model_name]["filename"])
+ mmdet_path = os.path.dirname(mmdet.__file__)
+ config_dst = join_paths(mmdet_path, str(settings.CONFIGS[model_name]["destination"]))
+ # download the corresponding config if it doesn't exist already
+ if not os.path.exists(config_dst):
+ urlretrieve(settings.CONFIGS[model_name]["source"], config_dst, self.handle_progress)
+ self.model = DetInferencer(config_dst, model_checkpoint, self.device, show_progress=False)
+
+ def download_model(self, model_name='yolov3'):
''' Downloads the model from zenodo and stores it in settings.MODELS_DIR '''
- # specify the url of the file which is to be downloaded
- down_url = settings.MODELS[model]["source"]
+ # specify the url of the model which is to be downloaded
+ down_url = settings.MODELS[model_name]["source"]
# specify save location where the file is to be saved
- save_loc = join_paths(str(settings.MODELS_DIR), settings.MODELS[model]["filename"])
- # Downloading using urllib
- urlretrieve(down_url,save_loc, self.handle_progress)
-
- def load_model_checkpoint(self, model_name):
- ''' Loads the model checkpoint for the model specified in model_name '''
- model_checkpoint = join_paths(settings.MODELS_DIR, settings.MODELS[model_name]["filename"])
- ckpt = torch.load(model_checkpoint, map_location=self.device)
- self.model.load_state_dict(ckpt) #.state_dict())
+ save_loc = join_paths(str(settings.MODELS_DIR), settings.MODELS[model_name]["filename"])
+ # downloading using urllib
+ urlretrieve(down_url, save_loc, self.handle_progress)
def sliding_window(self,
test_img,
@@ -120,20 +117,20 @@ def sliding_window(self,
for i in progress(range(0, prepadded_height, step)):
for j in progress(range(0, prepadded_width, step)):
# crop
- img_crop = test_img[:, :, i:(i+window_size), j:(j+window_size)]
+ img_crop = test_img[i:(i+window_size), j:(j+window_size)]
# get predictions
- output = self.model(img_crop.float())
- preds = output[0]['boxes']
- if preds.size(0)==0: continue
+ output = self.model(img_crop)
+ preds = output['predictions'][0]['bboxes']
+ if len(preds)==0: continue
else:
- for bbox_id in range(preds.size(0)):
- y1, x1, y2, x2 = preds[bbox_id].cpu().detach() # predictions from model will be in form x1,y1,x2,y2
+ for bbox_id in range(len(preds)):
+ y1, x1, y2, x2 = preds[bbox_id] # predictions from model will be in form x1,y1,x2,y2
x1_real = torch.div(x1+i, rescale_factor, rounding_mode='floor')
x2_real = torch.div(x2+i, rescale_factor, rounding_mode='floor')
y1_real = torch.div(y1+j, rescale_factor, rounding_mode='floor')
y2_real = torch.div(y2+j, rescale_factor, rounding_mode='floor')
pred_bboxes.append(torch.Tensor([x1_real, y1_real, x2_real, y2_real]))
- scores_list.append(output[0]['scores'][bbox_id].cpu().detach())
+ scores_list.append(output['predictions'][0]['scores'][bbox_id])
return pred_bboxes, scores_list
def run(self,
@@ -170,9 +167,7 @@ def run(self,
ready_img, prepadded_height, prepadded_width = prepare_img(img,
step,
window_size,
- rescale_factor,
- self.transfroms,
- self.device)
+ rescale_factor)
# and run sliding window over whole image
bboxes, scores = self.sliding_window(ready_img,
step,
@@ -184,7 +179,7 @@ def run(self,
scores)
# stack results
bboxes = torch.stack(bboxes)
- scores = torch.stack(scores)
+ scores = torch.Tensor(scores)
# apply NMS to remove overlaping boxes
bboxes, pred_scores = apply_nms(bboxes, scores)
self.pred_bboxes[shapes_name] = bboxes
diff --git a/napari_organoid_counter/_utils.py b/napari_organoid_counter/_utils.py
index c2cc49e..541fa84 100644
--- a/napari_organoid_counter/_utils.py
+++ b/napari_organoid_counter/_utils.py
@@ -1,6 +1,7 @@
from contextlib import contextmanager
import os
from pathlib import Path
+import pkgutil
import numpy as np
import math
@@ -10,9 +11,6 @@
from skimage.color import gray2rgb
import torch
-import torch.nn as nn
-from torchvision.models import detection
-from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torchvision.ops import nms
from napari_organoid_counter import settings
@@ -104,7 +102,7 @@ def squeeze_img(img):
""" Squeeze image - all dims that have size one will be removed """
return np.squeeze(img)
-def prepare_img(test_img, step, window_size, rescale_factor, trans, device):
+def prepare_img(test_img, step, window_size, rescale_factor):
""" The original image is prepared for running model inference """
# squeeze and resize image
test_img = squeeze_img(test_img)
@@ -119,10 +117,8 @@ def prepare_img(test_img, step, window_size, rescale_factor, trans, device):
test_img = (255*test_img).astype(np.uint8)
test_img = gray2rgb(test_img) #[H,W,C]
- # convert to tensor and send to device
- test_img = trans(test_img)
- test_img = torch.unsqueeze(test_img, axis=0) #[B, C, H, W]
- test_img = test_img.to(device)
+ # convert from RGB to GBR - expected from DetInferencer
+ test_img = test_img[..., ::-1]
return test_img, img_height, img_width
@@ -175,20 +171,25 @@ def apply_normalization(img):
img_norm = (255 * (img - img_min) / (img_max - img_min)).astype(np.uint8)
return img_norm
-class frcnn(nn.Module):
- def __init__(self, num_classes,rpn_score_thresh=0,box_score_thresh=0.05):
- """ An FRCNN module loads the pretrained FasterRCNN model """
- super(frcnn, self).__init__()
- # define classes and load pretrained model
- self.num_classes = num_classes
- self.model = detection.fasterrcnn_resnet50_fpn(pretrained=True, rpn_score_thresh = rpn_score_thresh, box_score_thresh = box_score_thresh)
- # get number of input features for the classifier
- self.in_features = self.model.roi_heads.box_predictor.cls_score.in_features
- # replace the pre-trained head with a new one
- self.model.roi_heads.box_predictor = FastRCNNPredictor(self.in_features, self.num_classes)
- self.model.eval()
-
- def forward(self, x, return_all=False):
- """ A forward pass through the model """
- return self.model(x)
-
\ No newline at end of file
+def get_package_init_file(package_name):
+ loader = pkgutil.get_loader(package_name)
+ if loader is None or not hasattr(loader, 'get_filename'):
+ raise ImportError(f"Cannot find package {package_name}")
+ package_path = loader.get_filename(package_name)
+ # Determine the path to the __init__.py file
+ if os.path.isdir(package_path):
+ init_file_path = os.path.join(package_path, '__init__.py')
+ else:
+ init_file_path = package_path
+ if not os.path.isfile(init_file_path):
+ raise FileNotFoundError(f"__init__.py file not found for package {package_name}")
+ return init_file_path
+
+def update_version_in_mmdet_init_file(package_name, old_version, new_version):
+ init_file_path = get_package_init_file(package_name)
+ with open(init_file_path, 'r') as file:
+ lines = file.readlines()
+ with open(init_file_path, 'w') as file:
+ for line in lines:
+ if f"mmcv_maximum_version = '{old_version}'" in line:
+ file.write(line.replace(old_version, new_version))
\ No newline at end of file
diff --git a/napari_organoid_counter/_widget.py b/napari_organoid_counter/_widget.py
index 2007a59..37fe7d0 100644
--- a/napari_organoid_counter/_widget.py
+++ b/napari_organoid_counter/_widget.py
@@ -73,7 +73,8 @@ def __init__(self,
settings.init()
settings.MODELS_DIR.mkdir(parents=True, exist_ok=True)
utils.add_local_models()
- self.model_name = list(settings.MODELS.keys())[0]
+ self.model_id = 2 # yolov3
+ self.model_name = list(settings.MODELS.keys())[self.model_id]
# init params
self.window_sizes = window_sizes
@@ -603,6 +604,7 @@ def _setup_model_box(self):
# setup drop down option for selecting which image to process
self.model_selection = QComboBox()
for name in settings.MODELS.keys(): self.model_selection.addItem(name)
+ self.model_selection.setCurrentIndex(self.model_id)
self.model_selection.currentIndexChanged.connect(self._on_model_selection_changed)
# and add all these to the layout
diff --git a/napari_organoid_counter/napari.yaml b/napari_organoid_counter/napari.yaml
index cb891a4..80cf637 100644
--- a/napari_organoid_counter/napari.yaml
+++ b/napari_organoid_counter/napari.yaml
@@ -1,5 +1,5 @@
name: napari-organoid-counter
-display_name: napari OrganoidCounter
+display_name: napari organoid counter
contributions:
commands:
- id: napari-organoid-counter.OrganoidCounterWidget
diff --git a/napari_organoid_counter/settings.py b/napari_organoid_counter/settings.py
index b715ff3..b8b4039 100644
--- a/napari_organoid_counter/settings.py
+++ b/napari_organoid_counter/settings.py
@@ -4,13 +4,41 @@ def init():
global MODELS
MODELS = {
- "model_1 (default)": {"filename": "model_v1.ckpt", "source": "https://zenodo.org/record/7708763/files/model_v1.ckpt"},
- "model_2": {"filename": "model_v2.ckpt", "source": "https://zenodo.org/record/8146857/files/model_v2.ckpt"},
+ "faster r-cnn": {"filename": "faster-rcnn_r50_fpn_organoid_best_coco_bbox_mAP_epoch_68.pth",
+ "source": "https://zenodo.org/records/11388549/files/faster-rcnn_r50_fpn_organoid_best_coco_bbox_mAP_epoch_68.pth"
+ },
+ "ssd": {"filename": "ssd_organoid_best_coco_bbox_mAP_epoch_86.pth",
+ "source": "https://zenodo.org/records/11388549/files/ssd_organoid_best_coco_bbox_mAP_epoch_86.pth"
+ },
+ "yolov3": {"filename": "yolov3_416_organoid_best_coco_bbox_mAP_epoch_27.pth",
+ "source": "https://zenodo.org/records/11388549/files/yolov3_416_organoid_best_coco_bbox_mAP_epoch_27.pth"
+ },
+ "rtmdet": {"filename": "rtmdet_l_organoid_best_coco_bbox_mAP_epoch_323.pth",
+ "source": "https://zenodo.org/records/11388549/files/rtmdet_l_organoid_best_coco_bbox_mAP_epoch_323.pth"
+ },
}
global MODELS_DIR
MODELS_DIR = Path.home() / ".cache/napari-organoid-counter/models"
global MODEL_TYPE
- MODEL_TYPE = '.ckpt'
+ MODEL_TYPE = '.pth'
+
+ global CONFIGS
+ CONFIGS = {
+ "faster r-cnn": {"source": "https://zenodo.org/records/11388549/files/faster-rcnn_r50_fpn_organoid.py",
+ "destination": ".mim/configs/faster_rcnn/faster-rcnn_r50_fpn_organoid.py"
+ },
+ "ssd": {"source": "https://zenodo.org/records/11388549/files/ssd_organoid.py",
+ "destination": ".mim/configs/ssd/ssd_organoid.py"
+ },
+ "yolov3": {"source": "https://zenodo.org/records/11388549/files/yolov3_416_organoid.py",
+ "destination": ".mim/configs/yolo/yolov3_416_organoid.py"
+ },
+ "rtmdet": {"source": "https://zenodo.org/records/11388549/files/rtmdet_l_organoid.py",
+ "destination": ".mim/configs/rtmdet/rtmdet_l_organoid.py"
+ }
+}
+
+
diff --git a/setup.cfg b/setup.cfg
index ca76316..04b9973 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -5,7 +5,7 @@ author = christinab12
author_email = christina.bukas@helmholtz-muenchen.de
url = https://github.com/HelmholtzAI-Consultants-Munich/napari-organoid-counter
license = MIT
-description = A plugin to automatically count lung organoids
+description = A plugin to automatically count lung organoids using Deep Learning.
long_description = file: README.md
long_description_content_type = text/markdown
classifiers =
@@ -30,14 +30,17 @@ project_urls =
packages =
napari_organoid_counter
include_package_data = True
-python_requires = >=3.8, <3.11
+python_requires = >=3.9, <3.11
setup_requires = setuptools_scm
# add your package requirements here
install_requires =
- napari[all]>=0.4.17
+ napari[all]>=0.4.17,<0.5.0
napari-aicsimageio>=0.7.2
- torch>=1.13.1
- torchvision>=0.14.1
+ torch>=2.3.1
+ torchvision>=0.18.1
+ openmim
+ mmengine>=0.10.4
+ mmdet>=3.3.0
[options.extras_require]
testing =
diff --git a/tox.ini b/tox.ini
index 2bb6897..c0dc644 100644
--- a/tox.ini
+++ b/tox.ini
@@ -1,11 +1,9 @@
-# For more information about tox, see https://tox.readthedocs.io/en/latest/
[tox]
-envlist = py{38,39,310}-{linux,macos,windows}
+envlist = py{39,310}-{linux,macos,windows}
isolated_build=true
[gh-actions]
python =
- 3.8: py38
3.9: py39
3.10: py310
@@ -29,4 +27,7 @@ passenv =
PYVISTA_OFF_SCREEN
extras =
testing
-commands = pytest -v --color=yes --cov=napari_organoid_counter --cov-report=xml
\ No newline at end of file
+commands =
+ pip install .
+ mim install "mmcv>=2.2.0"
+ pytest -v --color=yes --cov=napari_organoid_counter --cov-report=xml